Feature/cog 1358 local ollama model support for cognee #555
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feat: implements separate adapter for ollama
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,44 @@ | ||
| from typing import Type | ||
| from pydantic import BaseModel | ||
| import instructor | ||
| from cognee.infrastructure.llm.llm_interface import LLMInterface | ||
| from cognee.infrastructure.llm.config import get_llm_config | ||
| from openai import OpenAI | ||
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| class OllamaAPIAdapter(LLMInterface): | ||
| """Adapter for a Generic API LLM provider using instructor with an OpenAI backend.""" | ||
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| def __init__(self, endpoint: str, api_key: str, model: str, name: str, max_tokens: int): | ||
| self.name = name | ||
| self.model = model | ||
| self.api_key = api_key | ||
| self.endpoint = endpoint | ||
| self.max_tokens = max_tokens | ||
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| self.aclient = instructor.from_openai( | ||
| OpenAI(base_url=self.endpoint, api_key=self.api_key), mode=instructor.Mode.JSON | ||
| ) | ||
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| async def acreate_structured_output( | ||
| self, text_input: str, system_prompt: str, response_model: Type[BaseModel] | ||
| ) -> BaseModel: | ||
| """Generate a structured output from the LLM using the provided text and system prompt.""" | ||
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| response = self.aclient.chat.completions.create( | ||
| model=self.model, | ||
| messages=[ | ||
| { | ||
| "role": "user", | ||
| "content": f"Use the given format to extract information from the following input: {text_input}", | ||
| }, | ||
| { | ||
| "role": "system", | ||
| "content": system_prompt, | ||
| }, | ||
| ], | ||
| max_retries=5, | ||
| response_model=response_model, | ||
| ) | ||
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| return response | ||
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🛠️ Refactor suggestion
Add error handling and configuration options.
The method should handle API errors and allow configuration of model parameters.
async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: - """Generate a structured output from the LLM using the provided text and system prompt.""" + """Generate a structured output from Ollama using the provided text and system prompt. + + Args: + text_input: The input text to process + system_prompt: The system prompt to guide the model + response_model: Pydantic model for response structure + + Returns: + BaseModel: Structured response matching response_model + + Raises: + OpenAIError: If API call fails + ValueError: If input validation fails + """ + if not text_input or not system_prompt: + raise ValueError("text_input and system_prompt are required") - response = self.aclient.chat.completions.create( - model=self.model, - messages=[ - { - "role": "user", - "content": f"Use the given format to extract information from the following input: {text_input}", - }, - { - "role": "system", - "content": system_prompt, - }, - ], - max_retries=5, - response_model=response_model, - ) + try: + response = await self.aclient.chat.completions.create( + model=self.model, + messages=[ + { + "role": "user", + "content": f"Use the given format to extract information from the following input: {text_input}", + }, + { + "role": "system", + "content": system_prompt, + }, + ], + max_retries=5, + response_model=response_model, + temperature=0.7, # Add configurable parameters + timeout=30, # Add timeout + ) + return response + except Exception as e: + raise OpenAIError(f"Failed to generate structured output: {str(e)}") - return response📝 Committable suggestion