+
AI Resources
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-[](https://opensource.org/licenses/MIT)
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+
- +## Getting Started +New to Redis for AI applications? Here's how to get started: -
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+1. **First time with Redis?** Start with our [Redis Intro notebook](python-recipes/redis-intro/00_redis_intro.ipynb)
+2. **Want to try vector search?** Check our [Vector Search with RedisVL](python-recipes/vector-search/01_redisvl.ipynb) recipe
+3. **Building a RAG application?** Begin with [RAG from Scratch](python-recipes/RAG/01_redisvl.ipynb)
+4. **Ready to see it in action?** Play with the [Redis RAG Workbench](https://github.com/redis-developer/redis-rag-workbench) demo
-# Table of Contents
-- [Demos](#Demos)
-- [Recipes](#Recipes)
- - [RAG](#getting-started-with-rag)
- - [Semantic cache](#semantic-cache)
- - [Advanced RAG](#advanced-rag)
- - [Recommendation systems](#recommendation-systems)
- - [LLM Session Management](#llm-session-management)
-- [Integrations](#integrations)
-- [Additional content](#additional-content)
-- [Benchmarks](#benchmarks)
-- [Documentation](#documentation)
+
+ 
+ 
+ ✨ A curated repository of code recipes, demos, tutorials and resources for basic and advanced Redis use cases in the AI ecosystem. ✨ +
+ +
+
-+ Getting Started | + Demos | + Recipes | + Tutorials | + Integrations | + Resources +
- ✨ A curated repository of code recipes, demos, and resources for basic and advanced Redis use cases in the AI ecosystem. ✨
+
+- +## Getting Started +New to Redis for AI applications? Here's how to get started: -
-
-
+## Demos +No faster way to get started than by diving in and playing around with a demo. -# Demos -No faster way to get started than by diving in and playing around with one of our demos. +| Demo | Description | +|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| [Redis RAG Workbench](https://github.com/redis-developer/redis-rag-workbench) | Interactive demo to build a RAG-based chatbot over a user-uploaded PDF. Toggle different settings and configurations to improve chatbot performance and quality. Utilizes RedisVL, LangChain, RAGAs, and more. | +| [Redis VSS - Simple Streamlit Demo](https://github.com/antonum/Redis-VSS-Streamlit) | Streamlit demo of Redis Vector Search | +| [ArXiv Search](https://github.com/redis-developer/redis-arxiv-search) | Full stack implementation of Redis with React FE | +| [Product Search](https://github.com/redis-developer/redis-product-search) | Vector search with Redis Stack and Redis Enterprise | +| [ArxivChatGuru](https://github.com/redis-developer/ArxivChatGuru) | Streamlit demo of RAG over Arxiv documents with Redis & OpenAI | +| [Redis Movies Searcher](https://github.com/redis-developer/redis-movies-searcher) | Demo of hybrid search using Java, Spring Boot, and Redis OM | +| [My Jarvis Alexa Skill](https://github.com/redis-developer/my-jarvis-alexa-skill) | Complete example of an Alexa skill that can recall previously stored conversations and memories to provide contextual responses to users. Utilizes Redis Agent Memory Server, LangChain4J, Terraform, and AWS. It showcases how to implement context engineering to dynamically leverage RAG, tools, short-term and long-term memories. | -| Demo | Description | -| --- | --- | -| [Redis RAG Workbench](https://github.com/redis-developer/redis-rag-workbench) | Interactive demo to build a RAG-based chatbot over an arbitrary PDF. Toggle different settings and configurations to improve chatbot performance and quality. Integrates RedisVL, LangChain, RAGAs, and more. | -| [ArxivChatGuru](https://github.com/redis-developer/ArxivChatGuru) | Streamlit demo of RAG over Arxiv documents with Redis & OpenAI | -| [Redis VSS - Simple Streamlit Demo](https://github.com/antonum/Redis-VSS-Streamlit) | Streamlit demo of Redis Vector Search | -| [Vertex AI & Redis](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) | A tutorial featuring Redis with Vertex AI | -| [Agentic RAG](https://github.com/redis-developer/agentic-rag) | A tutorial focused on agentic RAG with LlamaIndex and Cohere | -| [ArXiv Search](https://github.com/redis-developer/redis-arxiv-search) | Full stack implementation of Redis with React FE | -| [Product Search](https://github.com/redis-developer/redis-product-search) | Vector search with Redis Stack and Redis Enterprise | -# Recipes +## Recipes -Need specific sample code to help get started with Redis? Start here. +Need quickstarts to begin your Redis AI journey? -## Getting started with Redis & Vector Search +### Getting started with Redis & Vector Search -| Recipe | Description | -| --- | --- | -| [/redis-intro/00_redis_intro.ipynb](/python-recipes/redis-intro/00_redis_intro.ipynb) | The place to start if brand new to Redis | -| [/vector-search/00_redispy.ipynb](/python-recipes/vector-search/00_redispy.ipynb) | Vector search with Redis python client | -| [/vector-search/01_redisvl.ipynb](/python-recipes/vector-search/01_redisvl.ipynb) | Vector search with Redis Vector Library | -## Getting started with RAG +| Recipe | GitHub | Google Colab | +| --- | --- | --- | +| 🏁 **Redis Intro** - The place to start if brand new to Redis | [](python-recipes/redis-intro/00_redis_intro.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/redis-intro/00_redis_intro.ipynb) | +| 🔍 **Vector Search with RedisPy** - Vector search with Redis python client | [](python-recipes/vector-search/00_redispy.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/00_redispy.ipynb) | +| 📚 **Vector Search with RedisVL** - Vector search with Redis Vector Library | [](python-recipes/vector-search/01_redisvl.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/01_redisvl.ipynb) | +| 🔄 **Hybrid Search** - Hybrid search techniques with Redis (BM25 + Vector) | [](python-recipes/vector-search/02_hybrid_search.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/02_hybrid_search.ipynb) | +| 🔢 **Data Type Support** - Shows how to convert a float32 index to float16 or integer dataypes | [](python-recipes/vector-search/03_dtype_support.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/03_dtype_support.ipynb) | +| 📊 **Benchmarking Basics** - Overview of search benchmarking basics with RedisVL and Python multiprocessing | [](python-recipes/vector-search/04_redisvl_benchmarking_basics.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/04_redisvl_benchmarking_basics.ipynb) | -**Retrieval Augmented Generation** (aka RAG) is a technique to enhance the ability of an LLM to respond to user queries. The **retrieval** part of RAG is supported by a vector database, which can return semantically relevant results to a user’s query, serving as contextual information to **augment** the **generative** capabilities of an LLM. -To get started with RAG, either from scratch or using a popular framework like Llamaindex or LangChain, go with these recipes: +### Retrieval Augmented Generation (RAG) -| Recipe | Description | -| --- | --- | -| [/RAG/01_redisvl.ipynb](python-recipes/RAG/01_redisvl.ipynb) | RAG from scratch with the Redis Vector Library | -| [/RAG/02_langchain.ipynb](python-recipes/RAG/02_langchain.ipynb) | RAG using Redis and LangChain | -| [/RAG/03_llamaindex.ipynb](python-recipes/RAG/03_llamaindex.ipynb) | RAG using Redis and LlamaIndex | -| [/RAG/04_advanced_redisvl.ipynb](python-recipes/RAG/04_advanced_redisvl.ipynb) | Advanced RAG with redisvl | -| [/RAG/05_nvidia_ai_rag_redis.ipynb](python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) | RAG using Redis and Nvidia | -| [/RAG/06_ragas_evaluation.ipynb](python-recipes/RAG/06_ragas_evaluation.ipynb) | Utilize RAGAS framework to evaluate RAG performance | - -## LLM Session Management -LLMs are stateless. To maintain context within a conversation chat sessions must be stored and resent to the LLM. Redis manages the storage and retrieval of chat sessions to maintain context and conversational relevance. -| Recipe | Description | -| --- | --- | -| [/llm-session-manager/00_session_manager.ipynb](python-recipes/llm-session-manager/00_llm_session_manager.ipynb) | LLM session manager with semantic similarity | -| [/llm-session-manager/01_multiple_sessions.ipynb](python-recipes/llm-session-manager/01_multiple_sessions.ipynb) | Handle multiple simultaneous chats with one instance | +**Retrieval Augmented Generation** (aka RAG) is a technique to enhance the ability of an LLM to respond to user queries. The **retrieval** part of RAG is supported by a vector database, which can return semantically relevant results to a user's query, serving as contextual information to **augment** the **generative** capabilities of an LLM. -## Semantic Cache -An estimated 31% of LLM queries are potentially redundant ([source](https://arxiv.org/pdf/2403.02694)). Redis enables semantic caching to help cut down on LLM costs quickly. - -| Recipe | Description | -| --- | --- | -| [/semantic-cache/doc2cache_llama3_1.ipynb](python-recipes/semantic-cache/doc2cache_llama3_1.ipynb) | Build a semantic cache using the Doc2Cache framework and Llama3.1 | -| [/semantic-cache/semantic_caching_gemini.ipynb](python-recipes/semantic-cache/semantic_caching_gemini.ipynb) | Build a semantic cache with Redis and Google Gemini | +To get started with RAG, either from scratch or using a popular framework like Llamaindex or LangChain, go with these recipes: -## Advanced RAG -For further insights on enhancing RAG applications with dense content representations, query re-writing, and other techniques. +| Recipe | GitHub | Google Colab | +| --- | --- | --- | +| 🧩 **RAG from Scratch** - RAG from scratch with the Redis Vector Library | [](python-recipes/RAG/01_redisvl.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/01_redisvl.ipynb) | +| ⛓️ **LangChain RAG** - RAG using Redis and LangChain | [](python-recipes/RAG/02_langchain.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/02_langchain.ipynb) | +| 🦙 **LlamaIndex RAG** - RAG using Redis and LlamaIndex | [](python-recipes/RAG/03_llamaindex.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/03_llamaindex.ipynb) | +| 🚀 **Advanced RAG** - Advanced RAG techniques | [](python-recipes/RAG/04_advanced_redisvl.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) | +| 🖥️ **NVIDIA RAG** - RAG using Redis and Nvidia NIMs | [](python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) | +| 📊 **RAGAS Evaluation** - Utilize the RAGAS framework to evaluate RAG performance | [](python-recipes/RAG/06_ragas_evaluation.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/06_ragas_evaluation.ipynb) | +| 🔒 **Role-Based RAG** - Implement a simple RBAC policy with vector search using Redis | [](python-recipes/RAG/07_user_role_based_rag.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/07_user_role_based_rag.ipynb) | + +### LLM Memory +LLMs are stateless. To maintain context within a conversation chat sessions must be stored and re-sent to the LLM. Redis manages the storage and retrieval of message histories to maintain context and conversational relevance. + +| Recipe | GitHub | Google Colab | +| --- | --- | --- | +| 💬 **Message History** - LLM message history with semantic similarity | [](python-recipes/llm-message-history/00_llm_message_history.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/llm-message-history/00_llm_message_history.ipynb) | +| 👥 **Multiple Sessions** - Handle multiple simultaneous chats with one instance | [](python-recipes/llm-message-history/01_multiple_sessions.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/llm-message-history/01_multiple_sessions.ipynb) | + +### Semantic Caching +An estimated 31% of LLM queries are potentially redundant ([source](https://arxiv.org/pdf/2403.02694)). Redis enables semantic caching to help cut down on LLM costs quickly. -| Recipe | Description | +| Recipe | GitHub | Google Colab | +| --- | --- | --- | +| 🧠 **Gemini Semantic Cache** - Build a semantic cache with Redis and Google Gemini | [](python-recipes/semantic-cache/00_semantic_caching_gemini.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/00_semantic_caching_gemini.ipynb) | +| 🦙 **Llama3.1 Doc2Cache** - Build a semantic cache using the Doc2Cache framework and Llama3.1 | [](python-recipes/semantic-cache/01_doc2cache_llama3_1.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/01_doc2cache_llama3_1.ipynb) | +| ⚙️ **Cache Optimization** - Use CacheThresholdOptimizer from [redis-retrieval-optimizer](https://pypi.org/project/redis-retrieval-optimizer/) to setup best cache config | [](python-recipes/semantic-cache/02_semantic_cache_optimization.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/02_semantic_cache_optimization.ipynb) | +| 🎯 **Context-Enabled Caching** - Context-aware semantic caching with Redis for enhanced LLM performance | [](python-recipes/semantic-cache/03_context_enabled_semantic_caching.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/03_context_enabled_semantic_caching.ipynb) | + +### Semantic Routing +Routing is a simple and effective way of preventing misuse with your AI application or for creating branching logic between data sources etc. + +| Recipe | GitHub | Google Colab | +| --- | --- | --- | +| 🔀 **Basic Routing** - Simple examples of how to build an allow/block list router in addition to a multi-topic router | [](python-recipes/semantic-router/00_semantic_routing.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/00_semantic_routing.ipynb) | +| ⚙️ **Router Optimization** - Use RouterThresholdOptimizer from [redis-retrieval-optimizer](https://pypi.org/project/redis-retrieval-optimizer/) to setup best router config | [](python-recipes/semantic-router/01_routing_optimization.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/01_routing_optimization.ipynb) | + + +### AI Gateways +AI gateways manage LLM traffic through a centralized, managed layer that can implement routing, rate limiting, caching, and more. + +| Recipe | GitHub | Google Colab | +| --- | --- | --- | +| 🚪 **LiteLLM Proxy** - Getting started with LiteLLM proxy and Redis | [](python-recipes/gateway/00_litellm_proxy_redis.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/gateway/00_litellm_proxy_redis.ipynb) | + + +### Agents + +| Recipe | GitHub | Google Colab | +| --- | --- | --- | +| 🕸️ **LangGraph Agents** - Notebook to get started with lang-graph and agents | [](python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) | +| 👥 **CrewAI Agents** - Notebook to get started with CrewAI and lang-graph | [](python-recipes/agents/01_crewai_langgraph_redis.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/01_crewai_langgraph_redis.ipynb) | +| 🧠 **Memory Agent** - Building an agent with short term and long term memory using Redis | [](python-recipes/agents/03_memory_agent.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/03_memory_agent.ipynb) | +| 🛠️ **Full-Featured Agent** - Notebook builds full tool calling agent with semantic cache and router | [](python-recipes/agents/02_full_featured_agent.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/02_full_featured_agent.ipynb) | +| 🥗 **Autogen Agent** - Builds a blog writing agent with Autogen and Redis memory | [](python-recipes/agents/04_autogen_agent.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/04_autogen_agent.ipynb) | + +### Computer Vision +| Recipe | GitHub | Google Colab | +| ------ | ------ | ------------ | +| 👤 **Facial Recognition** - Build a facial recognition system using the Facenet embedding model and RedisVL | [](python-recipes/computer-vision/00_facial_recognition_facenet.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/computer-vision/00_facial_recognition_facenet.ipynb) | + + +### Recommendation Systems + +| Recipe | GitHub | Google Colab | +| --- | --- | --- | +| 📋 **Content Filtering** - Intro content filtering example with redisvl | [](python-recipes/recommendation-systems/00_content_filtering.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/00_content_filtering.ipynb) | +| 👥 **Collaborative Filtering** - Intro collaborative filtering example with redisvl | [](python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) | +| 🏗️ **Two Towers** - Intro deep learning two tower example with redisvl | [](python-recipes/recommendation-systems/02_two_towers.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/02_two_towers.ipynb) | + +### Feature Store +| Recipe | GitHub | Google Colab | +| ------ | ------ | ------------ | +| 💳 **Credit Scoring** - Credit scoring system using Feast with Redis as the online store | [](python-recipes/feature-store/00_feast_credit_score.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/00_feast_credit_score.ipynb) | +| 🔍 **Transaction Search** - Real-time transaction feature search with Redis | [](python-recipes/feature-store/01_card_transaction_search.ipynb) | [](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/01_card_transaction_search.ipynb) | + +### ☕️ Java AI Recipes + +A set of Java recipes can be found under [/java-recipes](/java-recipes/README.md). + + +## Tutorials +Need a *deeper-dive* through different use cases and topics? + +
|
+ 🤖 Agentic RAG
+ + A tutorial focused on agentic RAG with LlamaIndex and Cohere + |
+
+ ☁️ RAG on VertexAI
+ + A RAG tutorial featuring Redis with Vertex AI + |
+
+ 🔍 Recommendation Systems
+ + Building realtime recsys with NVIDIA Merlin & Redis + |
+
|
+ 🧑🏻💻 Redis Movies Searcher Workshop
+ + A hands-on workshop to create the Redis Movies Searcher application + |
+ +## Integrations +Redis integrates with many different players in the AI ecosystem. Here's a curated list below: + +| Integration | Description | | --- | --- | -[/RAG/04_advanced_redisvl.ipynb](python-recipes/RAG/04_advanced_redisvl.ipynb) | Notebook for additional tips and techniques to improve RAG quality | +| [RedisVL](https://github.com/redis/redis-vl-python) | A dedicated Python client lib for Redis as a Vector DB | +| [AWS Bedrock](https://redis.io/docs/latest/integrate/amazon-bedrock/) | Streamlines GenAI deployment by offering foundational models as a unified API | +| [LangChain Python](https://github.com/langchain-ai/langchain) | Popular Python client lib for building LLM applications powered by Redis | +| [LangChain JS](https://github.com/langchain-ai/langchainjs) | Popular JS client lib for building LLM applications powered by Redis | +| [LlamaIndex](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/RedisIndexDemo.html) | LlamaIndex Integration for Redis as a vector Database (formerly GPT-index) | +| [LiteLLM](https://www.litellm.ai/) | Popular LLM proxy layer to help manage and streamline usage of multiple foundation models | +| [Semantic Kernel](https://github.com/microsoft/semantic-kernel/tree/main) | Popular lib by MSFT to integrate LLMs with plugins | +| [RelevanceAI](https://relevance.ai/) | Platform to tag, search and analyze unstructured data faster, built on Redis | +| [DocArray](https://docs.docarray.org/user_guide/storing/index_redis/) | DocArray Integration of Redis as a VectorDB by Jina AI | -## Agents -/Users/robert.shelton/Documents/redis-ai-resources/python-recipes/agents/01_crewai_langgraph_redis.ipynb -| Recipe | Description | -| --- | --- | -[/agents/00_langgraph_redis_agentic_rag.ipynb](python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) | Notebook to get started with lang-graph and agents | -[/agents/01_crewai_langgraph_redis.ipynb](python-recipes/agents/01_crewai_langgraph_redis.ipynb) | Notebook to get started with lang-graph and agents | +
-## Recommendation systems +# Other Helpful Resources -| Recipe | Description | -| --- | --- | -| [/recommendation-systems/content_filtering.ipynb](python-recipes/recommendation-systems/content_filtering.ipynb) | Intro content filtering example with redisvl | -| [/recommendation-systems/collaborative_filtering.ipynb](python-recipes/recommendation-systems/collaborative_filtering.ipynb) | Intro collaborative filtering example with redisvl | +- [Vector Databases and Large Language Models](https://youtu.be/GJDN8u3Y-T4) - Talk given at LLMs in Production Part 1 by Sam Partee. +- [Level-up RAG with RedisVL](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) +- [Improving RAG quality with RAGAs](https://redis.io/blog/get-better-rag-responses-with-ragas/) +- [Vector Databases and AI-powered Search Talk](https://www.youtube.com/watch?v=g2bNHLeKlAg) - Video "Vector Databases and AI-powered Search" given by Sam Partee at SDSC 2023. +- [NVIDIA RecSys with Redis](https://developer.nvidia.com/blog/offline-to-online-feature-storage-for-real-time-recommendation-systems-with-nvidia-merlin/) +- [Benchmarking results for vector databases](https://redis.io/blog/benchmarking-results-for-vector-databases/) - Benchmarking results for vector databases, including Redis and 7 other Vector Database players. +- [Redis Vector Library Docs](https://docs.redisvl.com) +- [Redis Vector Search API Docs](https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/) - Official Redis literature for Vector Similarity Search. +- [Redis Retrieval Optimizer](https://pypi.org/project/redis-retrieval-optimizer/) - Library for optimizing index, embedding, and search method usage within Redis. -### See also -An exciting example of how Redis can power production-ready systems is highlighted in our collaboration with [NVIDIA](https://developer.nvidia.com/blog/offline-to-online-feature-storage-for-real-time-recommendation-systems-with-nvidia-merlin/) to construct a state-of-the-art recommendation system. +
-Within [this repository](https://github.com/redis-developer/redis-nvidia-recsys), you'll find three examples, each escalating in complexity, showcasing the process of building such a system. +## Contributing +We welcome contributions to Redis AI Resources! Here's how you can help: -# Integrations/Tools -- [⭐ RedisVL](https://github.com/redis/redis-vl-python) - a dedicated Python client lib for Redis as a Vector DB. -- [⭐ AWS Bedrock](https://redis.io/docs/latest/integrate/amazon-bedrock/) - Streamlines GenAI deployment by offering foundational models as a unified API. -- [⭐ LangChain Python](https://github.com/langchain-ai/langchain) - popular Python client lib for building LLM applications. -powered by Redis. -- [⭐ LangChain JS](https://github.com/langchain-ai/langchainjs) - popular JS client lib for building LLM applications. -powered by Redis. -- [⭐ LlamaIndex](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/RedisIndexDemo.html) - LlamaIndex Integration for Redis as a vector Database (formerly GPT-index). -- [Semantic Kernel](https://github.com/microsoft/semantic-kernel/tree/main) - popular lib by MSFT to integrate LLMs with plugins. -- [RelevanceAI](https://relevance.ai/) - Platform to ag, search and analyze unstructured data faster, built on Redis. -- [DocArray](https://docs.docarray.org/user_guide/storing/index_redis/) - DocArray Integration of Redis as a VectorDB by Jina AI. +1. **Add a new recipe**: Create a Jupyter notebook demonstrating a Redis AI use case +2. **Improve documentation**: Enhance existing notebooks or README with clearer explanations +3. **Fix bugs**: Address issues in code samples or documentation +4. **Suggest improvements**: Open an issue with ideas for new content or enhancements +To contribute: +1. Fork the repository +2. Create a feature branch +3. Make your changes +4. Submit a pull request -# Additional content -- [Vector Similarity Search: From Basics to Production](https://mlops.community/vector-similarity-search-from-basics-to-production/) - Introductory blog post to VSS and Redis as a VectorDB. -- [AI-Powered Document Search](https://datasciencedojo.com/blog/ai-powered-document-search/) - Blog post covering AI Powered Document Search Use Cases & Architectures. -- [Vector Search on Azure](https://techcommunity.microsoft.com/t5/azure-developer-community-blog/vector-similarity-search-with-azure-cache-for-redis-enterprise/ba-p/3822059) - Using Azure Redis Enterprise for Vector Search -- [Vector Databases and Large Language Models](https://youtu.be/GJDN8u3Y-T4) - Talk given at LLMs in Production Part 1 by Sam Partee. -- [Vector Databases and AI-powered Search Talk](https://www.youtube.com/watch?v=g2bNHLeKlAg) - Video "Vector Databases and AI-powered Search" given by Sam Partee at SDSC 2023. -- [Engineering Lab Review](https://mlops.community/redis-vector-search-engineering-lab-review/) - Review of the first Redis VSS Hackathon. -- [Real-Time Product Recommendations](https://jina.ai/news/real-time-product-recommendation-using-redis-and-docarray/) - Content-based recsys design with Redis and DocArray. -- [LabLab AI Redis Tech Page](https://lablab.ai/tech/redis) -- [Storing and querying for embeddings with Redis](https://blog.baeke.info/2023/03/21/storing-and-querying-for-embeddings-with-redis/) -- [Building Intelligent Apps with Redis Vector Similarity Search](https://redis.com/blog/build-intelligent-apps-redis-vector-similarity-search/) -- [RedisDays Keynote](https://www.youtube.com/watch?v=EEIBTEpb2LI) - Video "Infuse Real-Time AI Into Your "Financial Services" Application". -- [RedisDays Trading Signals](https://www.youtube.com/watch?v=_Lrbesg4DhY) - Video "Using AI to Reveal Trading Signals Buried in Corporate Filings". - -# Benchmarks -- [Benchmarking results for vector databases](https://redis.io/blog/benchmarking-results-for-vector-databases/) - Benchmarking results for vector databases, including Redis and 7 other Vector Database players. -- [ANN Benchmarks](https://ann-benchmarks.com) - Standard ANN Benchmarks site. *Only using single Redis OSS instance/client.* - -# Documentation -- [Redis Vector Database QuickStart](https://redis.io/docs/get-started/vector-database/) -- [Redis Vector Similarity Docs](https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/) - Official Redis literature for Vector Similarity Search. -- [Redis-py Search Docs](https://redis.readthedocs.io/en/latest/redismodules.html#redisearch-commands) - Redis-py client library docs for RediSearch. -- [Redis-py General Docs](https://redis.readthedocs.io/en/latest/) - Redis-py client library documentation. -- [Redis Stack](https://redis.io/docs/stack/) - Redis Stack documentation. -- [Redis Clients](https://redis.io/docs/clients/) - Redis client list. +Please follow the existing style and format of the repository when adding content. diff --git a/assets/cache_diagram.png b/assets/cache_diagram.png new file mode 100644 index 00000000..fa59fda6 Binary files /dev/null and b/assets/cache_diagram.png differ diff --git a/assets/feature_store.png b/assets/feature_store.png new file mode 100644 index 00000000..662eb923 Binary files /dev/null and b/assets/feature_store.png differ diff --git a/assets/full_featured_agent.png b/assets/full_featured_agent.png new file mode 100644 index 00000000..23a74e72 Binary files /dev/null and b/assets/full_featured_agent.png differ diff --git a/assets/long-term-memory.png b/assets/long-term-memory.png new file mode 100644 index 00000000..309ed22c Binary files /dev/null and b/assets/long-term-memory.png differ diff --git a/assets/memory-agents.png b/assets/memory-agents.png new file mode 100644 index 00000000..7d0249f4 Binary files /dev/null and b/assets/memory-agents.png differ diff --git a/assets/role-based-rag.png b/assets/role-based-rag.png new file mode 100644 index 00000000..4c5d6a56 Binary files /dev/null and b/assets/role-based-rag.png differ diff --git a/assets/router_diagram.png b/assets/router_diagram.png new file mode 100644 index 00000000..49df72d8 Binary files /dev/null and b/assets/router_diagram.png differ diff --git a/assets/short-term-memory.png b/assets/short-term-memory.png new file mode 100644 index 00000000..41759488 Binary files /dev/null and b/assets/short-term-memory.png differ diff --git a/contributing.md b/contributing.md index ca4b3025..6136774f 100644 --- a/contributing.md +++ b/contributing.md @@ -11,17 +11,6 @@ Open a PR with your addition. We expect the following standards: 3. New additions should be added to the bottom of the list (unless otherwise noted). 4. New additions should not contain any profanity or offensive language. -### What it takes to get a Star - -When reviewing the PR, we will determine whether a new entry gets a star! - -Examples that: -- are well-documented and easy to follow -- pertain to a new or creative use case -- follow good coding/writing hygiene - -will be considered for getting a special star ⭐. - ## Updating your Pull Request Sometimes, a maintainer will ask you to edit your Pull Request before it is included. This is normally due to spelling errors or because your PR didn't match the list format. diff --git a/java-recipes/README.md b/java-recipes/README.md new file mode 100644 index 00000000..c8ba21f3 --- /dev/null +++ b/java-recipes/README.md @@ -0,0 +1,59 @@ +
+
+
+ +[**Notebooks**](#notebooks) | [**Applications**](#applications) | [**Example Applications**](#example-notebooks--applications) + +
+ Redis AI Java Resources
+
+
+[](https://opensource.org/licenses/MIT)
+
+
+
+
+
+ ✨ Java-based code examples, notebooks, and resources for using Redis in AI and ML applications. ✨
+
+
+
++ +[**Notebooks**](#notebooks) | [**Applications**](#applications) | [**Example Applications**](#example-notebooks--applications) + +
