mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-05-03 16:22:49 +00:00
fix: rag tool embeddings config
* fix: ensure config is not flattened, add tests * chore: refactor inits to model_validator * chore: refactor rag tool config parsing * chore: add initial docs * chore: add additional validation aliases for provider env vars * chore: add solid docs * chore: move imports to top * fix: revert circular import * fix: lazy import qdrant-client * fix: allow collection name config * chore: narrow model names for google * chore: update additional docs * chore: add backward compat on model name aliases * chore: add tests for config changes
This commit is contained in:
Greyson LaLonde
@@ -0,0 +1,10 @@
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from crewai.rag.embeddings.types import ProviderSpec
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from crewai_tools.tools.rag.types import RagToolConfig, VectorDbConfig
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__all__ = [
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"ProviderSpec",
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"RagToolConfig",
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"VectorDbConfig",
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]
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@@ -1,10 +1,74 @@
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from abc import ABC, abstractmethod
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import os
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from typing import Any, cast
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from typing import Any, Literal, cast
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from crewai.rag.embeddings.factory import get_embedding_function
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from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
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from crewai.rag.embeddings.factory import build_embedder
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from crewai.rag.embeddings.types import ProviderSpec
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from crewai.tools import BaseTool
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from pydantic import BaseModel, ConfigDict, Field, model_validator
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from pydantic import (
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BaseModel,
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ConfigDict,
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Field,
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TypeAdapter,
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ValidationError,
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field_validator,
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model_validator,
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)
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from typing_extensions import Self
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from crewai_tools.tools.rag.types import RagToolConfig, VectorDbConfig
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def _validate_embedding_config(
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value: dict[str, Any] | ProviderSpec,
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) -> dict[str, Any] | ProviderSpec:
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"""Validate embedding config and provide clearer error messages for union validation.
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This pre-validator catches Pydantic ValidationErrors from the ProviderSpec union
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and provides a cleaner, more focused error message that only shows the relevant
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provider's validation errors instead of all 18 union members.
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Args:
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value: The embedding configuration dictionary or validated ProviderSpec.
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Returns:
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A validated ProviderSpec instance, or the original value if already validated
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or missing required fields.
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Raises:
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ValueError: If the configuration is invalid for the specified provider.
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"""
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if not isinstance(value, dict):
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return value
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provider = value.get("provider")
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if not provider:
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return value
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try:
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type_adapter: TypeAdapter[ProviderSpec] = TypeAdapter(ProviderSpec)
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return type_adapter.validate_python(value)
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except ValidationError as e:
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provider_key = f"{provider.lower()}providerspec"
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provider_errors = [
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err for err in e.errors() if provider_key in str(err.get("loc", "")).lower()
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]
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if provider_errors:
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error_msgs = []
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for err in provider_errors:
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loc_parts = err["loc"]
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if str(loc_parts[0]).lower() == provider_key:
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loc_parts = loc_parts[1:]
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loc = ".".join(str(x) for x in loc_parts)
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error_msgs.append(f" - {loc}: {err['msg']}")
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raise ValueError(
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f"Invalid configuration for embedding provider '{provider}':\n"
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+ "\n".join(error_msgs)
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) from e
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raise
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class Adapter(BaseModel, ABC):
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@@ -46,139 +110,100 @@ class RagTool(BaseTool):
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summarize: bool = False
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similarity_threshold: float = 0.6
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limit: int = 5
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collection_name: str = "rag_tool_collection"
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adapter: Adapter = Field(default_factory=_AdapterPlaceholder)
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config: Any | None = None
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config: RagToolConfig = Field(
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default_factory=RagToolConfig,
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description="Configuration format accepted by RagTool.",
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)
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@field_validator("config", mode="before")
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@classmethod
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def _validate_config(cls, value: Any) -> Any:
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"""Validate config with improved error messages for embedding providers."""
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if not isinstance(value, dict):
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return value
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embedding_model = value.get("embedding_model")
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if embedding_model:
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try:
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value["embedding_model"] = _validate_embedding_config(embedding_model)
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except ValueError:
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raise
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return value
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@model_validator(mode="after")
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def _set_default_adapter(self):
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def _ensure_adapter(self) -> Self:
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if isinstance(self.adapter, RagTool._AdapterPlaceholder):
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from crewai_tools.adapters.crewai_rag_adapter import CrewAIRagAdapter
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parsed_config = self._parse_config(self.config)
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provider_cfg = self._parse_config(self.config)
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self.adapter = CrewAIRagAdapter(
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collection_name="rag_tool_collection",
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collection_name=self.collection_name,
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summarize=self.summarize,
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similarity_threshold=self.similarity_threshold,
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limit=self.limit,
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config=parsed_config,
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config=provider_cfg,
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)
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return self
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def _parse_config(self, config: Any) -> Any:
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"""Parse complex config format to extract provider-specific config.
