fix: changed embedding model to have api base and watsonx api endpoint

src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json

@@ -1921,7 +1921,7 @@
                   },
                   {
                     "name": "google",
-                    "version": "0.6.15"
+                    "version": "0.8.5"
                   },
                   {
                     "name": "googleapiclient",

src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json

@@ -1849,7 +1849,7 @@
             "legacy": false,
             "lf_version": "1.4.2",
             "metadata": {
-              "code_hash": "437f01c42e31",
+              "code_hash": "bfe1d527184b",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1939,6 +1939,36 @@
                 "type": "str",
                 "value": ""
               },
+              "base_url_ibm_watsonx": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "watsonx API Endpoint",
+                "dynamic": false,
+                "external_options": {},
+                "info": "The base URL of the API (IBM watsonx.ai only)",
+                "name": "base_url_ibm_watsonx",
+                "options": [
+                  "https://us-south.ml.cloud.ibm.com",
+                  "https://eu-de.ml.cloud.ibm.com",
+                  "https://eu-gb.ml.cloud.ibm.com",
+                  "https://au-syd.ml.cloud.ibm.com",
+                  "https://jp-tok.ml.cloud.ibm.com",
+                  "https://ca-tor.ml.cloud.ibm.com"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": false,
+                "show": false,
+                "title_case": false,
+                "toggle": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "https://us-south.ml.cloud.ibm.com"
+              },
               "chunk_size": {
                 "_input_type": "IntInput",
                 "advanced": true,
@@ -1973,7 +2003,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.ollama_constants import OLLAMA_EMBEDDING_MODELS\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import WATSONX_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageTextInput,\n    SecretStrInput,\n)\nfrom lfx.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n    display_name = \"Embedding Model\"\n    description = \"Generate embeddings using a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n    icon = \"binary\"\n    name = \"EmbeddingModel\"\n    category = \"models\"\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n            value=\"OpenAI\",\n            info=\"Select the embedding model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model Name\",\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n            info=\"Select the embedding model to use\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=True,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"api_base\",\n            display_name=\"API Base URL\",\n            info=\"Base URL for the API. Leave empty for default.\",\n            advanced=True,\n        ),\n        # Watson-specific inputs\n        MessageTextInput(\n            name=\"project_id\",\n            display_name=\"Project ID\",\n            info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n            show=False,\n        ),\n        IntInput(\n            name=\"dimensions\",\n            display_name=\"Dimensions\",\n            info=\"The number of dimensions the resulting output embeddings should have. \"\n            \"Only supported by certain models.\",\n            advanced=True,\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        provider = self.provider\n        model = self.model\n        api_key = self.api_key\n        api_base = self.api_base\n        dimensions = self.dimensions\n        chunk_size = self.chunk_size\n        request_timeout = self.request_timeout\n        max_retries = self.max_retries\n        show_progress_bar = self.show_progress_bar\n        model_kwargs = self.model_kwargs or {}\n\n        if provider == \"OpenAI\":\n            if not api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n            return OpenAIEmbeddings(\n                model=model,\n                dimensions=dimensions or None,\n                base_url=api_base or None,\n                api_key=api_key,\n                chunk_size=chunk_size,\n                max_retries=max_retries,\n                timeout=request_timeout or None,\n                show_progress_bar=show_progress_bar,\n                model_kwargs=model_kwargs,\n            )\n\n        if provider == \"Ollama\":\n            try:\n                from langchain_ollama import OllamaEmbeddings\n            except ImportError:\n                try:\n                    from langchain_community.embeddings import OllamaEmbeddings\n                except ImportError:\n                    msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n                    raise ImportError(msg) from None\n\n            return OllamaEmbeddings(\n                