+ +There are two types of Java Recipes: Notebooks and Applications. Notebooks are interactive, self-contained examples in Jupyter format that let you explore AI concepts step by step that mix code, explanations, and output in one place. Applications, on the other hand, are full Spring Boot projects meant for building real-world systems. They show how to structure, run, and scale actual AI-powered apps using Redis, embedding models, and Spring AI in a production-like setup. + +## Notebooks + +Notebooks require a Jupyter Notebook environment to run. Check out the [Setup Instructions & Implementation Details](./notebooks/README.md) for more details on how to set up your environment. + +| Notebook | Description | +|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------| +| [notebooks/RAG/spring_ai_redis_rag.ipynb](./notebooks/RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-ba sed beer recommendation chatbot using Spring AI and Redis as the vector store | + +## Applications + +| Application | Description | +|-------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------| +| [applications/vector-similarity-search/redis-om-spring](./applications/vector-similarity-search/redis-om-spring/spring_boot_redis_om_spring.md) | Demonstrates building a vector similarity search application using Spring Boot and Redis OM Spring | +| [applications/vector-similarity-search/spring-ai](./applications/vector-similarity-search/spring-ai/spring_boot_spring_ai.md) | Demonstrates building a vector similarity search application using Spring Boot and Spring AI | + + +## Example Notebooks & Applications + +### Beer Recommendation Chatbot + +The `spring-ai-rag.ipynb` notebook demonstrates: + +- Loading and embedding beer data into Redis Vector Store +- Using local transformer models for generating embeddings +- Connecting to OpenAI for LLM capabilities +- Building a RAG pipeline to answer beer-related queries +- Semantic search over beer properties and descriptions + +### Vector Similarity Search with Redis OM Spring and Spring Boot + +The `spring_boot_redis_om_spring` directory contains a Spring Boot application that demonstrates how to use Redis OM Spring for vector similarity search. The application allows you to: +- Add movies to the Redis database +- Search for movies based on semantic similarity on the synopsis of the movie +- Perform hybrid search by adding filters to genre, cast, and year + diff --git a/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/autocomplete.png b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/autocomplete.png new file mode 100644 index 00000000..37b58585 Binary files /dev/null and b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/autocomplete.png differ diff --git a/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/index-redis-insight.png b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/index-redis-insight.png new file mode 100644 index 00000000..42089ac3 Binary files /dev/null and b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/index-redis-insight.png differ diff --git a/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/pre-filtered-vector-search.png b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/pre-filtered-vector-search.png new file mode 100644 index 00000000..4db1b0ab Binary files /dev/null and b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/pre-filtered-vector-search.png differ diff --git a/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/redis-insight.png b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/redis-insight.png new file mode 100644 index 00000000..313e4e4b Binary files /dev/null and b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/redis-insight.png differ diff --git a/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/vector-search.png b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/vector-search.png new file mode 100644 index 00000000..bb33e9c1 Binary files /dev/null and b/java-recipes/applications/vector-similarity-search/redis-om-spring/readme-assets/vector-search.png differ diff --git a/java-recipes/applications/vector-similarity-search/redis-om-spring/spring_boot_redis_om_spring.md b/java-recipes/applications/vector-similarity-search/redis-om-spring/spring_boot_redis_om_spring.md new file mode 100644 index 00000000..fa5e873a --- /dev/null +++ b/java-recipes/applications/vector-similarity-search/redis-om-spring/spring_boot_redis_om_spring.md @@ -0,0 +1,227 @@ +# Vector Search with Redis OM Spring (SpringBoot) + +Vector similarity search (also known as semantic search) is a powerful technique that allows you to find items based on their semantic meaning rather than exact keyword matches. Redis Query Engine supports vector similarity search through its vector indexing capabilities, enabling you to implement semantic search applications with high performance and low latency. + +This demo showcases how to implement vector similarity search using Redis OM Spring, a library that simplifies working with Redis data models and the Redis Query Engine. + +## Learning resources: + +- Article: [Semantic Search with Spring Boot & Redis](https://raphaeldelio.com/2025/04/29/semantic-search-with-spring-boot-redis/) +- Video: [Autocomplete in Spring with Redis](https://www.youtube.com/watch?v=rjaR1PR5gVk) +- Video: [What is an embedding model?](https://youtu.be/0U1S0WSsPuE) +- Video: [Exact vs Approximate Nearest Neighbors - What's the difference?](https://youtu.be/9NvO-VdjY80) +- Video: [What is semantic search?](https://youtu.be/o3XN4dImESE) +- Video: [What is a vector database?](https://youtu.be/Yhv19le0sBw) + + +## Repository + +The repository for this demo can be found [here](https://github.com/redis-developer/redis-springboot-resources/tree/main/search/vector-search) + +## Requirements + +To run this demo, you’ll need the following installed on your system: +- Docker – [Install Docker](https://docs.docker.com/get-docker/) +- Docker Compose – Included with Docker Desktop or available via CLI installation guide + +## Running the demo + +The easiest way to run the demo is with Docker Compose, which sets up all required services in one command. + +### Step 1: Clone the repository + +If you haven’t already: + +```bash +git clone https://github.com/redis-developer/redis-springboot-recipes.git +cd redis-springboot-recipes/search/full-text-search-and-autocomplete +``` + +### Step 2: Start the services + +```bash +docker compose up --build +``` + +This will start: + +- redis: for storing documents +- redis-insight: a UI to explore the Redis data +- vector-search-app: the Spring Boot app that implements vector search + +## Using the demo + +When all of your services are up and running. Go to `localhost:8080` to access the demo. + +If you search using the extract box, the system will perform semantic search and find items on the database that are semantically similar to your query: + + + +You can also apply filters for pre-filtering the results before applying semantic search: + + + +This demo also supports autocompletion of the title: + + + +### Redis Insight + +RedisInsight is a graphical tool developed by Redis to help developers and administrators interact with and manage Redis databases more efficiently. It provides a visual interface for exploring keys, running commands, analyzing memory usage, and monitoring performance metrics in real-time. RedisInsight supports features like full-text search, time series, streams, and vector data structures, making it especially useful for working with more advanced Redis use cases. With its intuitive UI, it simplifies debugging, optimizing queries, and understanding data patterns without requiring deep familiarity with the Redis CLI. + +The Docker Compose file will also spin up an instance of Redis Insight. We can access it by going to `localhost:5540`: + +If we go to Redis Insight, we will be able to see the data stored in Redis: + + + +And if run the command `FT.INFO 'com.redis.fulltextsearchandautocomplete.domain.MovieIdx'`, we'll be able to see the schema that was created for indexing our documents efficiently: + + + +## How It Is Implemented + +The application uses Redis OM Spring to vectorize documents and perform vector similarity search. Here's how it works: + +### Defining Vector Fields with Redis OM Spring Annotations + +Documents are defined as Java classes with Redis OM Spring annotations that specify how they should be vectorized and indexed: + +```java +@Document +public class Movie { + // Other fields... + + @Vectorize( + destination = "embeddedExtract", + embeddingType = EmbeddingType.SENTENCE + ) + private String extract; + + @Indexed( + schemaFieldType = SchemaFieldType.VECTOR, + algorithm = VectorField.VectorAlgorithm.HNSW, + type = VectorType.FLOAT32, + dimension = 384, + distanceMetric = DistanceMetric.COSINE, + initialCapacity = 10 + ) + private float[] embeddedExtract; + + // Getters and setters... +} +``` + +Let's break down the annotations: + +- `@Vectorize`: Automatically generates vector embeddings for the text field + - `destination`: Specifies the field where the embedding will be stored + - `embeddingType`: Defines the granularity of the embedding (SENTENCE in this case) + +- `@Indexed` with vector parameters: + - `schemaFieldType = SchemaFieldType.VECTOR`: Marks this as a vector field + - `algorithm = VectorField.VectorAlgorithm.HNSW`: Uses the Hierarchical Navigable Small World algorithm for efficient approximate nearest neighbor search + - `type = VectorType.FLOAT32`: Specifies the vector data type + - `dimension = 384`: Sets the vector dimension (must match the number of dimensions output by the embedding model) + - `distanceMetric = DistanceMetric.COSINE`: Uses cosine similarity for distance calculation + +### Storing and Vectorizing Documents + +When documents are saved to Redis using the repository, Redis OM Spring automatically generates vector embeddings: + +```java +public void loadAndSaveMovies(String filePath) throws Exception { + // Load movies from JSON file + List
+
+
+ +[**Setup**](#setup) | [**Running the Project**](#running-the-project) | [**Notebooks**](#notebooks) | [**Project Structure**](#project-structure) | [**Implementation Details**](#implementation-details) + +
+ Redis AI Java Resources
+
+
+[](https://opensource.org/licenses/MIT)
+
+
+
+
+
+ ✨ Java-based code examples, notebooks, and resources for using Redis in AI and ML applications. ✨
+
+
+
++ +[**Setup**](#setup) | [**Running the Project**](#running-the-project) | [**Notebooks**](#notebooks) | [**Project Structure**](#project-structure) | [**Implementation Details**](#implementation-details) + +
+ +## Setup + +This project uses Docker Compose to set up a complete environment for running Java-based AI applications with Redis. The environment includes: + +- A Jupyter Notebook server with Java kernel support +- Redis Stack (includes Redis and RedisInsight) +- Pre-installed dependencies for AI/ML workloads + +### Prerequisites + +- [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) +- OpenAI API key (for notebooks that use OpenAI services) + +### Environment Configuration + +1. Create a `.env` file in the project root with your OpenAI API key: + +```bash +OPENAI_API_KEY=your_openai_api_key_here +``` + +## Running the Project + +1. Clone the repository (if you haven't already): + + ```bash + git clone https://github.com/redis-developer/redis-ai-resources.git + cd redis-ai-resources/java-resources + ``` + +2. Start the Docker containers: + + ```bash + docker-compose up -d + ``` + +3. Access the Jupyter environment: + - Open your browser and navigate to [http://localhost:8888](http://localhost:8888) + - The token is usually shown in the docker-compose logs. You can view them with: + + ```bash + docker-compose logs jupyter + ``` + +4. Access RedisInsight: + - Open your browser and navigate to [http://localhost:8001](http://localhost:8001) + - Connect to Redis using the following details: + - Host: redis-java + - Port: 6379 + - No password (unless configured) + +5. When finished, stop the containers: + + ```bash + docker-compose down + ``` + +## Notebooks + +| Notebook | Description | +| --- | --- | +| [RAG/spring_ai_redis_rag.ipynb](./RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store | + +## Project Structure + +```bash +notebooks/ +├── .env # Environment variables (create this) +├── docker-compose.yml # Docker Compose configuration +├── jupyter/ # Jupyter configuration files +│ ├── Dockerfile # Dockerfile for Jupyter with Java kernel +│ ├── environment.yml # Conda environment specification +│ ├── install.py # JJava kernel installation script +│ ├── kernel.json # Kernel specification +│ └── java/ # Java dependencies and configuration +│ └── pom.xml # Maven project file with dependencies +└── resources/ # Data files for notebooks + └── beers.json.gz # Compressed beer dataset +``` + +## Implementation Details + +### Java Jupyter Kernel + +The project uses [JJava](https://github.com/dflib/jjava), a Jupyter kernel for Java based on JShell. This allows for interactive Java development in Jupyter notebooks. + +Key components: + +- Java 21 for modern Java features +- Maven for dependency management +- JJava kernel for Jupyter integration + +### Spring AI Integration + +The Spring AI notebooks showcase how to use Spring's AI capabilities with Redis: + +- **Spring AI**: Framework for building AI-powered applications +- **Redis Vector Store**: Used for storing and querying vector embeddings +- **Transformer Models**: For generating embeddings locally +- **RAG Pattern**: Demonstrates the Retrieval Augmented Generation pattern + +### Docker Configuration + +The Docker setup includes: + +1. **Jupyter Container**: + - Based on minimal Jupyter notebook image + - Adds Java 21, Maven, and the JJava kernel + - Includes Python environment with PyTorch and other ML libraries + +2. **Redis Container**: + - Uses Redis Stack image with Vector Search capabilities + - Persists data using Docker volumes + - Exposes Redis on port 6379 and RedisInsight on port 8001 \ No newline at end of file diff --git a/java-recipes/notebooks/docker-compose.yml b/java-recipes/notebooks/docker-compose.yml new file mode 100644 index 00000000..5036afcf --- /dev/null +++ b/java-recipes/notebooks/docker-compose.yml @@ -0,0 +1,25 @@ +name: redis-ai-java +services: + jupyter: + build: + context: . + dockerfile: ./jupyter/Dockerfile + ports: + - "8888:8888" + environment: + - JUPYTER_ENABLE_LAB=yes + env_file: + - .env + volumes: + - ./:/home/jovyan/ + - ./resources:/home/jovyan/resources + redis-java: + image: redis/redis-stack:latest + ports: + - "6379:6379" # Redis database port + - "8001:8001" # RedisInsight port + volumes: + - redis-data:/data # Persist Redis data + +volumes: + redis-data: \ No newline at end of file diff --git a/java-recipes/notebooks/jupyter/Dockerfile b/java-recipes/notebooks/jupyter/Dockerfile new file mode 100644 index 00000000..a7604943 --- /dev/null +++ b/java-recipes/notebooks/jupyter/Dockerfile @@ -0,0 +1,59 @@ +FROM quay.io/jupyter/minimal-notebook:latest + +RUN mkdir /home/jovyan/resources + +USER root +WORKDIR /home/jovyan + +# Install dependencies: Java 21 and Maven +RUN apt-get update && apt-get install -y openjdk-21-jdk maven + +# Copy the pre-created Maven project and jjava-glue project +COPY ./jupyter/java /home/jovyan/java +COPY ./jupyter/install.py /home/jovyan/install.py + +# Use Maven to download dependencies for JJava +WORKDIR /home/jovyan/java + +# Download the JJava jar directly +RUN mvn dependency:get -Dartifact=org.dflib.jjava:jjava:1.0-M3 -Ddest=./ -Dtransitive=false +RUN mv jjava-1.0-M3.jar jjava.jar + +# Pre-download Spring AI Dependencies +RUN mvn dependency:get -Dartifact=org.springframework.ai:spring-ai-openai:1.0.0-M6 +RUN mvn dependency:get -Dartifact=org.springframework.ai:spring-ai-transformers:1.0.0-M6 +RUN mvn dependency:get -Dartifact=org.springframework.ai:spring-ai-redis-store:1.0.0-M6 +# Pre-download Jedis +RUN mvn dependency:get -Dartifact=redis.clients:jedis:5.2.0 +# Download all dependencies +RUN mvn dependency:copy-dependencies -DoutputDirectory=./lib + +# Create a list of dependencies for the classpath +RUN find ./lib -name "*.jar" | tr '\n' ':' > classpath.txt +# Add the jjava.jar to the classpath +RUN echo -n "/home/jovyan/java/jjava.jar:" >> classpath.txt + +# Install the kernel with classpath configuration +WORKDIR /home/jovyan +RUN python install.py --prefix /opt/conda/ --classpath $(cat /home/jovyan/java/classpath.txt) + +# Pre-download Transformer Models +RUN pip install transformers torch +RUN mkdir -p /home/jovyan/.cache/huggingface/hub +# Pre-download the specific model used in Spring AI Transformers +RUN python -c "from transformers import AutoModel; AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')" + +# Clean up Maven artifacts but keep the jjava.jar and lib directory +RUN rm -rf /home/jovyan/java/target /home/jovyan/java/.m2 /home/jovyan/java/pom.xml \ + /home/jovyan/java/classpath.txt \ + && rm -f /home/jovyan/install.py + +# Install conda packages from environment.yml +COPY ./jupyter/environment.yml /tmp/ +RUN conda env update -f /tmp/environment.yml && \ + conda clean --all -f -y && \ + fix-permissions "${CONDA_DIR}" && \ + fix-permissions "/home/${NB_USER}" + +WORKDIR /home/jovyan +USER $NB_UID \ No newline at end of file diff --git a/java-recipes/notebooks/jupyter/environment.yml b/java-recipes/notebooks/jupyter/environment.yml new file mode 100644 index 00000000..46dbe904 --- /dev/null +++ b/java-recipes/notebooks/jupyter/environment.yml @@ -0,0 +1,9 @@ +name: base +channels: + - pytorch + - conda-forge + - defaults +dependencies: + - pytorch + - torchtext + - gensim \ No newline at end of file diff --git a/java-recipes/notebooks/jupyter/install.py b/java-recipes/notebooks/jupyter/install.py new file mode 100644 index 00000000..78bca62c --- /dev/null +++ b/java-recipes/notebooks/jupyter/install.py @@ -0,0 +1,197 @@ +import argparse +import json +import os +import sys + +from jupyter_client.kernelspec import KernelSpecManager + +ALIASES = { + "IJAVA_CLASSPATH": { + }, + "IJAVA_COMPILER_OPTS": { + }, + "IJAVA_STARTUP_SCRIPTS_PATH": { + }, + "IJAVA_STARTUP_SCRIPT": { + }, + "IJAVA_TIMEOUT": { + "NO_TIMEOUT": "-1", + }, + +} + +NAME_MAP = { + "classpath": "IJAVA_CLASSPATH", + "comp-opts": "IJAVA_COMPILER_OPTS", + "startup-scripts-path": "IJAVA_STARTUP_SCRIPTS_PATH", + "startup-script": "IJAVA_STARTUP_SCRIPT", + "timeout": "IJAVA_TIMEOUT", + +} + +def type_assertion(name, type_fn): + env = NAME_MAP[name] + aliases = ALIASES.get(env, {}) + + def checker(value): + alias = aliases.get(value, value) + type_fn(alias) + return alias + setattr(checker, '__name__', getattr(type_fn, '__name__', 'type_fn')) + return checker + +class EnvVar(argparse.Action): + def __init__(self, option_strings, dest, aliases=None, name_map=None, list_sep=None, **kwargs): + super(EnvVar, self).__init__(option_strings, dest, **kwargs) + + if aliases is None: aliases = {} + if name_map is None: name_map = {} + + self.aliases = aliases + self.name_map = name_map + self.list_sep = list_sep + + for name in self.option_strings: + if name.lstrip('-') not in name_map: + raise ValueError('Name "%s" is not mapped to an environment variable' % name.lstrip('-')) + + + def __call__(self, parser, namespace, value, option_string=None): + if option_string is None: + raise ValueError('option_string is required') + + env = getattr(namespace, self.dest, None) + if env is None: + env = {} + + name = option_string.lstrip('-') + env_var = self.name_map[name] + + if self.list_sep: + old = env.get(env_var) + value = old + self.list_sep + str(value) if old is not None else str(value) + + env[env_var] = value + + setattr(namespace, self.dest, env) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Install the java kernel.') + + install_location = parser.add_mutually_exclusive_group() + install_location.add_argument( + '--user', + help='Install to the per-user kernel registry.', + action='store_true' + ) + install_location.add_argument( + '--sys-prefix', + help="Install to Python's sys.prefix. Useful in conda/virtual environments.", + action='store_true' + ) + install_location.add_argument( + '--prefix', + help=''' + Specify a prefix to install to, e.g. an env. + The kernelspec will be installed in PREFIX/share/jupyter/kernels/ + ''', + default='' + ) + + parser.add_argument( + '--replace', + help='Replace any existing kernel spec with this name.', + action='store_true' + ) + + parser.add_argument( + "--classpath", + dest="env", + action=EnvVar, + aliases=ALIASES, + name_map=NAME_MAP, + help="A file path separator delimited list of classpath entries that should be available to the user code. **Important:** no matter what OS, this should use forward slash \"/\" as the file separator. Also each path may actually be a simple glob.", + type=type_assertion("classpath", str), + list_sep=os.pathsep, + ) + parser.add_argument( + "--comp-opts", + dest="env", + action=EnvVar, + aliases=ALIASES, + name_map=NAME_MAP, + help="A space delimited list of command line options that would be passed to the `javac` command when compiling a project. For example `-parameters` to enable retaining parameter names for reflection.", + type=type_assertion("comp-opts", str), + list_sep=" ", + ) + parser.add_argument( + "--startup-scripts-path", + dest="env", + action=EnvVar, + aliases=ALIASES, + name_map=NAME_MAP, + help="A file path seperator delimited list of `.jshell` scripts to run on startup. This includes ijava-jshell-init.jshell and ijava-display-init.jshell. **Important:** no matter what OS, this should use forward slash \"/\" as the file separator. Also each path may actually be a simple glob.", + type=type_assertion("startup-scripts-path", str), + list_sep=os.pathsep, + ) + parser.add_argument( + "--startup-script", + dest="env", + action=EnvVar, + aliases=ALIASES, + name_map=NAME_MAP, + help="A block of java code to run when the kernel starts up. This may be something like `import my.utils;` to setup some default imports or even `void sleep(long time) { try {Thread.sleep(time); } catch (InterruptedException e) { throw new RuntimeException(e); }}` to declare a default utility method to use in the notebook.", + type=type_assertion("startup-script", str), + ) + parser.add_argument( + "--timeout", + dest="env", + action=EnvVar, + aliases=ALIASES, + name_map=NAME_MAP, + help="A duration specifying a timeout (in milliseconds by default) for a _single top level statement_. If less than `1` then there is no timeout. If desired a time may be specified with a `TimeUnit` may be given following the duration number (ex `\"30 SECONDS\"`).", + type=type_assertion("timeout", str), + ) + + + args = parser.parse_args() + + if not hasattr(args, "env") or getattr(args, "env") is None: + setattr(args, "env", {}) + + + # Install the kernel + install_dest = KernelSpecManager().install_kernel_spec( + os.path.join(os.path.dirname(os.path.abspath(__file__)), 'java'), + kernel_name='java', + user=args.user, + prefix=sys.prefix if args.sys_prefix else args.prefix, + replace=args.replace + ) + + # Connect the self referencing token left in the kernel.json to point to it's install location. + + # Prepare the token replacement string which should be properly escaped for use in a JSON string + # The [1:-1] trims the first and last " json.dumps adds for strings. + install_dest_json_fragment = json.dumps(install_dest)[1:-1] + + # Prepare the paths to the installed kernel.json and the one bundled with this installer. + local_kernel_json_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'java', 'kernel.json') + installed_kernel_json_path = os.path.join(install_dest, 'kernel.json') + + # Replace the @KERNEL_INSTALL_DIRECTORY@ token with the path to where the kernel was installed + # in the installed kernel.json from the local template. + with open(local_kernel_json_path, 'r') as template_kernel_json_file: + template_kernel_json_contents = template_kernel_json_file.read() + kernel_json_contents = template_kernel_json_contents.replace( + '@KERNEL_INSTALL_DIRECTORY@', + install_dest_json_fragment + ) + kernel_json_json_contents = json.loads(kernel_json_contents) + kernel_env = kernel_json_json_contents.setdefault('env', {}) + for k, v in args.env.items(): + kernel_env[k] = v + with open(installed_kernel_json_path, 'w') as installed_kernel_json_file: + json.dump(kernel_json_json_contents, installed_kernel_json_file, indent=4, sort_keys=True) + + print('Installed java kernel into "%s"' % install_dest) diff --git a/java-recipes/notebooks/jupyter/java/kernel.json b/java-recipes/notebooks/jupyter/java/kernel.json new file mode 100644 index 00000000..348e8789 --- /dev/null +++ b/java-recipes/notebooks/jupyter/java/kernel.json @@ -0,0 +1,13 @@ +{ + "argv": [ + "java", + "--add-opens", "jdk.jshell/jdk.jshell=ALL-UNNAMED", + "-jar", + "@KERNEL_INSTALL_DIRECTORY@/jjava.jar", + "{connection_file}" + ], + "display_name": "Java", + "language": "java", + "interrupt_mode": "message", + "env": {} +} \ No newline at end of file diff --git a/java-recipes/notebooks/jupyter/java/pom.xml b/java-recipes/notebooks/jupyter/java/pom.xml new file mode 100644 index 00000000..9e335f1e --- /dev/null +++ b/java-recipes/notebooks/jupyter/java/pom.xml @@ -0,0 +1,27 @@ + +
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R2-i8jBl9GRH"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "# RAG from scratch with the Redis Vector Library\n",
+ "\n",
+ "\n",
+ "In this recipe we will cover the basic of the Redis Vector Library and build a basic RAG app from scratch.\n",
+ "\n",
+ "## Let's Begin!\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rT9HzsnQ1uiz"
+ },
+ "source": [
+ "## Environment Setup\n",
+ "\n",
+ "### Pull Github Materials\n",
+ "Because you are likely running this notebook in **Google Colab**, we need to first\n",
+ "pull the necessary dataset and materials directly from GitHub.\n",
+ "\n",
+ "**If you are running this notebook locally**, FYI you may not need to perform this\n",
+ "step at all."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T04:41:18.607703Z",
+ "start_time": "2025-04-24T04:41:11.664107Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rT9HzsnQ1uiz"
- },
- "source": [
- "## Environment Setup\n",
- "\n",
- "### Pull Github Materials\n",
- "Because you are likely running this notebook in **Google Colab**, we need to first\n",
- "pull the necessary dataset and materials directly from GitHub.\n",
- "\n",
- "**If you are running this notebook locally**, FYI you may not need to perform this\n",
- "step at all."
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "AJJ2UW6M1ui0",
+ "outputId": "0f5773b7-a292-4ee6-f4bd-20dc40ca2aba"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "AJJ2UW6M1ui0",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "0f5773b7-a292-4ee6-f4bd-20dc40ca2aba"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Cloning into 'temp_repo'...\n",
- "remote: Enumerating objects: 384, done.\u001b[K\n",
- "remote: Counting objects: 100% (247/247), done.\u001b[K\n",
- "remote: Compressing objects: 100% (159/159), done.\u001b[K\n",
- "remote: Total 384 (delta 135), reused 153 (delta 74), pack-reused 137 (from 1)\u001b[K\n",
- "Receiving objects: 100% (384/384), 64.50 MiB | 8.97 MiB/s, done.\n",
- "Resolving deltas: 100% (159/159), done.\n"
- ]
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
- "!mv temp_repo/python-recipes/RAG/resources .\n",
- "!rm -rf temp_repo"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cloning into 'temp_repo'...\r\n",
+ "remote: Enumerating objects: 679, done.\u001b[K\r\n",
+ "remote: Counting objects: 100% (330/330), done.\u001b[Kjects: 82% (271/330)\u001b[K\r\n",
+ "remote: Compressing objects: 100% (214/214), done.\u001b[K\r\n",
+ "remote: Total 679 (delta 227), reused 148 (delta 115), pack-reused 349 (from 2)\u001b[K\r\n",
+ "Receiving objects: 100% (679/679), 57.80 MiB | 11.09 MiB/s, done.\r\n",
+ "Resolving deltas: 100% (295/295), done.\r\n",
+ "mv: rename temp_repo/python-recipes/RAG/resources to ./resources: Directory not empty\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
+ "!mv temp_repo/python-recipes/RAG/resources .\n",
+ "!rm -rf temp_repo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z67mf6T91ui2"
+ },
+ "source": [
+ "### Install Python Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T04:41:20.572419Z",
+ "start_time": "2025-04-24T04:41:18.616143Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Z67mf6T91ui2"
- },
- "source": [
- "### Install Python Dependencies"
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "DgxBQFXQ1ui2",
+ "outputId": "c3c399d6-e294-4a3a-a0a3-82d818509991"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "id": "DgxBQFXQ1ui2",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "c3c399d6-e294-4a3a-a0a3-82d818509991"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
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- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h"
- ]
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "!pip install -q redis redisvl langchain_community pypdf sentence-transformers langchain openai"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\r\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install -q \"redisvl>=0.6.0\" langchain-community pypdf sentence-transformers langchain openai pandas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "peC8ThuVJkD7"
+ },
+ "source": [
+ "### Install Redis Stack\n",
+ "\n",
+ "Later in this tutorial, Redis will be used to store, index, and query vector\n",
+ "embeddings created from PDF document chunks. **We need to make sure we have a Redis\n",
+ "instance available.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zMKHJ7oWJkD8"
+ },
+ "source": [
+ "#### For Colab\n",
+ "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly from the Redis package archive."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "peC8ThuVJkD7"
- },
- "source": [
- "### Install Redis Stack\n",
- "\n",
- "Later in this tutorial, Redis will be used to store, index, and query vector\n",
- "embeddings created from PDF document chunks. **We need to make sure we have a Redis\n",
- "instance available.**"
- ]
+ "id": "c0d5lfNxJkD8",
+ "outputId": "f96e72fa-b9f3-476f-bc9e-328bd30d1344"
+ },
+ "outputs": [],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "%%sh\n",
+ "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
+ "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
+ "sudo apt-get update > /dev/null 2>&1\n",
+ "sudo apt-get install redis-stack-server > /dev/null 2>&1\n",
+ "redis-stack-server --daemonize yes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2arb8Ic0JkD8"
+ },
+ "source": [
+ "#### For Alternative Environments\n",
+ "There are many ways to get the necessary redis-stack instance running\n",
+ "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
+ "own version of Redis Enterprise running, that works too!\n",
+ "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n",
+ "3. With docker: `docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DhP1w0R9JkD8"
+ },
+ "source": [
+ "### Define the Redis Connection URL\n",
+ "\n",
+ "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace REDIS_PASSWORD, REDIS_HOST and REDIS_PORT values with your own."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:45.583246Z",
+ "start_time": "2025-04-24T16:46:45.581177Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "zMKHJ7oWJkD8"
- },
- "source": [
- "#### For Colab\n",
- "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly from the Redis package archive."
- ]
+ "id": "ggh5TzhkJkD9"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "# Replace values below with your own if using Redis Cloud instance\n",
+ "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
+ "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\") # ex: 18374\n",
+ "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\") # ex: \"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
+ "\n",
+ "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
+ "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b3ErDmsIJkD9"
+ },
+ "source": [
+ "## Simplified Vector Search with RedisVL"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KrtWWU4I1ui3"
+ },
+ "source": [
+ "### Dataset Preparation (PDF Documents)\n",
+ "\n",
+ "To best demonstrate Redis as a vector database layer, we will load a single\n",
+ "financial (10k filings) doc and preprocess it using some helpers from LangChain:\n",
+ "\n",
+ "- `PyPDFLoader` is not the only document loader type that LangChain provides. Docs: https://python.langchain.com/docs/integrations/document_loaders/pypdfloader/\n",
+ "- `RecursiveCharacterTextSplitter` is what we use to create smaller chunks of text from the doc. Docs: https://python.langchain.com/docs/how_to/recursive_text_splitter/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:46.043726Z",
+ "start_time": "2025-04-24T16:46:45.600472Z"
},
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "c0d5lfNxJkD8",
- "outputId": "f96e72fa-b9f3-476f-bc9e-328bd30d1344"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb jammy main\n",
- "Starting redis-stack-server, database path /var/lib/redis-stack\n"
- ]
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "%%sh\n",
- "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
- "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
- "sudo apt-get update > /dev/null 2>&1\n",
- "sudo apt-get install redis-stack-server > /dev/null 2>&1\n",
- "redis-stack-server --daemonize yes"
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "uijl2qFH1ui3",
+ "outputId": "a99b3fcb-7cfd-4dbd-f258-57779cfcae3c"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "2arb8Ic0JkD8"
- },
- "source": [
- "#### For Alternative Environments\n",
- "There are many ways to get the necessary redis-stack instance running\n",
- "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
- "own version of Redis Enterprise running, that works too!\n",
- "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n",
- "3. With docker: `docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest`"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Listing available documents ... ['resources/nke-10k-2023.pdf', 'resources/amzn-10k-2023.pdf', 'resources/jnj-10k-2023.pdf', 'resources/aapl-10k-2023.pdf', 'resources/testset_15.csv', 'resources/retrieval_basic_rag_test.csv', 'resources/2022-chevy-colorado-ebrochure.pdf', 'resources/nvd-10k-2023.pdf', 'resources/testset.csv', 'resources/msft-10k-2023.pdf', 'resources/propositions.json', 'resources/generation_basic_rag_test.csv']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+ "from langchain_community.document_loaders import PyPDFLoader\n",
+ "\n",
+ "# Load list of pdfs from a folder\n",
+ "data_path = \"resources/\"\n",
+ "docs = [os.path.join(data_path, file) for file in os.listdir(data_path)]\n",
+ "\n",
+ "print(\"Listing available documents ...\", docs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:50.509810Z",
+ "start_time": "2025-04-24T16:46:46.104219Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "DhP1w0R9JkD8"
- },
- "source": [
- "### Define the Redis Connection URL\n",
- "\n",
- "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace REDIS_PASSWORD, REDIS_HOST and REDIS_PORT values with your own."
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "anya8hVnT6K_",
+ "outputId": "a8430acc-2e6d-45fd-fc8b-601fbbd8289b"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "id": "ggh5TzhkJkD9"
- },
- "outputs": [],
- "source": [
- "import os\n",
- "import warnings\n",
- "#warnings.filterwarnings('ignore')\n",
- "\n",
- "# Replace values below with your own if using Redis Cloud instance\n",
- "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
- "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\") # ex: 18374\n",
- "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\") # ex: \"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
- "\n",
- "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
- "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\""
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done preprocessing. Created 211 chunks of the original pdf resources/nke-10k-2023.pdf\n"
+ ]
+ }
+ ],
+ "source": [
+ "# pick out the Nike doc for this exercise\n",
+ "doc = [doc for doc in docs if \"nke\" in doc][0]\n",
+ "\n",
+ "# set up the file loader/extractor and text splitter to create chunks\n",
+ "text_splitter = RecursiveCharacterTextSplitter(\n",
+ " chunk_size=2500, chunk_overlap=0\n",
+ ")\n",
+ "loader = PyPDFLoader(doc, headers = None)\n",
+ "\n",
+ "# extract, load, and make chunks\n",
+ "chunks = loader.load_and_split(text_splitter)\n",
+ "\n",
+ "print(\"Done preprocessing. Created\", len(chunks), \"chunks of the original pdf\", doc)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fDN4XopTJkD9"
+ },
+ "source": [
+ "### Text embedding generation with RedisVL\n",
+ "RedisVL has built-in extensions and utilities to aid the GenAI development process. In the following snipit we utilize the HFTextVectorizer redisvl in tandem with the **all-MiniLM-L6-v2** class to generate vector embeddings for the chunks created above. These embeddings capture the \"meaning\" of the text so that we can retrieve the relevant chunks later when a user's query is semantically related."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:55.588165Z",
+ "start_time": "2025-04-24T16:46:50.528240Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "b3ErDmsIJkD9"
- },
- "source": [
- "## Simplified Vector Search with RedisVL"
- ]
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+ "base_uri": "https://localhost:8080/",
+ "height": 661,
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},
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+ "outputId": "b0f0d2c1-41dc-4932-990b-53d2912af19e"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "KrtWWU4I1ui3"
- },
- "source": [
- "### Dataset Preparation (PDF Documents)\n",
- "\n",
- "To best demonstrate Redis as a vector database layer, we will load a single\n",
- "financial (10k filings) doc and preprocess it using some helpers from LangChain:\n",
- "\n",
- "- `PyPDFLoader` is not the only document loader type that LangChain provides. Docs: https://python.langchain.com/docs/integrations/document_loaders/pypdfloader/\n",
- "- `RecursiveCharacterTextSplitter` is what we use to create smaller chunks of text from the doc. Docs: https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
+ "data": {
+ "text/plain": [
+ "True"
]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import warnings\n",
+ "import pandas as pd\n",
+ "from redisvl.utils.vectorize import HFTextVectorizer, BaseVectorizer\n",
+ "from redisvl.extensions.cache.embeddings import EmbeddingsCache\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
+ "\n",
+ "hf = HFTextVectorizer(\n",
+ " model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
+ " cache=EmbeddingsCache(\n",
+ " name=\"embedcache\",\n",
+ " ttl=600,\n",
+ " redis_url=REDIS_URL,\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# Embed each chunk content\n",
+ "embeddings = hf.embed_many([chunk.page_content for chunk in chunks])\n",
+ "\n",
+ "# Check to make sure we've created enough embeddings, 1 per document chunk\n",
+ "len(embeddings) == len(chunks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5baI0xDQ1ui-"
+ },
+ "source": [
+ "### Define a schema and create an index\n",
+ "\n",
+ "Below we connect to Redis and create an index that contains a text field, tag field, and vector field."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:55.611260Z",
+ "start_time": "2025-04-24T16:46:55.598846Z"
},
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "id": "uijl2qFH1ui3",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "a99b3fcb-7cfd-4dbd-f258-57779cfcae3c"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Listing available documents ... ['resources/jnj-10k-2023.pdf', 'resources/retrieval_basic_rag_test.csv', 'resources/aapl-10k-2023.pdf', 'resources/nke-10k-2023.pdf', 'resources/amzn-10k-2023.pdf', 'resources/testset_15.csv', 'resources/generation_basic_rag_test.csv', 'resources/testset.csv', 'resources/msft-10k-2023.pdf', 'resources/propositions.json', 'resources/nvd-10k-2023.pdf']\n"
- ]
- }
- ],
- "source": [
- "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
- "from langchain_community.document_loaders import PyPDFLoader\n",
- "\n",
- "# Load list of pdfs from a folder\n",
- "data_path = \"resources/\"\n",
- "docs = [os.path.join(data_path, file) for file in os.listdir(data_path)]\n",
- "\n",
- "print(\"Listing available documents ...\", docs)"
- ]
+ "id": "zB1EW_9n1ui-"
+ },
+ "outputs": [],
+ "source": [
+ "from redisvl.index import SearchIndex\n",
+ "\n",
+ "\n",
+ "index_name = \"redisvl\"\n",
+ "\n",
+ "schema = {\n",
+ " \"index\": {\n",
+ " \"name\": index_name,\n",
+ " \"prefix\": \"chunk\"\n",
+ " },\n",
+ " \"fields\": [\n",
+ " {\n",
+ " \"name\": \"chunk_id\",\n",
+ " \"type\": \"tag\",\n",
+ " \"attrs\": {\n",
+ " \"sortable\": True\n",
+ " }\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"content\",\n",
+ " \"type\": \"text\"\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"text_embedding\",\n",
+ " \"type\": \"vector\",\n",
+ " \"attrs\": {\n",
+ " \"dims\": 384,\n",
+ " \"distance_metric\": \"cosine\",\n",
+ " \"algorithm\": \"hnsw\",\n",
+ " \"datatype\": \"float32\"\n",
+ " }\n",
+ " }\n",
+ " ]\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:55.630056Z",
+ "start_time": "2025-04-24T16:46:55.620207Z"
},
+ "id": "LKuQku2CJkD9"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "id": "anya8hVnT6K_",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "a8430acc-2e6d-45fd-fc8b-601fbbd8289b"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Done preprocessing. Created 211 chunks of the original pdf resources/nke-10k-2023.pdf\n"
- ]
- }
- ],
- "source": [
- "# pick out the Nike doc for this exercise\n",
- "doc = [doc for doc in docs if \"nke\" in doc][0]\n",
- "\n",
- "# set up the file loader/extractor and text splitter to create chunks\n",
- "text_splitter = RecursiveCharacterTextSplitter(\n",
- " chunk_size=2500, chunk_overlap=0\n",
- ")\n",
- "loader = PyPDFLoader(doc, headers = None)\n",
- "\n",
- "# extract, load, and make chunks\n",
- "chunks = loader.load_and_split(text_splitter)\n",
- "\n",
- "print(\"Done preprocessing. Created\", len(chunks), \"chunks of the original pdf\", doc)"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "09:46:55 redisvl.index.index INFO Index already exists, overwriting.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# create an index from schema and the client\n",
+ "index = SearchIndex.from_dict(schema, redis_url=REDIS_URL)\n",
+ "index.create(overwrite=True, drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "fDN4XopTJkD9"
- },
- "source": [
- "### Text embedding generation with RedisVL\n",
- "RedisVL has built-in extensions and utilities to aid the GenAI development process. In the following snipit we utilize the HFTextVectorizer redisvl in tandem with the **all-MiniLM-L6-v2** class to generate vector embeddings for the chunks created above. These embeddings capture the \"meaning\" of the text so that we can retrieve the relevant chunks later when a user's query is semantically related."
- ]
+ "id": "L6GOqmeN1ui_",
+ "outputId": "91a199e3-d087-4b15-9544-d59efa6033c5"
+ },
+ "outputs": [],
+ "source": [
+ "# use the RedisVL CLI tool to list all indices\n",
+ "!rvl index listall"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:56.828176Z",
+ "start_time": "2025-04-24T16:46:56.283831Z"
},
- {
- "cell_type": "code",
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- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 661,
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- ]
- },
- "id": "N3iQ2aLEJkD9",
- "outputId": "b0f0d2c1-41dc-4932-990b-53d2912af19e"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "20:48:16 numexpr.utils INFO NumExpr defaulting to 2 threads.\n",
- "20:48:30 sentence_transformers.SentenceTransformer INFO Use pytorch device_name: cuda\n",
- "20:48:30 sentence_transformers.SentenceTransformer INFO Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
- "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
- "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
- "You will be able to reuse this secret in all of your notebooks.\n",
- "Please note that authentication is recommended but still optional to access public models or datasets.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "modules.json: 0%| | 0.00/349 [00:00[KNN 3 @text_embedding $vector AS vector_distance] RETURN 3 chunk_id content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 3'"
]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from redisvl.query import VectorQuery\n",
+ "\n",
+ "query = \"Nike profit margins and company performance\"\n",
+ "\n",
+ "query_embedding = hf.embed(query)\n",
+ "\n",
+ "vector_query = VectorQuery(\n",
+ " vector=query_embedding,\n",
+ " vector_field_name=\"text_embedding\",\n",
+ " num_results=3,\n",
+ " return_fields=[\"chunk_id\", \"content\"],\n",
+ " return_score=True\n",
+ ")\n",
+ "\n",
+ "# show the raw redis query\n",
+ "str(vector_query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:57.008139Z",
+ "start_time": "2025-04-24T16:46:56.999381Z"
},
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 143
+ },
+ "id": "5reL5qTW1ujC",
+ "outputId": "dd58f191-54f5-4226-c4e1-70207d58f2dc"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "id": "C70C-UWj1ujA",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "1fb7a2d6-ae6d-4536-b4b7-702620efd128"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\n",
- "\n",
- "Index Information:\n",
- "╭──────────────┬────────────────┬────────────┬─────────────────┬────────────╮\n",
- "│ Index Name │ Storage Type │ Prefixes │ Index Options │ Indexing │\n",
- "├──────────────┼────────────────┼────────────┼─────────────────┼────────────┤\n",
- "│ redisvl │ HASH │ ['chunk'] │ [] │ 0 │\n",
- "╰──────────────┴────────────────┴────────────┴─────────────────┴────────────╯\n",
- "Index Fields:\n",
- "╭────────────────┬────────────────┬────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
- "│ Name │ Attribute │ Type │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │\n",
- "├────────────────┼────────────────┼────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
- "│ chunk_id │ chunk_id │ TAG │ SEPARATOR │ , │ │ │ │ │ │ │ │ │ │ │\n",
- "│ content │ content │ TEXT │ WEIGHT │ 1 │ │ │ │ │ │ │ │ │ │ │\n",
- "│ text_embedding │ text_embedding │ VECTOR │ algorithm │ HNSW │ data_type │ FLOAT32 │ dim │ 384 │ distance_metric │ COSINE │ M │ 16 │ ef_construction │ 200 │\n",
- "╰────────────────┴────────────────┴────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
- ]
- }
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
],
- "source": [
- "# get info about the index\n",
- "!rvl index info -i redisvl"
+ "text/plain": [
+ " id vector_distance chunk_id \\\n",
+ "0 chunk:88 0.337694525719 88 \n",
+ "1 chunk:80 0.34205275774 80 \n",
+ "2 chunk:87 0.357761025429 87 \n",
+ "\n",
+ " content \n",
+ "0 Asia Pacific & Latin America 1,932 1,896 2 % 1... \n",
+ "1 Table of Contents\\nCONSOLIDATED OPERATING RESU... \n",
+ "2 Table of Contents\\nOPERATING SEGMENTS\\nAs disc... "
]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# execute the query with RedisVL\n",
+ "result=index.query(vector_query)\n",
+ "\n",
+ "# view the results\n",
+ "pd.DataFrame(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:57.075644Z",
+ "start_time": "2025-04-24T16:46:57.067304Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Qrj-jeGmBRTL"
- },
- "source": [
- "### Process and load dataset\n",
- "Below we use the RedisVL index to simply load the list of document chunks to Redis db."
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "rZrcd6n7T6LE",
+ "outputId": "fad67a63-76bd-43b9-f62b-b1842ba47605"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "id": "Zsg09Keg1ujA"
- },
- "outputs": [],
- "source": [
- "# load expects an iterable of dictionaries\n",
- "from redisvl.redis.utils import array_to_buffer\n",
- "\n",
- "data = [\n",
- " {\n",
- " 'chunk_id': i,\n",
- " 'content': chunk.page_content,\n",
- " # For HASH -- must convert embeddings to bytes\n",
- " 'text_embedding': array_to_buffer(embeddings[i], dtype='float32')\n",
- " } for i, chunk in enumerate(chunks)\n",
- "]\n",
- "\n",
- "# RedisVL handles batching automatically\n",
- "keys = index.load(data, id_field=\"chunk_id\")"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "88 0.337694525719\n",
+ "80 0.34205275774\n",
+ "87 0.357761025429\n"
+ ]
+ }
+ ],
+ "source": [
+ "# paginate through results\n",
+ "for result in index.paginate(vector_query, page_size=1):\n",
+ " print(result[0][\"chunk_id\"], result[0][\"vector_distance\"], flush=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0Ap6WqPLT6LE"
+ },
+ "source": [
+ "### Sort by alternative fields"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:57.172397Z",
+ "start_time": "2025-04-24T16:46:57.167834Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-ZsFB-6Z1ujB"
- },
- "source": [
- "### Query the database\n",
- "Now we can use the RedisVL index to perform similarity search operations with Redis"
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 175
},
+ "id": "daLVm6OkLn9T",
+ "outputId": "d77dfc4c-d451-4bf5-91c3-2155232570b9"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "id": "BkFv-_iC1ujB",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 85,
- "referenced_widgets": [
- "c75d5ab2049146e580efab9da9bbcdb0",
- "9ce1fb951e79468baa9d1aebfa4c4fae",
- "e96d1546380146078c18ec78363f7dac",
- "a3c36bb0d3b74c8ea56bf03521465b81",
- "9f306cfd66dc441aba923d4e051911fc",
- "9e3289444cb142c29ad7d569be2e25b8",
- "c20443e17308425596679c0544dab528",
- "f0bdd8f4d7b84bd5a1c209c591ce8787",
- "126743b52b254e54aa4f65bcb9e65aea",
- "debae380e6d24fb8ae712a6dd2226152",
- "aacb6f8ca39846d89e1e4e96656e3a36"
- ]
- },
- "outputId": "c398d356-6bb7-43a9-ca95-cb7f167d1f38"
- },
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Batches: 0%| | 0/1 [00:00[KNN 3 @text_embedding $vector AS vector_distance] RETURN 3 chunk_id content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 3'"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- }
- },
- "metadata": {},
- "execution_count": 13
- }
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "vector_distance | \n", + "chunk_id | \n", + "content | \n", + "
|---|---|---|---|---|
| 0 | \n", + "chunk:88 | \n", + "0.337694525719 | \n", + "88 | \n", + "Asia Pacific & Latin America 1,932 1,896 2 % 1... | \n", + "
| 1 | \n", + "chunk:80 | \n", + "0.34205275774 | \n", + "80 | \n", + "Table of Contents\\nCONSOLIDATED OPERATING RESU... | \n", + "
| 2 | \n", + "chunk:87 | \n", + "0.357761025429 | \n", + "87 | \n", + "Table of Contents\\nOPERATING SEGMENTS\\nAs disc... | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
],
- "source": [
- "from redisvl.query import VectorQuery\n",
- "\n",
- "query = \"Nike profit margins and company performance\"\n",
- "\n",
- "query_embedding = hf.embed(query)\n",
- "\n",
- "vector_query = VectorQuery(\n",
- " vector=query_embedding,\n",
- " vector_field_name=\"text_embedding\",\n",
- " num_results=3,\n",
- " return_fields=[\"chunk_id\", \"content\"],\n",
- " return_score=True\n",
- ")\n",
- "\n",
- "# show the raw redis query\n",
- "str(vector_query)"
+ "text/plain": [
+ " id payload vector_distance chunk_id\n",
+ "0 chunk:80 None 0.34205275774 80\n",
+ "1 chunk:83 None 0.378765881062 83\n",
+ "2 chunk:87 None 0.357761025429 87\n",
+ "3 chunk:88 None 0.337694525719 88"
]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Sort by chunk_id field after vector search limits to topK\n",
+ "vector_query = VectorQuery(\n",
+ " vector=query_embedding,\n",
+ " vector_field_name=\"text_embedding\",\n",
+ " num_results=4,\n",
+ " return_fields=[\"chunk_id\"],\n",
+ " return_score=True\n",
+ ")\n",
+ "\n",
+ "# Decompose vector_query into the core query and the params\n",
+ "query = vector_query.query\n",
+ "params = vector_query.params\n",
+ "\n",
+ "# Pass query and params direct to index.search()\n",
+ "result = index.search(\n",
+ " query.sort_by(\"chunk_id\", asc=True),\n",
+ " params\n",
+ ")\n",
+ "\n",
+ "pd.DataFrame([doc.__dict__ for doc in result.docs])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "81PoXomtT6LF"
+ },
+ "source": [
+ "### Add filters to vector queries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:57.279677Z",
+ "start_time": "2025-04-24T16:46:57.274997Z"
},
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "id": "5reL5qTW1ujC",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 143
- },
- "outputId": "dd58f191-54f5-4226-c4e1-70207d58f2dc"
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " id vector_distance chunk_id \\\n",
- "0 chunk:88 0.337694585323 88 \n",
- "1 chunk:80 0.342052936554 80 \n",
- "2 chunk:87 0.35776078701 87 \n",
- "\n",
- " content \n",
- "0 Asia Pacific & Latin America 1,932 1,896 2 % 1... \n",
- "1 Table of Contents\\nCONSOLIDATED OPERATING RESU... \n",
- "2 Table of Contents\\nOPERATING SEGMENTS\\nAs disc... "
- ],
- "text/html": [
- "\n",
- " | \n", + " | id | \n", + "payload | \n", + "vector_distance | \n", + "chunk_id | \n", + "
|---|---|---|---|---|
| 0 | \n", + "chunk:80 | \n", + "None | \n", + "0.34205275774 | \n", + "80 | \n", + "
| 1 | \n", + "chunk:83 | \n", + "None | \n", + "0.378765881062 | \n", + "83 | \n", + "
| 2 | \n", + "chunk:87 | \n", + "None | \n", + "0.357761025429 | \n", + "87 | \n", + "
| 3 | \n", + "chunk:88 | \n", + "None | \n", + "0.337694525719 | \n", + "88 | \n", + "
\n",
- "
\n"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "dataframe",
- "summary": "{\n \"name\": \"pd\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"chunk:88\",\n \"chunk:80\",\n \"chunk:87\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vector_distance\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"0.337694585323\",\n \"0.342052936554\",\n \"0.35776078701\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"chunk_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"88\",\n \"80\",\n \"87\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"content\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\n(1) Total NIKE Brand EBIT, Total NIKE, Inc. EBIT and EBIT Margin represent non-GAAP financial measures. See \\\"Use of Non-GAAP Financial Measures\\\" for further information.\\n(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\",\n \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\nmanaged through our central foreign exchange risk management program.\",\n \"Table of Contents\\nOPERATING SEGMENTS\\nAs discussed in Note 15 \\u2014 Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, our operating segments\\nare evidence of the structure of the Company's internal organization. The NIKE Brand segments are defined by geographic regions for operations participating in NIKE\\nBrand sales activity.\\nThe breakdown of Revenues is as follows:\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL 2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNorth America $ 21,608 $ 18,353 18 % 18 %$ 17,179 7 % 7 %\\nEurope, Middle East & Africa 13,418 12,479 8 % 21 % 11,456 9 % 12 %\\nGreater China 7,248 7,547 -4 % 4 % 8,290 -9 % -13 %\\nAsia Pacific & Latin America 6,431 5,955 8 % 17 % 5,343 11 % 16 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\n(1) The percent change excluding currency changes represents a non-GAAP financial measure. For further information, see \\\"Use of Non-GAAP Financial Measures\\\".\\n(2) For additional information on the transition of our NIKE Brand businesses within our CASA territory to a third-party distributor, see Note 18 \\u2014 Acquisitions and Divestitures of the Notes to ConsolidatedFinancial Statements contained in Item 8 of this Annual Report.\\n(3) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(4) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, butmanaged through our central foreign exchange risk management program.\\nThe primary financial measure used by the Company to evaluate performance is Earnings Before Interest and Taxes (\\\"EBIT\\\"). As discussed in Note 15 \\u2014 Operating\\nSegments and Related Information in the accompanying Notes to the Consolidated Financial Statements, certain corporate costs are not included in EBIT.\\nThe breakdown of EBIT is as follows:\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nNorth America $ 5,454 $ 5,114 7 % $ 5,089 0 %\\nEurope, Middle East & Africa 3,531 3,293 7 % 2,435 35 %\\nGreater China 2,283 2,365 -3 % 3,243 -27 %\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
- }
- },
- "metadata": {},
- "execution_count": 14
- }
- ],
- "source": [
- "# execute the query with RedisVL\n",
- "result=index.query(vector_query)\n",
- "\n",
- "# view the results\n",
- "pd.DataFrame(result)"
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 175
},
+ "id": "a11G3xXJ1ujC",
+ "outputId": "d968add5-704d-4e22-d3bd-97c1d1103a75"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "id": "rZrcd6n7T6LE",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "fad67a63-76bd-43b9-f62b-b1842ba47605"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "88 0.337694585323\n",
- "80 0.342052936554\n",
- "87 0.35776078701\n"
- ]
- }
+ "data": {
+ "text/html": [
+ "\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
\n",
- " \n",
- " | \n", - " | id | \n", - "vector_distance | \n", - "chunk_id | \n", - "content | \n", - "
|---|---|---|---|---|
| 0 | \n", - "chunk:88 | \n", - "0.337694585323 | \n", - "88 | \n", - "Asia Pacific & Latin America 1,932 1,896 2 % 1... | \n", - "
| 1 | \n", - "chunk:80 | \n", - "0.342052936554 | \n", - "80 | \n", - "Table of Contents\\nCONSOLIDATED OPERATING RESU... | \n", - "
| 2 | \n", - "chunk:87 | \n", - "0.35776078701 | \n", - "87 | \n", - "Table of Contents\\nOPERATING SEGMENTS\\nAs disc... | \n", - "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
],
- "source": [
- "# paginate through results\n",
- "for result in index.paginate(vector_query, page_size=1):\n",
- " print(result[0][\"chunk_id\"], result[0][\"vector_distance\"], flush=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0Ap6WqPLT6LE"
- },
- "source": [
- "### Sort by alternative fields"
+ "text/plain": [
+ " id vector_distance \\\n",
+ "0 chunk:83 0.378765881062 \n",
+ "1 chunk:129 0.418757200241 \n",
+ "2 chunk:73 0.465415120125 \n",
+ "3 chunk:63 0.49339401722 \n",
+ "\n",
+ " content \n",
+ "0 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C... \n",
+ "1 Table of Contents\\nNIKE, INC.\\nCONSOLIDATED ST... \n",
+ "2 Table of Contents\\nITEM 7. MANAGEMENT'S DISCUS... \n",
+ "3 existing businesses, such as our NIKE Direct o... "
]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from redisvl.query.filter import Text\n",
+ "\n",
+ "vector_query = VectorQuery(\n",
+ " vector=query_embedding,\n",
+ " vector_field_name=\"text_embedding\",\n",
+ " num_results=4,\n",
+ " return_fields=[\"content\"],\n",
+ " return_score=True\n",
+ ")\n",
+ "\n",
+ "# Set a text filter\n",
+ "text_filter = Text(\"content\") % \"profit\"\n",
+ "\n",
+ "vector_query.set_filter(text_filter)\n",
+ "\n",
+ "result=index.query(vector_query)\n",
+ "pd.DataFrame(result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5XvVv8zAT6LF"
+ },
+ "source": [
+ "### Range queries in RedisVL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:57.391116Z",
+ "start_time": "2025-04-24T16:46:57.389349Z"
},
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "id": "daLVm6OkLn9T",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 175
- },
- "outputId": "d77dfc4c-d451-4bf5-91c3-2155232570b9"
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " id payload vector_distance chunk_id\n",
- "0 chunk:80 None 0.342052936554 80\n",
- "1 chunk:83 None 0.37876611948 83\n",
- "2 chunk:87 None 0.35776078701 87\n",
- "3 chunk:88 None 0.337694585323 88"
- ],
- "text/html": [
- "\n",
- " | \n", + " | id | \n", + "vector_distance | \n", + "content | \n", + "
|---|---|---|---|
| 0 | \n", + "chunk:83 | \n", + "0.378765881062 | \n", + "Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C... | \n", + "
| 1 | \n", + "chunk:129 | \n", + "0.418757200241 | \n", + "Table of Contents\\nNIKE, INC.\\nCONSOLIDATED ST... | \n", + "
| 2 | \n", + "chunk:73 | \n", + "0.465415120125 | \n", + "Table of Contents\\nITEM 7. MANAGEMENT'S DISCUS... | \n", + "
| 3 | \n", + "chunk:63 | \n", + "0.49339401722 | \n", + "existing businesses, such as our NIKE Direct o... | \n", + "
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- "type": "dataframe",
- "repr_error": "Out of range float values are not JSON compliant: nan"
- }
- },
- "metadata": {},
- "execution_count": 16
- }
- ],
- "source": [
- "# Sort by chunk_id field after vector search limits to topK\n",
- "vector_query = VectorQuery(\n",
- " vector=query_embedding,\n",
- " vector_field_name=\"text_embedding\",\n",
- " num_results=4,\n",
- " return_fields=[\"chunk_id\"],\n",
- " return_score=True\n",
- ")\n",
- "\n",
- "# Decompose vector_query into the core query and the params\n",
- "query = vector_query.query\n",
- "params = vector_query.params\n",
- "\n",
- "# Pass query and params direct to index.search()\n",
- "result = index.search(\n",
- " query.sort_by(\"chunk_id\", asc=True),\n",
- " params\n",
- ")\n",
- "\n",
- "pd.DataFrame([doc.__dict__ for doc in result.docs])\n"
- ]
+ "id": "bCffoZRx1ujD"
+ },
+ "outputs": [],
+ "source": [
+ "from redisvl.query import RangeQuery\n",
+ "\n",
+ "range_query = RangeQuery(\n",
+ " vector=query_embedding,\n",
+ " vector_field_name=\"text_embedding\",\n",
+ " num_results=4,\n",
+ " return_fields=[\"content\"],\n",
+ " return_score=True,\n",
+ " distance_threshold=0.8 # find all items with a semantic distance of less than 0.8\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:57.499232Z",
+ "start_time": "2025-04-24T16:46:57.494328Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "81PoXomtT6LF"
- },
- "source": [
- "### Add filters to vector queries"
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 175
},
+ "id": "0gHmam1Q1ujD",
+ "outputId": "ac80a6ed-4eb8-44d3-881d-87c9271aa10e"
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+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "id": "a11G3xXJ1ujC",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 175
- },
- "outputId": "d968add5-704d-4e22-d3bd-97c1d1103a75"
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " id vector_distance \\\n",
- "0 chunk:83 0.37876611948 \n",
- "1 chunk:129 0.41875731945 \n",
- "2 chunk:168 0.657553255558 \n",
- "3 chunk:39 0.683842301369 \n",
- "\n",
- " content \n",
- "0 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C... \n",
- "1 Table of Contents\\nNIKE, INC.\\nCONSOLIDATED ST... \n",
- "2 Table of Contents\\nNOTE 10 — EARNINGS PER SHAR... \n",
- "3 manner. However, lead times for many of our pr... "
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| 3 | \n", - "chunk:88 | \n", - "None | \n", - "0.337694585323 | \n", - "88 | \n", - "
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- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "dataframe",
- "summary": "{\n \"name\": \"pd\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"chunk:129\",\n \"chunk:39\",\n \"chunk:83\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vector_distance\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"0.41875731945\",\n \"0.683842301369\",\n \"0.37876611948\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"content\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Table of Contents\\nNIKE, INC.\\nCONSOLIDATED STATEMENTS OF INCOME\\nYEAR ENDED MAY 31,\\n(In millions, except per share data) 2023 2022 2021\\nRevenues $ 51,217 $ 46,710 $ 44,538 \\nCost of sales 28,925 25,231 24,576 \\nGross profit 22,292 21,479 19,962 \\nDemand creation expense 4,060 3,850 3,114 \\nOperating overhead expense 12,317 10,954 9,911 \\nTotal selling and administrative expense 16,377 14,804 13,025 \\nInterest expense (income), net (6) 205 262 \\nOther (income) expense, net (280) (181) 14 \\nIncome before income taxes 6,201 6,651 6,661 \\nIncome tax expense 1,131 605 934 \\nNET INCOME $ 5,070 $ 6,046 $ 5,727 \\nEarnings per common share:\\nBasic $ 3.27 $ 3.83 $ 3.64 \\nDiluted $ 3.23 $ 3.75 $ 3.56 \\nWeighted average common shares outstanding:\\nBasic 1,551.6 1,578.8 1,573.0 \\nDiluted 1,569.8 1,610.8 1,609.4 \\nThe accompanying Notes to the Consolidated Financial Statements are an integral part of this statement.\\n2023 FORM 10-K 55\",\n \"manner. However, lead times for many of our products may make it more difficult for us to respond rapidly to new or changing product trends or consumer preferences. All\\nof our products are subject to changing consumer preferences that cannot be predicted with certainty. Our new products may not receive consumer acceptance as\\nconsumer preferences could shift rapidly to different types of performance products or away from these types of products altogether, and our future success depends in\\npart on our ability to anticipate and respond to these changes. If we fail to anticipate accurately and respond to trends and shifts in consumer preferences by adjusting the\\nmix of existing product offerings, developing new products, designs, styles and categories, and influencing sports and fitness preferences through extensive marketing, we\\ncould experience lower sales, excess inventories or lower profit margins, any of which could have an adverse effect on our results of operations and financial condition. In\\naddition, we market our products globally through a diverse spectrum of advertising and promotional programs and campaigns, including social media and other digital\\nadvertising networks. If we do not successfully market our products or if advertising and promotional costs increase, these factors could have an adverse effect on our\\nbusiness, financial condition and results of operations.\\nWe rely on technical innovation and high-quality products to compete in the market for our products.\\nTechnical innovation and quality control in the design and manufacturing processes of footwear, apparel, equipment and other products and services are essential to the\\ncommercial success of our products and development of new products. Research and development play a key role in technical innovation. We rely upon specialists in the\\nfields of biomechanics, chemistry, exercise physiology, engineering, digital technologies, industrial design, sustainability and related fields, as well as research committees\\nand advisory boards made up of athletes, coaches, trainers, equipment managers, orthopedists, podiatrists and other experts to develop and test cutting-edge\\nperformance products. While we strive to produce products that help to enhance athletic performance and reduce injury and maximize comfort, if we fail to introduce\\ntechnical innovation in our products, consumer demand for our products could decline, and if we experience problems with the quality of our products, we may incur\",\n \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nFor fiscal 2023, our consolidated gross profit increased 4% to $22,292 million compared to $21,479 million for fiscal 2022. Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
- }
- },
- "metadata": {},
- "execution_count": 17
- }
+ "data": {
+ "text/html": [
+ "\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
\n",
- " \n",
- " | \n", - " | id | \n", - "vector_distance | \n", - "content | \n", - "
|---|---|---|---|
| 0 | \n", - "chunk:83 | \n", - "0.37876611948 | \n", - "Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C... | \n", - "
| 1 | \n", - "chunk:129 | \n", - "0.41875731945 | \n", - "Table of Contents\\nNIKE, INC.\\nCONSOLIDATED ST... | \n", - "
| 2 | \n", - "chunk:168 | \n", - "0.657553255558 | \n", - "Table of Contents\\nNOTE 10 — EARNINGS PER SHAR... | \n", - "
| 3 | \n", - "chunk:39 | \n", - "0.683842301369 | \n", - "manner. However, lead times for many of our pr... | \n", - "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
],
- "source": [
- "from redisvl.query.filter import Text\n",
- "\n",
- "vector_query = VectorQuery(\n",
- " vector=query_embedding,\n",
- " vector_field_name=\"text_embedding\",\n",
- " num_results=4,\n",
- " return_fields=[\"content\"],\n",
- " return_score=True\n",
- ")\n",
- "\n",
- "# Set a text filter\n",
- "text_filter = Text(\"content\") % \"profit\"\n",
- "\n",
- "vector_query.set_filter(text_filter)\n",
- "\n",
- "result=index.query(vector_query)\n",
- "pd.DataFrame(result)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5XvVv8zAT6LF"
- },
- "source": [
- "### Range queries in RedisVL"
+ "text/plain": [
+ " id vector_distance content\n",
+ "0 chunk:88 0.337694525719 Asia Pacific & Latin America 1,932 1,896 2 % 1...\n",
+ "1 chunk:80 0.34205275774 Table of Contents\\nCONSOLIDATED OPERATING RESU...\n",
+ "2 chunk:87 0.357761025429 Table of Contents\\nOPERATING SEGMENTS\\nAs disc...\n",
+ "3 chunk:83 0.378765881062 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..."