model=model,\n                base_url=api_base or \"http://localhost:11434\",\n                **model_kwargs,\n            )\n\n        if provider == \"IBM watsonx.ai\":\n            try:\n                from langchain_ibm import WatsonxEmbeddings\n            except ImportError:\n                msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n                raise ImportError(msg) from None\n\n            if not api_key:\n                msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n                raise ValueError(msg)\n\n            project_id = self.project_id\n\n            if not project_id:\n                msg = \"Project ID is required for IBM watsonx.ai provider\"\n                raise ValueError(msg)\n\n            params = {\n                \"model_id\": model,\n                \"url\": api_base or \"https://us-south.ml.cloud.ibm.com\",\n                \"apikey\": api_key,\n            }\n\n            params[\"project_id\"] = project_id\n\n            return WatsonxEmbeddings(**params)\n\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\":\n            if field_value == \"OpenAI\":\n                build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n                build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n                build_config[\"api_key\"][\"required\"] = True\n                build_config[\"api_key\"][\"show\"] = True\n                build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n                build_config[\"api_base\"][\"advanced\"] = True\n                build_config[\"project_id\"][\"show\"] = False\n\n            elif field_value == \"Ollama\":\n                build_config[\"model\"][\"options\"] = OLLAMA_EMBEDDING_MODELS\n                build_config[\"model\"][\"value\"] = OLLAMA_EMBEDDING_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n                build_config[\"api_key\"][\"required\"] = False\n                build_config[\"api_key\"][\"show\"] = False\n                build_config[\"api_base\"][\"display_name\"] = \"Ollama Base URL\"\n                build_config[\"api_base\"][\"value\"] = \"http://localhost:11434\"\n                build_config[\"api_base\"][\"advanced\"] = True\n                build_config[\"project_id\"][\"show\"] = False\n\n            elif field_value == \"IBM watsonx.ai\":\n                build_config[\"model\"][\"options\"] = WATSONX_EMBEDDING_MODEL_NAMES\n                build_config[\"model\"][\"value\"] = WATSONX_EMBEDDING_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n                build_config[\"api_key\"][\"required\"] = True\n                build_config[\"api_key\"][\"show\"] = True\n                build_config[\"api_base\"][\"display_name\"] = \"IBM watsonx.ai URL\"\n                build_config[\"api_base\"][\"value\"] = \"https://us-south.ml.cloud.ibm.com\"\n                build_config[\"api_base\"][\"advanced\"] = False\n                build_config[\"project_id\"][\"show\"] = True\n\n        return build_config\n"
+                "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.ollama_constants import OLLAMA_EMBEDDING_MODELS\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS, WATSONX_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageTextInput,\n    SecretStrInput,\n)\nfrom lfx.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n    display_name = \"Embedding Model\"\n    description = \"Generate embeddings using a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n    icon = \"binary\"\n    name = \"EmbeddingModel\"\n    category = \"models\"\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n            value=\"OpenAI\",\n            info=\"Select the embedding model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n        ),\n        MessageTextInput(\n            name=\"api_base\",\n            display_name=\"API Base URL\",\n            info=\"Base URL for the API. Leave empty for default.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            show=False,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model Name\",\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n            info=\"Select the embedding model to use\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=True,\n            show=True,\n            real_time_refresh=True,\n        ),\n        # Watson-specific inputs\n        MessageTextInput(\n            name=\"project_id\",\n            display_name=\"Project ID\",\n            info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n            show=False,\n        ),\n        IntInput(\n            name=\"dimensions\",\n            display_name=\"Dimensions\",\n            info=\"The number of dimensions the resulting output embeddings should have. \"\n            \"Only supported by certain models.