]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "result=index.query(range_query)\n",
+ "pd.DataFrame(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:57.667013Z",
+ "start_time": "2025-04-24T16:46:57.662153Z"
},
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "id": "bCffoZRx1ujD"
- },
- "outputs": [],
- "source": [
- "from redisvl.query import RangeQuery\n",
- "\n",
- "range_query = RangeQuery(\n",
- " vector=query_embedding,\n",
- " vector_field_name=\"text_embedding\",\n",
- " num_results=4,\n",
- " return_fields=[\"content\"],\n",
- " return_score=True,\n",
- " distance_threshold=0.8 # find all items with a semantic distance of less than 0.8\n",
- ")"
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 175
},
+ "id": "YZg4U21r1ujD",
+ "outputId": "d3db5ac3-6ae9-42c4-aaee-874cecafe3ad"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "id": "0gHmam1Q1ujD",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 175
- },
- "outputId": "ac80a6ed-4eb8-44d3-881d-87c9271aa10e"
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- "outputs": [
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- "output_type": "execute_result",
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- "3 chunk:83 0.37876611948 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..."
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- "summary": "{\n \"name\": \"pd\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"chunk:80\",\n \"chunk:83\",\n \"chunk:88\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vector_distance\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"0.342052936554\",\n \"0.37876611948\",\n \"0.337694585323\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"content\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\nmanaged through our central foreign exchange risk management program.\",\n \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nFor fiscal 2023, our consolidated gross profit increased 4% to $22,292 million compared to $21,479 million for fiscal 2022. Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\",\n \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\n(1) Total NIKE Brand EBIT, Total NIKE, Inc. EBIT and EBIT Margin represent non-GAAP financial measures. See \\\"Use of Non-GAAP Financial Measures\\\" for further information.\\n(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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- "metadata": {},
- "execution_count": 19
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+ "0 chunk:88 0.337694525719 Asia Pacific & Latin America 1,932 1,896 2 % 1...\n",
+ "1 chunk:80 0.34205275774 Table of Contents\\nCONSOLIDATED OPERATING RESU...\n",
+ "2 chunk:87 0.357761025429 Table of Contents\\nOPERATING SEGMENTS\\nAs disc...\n",
+ "3 chunk:83 0.378765881062 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..."
]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Add filter to range query\n",
+ "range_query.set_filter(text_filter)\n",
+ "\n",
+ "index.query(range_query)\n",
+ "pd.DataFrame(result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zYYPTQN7T6LG"
+ },
+ "source": [
+ "## Building a basic RAG Pipeline from Scratch\n",
+ "We're going to build a basic RAG pipeline from scratch incorporating the following components:\n",
+ "\n",
+ "- Standard semantic search\n",
+ "- Integration with OpenAI for LLM\n",
+ "- Chat completion"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rCWlVR2OT6LG"
+ },
+ "source": [
+ "### Setup RedisVL AsyncSearchIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:46:57.734454Z",
+ "start_time": "2025-04-24T16:46:57.732810Z"
},
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- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "id": "YZg4U21r1ujD",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 175
- },
- "outputId": "d3db5ac3-6ae9-42c4-aaee-874cecafe3ad"
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " id vector_distance content\n",
- "0 chunk:88 0.337694585323 Asia Pacific & Latin America 1,932 1,896 2 % 1...\n",
- "1 chunk:80 0.342052936554 Table of Contents\\nCONSOLIDATED OPERATING RESU...\n",
- "2 chunk:87 0.35776078701 Table of Contents\\nOPERATING SEGMENTS\\nAs disc...\n",
- "3 chunk:83 0.37876611948 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..."
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- "type": "dataframe",
- "summary": "{\n \"name\": \"pd\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"chunk:80\",\n \"chunk:83\",\n \"chunk:88\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vector_distance\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"0.342052936554\",\n \"0.37876611948\",\n \"0.337694585323\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"content\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\nmanaged through our central foreign exchange risk management program.\",\n \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nFor fiscal 2023, our consolidated gross profit increased 4% to $22,292 million compared to $21,479 million for fiscal 2022. Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\",\n \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\n(1) Total NIKE Brand EBIT, Total NIKE, Inc. EBIT and EBIT Margin represent non-GAAP financial measures. See \\\"Use of Non-GAAP Financial Measures\\\" for further information.\\n(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
- }
- },
- "metadata": {},
- "execution_count": 20
- }
- ],
- "source": [
- "# Add filter to range query\n",
- "range_query.set_filter(text_filter)\n",
- "\n",
- "index.query(range_query)\n",
- "pd.DataFrame(result)"
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "zYYPTQN7T6LG"
- },
- "source": [
- "## Building a basic RAG Pipeline from Scratch\n",
- "We're going to build a basic RAG pipeline from scratch incorporating the following components:\n",
- "\n",
- "- Standard semantic search\n",
- "- Integration with OpenAI for LLM\n",
- "- Chat completion"
- ]
+ "id": "_esLGYzbT6LG",
+ "outputId": "d3314a08-8746-4239-dcb2-e7e41b51c640"
+ },
+ "outputs": [],
+ "source": [
+ "from redisvl.index import AsyncSearchIndex\n",
+ "\n",
+ "async_index = AsyncSearchIndex.from_dict(schema, redis_url=REDIS_URL)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "peK4C5xGJkED"
+ },
+ "source": [
+ "### Setup OpenAI API"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-04-24T16:47:12.289527Z",
+ "start_time": "2025-04-24T16:46:57.837857Z"
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rCWlVR2OT6LG"
- },
- "source": [
- "### Setup RedisVL AsyncSearchIndex"
- ]
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "id": "_esLGYzbT6LG",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "d3314a08-8746-4239-dcb2-e7e41b51c640"
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
\n",
- " \n",
- " | \n", - " | id | \n", - "vector_distance | \n", - "content | \n", - "
|---|---|---|---|
| 0 | \n", - "chunk:88 | \n", - "0.337694585323 | \n", - "Asia Pacific & Latin America 1,932 1,896 2 % 1... | \n", - "
| 1 | \n", - "chunk:80 | \n", - "0.342052936554 | \n", - "Table of Contents\\nCONSOLIDATED OPERATING RESU... | \n", - "
| 2 | \n", - "chunk:87 | \n", - "0.35776078701 | \n", - "Table of Contents\\nOPERATING SEGMENTS\\nAs disc... | \n", - "
| 3 | \n", - "chunk:83 | \n", - "0.37876611948 | \n", - "Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C... | \n", - "
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Improve accuracy with dense content representations\n",
- "In the basic example, we took raw chunks of text from our pdf documents and generated embeddings for them to be stored in the vector database. This is okay but one technique we can use to improve the quality of retrieval is to leverage an LLM from OpenAI during ETL. We will prompt the LLM to summarize and decompose the raw pdf text into more discrete propositional phrases. This will enhance the clarity of the text and improve semantic retrieval for RAG.\n",
- "\n",
- "The goal is to utilize a preprocessing technique similar to what's outlined here:\n",
- "https://github.com/langchain-ai/langchain/blob/master/templates/propositional-retrieval/propositional_retrieval/proposal_chain.py\n",
- "\n",
- "If you already have a redis-stack instance running locally from before feel free to jump ahead but if not execute the following commands to get the environment properly setup."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rT9HzsnQ1uiz"
- },
- "source": [
- "## Environment Setup\n",
- "\n",
- "### Pull Github Materials\n",
- "Because you are likely running this notebook in **Google Colab**, we need to first\n",
- "pull the necessary dataset and materials directly from GitHub.\n",
- "\n",
- "**If you are running this notebook locally**, FYI you may not need to perform this\n",
- "step at all."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "AJJ2UW6M1ui0"
- },
- "outputs": [],
- "source": [
- "# NBVAL_SKIP\n",
- "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
- "!mv temp_repo/python-recipes/RAG/resources .\n",
- "!rm -rf temp_repo"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Z67mf6T91ui2"
- },
- "source": [
- "### Install Python Dependencies"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "DgxBQFXQ1ui2"
- },
- "outputs": [],
- "source": [
- "# NBVAL_SKIP\n",
- "!pip install -q redis redisvl pandas \"unstructured[pdf]\" sentence-transformers langchain langchain-community openai tqdm"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Install Redis Stack\n",
- "\n",
- "Later in this tutorial, Redis will be used to store, index, and query vector\n",
- "embeddings created from PDF document chunks. **We need to make sure we have a Redis\n",
- "instance available.**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### For Colab\n",
- "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly\n",
- "from the Redis package archive."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# NBVAL_SKIP\n",
- "%%sh\n",
- "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
- "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
- "sudo apt-get update > /dev/null 2>&1\n",
- "sudo apt-get install redis-stack-server > /dev/null 2>&1\n",
- "redis-stack-server --daemonize yes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### For Alternative Environments\n",
- "There are many ways to get the necessary redis-stack instance running\n",
- "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
- "own version of Redis Enterprise running, that works too!\n",
- "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n",
- "3. With docker: `docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Define the Redis Connection URL\n",
- "\n",
- "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace REDIS_PASSWORD, REDIS_HOST and REDIS_PORT values with your own."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n",
- "\n",
- "# Replace values below with your own if using Redis Cloud instance\n",
- "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
- "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\") # ex: 18374\n",
- "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\") # ex: \"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
- "\n",
- "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
- "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Now that our environment is setup we can again load our financial documents"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "KrtWWU4I1ui3"
- },
- "source": [
- "### Dataset Preparation (PDF Documents)\n",
- "\n",
- "To best demonstrate Redis as a vector database layer, we will load a single\n",
- "financial (10k filings) doc and preprocess it using some helpers from LangChain:\n",
- "\n",
- "- `UnstructuredFileLoader` is not the only document loader type that LangChain provides. Docs: https://python.langchain.com/docs/integrations/document_loaders/unstructured_file\n",
- "- `RecursiveCharacterTextSplitter` is what we use to create smaller chunks of text from the doc. Docs: https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "id": "uijl2qFH1ui3"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Listing available documents ... ['resources/nke-10k-2023.pdf', 'resources/amzn-10k-2023.pdf', 'resources/jnj-10k-2023.pdf', 'resources/aapl-10k-2023.pdf', 'resources/retrieval_basic_rag_test.csv', 'resources/nvd-10k-2023.pdf', 'resources/testset.csv', 'resources/msft-10k-2023.pdf', 'resources/generation_basic_rag_test.csv']\n"
- ]
- }
- ],
- "source": [
- "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
- "from langchain.document_loaders import UnstructuredFileLoader\n",
- "\n",
- "# Load list of pdfs from a folder\n",
- "data_path = \"resources/\"\n",
- "docs = [os.path.join(data_path, file) for file in os.listdir(data_path)]\n",
- "\n",
- "print(\"Listing available documents ...\", docs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "id": "anya8hVnT6K_"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done preprocessing. Created 179 chunks of the original pdf resources/nke-10k-2023.pdf\n"
- ]
- }
- ],
- "source": [
- "# pick out the Nike doc for this exercise\n",
- "doc = [doc for doc in docs if \"nke\" in doc][0]\n",
- "\n",
- "# set up the file loader/extractor and text splitter to create chunks\n",
- "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=0)\n",
- "loader = UnstructuredFileLoader(doc, mode=\"single\", strategy=\"fast\")\n",
- "\n",
- "# extract, load, and make chunks\n",
- "chunks = loader.load_and_split(text_splitter)\n",
- "\n",
- "print(\"Done preprocessing. Created\", len(chunks), \"chunks of the original pdf\", doc)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### In the previous example, we would have gone ahead and embed the chunks as extracted here.\n",
- "\n",
- "Now we will instead leverage an LLM to create dense content representations to improve our retrieval accuracy."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Setup OpenAI as LLM"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import getpass\n",
- "import openai\n",
- "\n",
- "CHAT_MODEL = \"gpt-3.5-turbo-0125\"\n",
- "\n",
- "\n",
- "if \"OPENAI_API_KEY\" not in os.environ:\n",
- " os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OPENAI_API_KEY\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "import tqdm\n",
- "import json\n",
- "\n",
- "\n",
- "def create_dense_props(chunk):\n",
- " \"\"\"Create dense representation of raw text content.\"\"\"\n",
- "\n",
- " # The system message here should be HEAVILY customized for your specific use case\n",
- " SYSTEM_PROMPT = \"\"\"\n",
- " You are a helpful PDF extractor tool. You will be presented with segments from\n",
- " raw PDF documents composed of 10k SEC filings information about public companies.\n",
- "\n",
- " Decompose and summarize the raw content into clear and simple propositions,\n",
- " ensuring they are interpretable out of context. Consider the following rules:\n",
- " 1. Split compound sentences into simpler dense phrases that retain existing\n",
- " meaning.\n",
- " 2. Simplify technical jargon or wording if possible while retaining existing\n",
- " meaning.\n",
- " 2. For any named entity that is accompanied by additional descriptive information,\n",
- " separate this information into its own distinct proposition.\n",
- " 3. Decontextualize the proposition by adding necessary modifier to nouns or\n",
- " entire sentences and replacing pronouns (e.g., \"it\", \"he\", \"she\", \"they\", \"this\", \"that\")\n",
- " with the full name of the entities they refer to.\n",
- " 4. Present the results as a list of strings, formatted in JSON, under the key \"propositions\".\n",
- " \"\"\"\n",
- "\n",
- " response = openai.OpenAI().chat.completions.create(\n",
- " model=CHAT_MODEL,\n",
- " response_format={ \"type\": \"json_object\" },\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
- " {\"role\": \"user\", \"content\": f\"Decompose this raw content using the rules above:\\n{chunk.page_content} \"}\n",
- " ]\n",
- " )\n",
- " res = response.choices[0].message.content\n",
- "\n",
- " try:\n",
- " return json.loads(res)[\"propositions\"]\n",
- " except Exception as e:\n",
- " print(f\"Failed to parse propositions\", str(e), flush=True)\n",
- " # Retry\n",
- " return create_dense_props(chunk)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create text propositions using OpenAI"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Load from disk to save time or regenerate as needed.\n",
- "try:\n",
- " with open(\"resources/propositions.json\", \"r\") as f:\n",
- " propositions = json.load(f)\n",
- "except:\n",
- " # create props\n",
- " propositions = [create_dense_props(chunk) for chunk in tqdm.tqdm(chunks)]\n",
- " propositions = [\" \".join(prop) for prop in propositions]\n",
- "\n",
- " # Save to disk for faster reload..\n",
- " with open(\"resources/propositions.json\", \"w\") as f:\n",
- " json.dump(propositions, f)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's evaluate the proposition vs the raw chunk"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\"Registrant check: Well-known seasoned issuer (YES/NO) Registrant check: Required to file reports under Section 13 or 15(d) (YES/NO) Registrant check: Filed all reports required by Section 13 or 15(d) in the past 12 months (YES/NO) and subject to filing requirements for the past 90 days (YES/NO) Registrant check: Submitted all Interactive Data Files required by Rule 405 of Regulation S-T in the past 12 months (YES/NO) Registrant classification: Large accelerated filer (YES), Accelerated filer (NO), Non-accelerated filer (NO), Smaller reporting company (NO), Emerging growth company (NO) Emerging growth company check: Elected not to use extended transition period for new financial accounting standards (YES/NO) Registrant check: Filed a report and attestation on management's assessment of internal control over financial reporting under Section 404(b) of the Sarbanes-Oxley Act (YES/NO) Securities registered check: Registered under Section 12(b) and financial statements reflect correction of errors in previously issued financial statements (YES/NO) Error corrections check: Any restatements requiring recovery analysis of executive officers' incentive-based compensation during recovery period (YES/NO) Registrant check: Shell company status (YES/NO)\""
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "propositions[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Document(page_content=\"Indicate by check mark:YESNO•if the registrant is a well-known seasoned issuer, as defined in Rule 405 of the Securities Act.þ¨•if the registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act.¨þ•whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding12 months (or for such shorter period that the registrant was required to file such reports), and (2) has been subject to such filing requirements for thepast 90 days.þ¨•whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T(§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant was required to submit such files).þ¨•whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting company or an emerging growth company. See the definitions of “large accelerated filer,”“accelerated filer,” “smaller reporting company,” and “emerging growth company” in Rule 12b-2 of the Exchange Act.Large accelerated filerþAccelerated filer☐Non-accelerated filer☐Smaller reporting company☐Emerging growth company☐•if an emerging growth company, if the registrant has elected not to use the extended transition period for complying with any new or revised financialaccounting standards provided pursuant to Section 13(a) of the Exchange Act.¨•whether the registrant has filed a report on and attestation to its management's assessment of the effectiveness of its internal control over financialreporting under Section 404(b) of the Sarbanes-Oxley Act (15 U.S.C. 7262(b)) by the registered public accounting firm that prepared or issued its auditreport.þ•if securities are registered pursuant to Section 12(b) of the Act, whether the financial statements of the registrant included in the filing reflect thecorrection of an error to previously issued financial statements.¨•whether any of those error corrections are restatements that required a recovery analysis of incentive-based compensation received by any of theregistrant's executive officers during the relevant recovery period pursuant to § 240.10D-1(b).¨•whether the registrant is a shell company (as defined in Rule 12b-2 of the Act).☐þ\", metadata={'source': 'resources/nke-10k-2023.pdf'})"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "chunks[0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create embeddings from propositions data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from redisvl.utils.vectorize import HFTextVectorizer\n",
- "\n",
- "hf = HFTextVectorizer(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
- "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
- "\n",
- "prop_embeddings = hf.embed_many([\n",
- " proposition for proposition in propositions\n",
- "])\n",
- "\n",
- "# Check to make sure we've created enough embeddings, 1 per document chunk\n",
- "len(prop_embeddings) == len(propositions) == len(chunks)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5baI0xDQ1ui-"
- },
- "source": [
- "### Define a schema and create an index\n",
- "\n",
- "Below we connect to Redis and create an index that contains a text field, tag field, and vector field."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "id": "zB1EW_9n1ui-"
- },
- "outputs": [],
- "source": [
- "from redis import Redis\n",
- "from redisvl.index import SearchIndex\n",
- "\n",
- "\n",
- "index_name = \"redisvl\"\n",
- "\n",
- "\n",
- "schema = {\n",
- " \"index\": {\n",
- " \"name\": index_name,\n",
- " \"prefix\": \"chunk\"\n",
- " },\n",
- " \"fields\": [\n",
- " {\n",
- " \"name\": \"chunk_id\",\n",
- " \"type\": \"tag\",\n",
- " \"attrs\": {\n",
- " \"sortable\": True\n",
- " }\n",
- " },\n",
- " {\n",
- " \"name\": \"proposition\",\n",
- " \"type\": \"text\"\n",
- " },\n",
- " {\n",
- " \"name\": \"text_embedding\",\n",
- " \"type\": \"vector\",\n",
- " \"attrs\": {\n",
- " \"dims\": hf.dims,\n",
- " \"distance_metric\": \"cosine\",\n",
- " \"algorithm\": \"hnsw\",\n",
- " \"datatype\": \"float32\"\n",
- " }\n",
- " }\n",
- " ]\n",
- "}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "13:16:14 redisvl.index.index INFO Index already exists, overwriting.\n"
- ]
- }
- ],
- "source": [
- "# connect to redis\n",
- "client = Redis.from_url(REDIS_URL)\n",
- "\n",
- "# create an index from schema and the client\n",
- "index = SearchIndex.from_dict(schema)\n",
- "index.set_client(client)\n",
- "index.create(overwrite=True, drop=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "id": "C70C-UWj1ujA"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "Index Information:\n",
- "╭──────────────┬────────────────┬────────────┬─────────────────┬────────────╮\n",
- "│ Index Name │ Storage Type │ Prefixes │ Index Options │ Indexing │\n",
- "├──────────────┼────────────────┼────────────┼─────────────────┼────────────┤\n",
- "│ redisvl │ HASH │ ['chunk'] │ [] │ 0 │\n",
- "╰──────────────┴────────────────┴────────────┴─────────────────┴────────────╯\n",
- "Index Fields:\n",
- "╭────────────────┬────────────────┬────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
- "│ Name │ Attribute │ Type │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │\n",
- "├────────────────┼────────────────┼────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
- "│ chunk_id │ chunk_id │ TAG │ SEPARATOR │ , │ │ │ │ │ │ │ │ │ │ │\n",
- "│ proposition │ proposition │ TEXT │ WEIGHT │ 1 │ │ │ │ │ │ │ │ │ │ │\n",
- "│ text_embedding │ text_embedding │ VECTOR │ algorithm │ HNSW │ data_type │ FLOAT32 │ dim │ 384 │ distance_metric │ COSINE │ M │ 16 │ ef_construction │ 200 │\n",
- "╰────────────────┴────────────────┴────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
- ]
- }
- ],
- "source": [
- "# get info about the index\n",
- "# NBVAL_SKIP\n",
- "!rvl index info -i redisvl"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Qrj-jeGmBRTL"
- },
- "source": [
- "### Process and load dataset\n",
- "Below we use the RedisVL index to simply load the list of document chunks to Redis db."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "id": "Zsg09Keg1ujA"
- },
- "outputs": [],
- "source": [
- "# load expects an iterable of dictionaries\n",
- "from redisvl.redis.utils import array_to_buffer\n",
- "\n",
- "data = [\n",
- " {\n",
- " 'chunk_id': f'{i}',\n",
- " 'proposition': proposition,\n",
- " # For HASH -- must convert embeddings to bytes\n",
- " 'text_embedding': array_to_buffer(prop_embeddings[i], dtype=\"float32\")\n",
- " } for i, proposition in enumerate(propositions)\n",
- "]\n",
- "\n",
- "# RedisVL handles batching automatically\n",
- "keys = index.load(data, id_field=\"chunk_id\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Setup RedisVL AsyncSearchIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " question \\\n",
- "0 What is the trend in the company's revenue and... \n",
- "1 What are the company's primary revenue sources? \n",
- "2 How much debt does the company have, and what ... \n",
- "3 What does the company say about its environmen... \n",
- "4 What is the company's strategy for growth? \n",
- "\n",
- " answer \n",
- "0 The company experienced revenue growth in fisc... \n",
- "1 The company's primary revenue sources are from... \n",
- "2 As of May 31, 2023, the company had Long-term ... \n",
- "3 The company acknowledges the importance of env... \n",
- "4 The company's strategy for growth includes ide... "
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "import asyncio\n",
- "import pandas as pd\n",
- "\n",
- "results = await asyncio.gather(*[\n",
- " answer_question(index, question) for question in questions\n",
- "])\n",
- "\n",
- "pd.DataFrame(columns=[\"question\", \"answer\"], data=list(zip(questions, results)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "TnkK0NwIIM9q"
- },
- "source": [
- "### Improve accuracy with query rewriting / expansion\n",
- "\n",
- "We can also use the power on an LLM to rewrite or expand an input question.\n",
- "\n",
- "Example: https://github.com/langchain-ai/langchain/blob/master/templates/rewrite-retrieve-read/rewrite_retrieve_read/chain.py"
- ]
- },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R2-i8jBl9GRH"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "# Advanced RAG example\n",
+ "\n",
+ "Now that you have a good foundation in Redis data structures, search capabilities, and basic RAG with the redisvl client from [/getting_started/02_redisvl](../getting_started/02_redisvl.ipynb).\n",
+ "\n",
+ "We will extend the basic RAG example with a few special topics/techniques:\n",
+ "- Dense content representation\n",
+ "- Query rewriting / expansion\n",
+ "- Semantic caching\n",
+ "- Conversational memory persistence\n",
+ "\n",
+ "## Let's Begin!\n",
+ "| \n", - " | question | \n", - "answer | \n", - "
|---|---|---|
| 0 | \n", - "What is the trend in the company's revenue and... | \n", - "The company experienced revenue growth in fisc... | \n", - "
| 1 | \n", - "What are the company's primary revenue sources? | \n", - "The company's primary revenue sources are from... | \n", - "
| 2 | \n", - "How much debt does the company have, and what ... | \n", - "As of May 31, 2023, the company had Long-term ... | \n", - "
| 3 | \n", - "What does the company say about its environmen... | \n", - "The company acknowledges the importance of env... | \n", - "
| 4 | \n", - "What is the company's strategy for growth? | \n", - "The company's strategy for growth includes ide... | \n", - "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Improve accuracy with dense content representations\n",
+ "In the basic example, we took raw chunks of text from our pdf documents and generated embeddings for them to be stored in the vector database. This is okay but one technique we can use to improve the quality of retrieval is to leverage an LLM from OpenAI during ETL. We will prompt the LLM to summarize and decompose the raw pdf text into more discrete propositional phrases. This will enhance the clarity of the text and improve semantic retrieval for RAG.\n",
+ "\n",
+ "The goal is to utilize a preprocessing technique similar to what's outlined here:\n",
+ "https://github.com/langchain-ai/langchain/blob/master/templates/propositional-retrieval/propositional_retrieval/proposal_chain.py\n",
+ "\n",
+ "If you already have a redis-stack instance running locally from before feel free to jump ahead but if not execute the following commands to get the environment properly setup."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rT9HzsnQ1uiz"
+ },
+ "source": [
+ "## Environment Setup\n",
+ "\n",
+ "### Pull Github Materials\n",
+ "Because you are likely running this notebook in **Google Colab**, we need to first\n",
+ "pull the necessary dataset and materials directly from GitHub.\n",
+ "\n",
+ "**If you are running this notebook locally**, FYI you may not need to perform this\n",
+ "step at all."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "AJJ2UW6M1ui0"
+ },
+ "outputs": [],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
+ "!mv temp_repo/python-recipes/RAG/resources .\n",
+ "!rm -rf temp_repo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z67mf6T91ui2"
+ },
+ "source": [
+ "### Install Python Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "DgxBQFXQ1ui2"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "id": "XnWhfeiGYVrI"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Using dense content representation\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "\"Based on the provided context, we can see that the company in question is NIKE, Inc. The company has a significant presence globally with subsidiaries in various jurisdictions such as Delaware, Netherlands, China, Mexico, Japan, Korea, and Oregon. Additionally, the company's total revenues are substantial, with revenues in the United States alone amounting to $22,007 million in the fiscal year ended May 31, 2023. NIKE, Inc. also has a diverse range of financial assets, including cash, short-term investments, U.S. Treasury securities, commercial paper and bonds, money market funds, time deposits, and U.S. Agency securities.\\n\\nTherefore, based on the information provided, we can conclude that NIKE, Inc. is a large company with a significant global presence and substantial revenues.\""
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "# An example question that is a bit simplistic...\n",
- "await answer_question(index, \"How big is the company?\")"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install -q \"redisvl>=0.6.0\" pandas \"unstructured[pdf]\" sentence-transformers langchain langchain-community \"openai>=1.57.0\" tqdm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Install Redis Stack\n",
+ "\n",
+ "Later in this tutorial, Redis will be used to store, index, and query vector\n",
+ "embeddings created from PDF document chunks. **We need to make sure we have a Redis\n",
+ "instance available.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### For Colab\n",
+ "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly\n",
+ "from the Redis package archive."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "%%sh\n",
+ "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
+ "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
+ "sudo apt-get update > /dev/null 2>&1\n",
+ "sudo apt-get install redis-stack-server > /dev/null 2>&1\n",
+ "redis-stack-server --daemonize yes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### For Alternative Environments\n",
+ "There are many ways to get the necessary redis-stack instance running\n",
+ "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
+ "own version of Redis Enterprise running, that works too!\n",
+ "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n",
+ "3. With docker: `docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define the Redis Connection URL\n",
+ "\n",
+ "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace REDIS_PASSWORD, REDIS_HOST and REDIS_PORT values with your own."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import warnings\n",
+ "\n",
+ "import nest_asyncio\n",
+ "# Apply the nest_asyncio patch: let's us run async code in Jupyter\n",
+ "nest_asyncio.apply()\n",
+ "\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
+ "# Replace values below with your own if using Redis Cloud instance\n",
+ "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
+ "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\") # ex: 18374\n",
+ "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\") # ex: \"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
+ "\n",
+ "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
+ "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Now that our environment is setup we can again load our financial documents"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KrtWWU4I1ui3"
+ },
+ "source": [
+ "### Dataset Preparation (PDF Documents)\n",
+ "\n",
+ "To best demonstrate Redis as a vector database layer, we will load a single\n",
+ "financial (10k filings) doc and preprocess it using some helpers from LangChain:\n",
+ "\n",
+ "- `PyPDFLoader` is not the only document loader type that LangChain provides. Docs: https://python.langchain.com/docs/integrations/document_loaders/unstructured_file\n",
+ "- `RecursiveCharacterTextSplitter` is what we use to create smaller chunks of text from the doc. Docs: https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "uijl2qFH1ui3"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "id": "Tg55HqLFIRXJ"
- },
- "outputs": [],
- "source": [
- "# NBVAL_SKIP\n",
- "async def rewrite_query(query: str, prompt: str = None):\n",
- " \"\"\"Rewrite the user's original query\"\"\"\n",
- "\n",
- " SYSTEM_PROMPT = prompt if prompt else \"\"\"Given the user's input question below, find a better or\n",
- " more complete way to phrase this question in order to improve semantic search\n",
- " engine retrieval quality over a set of SEC 10K PDF docs. Return the rephrased\n",
- " question as a string in a JSON response under the key \"query\".\"\"\"\n",
- "\n",
- " response = await openai.AsyncClient().chat.completions.create(\n",
- " model=CHAT_MODEL,\n",
- " response_format={ \"type\": \"json_object\" },\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
- " {\"role\": \"user\", \"content\": f\"Original input question from user: {query}\"}\n",
- " ],\n",
- " temperature=0.1,\n",
- " seed=42\n",
- " )\n",
- " # Response provided by LLM\n",
- " rewritten_query = json.loads(response.choices[0].message.content)[\"query\"]\n",
- " return rewritten_query"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done preprocessing. Created 211 chunks of the original pdf resources/nke-10k-2023.pdf\n"
+ ]
+ }
+ ],
+ "source": [
+ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+ "from langchain_community.document_loaders import PyPDFLoader\n",
+ "\n",
+ "# pdf to load\n",
+ "path = 'resources/nke-10k-2023.pdf'\n",
+ "assert os.path.exists(path), f\"File not found: {path}\"\n",
+ "\n",
+ "# load and split\n",
+ "loader = PyPDFLoader(path)\n",
+ "pages = loader.load()\n",
+ "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=0)\n",
+ "chunks = text_splitter.split_documents(pages)\n",
+ "\n",
+ "print(\"Done preprocessing. Created\", len(chunks), \"chunks of the original pdf\", path)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "id": "8_ce8fC8KR50"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'What is the size of the company in terms of revenue, assets, and market capitalization?'"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "# Example Sinple Query Rewritten\n",
- "await rewrite_query(\"How big is the company?\")"
+ "data": {
+ "text/plain": [
+ "Document(metadata={'source': 'resources/nke-10k-2023.pdf', 'page': 0, 'page_label': '1'}, page_content=\"Table of Contents\\nUNITED STATES\\nSECURITIES AND EXCHANGE COMMISSION\\nWashington, D.C. 20549\\nFORM 10-K\\n(Mark One)\\n☑ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(D) OF THE SECURITIES EXCHANGE ACT OF 1934\\nFOR THE FISCAL YEAR ENDED MAY 31, 2023\\nOR\\n☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(D) OF THE SECURITIES EXCHANGE ACT OF 1934\\nFOR THE TRANSITION PERIOD FROM TO .\\nCommission File No. 1-10635\\nNIKE, Inc.\\n(Exact name of Registrant as specified in its charter)\\nOregon 93-0584541\\n(State or other jurisdiction of incorporation) (IRS Employer Identification No.)\\nOne Bowerman Drive, Beaverton, Oregon 97005-6453\\n(Address of principal executive offices and zip code)\\n(503) 671-6453\\n(Registrant's telephone number, including area code)\\nSECURITIES REGISTERED PURSUANT TO SECTION 12(B) OF THE ACT:\\nClass B Common Stock NKE New York Stock Exchange\\n(Title of each class) (Trading symbol) (Name of each exchange on which registered)\\nSECURITIES REGISTERED PURSUANT TO SECTION 12(G) OF THE ACT:\\nNONE\\nIndicate by check mark: YES NO\\n• if the registrant is a well-known seasoned issuer, as defined in Rule 405 of the Securities Act. þ ¨ \\n• if the registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act. ¨ þ \\n• whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding\\n12 months (or for such shorter period that the registrant was required to file such reports), and (2) has been subject to such filing requirements for the\\npast 90 days.\\nþ ¨ \\n• whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T\\n(§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant was required to submit such files).\\nþ ¨ \\n• whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting company or an emerging growth company. See the definitions of “large accelerated filer,”\\n“accelerated filer,” “smaller reporting company,” and “emerging growth company” in Rule 12b-2 of the Exchange Act.\\nLarge accelerated filer þ Accelerated filer ☐ Non-accelerated filer ☐ Smaller reporting company ☐ Emerging growth company ☐\")"
]
- },
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "chunks[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### In the previous example, we would have gone ahead and embed the chunks as extracted here.\n",
+ "\n",
+ "Now we will instead leverage an LLM to create dense content representations to improve our retrieval accuracy."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Setup OpenAI as LLM"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import getpass\n",
+ "import openai\n",
+ "\n",
+ "CHAT_MODEL = \"gpt-3.5-turbo-0125\"\n",
+ "\n",
+ "\n",
+ "if \"OPENAI_API_KEY\" not in os.environ:\n",
+ " os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OPENAI_API_KEY\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tqdm\n",
+ "import json\n",
+ "\n",
+ "\n",
+ "def create_dense_props(chunk):\n",
+ " \"\"\"Create dense representation of raw text content.\"\"\"\n",
+ "\n",
+ " # The system message here should be HEAVILY customized for your specific use case\n",
+ " SYSTEM_PROMPT = \"\"\"\n",
+ " You are a helpful PDF extractor tool. You will be presented with segments from\n",
+ " raw PDF documents composed of 10k SEC filings information about public companies.\n",
+ "\n",
+ " Decompose and summarize the raw content into clear and simple propositions,\n",
+ " ensuring they are interpretable out of context. Consider the following rules:\n",
+ " 1. Split compound sentences into simpler dense phrases that retain existing\n",
+ " meaning.\n",
+ " 2. Simplify technical jargon or wording if possible while retaining existing\n",
+ " meaning.\n",
+ " 2. For any named entity that is accompanied by additional descriptive information,\n",
+ " separate this information into its own distinct proposition.\n",
+ " 3. Decontextualize the proposition by adding necessary modifier to nouns or\n",
+ " entire sentences and replacing pronouns (e.g., \"it\", \"he\", \"she\", \"they\", \"this\", \"that\")\n",
+ " with the full name of the entities they refer to.\n",
+ " 4. Present the results as a list of strings, formatted in JSON, under the key \"propositions\".\n",
+ " \"\"\"\n",
+ "\n",
+ " response = openai.OpenAI().chat.completions.create(\n",
+ " model=CHAT_MODEL,\n",
+ " response_format={ \"type\": \"json_object\" },\n",
+ " messages=[\n",
+ " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+ " {\"role\": \"user\", \"content\": f\"Decompose this raw content using the rules above:\\n{chunk.page_content} \"}\n",
+ " ]\n",
+ " )\n",
+ " res = response.choices[0].message.content\n",
+ "\n",
+ " try:\n",
+ " return json.loads(res)[\"propositions\"]\n",
+ " except Exception as e:\n",
+ " print(f\"Failed to parse propositions\", str(e), flush=True)\n",
+ " # Retry\n",
+ " return create_dense_props(chunk)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create text propositions using OpenAI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load from disk to save time or regenerate as needed.\n",
+ "try:\n",
+ " with open(\"resources/propositions.json\", \"r\") as f:\n",
+ " propositions = json.load(f)\n",
+ "except:\n",
+ " # create props\n",
+ " propositions = [create_dense_props(chunk) for chunk in tqdm.tqdm(chunks)]\n",
+ " propositions = [\" \".join(prop) for prop in propositions]\n",
+ "\n",
+ " # Save to disk for faster reload..\n",
+ " with open(\"resources/propositions.json\", \"w\") as f:\n",
+ " json.dump(propositions, f)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Let's evaluate the proposition vs the raw chunk"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "id": "9ubNQrJOYL42"
- },
- "outputs": [],
- "source": [
- "async def answer_question(index: AsyncSearchIndex, query: str, **kwargs):\n",
- " \"\"\"Answer the user's question\"\"\"\n",
- "\n",
- " SYSTEM_PROMPT = \"\"\"You are a helpful financial analyst assistant that has access\n",
- " to public financial 10k documents in order to answer users questions about company\n",
- " performance, ethics, characteristics, and core information.\n",
- " \"\"\"\n",
- "\n",
- " # Rewrite the query using an LLM\n",
- " rewritten_query = await rewrite_query(query, **kwargs)\n",
- " print(\"User query updated to:\\n\", rewritten_query, flush=True)\n",
- "\n",
- " query_vector = hf.embed(rewritten_query)\n",
- " # Fetch context from Redis using vector search\n",
- " context = await retrieve_context(index, query_vector)\n",
- " print(\"Context retrieved\", flush=True)\n",
- "\n",
- " # Generate contextualized prompt and feed to OpenAI\n",
- " response = await openai.AsyncClient().chat.completions.create(\n",
- " model=CHAT_MODEL,\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
- " {\"role\": \"user\", \"content\": promptify(rewritten_query, context)}\n",
- " ],\n",
- " temperature=0.1,\n",
- " seed=42\n",
- " )\n",
- " # Response provided by LLM\n",
- " return response.choices[0].message.content"
+ "data": {
+ "text/plain": [
+ "\"Registrant check: Well-known seasoned issuer (YES/NO) Registrant check: Required to file reports under Section 13 or 15(d) (YES/NO) Registrant check: Filed all reports required by Section 13 or 15(d) in the past 12 months (YES/NO) and subject to filing requirements for the past 90 days (YES/NO) Registrant check: Submitted all Interactive Data Files required by Rule 405 of Regulation S-T in the past 12 months (YES/NO) Registrant classification: Large accelerated filer (YES), Accelerated filer (NO), Non-accelerated filer (NO), Smaller reporting company (NO), Emerging growth company (NO) Emerging growth company check: Elected not to use extended transition period for new financial accounting standards (YES/NO) Registrant check: Filed a report and attestation on management's assessment of internal control over financial reporting under Section 404(b) of the Sarbanes-Oxley Act (YES/NO) Securities registered check: Registered under Section 12(b) and financial statements reflect correction of errors in previously issued financial statements (YES/NO) Error corrections check: Any restatements requiring recovery analysis of executive officers' incentive-based compensation during recovery period (YES/NO) Registrant check: Shell company status (YES/NO)\""
]
- },
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "propositions[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "id": "BIO_jW6KYsMU"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "User query updated to:\n",
- " What is the size of the company in terms of revenue, assets, and market capitalization?\n",
- "Using dense content representation\n",
- "Context retrieved\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "\"Based on the provided context, the company's revenue, assets, and market capitalization figures are not explicitly mentioned. The information mainly focuses on financial assets, investments, return on invested capital, EBIT, and other financial metrics. Without specific details on revenue, assets, and market capitalization, I am unable to provide the exact size of the company in those terms.\""
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "# Now try again with query re-writing enabled\n",
- "await answer_question(index, \"How big is the company?\")"
+ "data": {
+ "text/plain": [
+ "Document(metadata={'source': 'resources/nke-10k-2023.pdf', 'page': 0, 'page_label': '1'}, page_content=\"Table of Contents\\nUNITED STATES\\nSECURITIES AND EXCHANGE COMMISSION\\nWashington, D.C. 20549\\nFORM 10-K\\n(Mark One)\\n☑ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(D) OF THE SECURITIES EXCHANGE ACT OF 1934\\nFOR THE FISCAL YEAR ENDED MAY 31, 2023\\nOR\\n☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(D) OF THE SECURITIES EXCHANGE ACT OF 1934\\nFOR THE TRANSITION PERIOD FROM TO .\\nCommission File No. 1-10635\\nNIKE, Inc.\\n(Exact name of Registrant as specified in its charter)\\nOregon 93-0584541\\n(State or other jurisdiction of incorporation) (IRS Employer Identification No.)\\nOne Bowerman Drive, Beaverton, Oregon 97005-6453\\n(Address of principal executive offices and zip code)\\n(503) 671-6453\\n(Registrant's telephone number, including area code)\\nSECURITIES REGISTERED PURSUANT TO SECTION 12(B) OF THE ACT:\\nClass B Common Stock NKE New York Stock Exchange\\n(Title of each class) (Trading symbol) (Name of each exchange on which registered)\\nSECURITIES REGISTERED PURSUANT TO SECTION 12(G) OF THE ACT:\\nNONE\\nIndicate by check mark: YES NO\\n• if the registrant is a well-known seasoned issuer, as defined in Rule 405 of the Securities Act. þ ¨ \\n• if the registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act. ¨ þ \\n• whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding\\n12 months (or for such shorter period that the registrant was required to file such reports), and (2) has been subject to such filing requirements for the\\npast 90 days.\\nþ ¨ \\n• whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T\\n(§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant was required to submit such files).\\nþ ¨ \\n• whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting company or an emerging growth company. See the definitions of “large accelerated filer,”\\n“accelerated filer,” “smaller reporting company,” and “emerging growth company” in Rule 12b-2 of the Exchange Act.\\nLarge accelerated filer þ Accelerated filer ☐ Non-accelerated filer ☐ Smaller reporting company ☐ Emerging growth company ☐\")"
]
- },
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "chunks[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create embeddings from propositions data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "p97uL4g9T6LQ"
- },
- "source": [
- "### Improve performance and cut costs with LLM caching"
+ "data": {
+ "text/plain": [
+ "False"
]
- },
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from redisvl.utils.vectorize import HFTextVectorizer\n",
+ "from redisvl.extensions.cache.embeddings import EmbeddingsCache\n",
+ "\n",
+ "\n",
+ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
+ "\n",
+ "hf = HFTextVectorizer(\n",
+ " model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
+ " cache=EmbeddingsCache(\n",
+ " name=\"embedcache\",\n",
+ " ttl=600,\n",
+ " redis_url=REDIS_URL,\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "prop_embeddings = hf.embed_many([\n",
+ " proposition for proposition in propositions\n",
+ "])\n",
+ "\n",
+ "# Check to make sure we've created enough embeddings, 1 per document chunk\n",
+ "len(prop_embeddings) == len(propositions) == len(chunks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5baI0xDQ1ui-"
+ },
+ "source": [
+ "### Define a schema and create an index\n",
+ "\n",
+ "Below we connect to Redis and create an index that contains a text field, tag field, and vector field."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "zB1EW_9n1ui-"
+ },
+ "outputs": [],
+ "source": [
+ "from redisvl.index import SearchIndex\n",
+ "\n",
+ "\n",
+ "index_name = \"redisvl\"\n",
+ "\n",
+ "\n",
+ "schema = {\n",
+ " \"index\": {\n",
+ " \"name\": index_name,\n",
+ " \"prefix\": \"chunk\"\n",
+ " },\n",
+ " \"fields\": [\n",
+ " {\n",
+ " \"name\": \"chunk_id\",\n",
+ " \"type\": \"tag\",\n",
+ " \"attrs\": {\n",
+ " \"sortable\": True\n",
+ " }\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"proposition\",\n",
+ " \"type\": \"text\"\n",
+ " },\n",
+ " {\n",
+ " \"name\": \"text_embedding\",\n",
+ " \"type\": \"vector\",\n",
+ " \"attrs\": {\n",
+ " \"dims\": hf.dims,\n",
+ " \"distance_metric\": \"cosine\",\n",
+ " \"algorithm\": \"hnsw\",\n",
+ " \"datatype\": \"float32\"\n",
+ " }\n",
+ " }\n",
+ " ]\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "id": "7geEAsYST6LQ"
- },
- "outputs": [],
- "source": [
- "from redisvl.extensions.llmcache import SemanticCache\n",
- "\n",
- "llmcache = SemanticCache(\n",
- " name=\"llmcache\",\n",
- " vectorizer=hf,\n",
- " redis_url=REDIS_URL,\n",
- " ttl=120,\n",
- " distance_threshold=0.2\n",
- ")"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "15:52:01 redisvl.index.index INFO Index already exists, overwriting.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# create an index from schema and the client\n",
+ "index = SearchIndex.from_dict(schema, redis_url=REDIS_URL)\n",
+ "index.create(overwrite=True, drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "C70C-UWj1ujA"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "id": "1ALcQXAqT6LQ"
- },
- "outputs": [],
- "source": [
- "from functools import wraps\n",
- "\n",
- "# Create an LLM caching decorator\n",
- "def cache(func):\n",
- " @wraps(func)\n",
- " async def wrapper(index, query_text, *args, **kwargs):\n",
- " query_vector = llmcache._vectorizer.embed(query_text)\n",
- "\n",
- " # Check the cache with the vector\n",
- " if result := llmcache.check(vector=query_vector):\n",
- " return result[0]['response']\n",
- "\n",
- " response = await func(index, query_text, query_vector=query_vector)\n",
- " llmcache.store(query_text, response, query_vector)\n",
- " return response\n",
- " return wrapper\n",
- "\n",
- "\n",
- "@cache\n",
- "async def answer_question(index: AsyncSearchIndex, query: str, **kwargs):\n",
- " \"\"\"Answer the user's question\"\"\"\n",
- "\n",
- " SYSTEM_PROMPT = \"\"\"You are a helpful financial analyst assistant that has access\n",
- " to public financial 10k documents in order to answer users questions about company\n",
- " performance, ethics, characteristics, and core information.\n",
- " \"\"\"\n",
- "\n",
- " context = await retrieve_context(index, kwargs[\"query_vector\"])\n",
- " response = await openai.AsyncClient().chat.completions.create(\n",
- " model=CHAT_MODEL,\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
- " {\"role\": \"user\", \"content\": promptify(query, context)}\n",
- " ],\n",
- " temperature=0.1,\n",
- " seed=42\n",
- " )\n",
- " # Response provided by GPT-3.5\n",
- " return response.choices[0].message.content"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "Index Information:\n",
+ "╭──────────────┬────────────────┬────────────┬─────────────────┬────────────╮\n",
+ "│ Index Name │ Storage Type │ Prefixes │ Index Options │ Indexing │\n",
+ "├──────────────┼────────────────┼────────────┼─────────────────┼────────────┤\n",
+ "│ redisvl │ HASH │ ['chunk'] │ [] │ 0 │\n",
+ "╰──────────────┴────────────────┴────────────┴─────────────────┴────────────╯\n",
+ "Index Fields:\n",
+ "╭────────────────┬────────────────┬────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
+ "│ Name │ Attribute │ Type │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │\n",
+ "├────────────────┼────────────────┼────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
+ "│ chunk_id │ chunk_id │ TAG │ SEPARATOR │ , │ │ │ │ │ │ │ │ │ │ │\n",
+ "│ proposition │ proposition │ TEXT │ WEIGHT │ 1 │ │ │ │ │ │ │ │ │ │ │\n",
+ "│ text_embedding │ text_embedding │ VECTOR │ algorithm │ HNSW │ data_type │ FLOAT32 │ dim │ 384 │ distance_metric │ COSINE │ M │ 16 │ ef_construction │ 200 │\n",
+ "╰────────────────┴────────────────┴────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get info about the index\n",
+ "# NBVAL_SKIP\n",
+ "!rvl index info -i redisvl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qrj-jeGmBRTL"
+ },
+ "source": [
+ "### Process and load dataset\n",
+ "Below we use the RedisVL index to simply load the list of document chunks to Redis db."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "Zsg09Keg1ujA"
+ },
+ "outputs": [],
+ "source": [
+ "# load expects an iterable of dictionaries\n",
+ "from redisvl.redis.utils import array_to_buffer\n",
+ "\n",
+ "data = [\n",
+ " {\n",
+ " 'chunk_id': f'{i}',\n",
+ " 'proposition': proposition,\n",
+ " # For HASH -- must convert embeddings to bytes\n",
+ " 'text_embedding': array_to_buffer(prop_embeddings[i], dtype=\"float32\")\n",
+ " } for i, proposition in enumerate(propositions)\n",
+ "]\n",
+ "\n",
+ "# RedisVL handles batching automatically\n",
+ "keys = index.load(data, id_field=\"chunk_id\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Setup RedisVL AsyncSearchIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from redisvl.index import AsyncSearchIndex\n",
+ "\n",
+ "index = AsyncSearchIndex.from_dict(schema, redis_url=REDIS_URL)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Test the updated RAG workflow"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from redisvl.query import VectorQuery\n",
+ "from redisvl.index import AsyncSearchIndex\n",
+ "\n",
+ "\n",
+ "def promptify(query: str, context: str) -> str:\n",
+ " return f'''Use the provided context below derived from public financial\n",
+ " documents to answer the user's question. If you can't answer the user's\n",
+ " question, based on the context; do not guess. If there is no context at all,\n",
+ " respond with \"I don't know\".\n",
+ "\n",
+ " User question:\n",
+ "\n",
+ " {query}\n",
+ "\n",
+ " Helpful context:\n",
+ "\n",
+ " {context}\n",
+ "\n",
+ " Answer:\n",
+ " '''\n",
+ "\n",
+ "# Update the retrieval helper to use propositions\n",
+ "async def retrieve_context(index: AsyncSearchIndex, query_vector) -> str:\n",
+ " \"\"\"Fetch the relevant context from Redis using vector search\"\"\"\n",
+ " print(\"Using dense content representation\", flush=True)\n",
+ " results = await index.query(\n",
+ " VectorQuery(\n",
+ " vector=query_vector,\n",
+ " vector_field_name=\"text_embedding\",\n",
+ " return_fields=[\"proposition\"],\n",
+ " num_results=3\n",
+ " )\n",
+ " )\n",
+ " content = \"\\n\".join([result[\"proposition\"] for result in results])\n",
+ " return content\n",
+ "\n",
+ "# Update the answer_question method\n",
+ "async def answer_question(index: AsyncSearchIndex, query: str):\n",
+ " \"\"\"Answer the user's question\"\"\"\n",
+ "\n",
+ " SYSTEM_PROMPT = \"\"\"You are a helpful financial analyst assistant that has access\n",
+ " to public financial 10k documents in order to answer users questions about company\n",
+ " performance, ethics, characteristics, and core information.\n",
+ " \"\"\"\n",
+ "\n",
+ " query_vector = hf.embed(query)\n",
+ " # Fetch context from Redis using vector search\n",
+ " context = await retrieve_context(index, query_vector)\n",
+ " # Generate contextualized prompt and feed to OpenAI\n",
+ " response = await openai.AsyncClient().chat.completions.create(\n",
+ " model=CHAT_MODEL,\n",
+ " messages=[\n",
+ " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+ " {\"role\": \"user\", \"content\": promptify(query, context)}\n",
+ " ],\n",
+ " temperature=0.1,\n",
+ " seed=42\n",
+ " )\n",
+ " # Response provided by LLM\n",
+ " return response.choices[0].message.content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Generate a list of questions\n",
+ "questions = [\n",
+ " \"What is the trend in the company's revenue and profit over the past few years?\",\n",
+ " \"What are the company's primary revenue sources?\",\n",
+ " \"How much debt does the company have, and what are its capital expenditure plans?\",\n",
+ " \"What does the company say about its environmental, social, and governance (ESG) practices?\",\n",
+ " \"What is the company's strategy for growth?\"\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {
- "id": "BXK_BXuhT6LQ"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Using dense content representation\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "\"Nike's total revenue for fiscal year 2023 was $27.4 billion from sales to wholesale customers and $21.3 billion through direct-to-consumer channels. Comparing this to the previous year, the total revenue for fiscal year 2022 was not explicitly provided in the context.\""
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "query = \"What was Nike's revenue last year compared to this year??\"\n",
- "\n",
- "await answer_question(index, query)"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using dense content representation\n",
+ "Using dense content representation\n",
+ "Using dense content representation\n",
+ "Using dense content representation\n",
+ "Using dense content representation\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {
- "id": "7mZpSpf9T6LQ"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'id': 'llmcache:c011dfed42a8227e11ba9a817fbbeb07e2623058add52e96066ee01b49fe9986', 'vector_distance': '0.0202275514603', 'entry_id': 'c011dfed42a8227e11ba9a817fbbeb07e2623058add52e96066ee01b49fe9986', 'prompt': \"What was Nike's revenue last year compared to this year??\", 'response': \"Nike's total revenue for fiscal year 2023 was $27.4 billion from sales to wholesale customers and $21.3 billion through direct-to-consumer channels. Comparing this to the previous year, the total revenue for fiscal year 2022 was not explicitly provided in the context.\", 'inserted_at': '1723223894.9', 'updated_at': '1723223894.9'}\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "\"Nike's total revenue for fiscal year 2023 was $27.4 billion from sales to wholesale customers and $21.3 billion through direct-to-consumer channels. Comparing this to the previous year, the total revenue for fiscal year 2022 was not explicitly provided in the context.\""
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
],
- "source": [
- "# NBVAL_SKIP\n",
- "query = \"What was Nike's total revenue in the last year compared to now??\"\n",
- "\n",
- "await answer_question(index, query)\n",
- "\n",
- "# notice no HTTP request to OpenAI since this question is \"close enough\" to the last one"
+ "text/plain": [
+ " question \\\n",
+ "0 What is the trend in the company's revenue and... \n",
+ "1 What are the company's primary revenue sources? \n",
+ "2 How much debt does the company have, and what ... \n",
+ "3 What does the company say about its environmen... \n",
+ "4 What is the company's strategy for growth? \n",
+ "\n",
+ " answer \n",
+ "0 The company experienced revenue growth in fisc... \n",
+ "1 The company's primary revenue sources are from... \n",
+ "2 As of May 31, 2023, the company had Long-term ... \n",
+ "3 The company acknowledges the importance of env... \n",
+ "4 The company's strategy for growth includes ide... "
]
- },
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import asyncio\n",
+ "import pandas as pd\n",
+ "\n",
+ "results = await asyncio.gather(*[\n",
+ " answer_question(index, question) for question in questions\n",
+ "])\n",
+ "\n",
+ "pd.DataFrame(columns=[\"question\", \"answer\"], data=list(zip(questions, results)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TnkK0NwIIM9q"
+ },
+ "source": [
+ "### Improve accuracy with query rewriting / expansion\n",
+ "\n",
+ "We can also use the power on an LLM to rewrite or expand an input question.\n",
+ "\n",
+ "Example: https://github.com/langchain-ai/langchain/blob/master/templates/rewrite-retrieve-read/rewrite_retrieve_read/chain.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "UaiF_ws7itsi"
- },
- "source": [
- "### Improve personalization with including chat session history\n",
- "\n",
- "In order to preserve state in the conversation, it's imperitive to offload conversation history to a database that can handle high transaction throughput for writes/reads to limit system latency.\n",
- "\n",
- "We can store message history for a particular user session in a Redis List data type.\n"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using dense content representation\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {
- "id": "WMOF7fJQdhgN"
- },
- "outputs": [],
- "source": [
- "import json\n",
- "\n",
- "\n",
- "class ChatBot:\n",
- " def __init__(self, index: AsyncSearchIndex, user: str):\n",
- " self.index = index\n",
- " self.user = user\n",
- "\n",
- " async def get_messages(self) -> list:\n",
- " \"\"\"Get all messages associated with a session\"\"\"\n",
- " return [\n",
- " json.loads(msg) for msg in await self.index.client.lrange(f\"messages:{self.user}\", 0, -1)\n",
- " ]\n",
- "\n",
- " async def add_messages(self, messages: list):\n",
- " \"\"\"Add chat messages to a Redis List\"\"\"\n",
- " return await self.index.client.rpush(\n",
- " f\"messages:{self.user}\", *[json.dumps(msg) for msg in messages]\n",
- " )\n",
- "\n",
- " async def clear_history(self):\n",
- " \"\"\"Clear session chat\"\"\"\n",
- " await index.client.delete(f\"messages:{self.user}\")\n",
- "\n",
- " @staticmethod\n",
- " def promptify(query: str, context: str) -> str:\n",
- " return f'''Use the provided context below derived from public financial\n",
- " documents to answer the user's question. If you can't answer the user's\n",
- " question, based on the context; do not guess. If there is no context at all,\n",
- " respond with \"I don't know\".\n",
- "\n",
- " User question:\n",
- "\n",
- " {query}\n",
- "\n",
- " Helpful context:\n",
- "\n",
- " {context}\n",
- "\n",
- " Answer:\n",
- " '''\n",
- "\n",
- " async def retrieve_context(self, query_vector) -> str:\n",
- " \"\"\"Fetch the relevant context from Redis using vector search\"\"\"\n",
- " results = await self.index.query(\n",
- " VectorQuery(\n",
- " vector=query_vector,\n",
- " vector_field_name=\"text_embedding\",\n",
- " return_fields=[\"proposition\"],\n",
- " num_results=3\n",
- " )\n",
- " )\n",
- " content = \"\\n\".join([result[\"proposition\"] for result in results])\n",
- " return content\n",
- "\n",
- " async def answer_question(self, query: str):\n",
- " \"\"\"Answer the user's question with historical context and caching baked-in\"\"\"\n",
- "\n",
- " SYSTEM_PROMPT = \"\"\"You are a helpful financial analyst assistant that has access\n",
- " to public financial 10k documents in order to answer users questions about company\n",
- " performance, ethics, characteristics, and core information.\n",
- " \"\"\"\n",
- "\n",
- " # Create query vector\n",
- " query_vector = llmcache._vectorizer.embed(query)\n",
- "\n",
- " # TODO - implement semantic gaurdrails?\n",
- "\n",
- " # Check the cache with the vector\n",
- " if result := llmcache.check(vector=query_vector):\n",
- " answer = result[0]['response']\n",
- " else:\n",
- " # TODO - implement query rewriting?\n",
- " context = await self.retrieve_context(query_vector)\n",
- " session = await self.get_messages()\n",
- " # TODO - implement session summarization?\n",
- " messages = (\n",
- " [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}] +\n",
- " session +\n",
- " [{\"role\": \"user\", \"content\": self.promptify(query, context)}]\n",
- " )\n",
- " # Response provided by GPT-3.5\n",
- " response = await openai.AsyncClient().chat.completions.create(\n",
- " model=CHAT_MODEL,\n",
- " messages=messages,\n",
- " temperature=0.1,\n",
- " seed=42\n",
- " )\n",
- " answer = response.choices[0].message.content\n",
- " llmcache.store(query, answer, query_vector)\n",
- "\n",
- " # Add message history\n",
- " await self.add_messages([\n",
- " {\"role\": \"user\", \"content\": query},\n",
- " {\"role\": \"assistant\", \"content\": answer}\n",
- " ])\n",
- "\n",
- " return answer"
+ "data": {
+ "text/plain": [
+ "\"Based on the provided context, we can see that the company in question is NIKE, Inc. The company has a significant presence globally with subsidiaries in various jurisdictions such as Delaware, Netherlands, China, Mexico, Missouri, Japan, Korea, and Oregon. Additionally, the company's total revenues are substantial, with revenues in the United States alone amounting to $22,007 million in the fiscal year ended May 31, 2023. NIKE, Inc. also has a diverse range of financial assets, accounts receivable, inventories, and property, plant, and equipment across different regions, indicating a large and well-established company.\""
]
- },
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# An example question that is a bit simplistic...\n",
+ "await answer_question(index, \"How big is the company?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "id": "Tg55HqLFIRXJ"
+ },
+ "outputs": [],
+ "source": [
+ "async def rewrite_query(query: str, prompt: str = None):\n",
+ " \"\"\"Rewrite the user's original query\"\"\"\n",
+ "\n",
+ " SYSTEM_PROMPT = prompt if prompt else \"\"\"Given the user's input question below, find a better or\n",
+ " more complete way to phrase this question in order to improve semantic search\n",
+ " engine retrieval quality over a set of SEC 10K PDF docs. Return the rephrased\n",
+ " question as a string in a JSON response under the key \"query\".\"\"\"\n",
+ "\n",
+ " response = await openai.AsyncClient().chat.completions.create(\n",
+ " model=CHAT_MODEL,\n",
+ " response_format={ \"type\": \"json_object\" },\n",
+ " messages=[\n",
+ " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+ " {\"role\": \"user\", \"content\": f\"Original input question from user: {query}\"}\n",
+ " ],\n",
+ " temperature=0.1,\n",
+ " seed=42\n",
+ " )\n",
+ " # Response provided by LLM\n",
+ " rewritten_query = json.loads(response.choices[0].message.content)[\"query\"]\n",
+ " return rewritten_query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Test the entire RAG workflow"
+ "data": {
+ "text/plain": [
+ "'What is the size of the company in terms of revenue, assets, and market capitalization?'"