\",\n            advanced=True,\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        provider = self.provider\n        model = self.model\n        api_key = self.api_key\n        api_base = self.api_base\n        base_url_ibm_watsonx = self.base_url_ibm_watsonx\n        dimensions = self.dimensions\n        chunk_size = self.chunk_size\n        request_timeout = self.request_timeout\n        max_retries = self.max_retries\n        show_progress_bar = self.show_progress_bar\n        model_kwargs = self.model_kwargs or {}\n\n        if provider == \"OpenAI\":\n            if not api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n            return OpenAIEmbeddings(\n                model=model,\n                dimensions=dimensions or None,\n                base_url=api_base or None,\n                api_key=api_key,\n                chunk_size=chunk_size,\n                max_retries=max_retries,\n                timeout=request_timeout or None,\n                show_progress_bar=show_progress_bar,\n                model_kwargs=model_kwargs,\n            )\n\n        if provider == \"Ollama\":\n            try:\n                from langchain_ollama import OllamaEmbeddings\n            except ImportError:\n                try:\n                    from langchain_community.embeddings import OllamaEmbeddings\n                except ImportError:\n                    msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n                    raise ImportError(msg) from None\n\n            return OllamaEmbeddings(\n                model=model,\n                base_url=api_base or \"http://localhost:11434\",\n                **model_kwargs,\n            )\n\n        if provider == \"IBM watsonx.ai\":\n            try:\n                from langchain_ibm import WatsonxEmbeddings\n            except ImportError:\n                msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n                raise ImportError(msg) from None\n\n            if not api_key:\n                msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n                raise ValueError(msg)\n\n            project_id = self.project_id\n\n            if not project_id:\n                msg = \"Project ID is required for IBM watsonx.ai provider\"\n                raise ValueError(msg)\n\n            params = {\n                \"model_id\": model,\n                \"url\": base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n                \"apikey\": api_key,\n            }\n\n            params[\"project_id\"] = project_id\n\n            return WatsonxEmbeddings(**params)\n\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\":\n            if field_value == \"OpenAI\":\n                build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n                build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n                build_config[\"api_key\"][\"required\"] = True\n                build_config[\"api_key\"][\"show\"] = True\n                build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n                build_config[\"api_base\"][\"advanced\"] = True\n                build_config[\"project_id\"][\"show\"] = False\n                build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n            elif field_value == \"Ollama\":\n                build_config[\"model\"][\"options\"] = OLLAMA_EMBEDDING_MODELS\n                build_config[\"model\"][\"value\"] = OLLAMA_EMBEDDING_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n                build_config[\"api_key\"][\"required\"] = False\n                build_config[\"api_key\"][\"show\"] = False\n                build_config[\"api_base\"][\"display_name\"] = \"Ollama Base URL\"\n                build_config[\"api_base\"][\"value\"] = \"http://localhost:11434\"\n                build_config[\"api_base\"][\"advanced\"] = False\n                build_config[\"project_id\"][\"show\"] = False\n                build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n            elif field_value == \"IBM watsonx.ai\":\n                build_config[\"model\"][\"options\"] = WATSONX_EMBEDDING_MODEL_NAMES\n                build_config[\"model\"][\"value\"] = WATSONX_EMBEDDING_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n                build_config[\"api_key\"][\"required\"] = True\n                build_config[\"api_key\"][\"show\"] = True\n                build_config[\"api_base\"][\"show\"] = False\n                build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n                build_config[\"project_id\"][\"show\"] = True\n\n        return build_config\n"
               },
               "dimensions": {
                 "_input_type": "IntInput",

src/lfx/src/lfx/base/models/watsonx_constants.py

@@ -10,3 +10,12 @@
 ]
 
 WATSONX_EMBEDDING_MODEL_NAMES = [metadata["name"] for metadata in WATSONX_EMBEDDING_MODELS_DETAILED]
+
+IBM_WATSONX_URLS = [
+    "https://us-south.ml.cloud.ibm.com",
+    "https://eu-de.ml.cloud.ibm.com",
+    "https://eu-gb.ml.cloud.ibm.com",
+    "https://au-syd.ml.cloud.ibm.com",
+    "https://jp-tok.ml.cloud.ibm.com",
+    "https://ca-tor.ml.cloud.ibm.com",
+]