]
- },
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Example Sinple Query Rewritten\n",
+ "await rewrite_query(\"How big is the company?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "9ubNQrJOYL42"
+ },
+ "outputs": [],
+ "source": [
+ "async def answer_question(index: AsyncSearchIndex, query: str, **kwargs):\n",
+ " \"\"\"Answer the user's question\"\"\"\n",
+ "\n",
+ " SYSTEM_PROMPT = \"\"\"You are a helpful financial analyst assistant that has access\n",
+ " to public financial 10k documents in order to answer users questions about company\n",
+ " performance, ethics, characteristics, and core information.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Rewrite the query using an LLM\n",
+ " rewritten_query = await rewrite_query(query, **kwargs)\n",
+ " print(\"User query updated to:\\n\", rewritten_query, flush=True)\n",
+ "\n",
+ " query_vector = hf.embed(rewritten_query)\n",
+ " # Fetch context from Redis using vector search\n",
+ " context = await retrieve_context(index, query_vector)\n",
+ " print(\"Context retrieved\", flush=True)\n",
+ "\n",
+ " # Generate contextualized prompt and feed to OpenAI\n",
+ " response = await openai.AsyncClient().chat.completions.create(\n",
+ " model=CHAT_MODEL,\n",
+ " messages=[\n",
+ " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+ " {\"role\": \"user\", \"content\": promptify(rewritten_query, context)}\n",
+ " ],\n",
+ " temperature=0.1,\n",
+ " seed=42\n",
+ " )\n",
+ " # Response provided by LLM\n",
+ " return response.choices[0].message.content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "BIO_jW6KYsMU"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {
- "id": "_Z3RUvyxdhiz"
- },
- "outputs": [],
- "source": [
- "# Setup Session\n",
- "chat = ChatBot(index, \"tyler\")\n",
- "await chat.clear_history()"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "User query updated to:\n",
+ " What is the size of the company in terms of revenue, assets, and market capitalization?\n",
+ "Using dense content representation\n",
+ "Context retrieved\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello! How can I assist you today?\n"
- ]
- }
- ],
- "source": [
- "# Run a simple chat\n",
- "stopterms = [\"exit\", \"quit\", \"end\", \"cancel\"]\n",
- "\n",
- "# Simple Chat\n",
- "# NBVAL_SKIP\n",
- "while True:\n",
- " user_query = input()\n",
- " if user_query.lower() in stopterms:\n",
- " break\n",
- " answer = await chat.answer_question(user_query)\n",
- " print(answer, flush=True)"
+ "data": {
+ "text/plain": [
+ "\"Based on the provided context, the company's revenue, assets, and market capitalization figures are not explicitly mentioned. The information mainly focuses on financial assets, investments, return on invested capital, EBIT, and other financial metrics. Without specific details on revenue, assets, and market capitalization, I am unable to provide the exact size of the company in those terms.\""
]
- },
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "# Now try again with query re-writing enabled\n",
+ "await answer_question(index, \"How big is the company?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "p97uL4g9T6LQ"
+ },
+ "source": [
+ "### Improve performance and cut costs with LLM caching"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "7geEAsYST6LQ"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "id": "ZoPQMAShZ5Uy"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'user', 'content': 'what are the expected next year earnings?'},\n",
- " {'role': 'assistant',\n",
- " 'content': 'Based on the provided context from the financial documents, the expected next year earnings for the company are not explicitly mentioned. The information primarily focuses on the financial performance and results for fiscal year 2023. Therefore, without specific details or guidance on future earnings, I am unable to provide an estimate for the expected next year earnings.'}]"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "await chat.get_messages()"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "15:53:30 redisvl.index.index INFO Index already exists, not overwriting.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from redisvl.extensions.llmcache import SemanticCache\n",
+ "\n",
+ "llmcache = SemanticCache(\n",
+ " name=\"llmcache\",\n",
+ " vectorizer=hf,\n",
+ " redis_url=REDIS_URL,\n",
+ " ttl=120,\n",
+ " distance_threshold=0.2\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "1ALcQXAqT6LQ"
+ },
+ "outputs": [],
+ "source": [
+ "from functools import wraps\n",
+ "\n",
+ "# Create an LLM caching decorator\n",
+ "def cache(func):\n",
+ " @wraps(func)\n",
+ " async def wrapper(index, query_text, *args, **kwargs):\n",
+ " query_vector = llmcache._vectorizer.embed(query_text)\n",
+ "\n",
+ " # Check the cache with the vector\n",
+ " if result := llmcache.check(vector=query_vector):\n",
+ " return result[0]['response']\n",
+ "\n",
+ " response = await func(index, query_text, query_vector=query_vector)\n",
+ " llmcache.store(query_text, response, query_vector)\n",
+ " return response\n",
+ " return wrapper\n",
+ "\n",
+ "\n",
+ "@cache\n",
+ "async def answer_question(index: AsyncSearchIndex, query: str, **kwargs):\n",
+ " \"\"\"Answer the user's question\"\"\"\n",
+ "\n",
+ " SYSTEM_PROMPT = \"\"\"You are a helpful financial analyst assistant that has access\n",
+ " to public financial 10k documents in order to answer users questions about company\n",
+ " performance, ethics, characteristics, and core information.\n",
+ " \"\"\"\n",
+ "\n",
+ " context = await retrieve_context(index, kwargs[\"query_vector\"])\n",
+ " response = await openai.AsyncClient().chat.completions.create(\n",
+ " model=CHAT_MODEL,\n",
+ " messages=[\n",
+ " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+ " {\"role\": \"user\", \"content\": promptify(query, context)}\n",
+ " ],\n",
+ " temperature=0.1,\n",
+ " seed=42\n",
+ " )\n",
+ " # Response provided by GPT-3.5\n",
+ " return response.choices[0].message.content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "BXK_BXuhT6LQ"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "5l4uEgKzljes"
- },
- "source": [
- "## Your Next Steps\n",
- "\n",
- "While a good start, there is still more to do. **For example**:\n",
- "- we could utilize message history to generate an updated and contextualized query to use for retrieval and answer generation (with an LLM). Otherwise, there can be a disconnect between what a user is asking (in context) and what they are asking in isolation.\n",
- "- we could utilize an LLM to summarize conversation history to use as context instead of passing the whole slew of messages to the Chat endpoint.\n",
- "- we could utilize semantic properties of the message history (or summaries) in order to fetch only relevant conversation bits (vector search).\n",
- "- we could utilize a technique like HyDE ( a form of query rewriting ) to improve the retrieval quality from raw user input to source documents OR try to break down user questions into sub questions and fetch / join context based on the different searces.\n",
- "- we could incorporate semantic routing to take a broken down question and route to different data sources, indices, or query types (etc).\n",
- "- we could add semantic guardrails on the front end or back end of the conversation I/O to ensure we are within bounds of approved topics."
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using dense content representation\n"
+ ]
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "Wscs4Mvo1ujD"
- },
- "source": [
- "## Cleanup\n",
- "\n",
- "Clean up the database."
+ "data": {
+ "text/plain": [
+ "\"Nike's total revenue for the fiscal year 2023 was $27.4 billion from sales to wholesale customers and $21.3 billion through direct-to-consumer channels. Comparing this to the previous year, the total revenue for the fiscal year 2022 was not explicitly provided in the context.\""
]
- },
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "query = \"What was Nike's revenue last year compared to this year??\"\n",
+ "\n",
+ "await answer_question(index, query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "id": "7mZpSpf9T6LQ"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {
- "id": "On6yNuQn1ujD"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NBVAL_SKIP\n",
- "await index.client.flushall()"
+ "data": {
+ "text/plain": [
+ "\"Nike's total revenue for the fiscal year 2023 was $27.4 billion from sales to wholesale customers and $21.3 billion through direct-to-consumer channels. Comparing this to the previous year, the total revenue for the fiscal year 2022 was not explicitly provided in the context.\""
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "gpuType": "T4",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.9"
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "query = \"What was Nike's total revenue in the last year compared to now??\"\n",
+ "\n",
+ "await answer_question(index, query)\n",
+ "\n",
+ "# notice no HTTP request to OpenAI since this question is \"close enough\" to the last one"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UaiF_ws7itsi"
+ },
+ "source": [
+ "### Improve personalization with including chat session history\n",
+ "\n",
+ "In order to preserve state in the conversation, it's imperitive to offload conversation history to a database that can handle high transaction throughput for writes/reads to limit system latency.\n",
+ "\n",
+ "We can store message history for a particular user session in a Redis List data type.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "id": "WMOF7fJQdhgN"
+ },
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "\n",
+ "class ChatBot:\n",
+ " def __init__(self, index: AsyncSearchIndex, user: str):\n",
+ " self.index = index\n",
+ " self.user = user\n",
+ "\n",
+ " async def get_messages(self) -> list:\n",
+ " \"\"\"Get all messages associated with a session\"\"\"\n",
+ " return [\n",
+ " json.loads(msg) for msg in await self.index.client.lrange(f\"messages:{self.user}\", 0, -1)\n",
+ " ]\n",
+ "\n",
+ " async def add_messages(self, messages: list):\n",
+ " \"\"\"Add chat messages to a Redis List\"\"\"\n",
+ " return await self.index.client.rpush(\n",
+ " f\"messages:{self.user}\", *[json.dumps(msg) for msg in messages]\n",
+ " )\n",
+ "\n",
+ " async def clear_history(self):\n",
+ " \"\"\"Clear session chat\"\"\"\n",
+ " await index.client.delete(f\"messages:{self.user}\")\n",
+ "\n",
+ " @staticmethod\n",
+ " def promptify(query: str, context: str) -> str:\n",
+ " return f'''Use the provided context below derived from public financial\n",
+ " documents to answer the user's question. If you can't answer the user's\n",
+ " question, based on the context; do not guess. If there is no context at all,\n",
+ " respond with \"I don't know\".\n",
+ "\n",
+ " User question:\n",
+ "\n",
+ " {query}\n",
+ "\n",
+ " Helpful context:\n",
+ "\n",
+ " {context}\n",
+ "\n",
+ " Answer:\n",
+ " '''\n",
+ "\n",
+ " async def retrieve_context(self, query_vector) -> str:\n",
+ " \"\"\"Fetch the relevant context from Redis using vector search\"\"\"\n",
+ " results = await self.index.query(\n",
+ " VectorQuery(\n",
+ " vector=query_vector,\n",
+ " vector_field_name=\"text_embedding\",\n",
+ " return_fields=[\"proposition\"],\n",
+ " num_results=3\n",
+ " )\n",
+ " )\n",
+ " content = \"\\n\".join([result[\"proposition\"] for result in results])\n",
+ " return content\n",
+ "\n",
+ " async def answer_question(self, query: str):\n",
+ " \"\"\"Answer the user's question with historical context and caching baked-in\"\"\"\n",
+ "\n",
+ " SYSTEM_PROMPT = \"\"\"You are a helpful financial analyst assistant that has access\n",
+ " to public financial 10k documents in order to answer users questions about company\n",
+ " performance, ethics, characteristics, and core information.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Create query vector\n",
+ " query_vector = llmcache._vectorizer.embed(query)\n",
+ "\n",
+ " # TODO - implement semantic gaurdrails?\n",
+ "\n",
+ " # Check the cache with the vector\n",
+ " if result := llmcache.check(vector=query_vector):\n",
+ " answer = result[0]['response']\n",
+ " else:\n",
+ " # TODO - implement query rewriting?\n",
+ " context = await self.retrieve_context(query_vector)\n",
+ " session = await self.get_messages()\n",
+ " # TODO - implement session summarization?\n",
+ " messages = (\n",
+ " [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}] +\n",
+ " session +\n",
+ " [{\"role\": \"user\", \"content\": self.promptify(query, context)}]\n",
+ " )\n",
+ " # Response provided by GPT-3.5\n",
+ " response = await openai.AsyncClient().chat.completions.create(\n",
+ " model=CHAT_MODEL,\n",
+ " messages=messages,\n",
+ " temperature=0.1,\n",
+ " seed=42\n",
+ " )\n",
+ " answer = response.choices[0].message.content\n",
+ " llmcache.store(query, answer, query_vector)\n",
+ "\n",
+ " # Add message history\n",
+ " await self.add_messages([\n",
+ " {\"role\": \"user\", \"content\": query},\n",
+ " {\"role\": \"assistant\", \"content\": answer}\n",
+ " ])\n",
+ "\n",
+ " return answer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Test the entire RAG workflow"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "id": "_Z3RUvyxdhiz"
+ },
+ "outputs": [],
+ "source": [
+ "# Setup Session\n",
+ "chat = ChatBot(index, \"tyler\")\n",
+ "await chat.clear_history()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run a simple chat\n",
+ "stopterms = [\"exit\", \"quit\", \"end\", \"cancel\"]\n",
+ "\n",
+ "# Simple Chat\n",
+ "# NBVAL_SKIP\n",
+ "while True:\n",
+ " user_query = input()\n",
+ " if user_query.lower() in stopterms:\n",
+ " break\n",
+ " answer = await chat.answer_question(user_query)\n",
+ " print(answer, flush=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ZoPQMAShZ5Uy"
+ },
+ "outputs": [],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "await chat.get_messages()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5l4uEgKzljes"
+ },
+ "source": [
+ "## Your Next Steps\n",
+ "\n",
+ "While a good start, there is still more to do. **For example**:\n",
+ "- we could utilize message history to generate an updated and contextualized query to use for retrieval and answer generation (with an LLM). Otherwise, there can be a disconnect between what a user is asking (in context) and what they are asking in isolation.\n",
+ "- we could utilize an LLM to summarize conversation history to use as context instead of passing the whole slew of messages to the Chat endpoint.\n",
+ "- we could utilize semantic properties of the message history (or summaries) in order to fetch only relevant conversation bits (vector search).\n",
+ "- we could utilize a technique like HyDE ( a form of query rewriting ) to improve the retrieval quality from raw user input to source documents OR try to break down user questions into sub questions and fetch / join context based on the different searces.\n",
+ "- we could incorporate semantic routing to take a broken down question and route to different data sources, indices, or query types (etc).\n",
+ "- we could add semantic guardrails on the front end or back end of the conversation I/O to ensure we are within bounds of approved topics."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wscs4Mvo1ujD"
+ },
+ "source": [
+ "## Cleanup\n",
+ "\n",
+ "Clean up the database."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "On6yNuQn1ujD"
+ },
+ "outputs": [],
+ "source": [
+ "await index.client.flushall()"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
}
diff --git a/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb b/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb
index 0c60a7f3..f4e05a21 100644
--- a/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb
+++ b/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb
@@ -53,7 +53,7 @@
"source": [
"%pip install --upgrade -q langchain-core langchain-community langchain-nvidia-ai-endpoints\n",
"%pip install -q \"unstructured[pdf]\" sentence-transformers\n",
- "%pip install -q redisvl>=0.3.0"
+ "%pip install -q \"redisvl>=0.4.1\""
]
},
{
@@ -608,7 +608,8 @@
"name": "python3"
},
"language_info": {
- "name": "python"
+ "name": "python",
+ "version": "3.11.9"
}
},
"nbformat": 4,
diff --git a/python-recipes/RAG/06_ragas_evaluation.ipynb b/python-recipes/RAG/06_ragas_evaluation.ipynb
index dc06921d..c3b112e8 100644
--- a/python-recipes/RAG/06_ragas_evaluation.ipynb
+++ b/python-recipes/RAG/06_ragas_evaluation.ipynb
@@ -1,1231 +1,1229 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Evaluating RAG\n",
- "\n",
- "This notebook uses the [ragas library](https://docs.ragas.io/en/stable/getstarted/index.html) and [Redis](https://redis.com) to evaluate the performance of sample RAG application. Also see the original [source paper](https://arxiv.org/pdf/2309.15217) to build a more detailed understanding.\n",
- "\n",
- "## Let's Begin!\n",
- "| \n", + " | question | \n", + "answer | \n", + "
|---|---|---|
| 0 | \n", + "What is the trend in the company's revenue and... | \n", + "The company experienced revenue growth in fisc... | \n", + "
| 1 | \n", + "What are the company's primary revenue sources? | \n", + "The company's primary revenue sources are from... | \n", + "
| 2 | \n", + "How much debt does the company have, and what ... | \n", + "As of May 31, 2023, the company had Long-term ... | \n", + "
| 3 | \n", + "What does the company say about its environmen... | \n", + "The company acknowledges the importance of env... | \n", + "
| 4 | \n", + "What is the company's strategy for growth? | \n", + "The company's strategy for growth includes ide... | \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To start, we need a RAG app to evaluate. Let's create one using LangChain and connect it with Redis as the vector DB.\n",
- "\n",
- "## Init redis, data prep, and populating the vector DB"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- }
- ],
- "source": [
- "# install deps\n",
- "# NBVAL_SKIP\n",
- "%pip install -q redis \"unstructured[pdf]\" sentence-transformers langchain langchain-redis langchain-huggingface langchain-openai ragas datasets"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Running Redis in Colab\n",
- "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly from the Redis package archive."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# NBVAL_SKIP\n",
- "%%sh\n",
- "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
- "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
- "sudo apt-get update > /dev/null 2>&1\n",
- "sudo apt-get install redis-stack-server > /dev/null 2>&1\n",
- "redis-stack-server --daemonize yes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### For Alternative Environments\n",
- "There are many ways to get the necessary redis-stack instance running\n",
- "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
- "own version of Redis Enterprise running, that works too!\n",
- "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n",
- "3. With docker: `docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n",
- "\n",
- "# Replace values below with your own if using Redis Cloud instance\n",
- "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
- "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\") # ex: 18374\n",
- "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\") # ex: \"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
- "\n",
- "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
- "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
- "from langchain.document_loaders import UnstructuredFileLoader\n",
- "\n",
- "CHUNK_SIZE = 2500\n",
- "CHUNK_OVERLAP = 0\n",
- "\n",
- "source_doc = \"resources/nke-10k-2023.pdf\"\n",
- "\n",
- "loader = UnstructuredFileLoader(\n",
- " source_doc, mode=\"single\", strategy=\"fast\"\n",
- ")\n",
- "\n",
- "text_splitter = RecursiveCharacterTextSplitter(\n",
- " chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP\n",
- ")\n",
- "\n",
- "chunks = loader.load_and_split(text_splitter)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 95,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Document(metadata={'source': 'resources/nke-10k-2023.pdf'}, page_content=\"Table of ContentsUNITED STATESSECURITIES AND EXCHANGE COMMISSIONWashington, D.C. 20549FORM 10-K(Mark One)☑ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(D) OF THE SECURITIES EXCHANGE ACT OF 1934FOR THE FISCAL YEAR ENDED MAY 31, 2023OR☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(D) OF THE SECURITIES EXCHANGE ACT OF 1934FOR THE TRANSITION PERIOD FROM TO .Commission File No. 1-10635\\n\\nAs of November 30, 2022, the aggregate market values of the Registrant's Common Stock held by non-affiliates were:Class A$7,831,564,572 Class B136,467,702,472 $144,299,267,044\\n\\nNIKE, Inc.(Exact name of Registrant as specified in its charter)Oregon93-0584541(State or other jurisdiction of incorporation)(IRS Employer Identification No.)One Bowerman Drive, Beaverton, Oregon 97005-6453(Address of principal executive offices and zip code)(503) 671-6453(Registrant's telephone number, including area code)SECURITIES REGISTERED PURSUANT TO SECTION 12(B) OF THE ACT:Class B Common StockNKENew York Stock Exchange(Title of each class)(Trading symbol)(Name of each exchange on which registered)SECURITIES REGISTERED PURSUANT TO SECTION 12(G) OF THE ACT:NONE\")"
- ]
- },
- "execution_count": 95,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "chunks[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain_huggingface import HuggingFaceEmbeddings\n",
- "\n",
- "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain_redis import RedisVectorStore\n",
- "\n",
- "# set the index name for this example\n",
- "index_name = \"ragas_ex\"\n",
- "\n",
- "# construct the vector store class from texts and metadata\n",
- "rds = RedisVectorStore.from_documents(\n",
- " chunks,\n",
- " embeddings,\n",
- " index_name=index_name,\n",
- " redis_url=REDIS_URL,\n",
- " metadata_schema=[\n",
- " {\n",
- " \"name\": \"source\",\n",
- " \"type\": \"text\"\n",
- " },\n",
- " ]\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Test the vector store"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 98,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'As discussed in Note 15 — Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, our operating segments are evidence of the structure of the Company\\'s internal organization. The NIKE Brand segments are defined by geographic regions for operations participating in NIKE Brand sales activity.\\n\\nThe breakdown of Revenues is as follows:\\n\\n(Dollars in millions)\\n\\nFISCAL 2023 FISCAL 2022\\n\\n% CHANGE\\n\\n% CHANGE EXCLUDING CURRENCY (1) CHANGES FISCAL 2021\\n\\n% CHANGE\\n\\nNorth America Europe, Middle East & Africa Greater China\\n\\n$\\n\\n21,608 $ 13,418 7,248\\n\\n18,353 12,479 7,547\\n\\n18 % 8 % -4 %\\n\\n18 % $ 21 % 4 %\\n\\n17,179 11,456 8,290\\n\\n7 % 9 % -9 %\\n\\nAsia Pacific & Latin America Global Brand Divisions\\n\\n(3)\\n\\n(2)\\n\\n6,431 58\\n\\n5,955 102\\n\\n8 % -43 %\\n\\n17 % -43 %\\n\\n5,343 25\\n\\n11 % 308 %\\n\\nTOTAL NIKE BRAND Converse\\n\\n$\\n\\n48,763 $ 2,427\\n\\n44,436 2,346\\n\\n10 % 3 %\\n\\n16 % $ 8 %\\n\\n42,293 2,205\\n\\n5 % 6 %\\n\\n(4)\\n\\nCorporate TOTAL NIKE, INC. REVENUES\\n\\n$\\n\\n27\\n\\n51,217 $\\n\\n(72) 46,710\\n\\n— 10 %\\n\\n— 16 % $\\n\\n40 44,538\\n\\n— 5 %\\n\\n(1) The percent change excluding currency changes represents a non-GAAP financial measure. For further information, see \"Use of Non-GAAP Financial Measures\".\\n\\n(2) For additional information on the transition of our NIKE Brand businesses within our CASA territory to a third-party distributor, see Note 18 — Acquisitions and Divestitures of the Notes to Consolidated\\n\\nFinancial Statements contained in Item 8 of this Annual Report.\\n\\n(3) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n\\n(4) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\n\\nmanaged through our central foreign exchange risk management program.\\n\\nThe primary financial measure used by the Company to evaluate performance is Earnings Before Interest and Taxes (\"EBIT\"). As discussed in Note 15 — Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, certain corporate costs are not included in EBIT.\\n\\nThe breakdown of EBIT is as follows:\\n\\n(Dollars in millions)\\n\\nFISCAL 2023\\n\\nFISCAL 2022\\n\\n% CHANGE\\n\\nFISCAL 2021\\n\\nNorth America Europe, Middle East & Africa Greater China\\n\\n$\\n\\n5,454 3,531 2,283\\n\\n$\\n\\n5,114 3,293 2,365\\n\\n7 % $ 7 % -3 %\\n\\n5,089 2,435 3,243\\n\\nAsia Pacific & Latin America Global Brand Divisions (1)'"
- ]
- },
- "execution_count": 98,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rds.similarity_search(\"What was nike's revenue last year?\")[0].page_content"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup RAG\n",
- "\n",
- "Now that the vector db is populated let's initialize our RAG app."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 99,
- "metadata": {},
- "outputs": [],
- "source": [
- "import getpass\n",
- "from langchain_openai import ChatOpenAI\n",
- "\n",
- "if \"OPENAI_API_KEY\" not in os.environ:\n",
- " os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OPENAI_API_KEY\")\n",
- "\n",
- "llm = ChatOpenAI(\n",
- " openai_api_key=os.environ[\"OPENAI_API_KEY\"],\n",
- " model=\"gpt-3.5-turbo-16k\",\n",
- " max_tokens=None\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 108,
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain_core.prompts import ChatPromptTemplate\n",
- "\n",
- "system_prompt = \"\"\"\n",
- " Use the following pieces of context from financial 10k filings data to answer the user question at the end. \n",
- " If you don't know the answer, say that you don't know, don't try to make up an answer.\n",
- "\n",
- " Context:\n",
- " ---------\n",
- " {context}\n",
- "\"\"\"\n",
- "\n",
- "def format_docs(docs):\n",
- " return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
- "\n",
- "prompt = ChatPromptTemplate.from_messages(\n",
- " [\n",
- " (\"system\", system_prompt),\n",
- " (\"human\", \"{input}\")\n",
- " ]\n",
- ")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Test it out"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 109,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'input': \"What was nike's revenue last year?\",\n",
- " 'context': [Document(metadata={'source': 'resources/nke-10k-2023.pdf'}, page_content='As discussed in Note 15 — Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, our operating segments are evidence of the structure of the Company\\'s internal organization. The NIKE Brand segments are defined by geographic regions for operations participating in NIKE Brand sales activity.\\n\\nThe breakdown of Revenues is as follows:\\n\\n(Dollars in millions)\\n\\nFISCAL 2023 FISCAL 2022\\n\\n% CHANGE\\n\\n% CHANGE EXCLUDING CURRENCY (1) CHANGES FISCAL 2021\\n\\n% CHANGE\\n\\nNorth America Europe, Middle East & Africa Greater China\\n\\n$\\n\\n21,608 $ 13,418 7,248\\n\\n18,353 12,479 7,547\\n\\n18 % 8 % -4 %\\n\\n18 % $ 21 % 4 %\\n\\n17,179 11,456 8,290\\n\\n7 % 9 % -9 %\\n\\nAsia Pacific & Latin America Global Brand Divisions\\n\\n(3)\\n\\n(2)\\n\\n6,431 58\\n\\n5,955 102\\n\\n8 % -43 %\\n\\n17 % -43 %\\n\\n5,343 25\\n\\n11 % 308 %\\n\\nTOTAL NIKE BRAND Converse\\n\\n$\\n\\n48,763 $ 2,427\\n\\n44,436 2,346\\n\\n10 % 3 %\\n\\n16 % $ 8 %\\n\\n42,293 2,205\\n\\n5 % 6 %\\n\\n(4)\\n\\nCorporate TOTAL NIKE, INC. REVENUES\\n\\n$\\n\\n27\\n\\n51,217 $\\n\\n(72) 46,710\\n\\n— 10 %\\n\\n— 16 % $\\n\\n40 44,538\\n\\n— 5 %\\n\\n(1) The percent change excluding currency changes represents a non-GAAP financial measure. For further information, see \"Use of Non-GAAP Financial Measures\".\\n\\n(2) For additional information on the transition of our NIKE Brand businesses within our CASA territory to a third-party distributor, see Note 18 — Acquisitions and Divestitures of the Notes to Consolidated\\n\\nFinancial Statements contained in Item 8 of this Annual Report.\\n\\n(3) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n\\n(4) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\n\\nmanaged through our central foreign exchange risk management program.\\n\\nThe primary financial measure used by the Company to evaluate performance is Earnings Before Interest and Taxes (\"EBIT\"). As discussed in Note 15 — Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, certain corporate costs are not included in EBIT.\\n\\nThe breakdown of EBIT is as follows:\\n\\n(Dollars in millions)\\n\\nFISCAL 2023\\n\\nFISCAL 2022\\n\\n% CHANGE\\n\\nFISCAL 2021\\n\\nNorth America Europe, Middle East & Africa Greater China\\n\\n$\\n\\n5,454 3,531 2,283\\n\\n$\\n\\n5,114 3,293 2,365\\n\\n7 % $ 7 % -3 %\\n\\n5,089 2,435 3,243\\n\\nAsia Pacific & Latin America Global Brand Divisions (1)'),\n",
- " Document(metadata={'source': 'resources/nke-10k-2023.pdf'}, page_content=\"NIKE, INC. CONSOLIDATED STATEMENTS OF INCOME\\n\\n(In millions, except per share data)\\n\\nRevenues Cost of sales\\n\\nGross profit\\n\\nDemand creation expense Operating overhead expense\\n\\nTotal selling and administrative expense\\n\\nInterest expense (income), net\\n\\nOther (income) expense, net Income before income taxes\\n\\nIncome tax expense NET INCOME\\n\\nEarnings per common share:\\n\\nBasic Diluted\\n\\nWeighted average common shares outstanding:\\n\\nBasic Diluted\\n\\nThe accompanying Notes to the Consolidated Financial Statements are an integral part of this statement.\\n\\n$\\n\\n$\\n\\n$ $\\n\\nYEAR ENDED MAY 31,\\n\\n2023\\n\\n2022\\n\\n2021\\n\\n51,217 $ 28,925\\n\\n46,710 $ 25,231\\n\\n44,538 24,576\\n\\n22,292 4,060 12,317\\n\\n21,479 3,850 10,954\\n\\n19,962 3,114 9,911\\n\\n16,377 (6)\\n\\n14,804 205\\n\\n13,025 262\\n\\n(280) 6,201\\n\\n(181) 6,651\\n\\n14 6,661\\n\\n1,131 5,070 $\\n\\n605 6,046 $\\n\\n934 5,727\\n\\n3.27 $ 3.23 $\\n\\n3.83 $ 3.75 $\\n\\n3.64 3.56\\n\\n1,551.6 1,569.8\\n\\n1,578.8 1,610.8\\n\\n1,573.0 1,609.4\\n\\n2023 FORM 10-K 55\\n\\nTable of Contents\\n\\nNIKE, INC. CONSOLIDATED STATEMENTS OF COMPREHENSIVE INCOME\\n\\nYEAR ENDED MAY 31,\\n\\n(Dollars in millions)\\n\\n2023\\n\\n2022\\n\\nNet income Other comprehensive income (loss), net of tax:\\n\\n$\\n\\n5,070 $\\n\\n6,046 $\\n\\nChange in net foreign currency translation adjustment\\n\\n267\\n\\n(522)\\n\\nChange in net gains (losses) on cash flow hedges Change in net gains (losses) on other\\n\\n(348) (6)\\n\\n1,214 6\\n\\nTotal other comprehensive income (loss), net of tax TOTAL COMPREHENSIVE INCOME\\n\\n$\\n\\n(87) 4,983 $\\n\\n698 6,744 $\\n\\nThe accompanying Notes to the Consolidated Financial Statements are an integral part of this statement.\\n\\n2023 FORM 10-K 56\\n\\n2021\\n\\n5,727\\n\\n496\\n\\n(825) 5\\n\\n(324) 5,403\\n\\nTable of Contents\\n\\nNIKE, INC. CONSOLIDATED BALANCE SHEETS\\n\\n(In millions)\\n\\nASSETS\\n\\nCurrent assets:\\n\\nCash and equivalents Short-term investments\\n\\nAccounts receivable, net Inventories Prepaid expenses and other current assets\\n\\nTotal current assets\\n\\nProperty, plant and equipment, net\\n\\nOperating lease right-of-use assets, net Identifiable intangible assets, net Goodwill\\n\\nDeferred income taxes and other assets\\n\\nTOTAL ASSETS\\n\\nLIABILITIES AND SHAREHOLDERS' EQUITY Current liabilities:\\n\\nCurrent portion of long-term debt Notes payable Accounts payable\\n\\nCurrent portion of operating lease liabilities Accrued liabilities Income taxes payable\\n\\nTotal current liabilities\\n\\nLong-term debt\\n\\nOperating lease liabilities Deferred income taxes and other liabilities Commitments and contingencies (Note 16)\\n\\nRedeemable preferred stock Shareholders' equity: Common stock at stated value:\"),\n",
- " Document(metadata={'source': 'resources/nke-10k-2023.pdf'}, page_content=\"Tax (expense) benefit Gain (loss) net of tax\\n\\n5 (14)\\n\\n(9) 22\\n\\nTotal net gain (loss) reclassified for the period\\n\\n$\\n\\n463 $\\n\\n30\\n\\n2023 FORM 10-K 82\\n\\nTable of Contents\\n\\nNOTE 14 — REVENUES\\n\\nDISAGGREGATION OF REVENUES The following tables present the Company's Revenues disaggregated by reportable operating segment, major product line and distribution channel:\\n\\n(Dollars in millions)\\n\\nNORTH AMERICA\\n\\nEUROPE, MIDDLE EAST & AFRICA\\n\\nGREATER CHINA\\n\\nYEAR ENDED MAY 31, 2023 ASIA PACIFIC & LATIN (1)\\n\\nGLOBAL BRAND DIVISIONS\\n\\nTOTAL NIKE\\n\\nAMERICA\\n\\nBRAND CONVERSE CORPORATE\\n\\nTOTAL NIKE, INC.\\n\\nRevenues by: Footwear\\n\\n$\\n\\n14,897 $\\n\\n8,260 $\\n\\n5,435 $\\n\\n4,543 $\\n\\n— $\\n\\n33,135 $\\n\\n2,155 $\\n\\n— $\\n\\n35,290\\n\\nApparel Equipment Other\\n\\n5,947 764 —\\n\\n4,566 592 —\\n\\n1,666 147 —\\n\\n1,664 224 —\\n\\n— — 58\\n\\n13,843 1,727 58\\n\\n90 28 154\\n\\n— — 27\\n\\n13,933 1,755 239\\n\\nTOTAL REVENUES\\n\\n$\\n\\n21,608 $\\n\\n13,418 $\\n\\n7,248 $\\n\\n6,431 $\\n\\n58 $\\n\\n48,763 $\\n\\n2,427 $\\n\\n27 $\\n\\n51,217\\n\\nRevenues by:\\n\\nSales to Wholesale Customers Sales through Direct to Consumer\\n\\n$\\n\\n11,273 $ 10,335\\n\\n8,522 $ 4,896\\n\\n3,866 $ 3,382\\n\\n3,736 $ 2,695\\n\\n— $ —\\n\\n27,397 $ 21,308\\n\\n1,299 $ 974\\n\\n— $ —\\n\\n28,696 22,282\\n\\nOther\\n\\nTOTAL REVENUES\\n\\n$\\n\\n—\\n\\n21,608 $\\n\\n—\\n\\n13,418 $\\n\\n— 7,248 $\\n\\n— 6,431 $\\n\\n58 58 $\\n\\n58\\n\\n48,763 $\\n\\n154 2,427 $\\n\\n27 27 $\\n\\n239 51,217\\n\\n(1) Refer to Note 18 — Acquisitions and Divestitures for additional information on the transition of the Company's NIKE Brand businesses in its CASA territory to third-party distributors.\\n\\nYEAR ENDED MAY 31, 2022\\n\\n(Dollars in millions)\\n\\nNORTH AMERICA\\n\\nEUROPE, MIDDLE EAST & AFRICA\\n\\nGREATER CHINA\\n\\nASIA PACIFIC & LATIN AMERICA\\n\\nGLOBAL BRAND DIVISIONS\\n\\nTOTAL NIKE\\n\\nBRAND CONVERSE CORPORATE\\n\\nTOTAL NIKE, INC.\\n\\nRevenues by: Footwear Apparel\\n\\n$\\n\\n12,228 $ 5,492\\n\\n7,388 $ 4,527\\n\\n5,416 $ 1,938\\n\\n4,111 $ 1,610\\n\\n— $ —\\n\\n29,143 $ 13,567\\n\\n2,094 $ 103\\n\\n— $ —\\n\\n31,237 13,670\\n\\nEquipment Other\\n\\n633 —\\n\\n564 —\\n\\n193 —\\n\\n234 —\\n\\n— 102\\n\\n1,624 102\\n\\n26 123\\n\\n— (72)\\n\\n1,650 153\\n\\nTOTAL REVENUES Revenues by:\\n\\n$\\n\\n18,353 $\\n\\n12,479 $\\n\\n7,547 $\\n\\n5,955 $\\n\\n102 $\\n\\n44,436 $\\n\\n2,346 $\\n\\n(72) $\\n\\n46,710\\n\\nSales to Wholesale Customers Sales through Direct to Consumer Other\\n\\n$\\n\\n9,621 $ 8,732 —\\n\\n8,377 $ 4,102 —\\n\\n4,081 $ 3,466 —\\n\\n3,529 $ 2,426 —\\n\\n— $ — 102\\n\\n25,608 $ 18,726 102\\n\\n1,292 $ 931 123\\n\\n— $ — (72)\\n\\n26,900 19,657 153\\n\\nTOTAL REVENUES\\n\\n$\\n\\n18,353 $\\n\\n12,479 $\\n\\n7,547 $\\n\\n5,955 $\\n\\n102 $\\n\\n44,436 $\\n\\n2,346 $\\n\\n(72) $\\n\\n46,710\\n\\n2023 FORM 10-K 83\\n\\nTable of Contents\\n\\nYEAR ENDED MAY 31, 2021\\n\\n(Dollars in millions)\\n\\nNORTH AMERICA\\n\\nEUROPE, MIDDLE EAST & AFRICA\\n\\nGREATER CHINA\"),\n",
- " Document(metadata={'source': 'resources/nke-10k-2023.pdf'}, page_content=\"ASIA PACIFIC & LATIN AMERICA\\n\\n(1)\\n\\nGLOBAL BRAND DIVISIONS\\n\\nTOTAL NIKE BRAND\\n\\nCONVERSE CORPORATE\\n\\nTOTAL NIKE, INC.\\n\\nRevenues by:\\n\\nFootwear Apparel Equipment\\n\\n$\\n\\n11,644 $ 5,028 507\\n\\n6,970 $ 3,996 490\\n\\n5,748 $ 2,347 195\\n\\n3,659 $ 1,494 190\\n\\n— $ — —\\n\\n28,021 $ 12,865 1,382\\n\\n1,986 $ 104 29\\n\\n— $ — —\\n\\n30,007 12,969 1,411\\n\\nOther\\n\\nTOTAL REVENUES\\n\\n$\\n\\n—\\n\\n17,179 $\\n\\n—\\n\\n11,456 $\\n\\n— 8,290 $\\n\\n— 5,343 $\\n\\n25 25 $\\n\\n25\\n\\n42,293 $\\n\\n86 2,205 $\\n\\n40 40 $\\n\\n151 44,538\\n\\nRevenues by:\\n\\nSales to Wholesale Customers $\\n\\n10,186 $\\n\\n7,812 $\\n\\n4,513 $\\n\\n3,387 $\\n\\n— $\\n\\n25,898 $\\n\\n1,353 $\\n\\n— $\\n\\n27,251\\n\\nSales through Direct to Consumer Other\\n\\n6,993 —\\n\\n3,644 —\\n\\n3,777 —\\n\\n1,956 —\\n\\n— 25\\n\\n16,370 25\\n\\n766 86\\n\\n— 40\\n\\n17,136 151\\n\\nTOTAL REVENUES\\n\\n$\\n\\n17,179 $\\n\\n11,456 $\\n\\n8,290 $\\n\\n5,343 $\\n\\n25 $\\n\\n42,293 $\\n\\n2,205 $\\n\\n40 $\\n\\n44,538\\n\\n(1) Refer to Note 18 — Acquisitions and Divestitures for additional information on the transition of the Company's NIKE Brand business in Brazil to a third-party distributor.\\n\\nFor the fiscal years ended May 31, 2023, 2022 and 2021, Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment. Converse Other revenues were primarily attributable to licensing businesses. Corporate revenues primarily consisted of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse but managed through the Company's central foreign exchange risk management program.\\n\\nAs of May 31, 2023 and 2022, the Company did not have any contract assets and had an immaterial amount of contract liabilities recorded in Accrued liabilities on the Consolidated Balance Sheets.\\n\\nSALES-RELATED RESERVES\\n\\nAs of May 31, 2023 and 2022, the Company's sales-related reserve balance, which includes returns, post-invoice sales discounts and miscellaneous claims, was $994 million and $1,015 million, respectively, recorded in Accrued liabilities on the Consolidated Balance Sheets. The estimated cost of inventory for expected product returns was $226 million and $194 million as of May 31, 2023 and 2022, respectively, and was recorded in Prepaid expenses and other current assets on the Consolidated Balance Sheets.\\n\\nNOTE 15 — OPERATING SEGMENTS AND RELATED INFORMATION\")],\n",
- " 'answer': \"Nike's revenue last year was $51,217 million.\"}"
- ]
- },
- "execution_count": 109,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from langchain.chains import create_retrieval_chain\n",
- "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
- "\n",
- "question_answer_chain = create_stuff_documents_chain(llm, prompt)\n",
- "rag_chain = create_retrieval_chain(rds.as_retriever(), question_answer_chain)\n",
- "\n",
- "rag_chain.invoke({\"input\": \"What was nike's revenue last year?\"})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## (Optional) Creating a test set\n",
- "\n",
- "Now that our setup is complete and we have our RAG app to evaluate we need a test set to evaluate against. The ragas library provides a helpful class for generating a synthetic test set given our data as input that we will use here. The output of this generation is a set of `questions`, `contexts`, and `ground_truth`. \n",
- "\n",
- "The questions are generated by an LLM based on slices of context from the provided doc and the ground_truth is determined via a critic LLM. Note there is nothing special about this data itself and you can provide your own `questions` and `ground_truth` for evaluation purposes. When starting a project however, there is often a lack of quality human labeled data to be used for evaluation and a synthetic dataset is a valuable place to start if pre live user/process data (which should be incorporated as an ultimate goal).\n",
- "\n",
- "For more detail see [the docs](https://docs.ragas.io/en/stable/concepts/testset_generation.html)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "# NBVAL_SKIP\n",
- "# source: https://docs.ragas.io/en/latest/getstarted/testset_generation.html\n",
- "from ragas.testset.generator import TestsetGenerator\n",
- "from ragas.testset.evolutions import simple, reasoning, multi_context\n",
- "from ragas.run_config import RunConfig\n",
- "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
- "\n",
- "run_config = RunConfig(\n",
- " timeout=200,\n",
- " max_wait=160,\n",
- " max_retries=3,\n",
- ")\n",
- "\n",
- "# generator with openai models\n",
- "generator_llm = ChatOpenAI(model=\"gpt-3.5-turbo-16k\")\n",
- "critic_llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
- "embeddings = OpenAIEmbeddings()\n",
- "\n",
- "generator = TestsetGenerator.from_langchain(\n",
- " generator_llm,\n",
- " critic_llm,\n",
- " embeddings,\n",
- " run_config=run_config,\n",
- ")\n",
- "\n",
- "testset = generator.generate_with_langchain_docs(\n",
- " chunks,\n",
- " test_size=10,\n",
- " distributions={\n",
- " simple: 0.5,\n",
- " reasoning: 0.25,\n",
- " multi_context: 0.25\n",
- " },\n",
- " run_config=run_config\n",
- ")\n",
- "\n",
- "# save to csv since this can be a time consuming process\n",
- "testset.to_pandas().to_csv(\"resources/new_testset.csv\", index=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Evaluation helper functions\n",
- "\n",
- "The following code takes a RetrievalQA chain, testset dataframe, and the metrics to be evaluated and returns a dataframe including the metrics calculated."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 110,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "from datasets import Dataset\n",
- "from ragas import evaluate\n",
- "from ragas.run_config import RunConfig\n",
- "\n",
- "def parse_contexts(source_docs):\n",
- " return [doc.page_content for doc in source_docs]\n",
- "\n",
- "def create_evaluation_dataset(chain, testset):\n",
- " res_set = {\n",
- " \"question\": [],\n",
- " \"answer\": [],\n",
- " \"contexts\": [],\n",
- " \"ground_truth\": []\n",
- " }\n",
- "\n",
- " for _, row in testset.iterrows():\n",
- " result = chain.invoke({\"input\": row[\"question\"]})\n",
- "\n",
- " res_set[\"question\"].append(row[\"question\"])\n",
- " res_set[\"answer\"].append(result[\"answer\"])\n",
- "\n",
- " contexts = parse_contexts(result[\"context\"])\n",
- "\n",
- " if not len(contexts):\n",
- " print(f\"no contexts found for question: {row['question']}\")\n",
- " res_set[\"contexts\"].append(contexts)\n",
- " res_set[\"ground_truth\"].append(str(row[\"ground_truth\"]))\n",
- "\n",
- " return Dataset.from_dict(res_set)\n",
- "\n",
- "def evaluate_dataset(eval_dataset, metrics, llm, embeddings):\n",
- "\n",
- " run_config = RunConfig(max_retries=1) # see ragas docs for more run_config options\n",
- "\n",
- " eval_result = evaluate(\n",
- " eval_dataset,\n",
- " metrics=metrics,\n",
- " run_config=run_config,\n",
- " llm=llm,\n",
- " embeddings=embeddings\n",
- " )\n",
- "\n",
- " eval_df = eval_result.to_pandas()\n",
- " return eval_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Create the evaluation data\n",
- "\n",
- "Input: chain to be evaluated and a pregenerated test set\n", - "Output: dataset formatted for use with ragas evaluation function" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " question \\\n",
- "0 What are short-term investments and how are th... \n",
- "1 What are some of the risks and uncertainties a... \n",
- "2 What is NIKE's policy regarding securities ana... \n",
- "3 What are the revenues for the Footwear and App... \n",
- "4 How do master netting arrangements impact the ... \n",
- "\n",
- " contexts \\\n",
- "0 [\"CASH AND EQUIVALENTS Cash and equivalents re... \n",
- "1 ['Our NIKE Direct operations, including our re... \n",
- "2 [\"Investors should also be aware that while NI... \n",
- "3 ['(Dollars in millions, except per share data)... \n",
- "4 [\"The Company records the assets and liabiliti... \n",
- "\n",
- " ground_truth evolution_type \\\n",
- "0 Short-term investments are highly liquid inves... simple \n",
- "1 Many factors unique to retail operations, some... simple \n",
- "2 NIKE's policy is to not disclose any material ... simple \n",
- "3 The revenues for the Footwear and Apparel cate... simple \n",
- "4 The Company records the assets and liabilities... simple \n",
- "\n",
- " metadata episode_done \n",
- "0 [{'source': 'resources/nke-10k-2023.pdf'}] True \n",
- "1 [{'source': 'resources/nke-10k-2023.pdf'}] True \n",
- "2 [{'source': 'resources/nke-10k-2023.pdf'}] True \n",
- "3 [{'source': 'resources/nke-10k-2023.pdf'}] True \n",
- "4 [{'source': 'resources/nke-10k-2023.pdf'}] True "
- ]
- },
- "execution_count": 111,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "testset_df = pd.read_csv(\"resources/testset_15.csv\")\n",
- "testset_df.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 112,
- "metadata": {},
- "outputs": [],
- "source": [
- "eval_dataset = create_evaluation_dataset(rag_chain, testset_df)\n",
- "eval_dataset.to_pandas().shape"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Evaluate generation metrics\n",
- "Generation metrics quantify how well the RAG app did creating answers to the provided questions (i.e. the G in **R**etrival **A**ugments **G**eneration). We will calculate the generation metrics **faithfulness** and **answer relevancy** for this example.\n",
- "\n",
- "The ragas libary conveniently abstracts the calculation of these metrics so we don't have to write redundant code but please review the following definitions in order to build intuition around what these metrics actually measure.\n",
- "\n",
- "Note: the following examples are paraphrased from the [ragas docs](https://docs.ragas.io/en/stable/concepts/metrics/index.html)\n",
- "\n",
- "------\n",
- "\n",
- "### Faithfulness\n",
- "\n",
- "An answer to a question can be said to be \"faithful\" if the **claims** that are made in the answer **can be inferred** from the **context**.\n",
- "\n",
- "#### Mathematically:\n",
- "\n",
- "$$\n",
- "Faithfullness\\ score = \\frac{Number\\ of\\ claims\\ in\\ the\\ generated\\ answer\\ that\\ can\\ be\\ inferred\\ from\\ the\\ given\\ context}{Total\\ number\\ of\\ claim\\ in\\ the\\ generated\\ answer}\n",
- "$$\n",
- "\n",
- "#### Example process:\n",
- "\n",
- "> Question: Where and when was Einstein born?\n",
- "> \n",
- "> Context: Albert Einstein (born 14 March 1879) was a German-born theoretical physicist, widely held to be one of the greatest and most influential scientists of all time\n",
- ">\n",
- "> answer: Einstein was born in Germany on 20th March 1879.\n",
- "\n",
- "Step 1: Use LLM to break generated answer into individual statements.\n",
- "- “Einstein was born in Germany.”\n",
- "- “Einstein was born on 20th March 1879.”\n",
- "\n",
- "Step 2: For each statement use LLM to verify if it can be inferred from the context.\n",
- "- “Einstein was born in Germany.” => yes. \n",
- "- “Einstein was born on 20th March 1879.” => no.\n",
- "\n",
- "Step 3: plug into formula\n",
- "\n",
- "Number of claims inferred from context = 1\n",
- "Total number of claims = 2\n",
- "Faithfulness = 1/2\n",
- "\n",
- "### Answer Relevance\n",
- "\n",
- "An answer can be said to be relevant if it directly addresses the question (intuitively).\n",
- "\n",
- "#### Example process:\n",
- "\n",
- "1. Use an LLM to generate \"hypothetical\" questions to a given answer with the following prompt:\n",
- "\n",
- " > Generate a question for the given answer.\n",
- " > answer: [answer]\n",
- "\n",
- "2. Embed the generated \"hypothetical\" questions as vectors.\n",
- "3. Calculate the cosine similarity of the hypothetical questions and the original question, sum those similarities, and divide by n.\n",
- "\n",
- "With data:\n",
- "\n",
- "> Question: Where is France and what is it’s capital?\n",
- "> \n",
- "> answer: France is in western Europe.\n",
- "\n",
- "Step 1 - use LLM to create 'n' variants of question from the generated answer.\n",
- "\n",
- "- “In which part of Europe is France located?”\n",
- "- “What is the geographical location of France within Europe?”\n",
- "- “Can you identify the region of Europe where France is situated?”\n",
- "\n",
- "Step 2 - Calculate the mean cosine similarity between the generated questions and the actual question.\n",
- "\n",
- "## Now let's implement using our helper functions\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 114,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "dd9cabb4b0c448b08cad96d2ef3391a2",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Evaluating: 0%| | 0/15 [00:00\n",
- "\n",
- "| \n", - " | question | \n", - "contexts | \n", - "ground_truth | \n", - "evolution_type | \n", - "metadata | \n", - "episode_done | \n", - "
|---|---|---|---|---|---|---|
| 0 | \n", - "What are short-term investments and how are th... | \n", - "[\"CASH AND EQUIVALENTS Cash and equivalents re... | \n", - "Short-term investments are highly liquid inves... | \n", - "simple | \n", - "[{'source': 'resources/nke-10k-2023.pdf'}] | \n", - "True | \n", - "
| 1 | \n", - "What are some of the risks and uncertainties a... | \n", - "['Our NIKE Direct operations, including our re... | \n", - "Many factors unique to retail operations, some... | \n", - "simple | \n", - "[{'source': 'resources/nke-10k-2023.pdf'}] | \n", - "True | \n", - "
| 2 | \n", - "What is NIKE's policy regarding securities ana... | \n", - "[\"Investors should also be aware that while NI... | \n", - "NIKE's policy is to not disclose any material ... | \n", - "simple | \n", - "[{'source': 'resources/nke-10k-2023.pdf'}] | \n", - "True | \n", - "
| 3 | \n", - "What are the revenues for the Footwear and App... | \n", - "['(Dollars in millions, except per share data)... | \n", - "The revenues for the Footwear and Apparel cate... | \n", - "simple | \n", - "[{'source': 'resources/nke-10k-2023.pdf'}] | \n", - "True | \n", - "
| 4 | \n", - "How do master netting arrangements impact the ... | \n", - "[\"The Company records the assets and liabiliti... | \n", - "The Company records the assets and liabilities... | \n", - "simple | \n", - "[{'source': 'resources/nke-10k-2023.pdf'}] | \n", - "True | \n", - "
| \n", - " | faithfulness | \n", - "answer_relevancy | \n", - "
|---|---|---|
| count | \n", - "15.000000 | \n", - "15.000000 | \n", - "
| mean | \n", - "0.781229 | \n", - "0.938581 | \n", - "
| std | \n", - "0.362666 | \n", - "0.085342 | \n", - "
| min | \n", - "0.000000 | \n", - "0.736997 | \n", - "
| 25% | \n", - "0.652778 | \n", - "0.926596 | \n", - "
| 50% | \n", - "1.000000 | \n", - "0.975230 | \n", - "
| 75% | \n", - "1.000000 | \n", - "0.994168 | \n", - "
| max | \n", - "1.000000 | \n", - "1.000000 | \n", - "
![](\"https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/resources/short-term-memory.png?raw=true\")
\n",
+ "\n",
+ "To prevent context window pollution, the agent summarizes conversations when they exceed a configurable length.\n",
+ "\n",
+ "## Long-term Memory\n",
+ "\n",
+ "Long-term memories are stored & indexed in Redis using the RedisVL client, with two types:\n",
+ "- **Episodic**: User preferences and experiences\n",
+ "- **Semantic**: General travel knowledge\n",
+ "\n",
+ "![](\"https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/resources/long-term-memory.png?raw=true\")
\n",
+ "\n",
+ ">**NOTE**: These memory types align with the [CoALA](https://arxiv.org/abs/2309.02427) paper's concepts. Our agent's procedural memory is encoded in its Python workflow."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let's Begin\n",
+ "![\"Memory](\"https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/resources/memory-agents.png?raw=true\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BmwB-sUJ1nfh"
+ },
+ "source": [
+ "### Define Agent Tools\n",
+ "\n",
+ "Now that we have our storage functions defined, we can create the tools that will enable our agent to interact with the memory system. These tools will be used by the LLM to manage memories during conversations.\n",
+ "\n",
+ "Let's start with the Store Memory Tool:\n",
+ "\n",
+ "#### Store Memory Tool\n",
+ "\n",
+ "This tool enables the agent to save important information as long-term memories in Redis. It's particularly useful for capturing:\n",
+ "- User preferences and habits\n",
+ "- Personal experiences and anecdotes\n",
+ "- Important facts and knowledge shared during conversations\n",
+ "\n",
+ "The tool accepts the following parameters:\n",
+ "- `content`: The actual memory content to store (e.g., \"User prefers window seats on flights\")\n",
+ "- `memory_type`: The type of memory (e.g., `MemoryType.EPISODIC` for personal experiences, `MemoryType.SEMANTIC` for general knowledge)\n",
+ "- `metadata`: Optional dictionary for additional context (e.g., timestamps, source, confidence)\n",
+ "- `config`: Optional configuration for user/thread context (automatically handled by the agent)\n",
+ "\n",
+ "When called, the tool:\n",
+ "1. Validates the input parameters\n",
+ "2. Stores the memory in Redis with proper indexing\n",
+ "3. Returns a success message with the stored content\n",
+ "4. Handles errors gracefully with informative messages\n",
+ "\n",
+ "This tool is designed to be used by the LLM to build a persistent memory of the user's preferences and experiences, enabling more personalized and context-aware interactions over time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "T-S0eN4B1nfh"
+ },
+ "outputs": [],
+ "source": [
+ "from typing import Dict, Optional\n",
+ "\n",
+ "from langchain_core.tools import tool\n",
+ "from langchain_core.runnables.config import RunnableConfig\n",
+ "\n",
+ "\n",
+ "@tool\n",
+ "def store_memory_tool(\n",
+ " content: str,\n",
+ " memory_type: MemoryType,\n",
+ " metadata: Optional[Dict[str, str]] = None,\n",
+ " config: Optional[RunnableConfig] = None,\n",
+ ") -> str:\n",
+ " \"\"\"\n",
+ " Store a long-term memory in the system.\n",
+ "\n",
+ " Use this tool to save important information about user preferences,\n",
+ " experiences, or general knowledge that might be useful in future\n",
+ " interactions.\n",
+ " \"\"\"\n",
+ " config = config or RunnableConfig()\n",
+ " user_id = config.get(\"user_id\", SYSTEM_USER_ID)\n",
+ " thread_id = config.get(\"thread_id\")\n",
+ "\n",
+ " try:\n",
+ " # Store in long-term memory\n",
+ " store_memory(\n",
+ " content=content,\n",
+ " memory_type=memory_type,\n",
+ " user_id=user_id,\n",
+ " thread_id=thread_id,\n",
+ " metadata=str(metadata) if metadata else None,\n",
+ " )\n",
+ "\n",
+ " return f\"Successfully stored {memory_type} memory: {content}\"\n",
+ " except Exception as e:\n",
+ " return f\"Error storing memory: {str(e)}\"\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9Am1Z_hItKpc"
+ },
+ "source": [
+ "Test the tool:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "H1-HPwag-im_",
+ "outputId": "4b883edc-29e2-4666-84ae-4e156b03661c"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Successfully stored MemoryType.EPISODIC memory: I like flying on Delta when possible'"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "store_memory_tool.invoke({\"content\": \"I like flying on Delta when possible\", \"memory_type\": \"episodic\"})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MkjIWht9vKOG"
+ },
+ "source": [
+ "Now that we've seen how to store memories, let's look at how to retrieve them.\n",
+ "\n",
+ "#### Retrieve Memoreis Tool\n",
+ "This tool allows us to search through our stored memories using semantic similarity and filtering.\n",
+ "\n",
+ "This tool is particularly useful when you want to:\n",
+ "- Find relevant past experiences or preferences\n",
+ "- Filter memories by type (episodic or semantic)\n",
+ "- Get user-specific information\n",
+ "- Limit the number of results to keep responses focused\n",
+ "\n",
+ "The tool works by:\n",
+ "1. Taking a query string and searching for semantically similar memories\n",
+ "2. Filtering results based on memory type\n",
+ "3. Applying a similarity threshold to ensure relevance\n",
+ "4. Formatting the results in a clear, readable way"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "NEqm-q1ovKOG"
+ },
+ "outputs": [],
+ "source": [
+ "@tool\n",
+ "def retrieve_memories_tool(\n",
+ " query: str,\n",
+ " memory_type: List[MemoryType],\n",
+ " limit: int = 5,\n",
+ " config: Optional[RunnableConfig] = None,\n",
+ ") -> str:\n",
+ " \"\"\"\n",
+ " Retrieve long-term memories relevant to the query.\n",
+ "\n",
+ " Use this tool to access previously stored information about user\n",
+ " preferences, experiences, or general knowledge.\n",
+ " \"\"\"\n",
+ " config = config or RunnableConfig()\n",
+ " user_id = config.get(\"user_id\", SYSTEM_USER_ID)\n",
+ "\n",
+ " try:\n",
+ " # Get long-term memories\n",
+ " stored_memories = retrieve_memories(\n",
+ " query=query,\n",
+ " memory_type=memory_type,\n",
+ " user_id=user_id,\n",
+ " limit=limit,\n",
+ " distance_threshold=0.3,\n",
+ " )\n",
+ "\n",
+ " # Format the response\n",
+ " response = []\n",
+ "\n",
+ " if stored_memories:\n",
+ " response.append(\"Long-term memories:\")\n",
+ " for memory in stored_memories:\n",
+ " response.append(f\"- [{memory.memory_type}] {memory.content}\")\n",
+ "\n",
+ " return \"\\n\".join(response) if response else \"No relevant memories found.\"\n",
+ "\n",
+ " except Exception as e:\n",
+ " return f\"Error retrieving memories: {str(e)}\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4irYew3pvKON"
+ },
+ "source": [
+ "Test the tool:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "CMlAHmTe9vhN",
+ "outputId": "95304a90-39c3-42d3-bcdc-d7d6ea6e2191"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Long-term memories:\\n- [MemoryType.EPISODIC] I like flying on Delta when possible'"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "retrieve_memories_tool.invoke({\"query\": \"Airline preferences\", \"memory_type\": [\"episodic\"]})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PftV2tTG1nfh"
+ },
+ "source": [
+ "# 🌎 Build the Travel Agent\n",
+ "\n",
+ "## Setting Up the ReAct Agent\n",
+ "\n",
+ "We'll use LangGraph's prebuilt components to create a ReAct agent with memory capabilities:\n",
+ "\n",
+ "1. **Short-term Memory**: A checkpoint saver tracks conversation history per thread\n",
+ "2. **Long-term Memory**: We'll extract and store key information from conversations\n",
+ " - Episodic memories: User preferences and experiences\n",
+ " - Semantic memories: General travel knowledge\n",
+ "\n",
+ "The system will automatically summarize conversations to manage context while preserving important details in long-term storage.\n",
+ "\n",
+ "Below we start with setting up the Redis checkpointer (`RedisSaver`) that will handle short term memory for the agent."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "QSouau_jvKON"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_core.messages import AIMessage, SystemMessage\n",
+ "from langchain_openai import ChatOpenAI\n",
+ "from langgraph.prebuilt.chat_agent_executor import create_react_agent\n",
+ "from langgraph.checkpoint.redis import RedisSaver\n",
+ "\n",
+ "# Set up the Redis checkpointer for short term memory\n",
+ "redis_saver = RedisSaver(redis_client=redis_client)\n",
+ "redis_saver.setup()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "a8LEro_PvKON"
+ },
+ "source": [
+ "Next we define the set of tools for the agent."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "id": "EtZo92KuvKON"
+ },
+ "outputs": [],
+ "source": [
+ "# Define the set of tools\n",
+ "tools = [store_memory_tool, retrieve_memories_tool]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e-2IMJaLvKON"
+ },
+ "source": [
+ "Configure the LLM from OpenAI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "id": "kWz7rC5_vKON"
+ },
+ "outputs": [],
+ "source": [
+ "# Configure an LLM for the agent with a more creative temperature.\n",
+ "llm = ChatOpenAI(model=\"gpt-4o\", temperature=0.7).bind_tools(tools)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JLKB-V9HvKON"
+ },
+ "source": [
+ "Assemble the ReAct agent combining the LLM, tools, checkpointer, and system prompt!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "id": "e-TpYxYb1nfh"
+ },
+ "outputs": [],
+ "source": [
+ "# Defint the travel agent\n",
+ "travel_agent = create_react_agent(\n",
+ " model=llm,\n",
+ " tools=tools, # Long-term memory: provided as a set of custom tools\n",
+ " checkpointer=redis_saver, # Short-term memory: the conversation history\n",
+ " prompt=SystemMessage(\n",
+ " content=\"\"\"\n",
+ " You are a travel assistant helping users plan their trips. You remember user preferences\n",
+ " and provide personalized recommendations based on past interactions.\n",
+ "\n",
+ " You have access to the following types of memory:\n",
+ " 1. Short-term memory: The current conversation thread\n",
+ " 2. Long-term memory:\n",
+ " - Episodic: User preferences and past trip experiences (e.g., \"User prefers window seats\")\n",
+ " - Semantic: General knowledge about travel destinations and requirements\n",
+ "\n",
+ " Your procedural knowledge (how to search, book flights, etc.) is built into your tools and prompts.\n",
+ "\n",
+ " Always be helpful, personal, and context-aware in your responses.\n",
+ " \"\"\"\n",
+ " ),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "htuJmhkY1nfi"
+ },
+ "source": [
+ "✅ Now that we have the basic agent in place, we will build a LangGraph workflow that invokes this agent as a node. The graph will consist of three nodes in total. We will move through each one separately."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R2mZwvHj1nfi"
+ },
+ "source": [
+ "## Node 1: Respond to the user\n",
+ "In LangGraph, a **node** represents a discrete unit of processing in a workflow. Each node is a function that takes a state object and configuration as input, processes the data, and returns an updated state. Nodes can be connected to form a directed graph that defines the flow of execution.\n",
+ "\n",
+ "The `respond_to_user` node (below) is our first node in the travel agent workflow. It serves as the entry point for user interactions and handles the core conversation flow. Here's how it works:\n",
+ "\n",
+ "1. It receives the current conversation state and configuration\n",
+ "2. Extracts any human messages from the state\n",
+ "3. Invokes our travel agent to generate a response\n",
+ "4. Handles any errors gracefully\n",
+ "5. Updates the conversation state with the agent's response\n",
+ "\n",
+ "The node uses a custom `RuntimeState` class that inherits from `MessagesState` to maintain the conversation history. This state object is passed between nodes in the graph, allowing each node to access and modify the conversation context as needed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "PFdGi8fd1nfi"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_core.messages import HumanMessage\n",
+ "from langgraph.graph.message import MessagesState\n",
+ "\n",
+ "\n",
+ "class RuntimeState(MessagesState):\n",
+ " \"\"\"Runtime state for the travel agent.\"\"\"\n",
+ " pass\n",
+ "\n",
+ "\n",
+ "def respond_to_user(state: RuntimeState, config: RunnableConfig) -> RuntimeState:\n",
+ " \"\"\"Invoke the travel agent to generate a response.\"\"\"\n",
+ " human_messages = [m for m in state[\"messages\"] if isinstance(m, HumanMessage)]\n",
+ " if not human_messages:\n",
+ " logger.warning(\"No HumanMessage found in state\")\n",
+ " return state\n",
+ "\n",
+ " try:\n",
+ " # Single agent invocation, not streamed (simplified for reliability)\n",
+ " result = travel_agent.invoke({\"messages\": state[\"messages\"]}, config=config)\n",
+ " agent_message = result[\"messages\"][-1]\n",
+ " state[\"messages\"].append(agent_message)\n",
+ " except Exception as e:\n",
+ " logger.error(f\"Error invoking travel agent: {e}\")\n",
+ " agent_message = AIMessage(\n",
+ " content=\"I'm sorry, I encountered an error processing your request.\"\n",
+ " )\n",
+ " state[\"messages\"].append(agent_message)\n",
+ "\n",
+ " return state"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kZyQE3MeyoQw"
+ },
+ "source": [
+ "## Node 2: Execute Tools\n",
+ "\n",
+ "The `execute_tools` node is a critical component in our travel agent's workflow that bridges the gap between the LLM's decisions and actual tool execution. Positioned after the `respond_to_user` node, it handles the practical side of the agent's tool-using capabilities.\n",
+ "\n",
+ "When the LLM determines it needs to use a tool, it includes tool calls in its response. This node then:\n",
+ "\n",
+ "1. Scans the conversation history to find the most recent AI message containing tool calls\n",
+ "2. For each tool call found:\n",
+ " - Extracts the tool name, arguments, and call ID from the message\n",
+ " - Matches the tool name against our available tools\n",
+ " - Executes the tool with the provided arguments\n",
+ " - Creates a ToolMessage containing the result\n",
+ "3. Handles any errors that occur during tool execution\n",
+ "4. Adds all tool results back to the conversation history\n",
+ "\n",
+ "This node is essential because it enables our agent to interact with external systems and services while maintaining a coherent conversation flow. Without it, the agent would be limited to just generating text responses without the ability to perform actual actions or retrieve real-time information.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "bkFA2_vgyrdZ"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_core.messages import ToolMessage\n",
+ "\n",
+ "\n",
+ "def execute_tools(state: RuntimeState, config: RunnableConfig) -> RuntimeState:\n",
+ " \"\"\"Execute tools specified in the latest AIMessage and append ToolMessages.\"\"\"\n",
+ " messages = state[\"messages\"]\n",
+ " latest_ai_message = next(\n",
+ " (m for m in reversed(messages) if isinstance(m, AIMessage) and m.tool_calls),\n",
+ " None\n",
+ " )\n",
+ "\n",
+ " if not latest_ai_message:\n",
+ " return state # No tool calls to process\n",
+ "\n",
+ " tool_messages = []\n",
+ " for tool_call in latest_ai_message.tool_calls:\n",
+ " tool_name = tool_call[\"name\"]\n",
+ " tool_args = tool_call[\"args\"]\n",
+ " tool_id = tool_call[\"id\"]\n",
+ "\n",
+ " # Find the corresponding tool\n",
+ " tool = next((t for t in tools if t.name == tool_name), None)\n",
+ " if not tool:\n",
+ " continue # Skip if tool not found\n",
+ "\n",
+ " try:\n",
+ " # Execute the tool with the provided arguments\n",
+ " result = tool.invoke(tool_args, config=config)\n",
+ " # Create a ToolMessage with the result\n",
+ " tool_message = ToolMessage(\n",
+ " content=str(result),\n",
+ " tool_call_id=tool_id,\n",
+ " name=tool_name\n",
+ " )\n",
+ " tool_messages.append(tool_message)\n",
+ " except Exception as e:\n",
+ " # Handle tool execution errors\n",
+ " error_message = ToolMessage(\n",
+ " content=f\"Error executing tool '{tool_name}': {str(e)}\",\n",
+ " tool_call_id=tool_id,\n",
+ " name=tool_name\n",
+ " )\n",
+ " tool_messages.append(error_message)\n",
+ "\n",
+ " # Append the ToolMessages to the message history\n",
+ " messages.extend(tool_messages)\n",
+ " state[\"messages\"] = messages\n",
+ " return state"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LM3oPg101nfi"
+ },
+ "source": [
+ "## Node 3: Conversation Summarization\n",
+ "\n",
+ "While our Redis-based long-term memory system helps store important information, we still need to manage the immediate conversation context. As the chat progresses, the message history grows, potentially overwhelming the LLM's context window. This is where our third node comes in.\n",
+ "\n",
+ "The conversation summarization node acts as a context manager, periodically condensing the chat history into a concise summary. Here's how it works:\n",
+ "\n",
+ "1. **Trigger**: The node monitors the message count and triggers summarization after every 6 messages (configurable via `MESSAGE_SUMMARIZATION_THRESHOLD`)\n",
+ "\n",
+ "2. **Summarization Process**:\n",
+ " - Uses GPT-4o with a low temperature (0.3) to ensure consistent, focused summaries\n",
+ " - Preserves critical information like travel preferences, trip details, and pending questions\n",
+ " - Replaces older messages with the summary while keeping recent context\n",
+ "\n",
+ "3. **Benefits**:\n",
+ " - Prevents context window overflow\n",
+ " - Maintains conversation coherence\n",
+ " - Optimizes token usage while preserving essential context\n",
+ "\n",
+ "The resulting summary becomes part of the conversation history, allowing the agent to reference past interactions without carrying the full message load."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "KUYw18Xb1nfi"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_core.messages import RemoveMessage\n",
+ "\n",
+ "# An LLM configured for summarization.\n",
+ "summarizer = ChatOpenAI(model=\"gpt-4o\", temperature=0.3)\n",
+ "\n",
+ "# The number of messages after which we'll summarize the conversation.\n",
+ "MESSAGE_SUMMARIZATION_THRESHOLD = 6\n",
+ "\n",
+ "\n",
+ "def summarize_conversation(\n",
+ " state: RuntimeState, config: RunnableConfig\n",
+ ") -> RuntimeState:\n",
+ " \"\"\"\n",
+ " Summarize a list of messages into a concise summary to reduce context length\n",
+ " while preserving important information.\n",
+ " \"\"\"\n",
+ " messages = state[\"messages\"]\n",
+ " current_message_count = len(messages)\n",
+ " if current_message_count < MESSAGE_SUMMARIZATION_THRESHOLD:\n",
+ " logger.debug(f\"Not summarizing conversation: {current_message_count}\")\n",
+ " return state\n",
+ "\n",
+ " system_prompt = \"\"\"\n",
+ " You are a conversation summarizer. Create a concise summary of the previous\n",
+ " conversation between a user and a travel assistant.\n",
+ "\n",
+ " The summary should:\n",
+ " 1. Highlight key topics, preferences, and decisions\n",
+ " 2. Include any specific trip details (destinations, dates, preferences)\n",
+ " 3. Note any outstanding questions or topics that need follow-up\n",
+ " 4. Be concise but informative\n",
+ "\n",
+ " Format your summary as a brief narrative paragraph.\n",
+ " \"\"\"\n",
+ "\n",
+ " message_content = \"\\n\".join(\n",
+ " [\n",
+ " f\"{'User' if isinstance(msg, HumanMessage) else 'Assistant'}: {msg.content}\"\n",
+ " for msg in messages\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ " # Invoke the summarizer\n",
+ " summary_messages = [\n",
+ " SystemMessage(content=system_prompt),\n",
+ " HumanMessage(\n",
+ " content=f\"Please summarize this conversation:\\n\\n{message_content}\"\n",
+ " ),\n",
+ " ]\n",
+ "\n",
+ " summary_response = summarizer.invoke(summary_messages)\n",
+ "\n",
+ " logger.info(f\"Summarized {len(messages)} messages into a conversation summary\")\n",
+ "\n",
+ " summary_message = SystemMessage(\n",
+ " content=f\"\"\"\n",
+ " Summary of the conversation so far:\n",
+ "\n",
+ " {summary_response.content}\n",
+ "\n",
+ " Please continue the conversation based on this summary and the recent messages.\n",
+ " \"\"\"\n",
+ " )\n",
+ " remove_messages = [\n",
+ " RemoveMessage(id=msg.id) for msg in messages if msg.id is not None\n",
+ " ]\n",
+ "\n",
+ " state[\"messages\"] = [ # type: ignore\n",
+ " *remove_messages,\n",
+ " summary_message,\n",
+ " state[\"messages\"][-1],\n",
+ " ]\n",
+ "\n",
+ " return state.copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dpzjQxXi1nfi"
+ },
+ "source": [
+ "## Assemble the full graph\n",
+ "\n",
+ "🚧 It's time to assemble our graph for end-to-end agent execution. We will attach all three **nodes** we defined above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "h6TvQaob1nfi"
+ },
+ "outputs": [],
+ "source": [
+ "from langgraph.graph import StateGraph, END\n",
+ "\n",
+ "workflow = StateGraph(RuntimeState)\n",
+ "\n",
+ "# Add nodes to the graph\n",
+ "workflow.add_node(\"agent\", respond_to_user)\n",
+ "workflow.add_node(\"execute_tools\", execute_tools)\n",
+ "workflow.add_node(\"summarize_conversation\", summarize_conversation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cYGE-DLuvKOO"
+ },
+ "source": [
+ "Next, we will tie the nodes together using **edges** which control process flow. There is a conditional edge between the agent node and what comes next. What comes next is based on whether we need to handle + execute a tool call or proceed..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "61Un_szhvKOO"
+ },
+ "outputs": [],
+ "source": [
+ "def decide_next_step(state):\n",
+ " latest_ai_message = next((m for m in reversed(state[\"messages\"]) if isinstance(m, AIMessage)), None)\n",
+ " if latest_ai_message and latest_ai_message.tool_calls:\n",
+ " return \"execute_tools\"\n",
+ " return \"summarize_conversation\"\n",
+ "\n",
+ "\n",
+ "workflow.set_entry_point(\"agent\")\n",
+ "workflow.add_conditional_edges(\n",
+ " \"agent\",\n",
+ " decide_next_step,\n",
+ " {\"execute_tools\": \"execute_tools\", \"summarize_conversation\": \"summarize_conversation\"},\n",
+ ")\n",
+ "workflow.add_edge(\"execute_tools\", \"agent\")\n",
+ "workflow.add_edge(\"summarize_conversation\", END)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3L_OEc80vKOO"
+ },
+ "source": [
+ "Compile the graph!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "kuwdsVhYvKOO"
+ },
+ "outputs": [],
+ "source": [
+ "graph = workflow.compile(checkpointer=redis_saver)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sQSfnQDK1nfo"
+ },
+ "source": [
+ "## Testing the Main Agent Loop\n",
+ "\n",
+ "Now that we have our workflow graph set up, let's test the main interaction loop. This loop will:\n",
+ "1. Initialize the conversation state and configuration\n",
+ "2. Process user input through our workflow\n",
+ "3. Display the agent's responses\n",
+ "4. Handle any errors gracefully\n",
+ "\n",
+ "The main loop implements the following workflow:\n",
+ "\n",
+ "1. Initialization\n",
+ " - Creates a unique thread ID for conversation tracking\n",
+ " - Initializes an empty message state for the conversation\n",
+ "\n",
+ "2. Input Processing\n",
+ " - Prompts for user input in a continuous loop\n",
+ " - Handles empty inputs by skipping to next iteration\n",
+ " - Provides exit commands (\"exit\" or \"quit\") to end the session\n",
+ "\n",
+ "3. Message Flow\n",
+ " - Converts user input into a HumanMessage\n",
+ " - Streams the message through our workflow graph\n",
+ " - Updates conversation state with each processing step\n",
+ " - Maintains conversation history for context\n",
+ "\n",
+ "4. Response Generation\n",
+ " - Processes the state through our agent workflow\n",
+ " - Extracts the most recent AI response\n",
+ " - Displays the response to the user\n",
+ " - Handles cases where no response is generated\n",
+ "\n",
+ "5. Error Handling\n",
+ " - Catches and logs any processing errors\n",
+ " - Provides user-friendly error messages\n",
+ " - Preserves conversation state even when errors occur\n",
+ " - Ensures graceful recovery from failures"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "id": "xD1BTjXY1nfp"
+ },
+ "outputs": [],
+ "source": [
+ "def main(thread_id: str = \"book_flight\", user_id: str = \"demo_user\"):\n",
+ " \"\"\"Main interaction loop for the travel agent\"\"\"\n",
+ "\n",
+ " print(\"Welcome to the Travel Assistant! (Type 'exit' to quit)\")\n",
+ "\n",
+ " config = RunnableConfig(configurable={\"thread_id\": thread_id, \"user_id\": user_id})\n",
+ " state = RuntimeState(messages=[])\n",
+ "\n",
+ " while True:\n",
+ " user_input = input(\"\\nYou (type 'quit' to quit): \")\n",
+ "\n",
+ " if not user_input:\n",
+ " continue\n",
+ "\n",
+ " if user_input.lower() in [\"exit\", \"quit\"]:\n",
+ " print(\"Thank you for using the Travel Assistant. Goodbye!\")\n",
+ " break\n",
+ "\n",
+ " state[\"messages\"].append(HumanMessage(content=user_input))\n",
+ "\n",
+ " try:\n",
+ " # Process user input through the graph\n",
+ " for result in graph.stream(state, config=config, stream_mode=\"values\"):\n",
+ " state = RuntimeState(**result)\n",
+ "\n",
+ " logger.debug(f\"# of messages after run: {len(state['messages'])}\")\n",
+ "\n",
+ " # Find the most recent AI message, so we can print the response\n",
+ " ai_messages = [m for m in state[\"messages\"] if isinstance(m, AIMessage)]\n",
+ " if ai_messages:\n",
+ " message = ai_messages[-1].content\n",
+ " else:\n",
+ " logger.error(\"No AI messages after run\")\n",
+ " message = \"I'm sorry, I couldn't process your request properly.\"\n",
+ " # Add the error message to the state\n",
+ " state[\"messages\"].append(AIMessage(content=message))\n",
+ "\n",
+ " print(f\"\\nAssistant: {message}\")\n",
+ "\n",
+ " except Exception as e:\n",
+ " logger.exception(f\"Error processing request: {e}\")\n",
+ " error_message = \"I'm sorry, I encountered an error processing your request.\"\n",
+ " print(f\"\\nAssistant: {error_message}\")\n",
+ " # Add the error message to the state\n",
+ " state[\"messages\"].append(AIMessage(content=error_message))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "P51RdhnzfZa1"
+ },
+ "source": [
+ "Before you try your own, take a look at the current conversation between Tyler and the travel agent. Notice the memory storage actions, the calls to the LLM, and also the conversation summarization that take place during the workflow!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "C5fg4PH97YGY",
+ "outputId": "1a6fd03c-e0f5-46a8-9462-76f46260e901"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Enter a user ID: tyler\n",
+ "Enter a thread ID: 123\n",
+ "Welcome to the Travel Assistant! (Type 'exit' to quit)\n",
+ "13:51:57 __main__ INFO Starting memory consolidation for user tyler\n",
+ "\n",
+ "You (type 'quit' to quit): Hi I plan to go to singapore with my wife this summer. We love outdoors activities and trying new kinds of foods. Any good recommendations?\n",
+ "13:52:30 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:52:30 __main__ INFO Preparing to store memory: User plans to visit Singapore this summer with his wife and they love outdoor activities and trying new kinds of foods.\n",
+ "13:52:31 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:52:31 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:52:31 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:52:31 __main__ INFO Stored MemoryType.EPISODIC memory: User plans to visit Singapore this summer with his wife and they love outdoor activities and trying new kinds of foods.\n",
+ "13:52:37 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "\n",
+ "Assistant: Singapore is a fantastic destination for outdoor activities and culinary adventures! Here are some recommendations that you and your wife might enjoy:\n",
+ "\n",
+ "### Outdoor Activities:\n",
+ "1. **Gardens by the Bay**: Explore the stunning gardens and the iconic Supertree Grove. You can also walk along the OCBC Skyway for a breathtaking view of the Marina Bay area.\n",
+ "\n",
+ "2. **Sentosa Island**: Enjoy a day at the beach, try zip-lining, or explore the numerous attractions like Universal Studios Singapore.\n",
+ "\n",
+ "3. **MacRitchie Reservoir**: Go for a hike along the MacRitchie Trails and experience the TreeTop Walk, a suspension bridge spanning the forest canopy.\n",
+ "\n",
+ "4. **Pulau Ubin**: Rent a bicycle and explore this rustic island. It's a great place to enjoy nature and see what Singapore was like in the past.\n",
+ "\n",
+ "### Food Experiences:\n",
+ "1. **Hawker Centers**: Visit places like Maxwell Food Centre or Lau Pa Sat to try local dishes such as Hainanese chicken rice, laksa, and chili crab.\n",
+ "\n",
+ "2. **Peranakan Cuisine**: Try something different with Peranakan or Nyonya food, which is a blend of Chinese and Malay culinary traditions. \n",
+ "\n",
+ "3. **Jumbo Seafood**: Known for their chili crab, this is a must-try for seafood lovers. There are several locations around the city.\n",
+ "\n",
+ "4. **Food Tours**: Consider joining a food tour to explore the diverse culinary scene in Singapore and learn about the history and culture behind each dish.\n",
+ "\n",
+ "Feel free to ask if you need more details or have specific interests!\n",
+ "\n",
+ "You (type 'quit' to quit): Excellent thank you. I would love help booking flights. What are the best routes typically flown from Atlanta to Singapore?\n",
+ "13:53:24 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "\n",
+ "Assistant: Flying from Atlanta to Singapore usually involves at least one stopover, as there are no direct flights. Here are some of the best routes typically flown:\n",
+ "\n",
+ "1. **Atlanta (ATL) to Singapore (SIN) via Tokyo (NRT/HND)**:\n",
+ " - Airlines: Delta Air Lines, Japan Airlines\n",
+ " - This route often involves a stop in Tokyo, which can be a great opportunity to explore Japan if you have a long layover.\n",
+ "\n",
+ "2. **Atlanta (ATL) to Singapore (SIN) via Seoul (ICN)**:\n",
+ " - Airlines: Korean Air, Delta Air Lines\n",
+ " - A stopover in Seoul offers another chance for a brief visit in South Korea.\n",
+ "\n",
+ "3. **Atlanta (ATL) to Singapore (SIN) via Doha (DOH)**:\n",
+ " - Airline: Qatar Airways\n",
+ " - Qatar Airways offers a stop in Doha, which is known for its luxurious airport and facilities.\n",
+ "\n",
+ "4. **Atlanta (ATL) to Singapore (SIN) via Dubai (DXB)**:\n",
+ " - Airline: Emirates\n",
+ " - Stopping in Dubai can be a fantastic experience with plenty of attractions to explore.\n",
+ "\n",
+ "5. **Atlanta (ATL) to Singapore (SIN) via Frankfurt (FRA) or Munich (MUC)**:\n",
+ " - Airlines: Lufthansa, Singapore Airlines\n",
+ " - A European stopover provides a different cultural experience.\n",
+ "\n",
+ "When booking flights, it's a good idea to consider the total travel time, layover duration, and the amenities offered by the airlines. Let me know if you would like assistance with booking or have any preferences, such as specific airlines or layover cities!\n",
+ "\n",
+ "You (type 'quit' to quit): Ok great. As you know we like Delta. But we also prefer to sit first class if possible. Do these kinds of routes offer upgrades normally?\n",
+ "13:53:32 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:53:32 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:53:37 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:53:41 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:53:41 __main__ INFO Summarized 6 messages into a conversation summary\n",
+ "\n",
+ "Assistant: Since you prefer Delta Air Lines, you might consider the following route:\n",
+ "\n",
+ "### Atlanta (ATL) to Singapore (SIN) via Tokyo (NRT/HND):\n",
+ "- **Airlines**: Delta Air Lines, Japan Airlines\n",
+ "- **Class Options**: Delta offers Delta One, their premium cabin, which includes lie-flat seats, premium dining, and other luxury amenities.\n",
+ "\n",
+ "Delta typically allows for upgrades to first-class or their premium Delta One cabin, especially if you're a SkyMiles member or have elite status. Upgrades can often be requested at the time of booking or closer to the departure date, depending on availability.\n",
+ "\n",
+ "For the best chance of securing an upgrade, consider:\n",
+ "- Booking early and using any available miles or upgrade certificates.\n",
+ "- Checking the Delta app or website regularly for upgrade availability.\n",
+ "- Contacting Delta customer service if you have specific requests or need assistance with your booking.\n",
+ "\n",
+ "Let me know if you need help with booking or have any other questions!\n",
+ "\n",
+ "You (type 'quit' to quit): Let's hold on booking for now. Back to activities. Based on what you know about me, what do you think we should do? Design the perfect Sunday for me and my wife in Singapore.\n",
+ "13:54:05 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:54:06 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:54:13 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "\n",
+ "Assistant: Here's a perfect Sunday itinerary in Singapore, tailored to your love for outdoor activities and culinary experiences:\n",
+ "\n",
+ "### Morning:\n",
+ "- **Breakfast at Tiong Bahru Bakery**: Start your day with a delicious breakfast at this popular bakery known for its croissants and artisanal coffee.\n",
+ "- **Visit Gardens by the Bay**: Spend the morning exploring this iconic attraction. Don't miss the Supertree Grove and Cloud Forest Dome for a mix of nature and futuristic architecture.\n",
+ "\n",
+ "### Midday:\n",
+ "- **Lunch at Lau Pa Sat Hawker Centre**: Head to this historic food market for a taste of Singapore's diverse street food. Try local favorites like Hainanese chicken rice, satay, and laksa.\n",
+ "- **Stroll Along Marina Bay**: Enjoy a leisurely walk along Marina Bay and take in the stunning skyline views. You can also visit the Merlion Park for some iconic photo opportunities.\n",
+ "\n",
+ "### Afternoon:\n",
+ "- **Biking at East Coast Park**: Rent a bike and enjoy a ride along the scenic coastline. The park offers a beautiful setting for outdoor activities and relaxation.\n",
+ "- **Explore Katong and Joo Chiat**: Discover the colorful shophouses and Peranakan culture in these charming neighborhoods. You can also stop by for some traditional Peranakan snacks.\n",
+ "\n",
+ "### Evening:\n",
+ "- **Dinner at a Rooftop Restaurant**: End your day with a romantic dinner at a rooftop restaurant like Level33 or Ce La Vi, offering panoramic views of the city skyline.\n",
+ "- **Night Safari at Singapore Zoo**: If you're up for some adventure, consider the Night Safari for a unique experience of seeing nocturnal animals in their natural habitat.\n",
+ "\n",
+ "This itinerary combines your love for the outdoors with Singapore's rich culinary scene, ensuring a memorable day for you and your wife. Let me know if you'd like more details or adjustments!\n",
+ "\n",
+ "You (type 'quit' to quit): Great as long as there are no shellfish items featured as my wife is very allergic.\n",
+ "13:54:52 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:54:52 __main__ INFO Preparing to store memory: User's wife is allergic to shellfish.\n",
+ "13:54:58 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:54:58 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:54:58 __main__ INFO Stored MemoryType.EPISODIC memory: User's wife is allergic to shellfish.\n",
+ "13:55:00 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:55:03 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:55:03 __main__ INFO Summarized 6 messages into a conversation summary\n",
+ "\n",
+ "Assistant: I've noted that your wife is allergic to shellfish. When dining at places like hawker centers or restaurants, it's a good idea to inform the staff about her allergy to ensure a safe dining experience. \n",
+ "\n",
+ "If you need more personalized recommendations or adjustments to your itinerary, feel free to ask!\n",
+ "\n",
+ "You (type 'quit' to quit): Excellent. Back to booking flights. For our preferred airline, will you recommend best route to get there from the US?\n",
+ "13:55:32 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:55:32 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:55:34 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "\n",
+ "Assistant: Given your preference for Delta Air Lines, I recommend flying from Atlanta (ATL) to Singapore (SIN) with a layover in Tokyo (NRT). Delta offers convenient connections through Tokyo, and this route is popular for travelers heading to Singapore.\n",
+ "\n",
+ "Would you like me to assist with finding specific flight options or any other details regarding the booking?\n",
+ "\n",
+ "You (type 'quit' to quit): Sounds great. We will go with this one. I havent been to Singapore since I was 15 years old when I went with my family during my dads business trip. We had such a great time. I think you are going to help us have a fantastic trip!!\n",
+ "13:55:52 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:55:52 __main__ INFO Preparing to store memory: User visited Singapore at the age of 15 with their family during their dad's business trip and had a great time.\n",
+ "13:55:52 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:55:52 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "13:55:52 __main__ INFO Stored MemoryType.EPISODIC memory: User visited Singapore at the age of 15 with their family during their dad's business trip and had a great time.\n",
+ "13:55:54 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:55:57 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+ "13:55:57 __main__ INFO Summarized 6 messages into a conversation summary\n",
+ "\n",
+ "Assistant: I'm thrilled to be part of planning your trip back to Singapore! It sounds like you have fond memories from your last visit, and I'm here to help make this trip just as memorable. If you need any more assistance with flights or have questions about your itinerary, just let me know!\n",
+ "\n",
+ "You (type 'quit' to quit): quit\n",
+ "Thank you for using the Travel Assistant. Goodbye!\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "\n",
+ "try:\n",
+ " user_id = input(\"Enter a user ID: \") or \"demo_user\"\n",
+ " thread_id = input(\"Enter a thread ID: \") or \"demo_thread\"\n",
+ "except Exception:\n",
+ " # If we're running in CI, we don't have a terminal to input from, so just exit\n",
+ " exit()\n",
+ "else:\n",
+ " main(thread_id, user_id)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xmFwUzVo2qxB"
+ },
+ "source": [
+ "Let's review what the agent learned about me during the process!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-txziZxw2jik",
+ "outputId": "1ac4bf0b-ec10-4fd2-ae28-8568e9be5829"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "13:56:11 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['Long-term memories:',\n",
+ " '- [MemoryType.EPISODIC] User plans to visit Singapore this summer with his wife and they love outdoor activities and trying new kinds of foods.',\n",
+ " \"- [MemoryType.EPISODIC] User visited Singapore at the age of 15 with their family during their dad's business trip and had a great time.\",\n",
+ " '- [MemoryType.EPISODIC] I like flying on Delta when possible',\n",
+ " \"- [MemoryType.EPISODIC] User's wife is allergic to shellfish.\"]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# NBVAL_SKIP\n",
+ "res = retrieve_memories_tool.invoke({\"query\": \"Travel, activity, and dietary preferences\", \"memory_type\": [\"episodic\", \"semantic\"]})\n",
+ "res.split(\"\\n\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "M2UrJxte5HRT"
+ },
+ "source": [
+ "Don't forget, we have the RedisVL index we can use to manually query or work with as needed:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qYm3HQj54WPX",
+ "outputId": "5db66cb4-e07b-40dd-e4ae-35fed24e283d"
+ },
+ "outputs": [],
+ "source": [
+ "from redisvl.query import CountQuery\n",
+ "\n",
+ "# count total long-term memories in Redis\n",
+ "long_term_memory_index.query(CountQuery())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "recap-summary"
+ },
+ "source": [
+ "___\n",
+ "\n",
+ "# 🎓 Recap\n",
+ "\n",
+ "You've now learned the fundamentals from scratch and built a **production-ready memory-enabled AI agent** from the ground up. Let's recap the key accomplishments:\n",
+ "\n",
+ "## 🏗️ What we built\n",
+ "\n",
+ "1. ✅ **Dual-Memory Architecture**: Short-term conversation state + long-term persistent knowledge with LangGraph and Redis\n",
+ "2. ✅ **Vector-Powered Memory**: Semantic search using RedisVL\n",
+ "3. ✅ **Smart Deduplication**: Prevents storing similar memories multiple times \n",
+ "4. ✅ **Tool-Based Memory Management**: LLM controls when to store/retrieve memories \n",
+ "5. ✅ **Conversation Summarization**: Automatic context window management \n",
+ "\n",
+ "**Why Redis?**\n",
+ "\n",
+ "- **Performance**: Sub-millisecond memory retrieval at scale \n",
+ "- **Versatility**: Handles both structured state (checkpoints) and unstructured data (vectors) \n",
+ "- **Production-Ready**: Built-in persistence, clustering, and high availability \n",
+ "- **Developer Experience**: Rich ecosystem with tools like RedisVL and AI framework integrations \n",
+ "\n",
+ "## 🔧 Alternative memory dev frameworks\n",
+ "\n",
+ "While this tutorial shows hands-on implementation, consider these frameworks for faster development:\n",
+ "\n",
+ "- **[LangMem](https://github.com/langchain-ai/langmem)**: LangChain's official memory framework\n",
+ "- **[Mem0](https://github.com/mem0-ai/mem0)**: Dedicated memory layer for AI applications\n",
+ "\n",
+ "**When to Use Each Approach:**\n",
+ "- **Custom Implementation** (this tutorial): Maximum control, specific requirements, learning\n",
+ "- **LangMem**: LangChain ecosystem integration, rapid prototyping\n",
+ "- **Mem0**: Multi-application memory sharing, enterprise features\n",
+ "\n",
+ "## 🔄 Next Steps\n",
+ "\n",
+ "You now have the foundation to build sophisticated, memory-enabled AI agents that feel truly intelligent and personalized.\n",
+ "\n",
+ "**Want to learn more?**\n",
+ "1. [Read more](https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/) about agent memory patterns with Redis\n",
+ "2. [Meet with our experts](https://redis.io/meeting/) to get a consultation on your architecture and where Redis can help."
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python-recipes/agents/04_autogen_agent.ipynb b/python-recipes/agents/04_autogen_agent.ipynb
new file mode 100644
index 00000000..b8032d57
--- /dev/null
+++ b/python-recipes/agents/04_autogen_agent.ipynb
@@ -0,0 +1,653 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "# Agentic tasks with AutoGen and Redis\n",
+ "\n",
+ "This notebook demonstrates how to build an agent using Microsoft's [AutoGen](https://microsoft.github.io/autogen/stable//index.html) agent framework and the RedisMemory integration.\n",
+ "\n",
+ "We'll define an agent, give it access to tools and memory, then set in on a task to see how it uses its abilities.\n",
+ "\n",
+ "## Let's Begin!\n",
+ "\n",
+ "![\"Redis\"](\"https://redis.io/wp-content/uploads/2024/04/Logotype.svg\")
\n",
+ " ![\"LiteLLM\"](\"https://awsmp-logos.s3.amazonaws.com/seller-xw5kijmvmzasy/c233c9ade2ccb5491072ae232c814942.png\")
\n",
+ "