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def _parse_config(self, config: RagToolConfig) -> Any:
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"""Normalize the RagToolConfig into a provider-specific config object.
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Raises:
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ValueError: If the config format is invalid or uses unsupported providers.
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Defaults to 'chromadb' with no extra provider config if none is supplied.
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"""
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if config is None:
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return None
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if not config:
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return self._create_provider_config("chromadb", {}, None)
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if isinstance(config, dict) and "provider" in config:
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return config
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vectordb_cfg = cast(VectorDbConfig, config.get("vectordb", {}))
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provider: Literal["chromadb", "qdrant"] = vectordb_cfg.get(
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"provider", "chromadb"
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)
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provider_config: dict[str, Any] = vectordb_cfg.get("config", {})
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if isinstance(config, dict):
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if "vectordb" in config:
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vectordb_config = config["vectordb"]
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if isinstance(vectordb_config, dict) and "provider" in vectordb_config:
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provider = vectordb_config["provider"]
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provider_config = vectordb_config.get("config", {})
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supported = ("chromadb", "qdrant")
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if provider not in supported:
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raise ValueError(
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f"Unsupported vector database provider: '{provider}'. "
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f"CrewAI RAG currently supports: {', '.join(supported)}."
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)
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supported_providers = ["chromadb", "qdrant"]
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if provider not in supported_providers:
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raise ValueError(
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f"Unsupported vector database provider: '{provider}'. "
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f"CrewAI RAG currently supports: {', '.join(supported_providers)}."
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)
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embedding_spec: ProviderSpec | None = config.get("embedding_model")
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if embedding_spec:
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embedding_spec = cast(
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ProviderSpec, _validate_embedding_config(embedding_spec)
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)
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embedding_config = config.get("embedding_model")
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embedding_function = None
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if embedding_config and isinstance(embedding_config, dict):
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embedding_function = self._create_embedding_function(
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embedding_config, provider
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)
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return self._create_provider_config(
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provider, provider_config, embedding_function
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)
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return None
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embedding_config = config.get("embedding_model")
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embedding_function = None
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if embedding_config and isinstance(embedding_config, dict):
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embedding_function = self._create_embedding_function(
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embedding_config, "chromadb"
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)
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return self._create_provider_config("chromadb", {}, embedding_function)
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return config
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@staticmethod
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def _create_embedding_function(embedding_config: dict, provider: str) -> Any:
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"""Create embedding function for the specified vector database provider."""
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embedding_provider = embedding_config.get("provider")
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embedding_model_config = embedding_config.get("config", {}).copy()
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if "model" in embedding_model_config:
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embedding_model_config["model_name"] = embedding_model_config.pop("model")
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factory_config = {"provider": embedding_provider, **embedding_model_config}
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if embedding_provider == "openai" and "api_key" not in factory_config:
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api_key = os.getenv("OPENAI_API_KEY")
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if api_key:
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factory_config["api_key"] = api_key
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if provider == "chromadb":
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return get_embedding_function(factory_config) # type: ignore[call-overload]
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if provider == "qdrant":
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chromadb_func = get_embedding_function(factory_config) # type: ignore[call-overload]
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def qdrant_embed_fn(text: str) -> list[float]:
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"""Embed text using ChromaDB function and convert to list of floats for Qdrant.
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Args:
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text: The input text to embed.
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Returns:
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A list of floats representing the embedding.
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"""
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embeddings = chromadb_func([text])
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return embeddings[0] if embeddings and len(embeddings) > 0 else []
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return cast(Any, qdrant_embed_fn)
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return None
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embedding_function = build_embedder(embedding_spec) if embedding_spec else None
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return self._create_provider_config(
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provider, provider_config, embedding_function
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)
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@staticmethod
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def _create_provider_config(
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provider: str, provider_config: dict, embedding_function: Any
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provider: Literal["chromadb", "qdrant"],
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provider_config: dict[str, Any],
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embedding_function: EmbeddingFunction[Any] | None,
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) -> Any:
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"""Create proper provider config object."""
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"""Instantiate provider config with optional embedding_function injected."""
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if provider == "chromadb":
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from crewai.rag.chromadb.config import ChromaDBConfig
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config_kwargs = {}
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if embedding_function:
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config_kwargs["embedding_function"] = embedding_function
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config_kwargs.update(provider_config)
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return ChromaDBConfig(**config_kwargs)
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kwargs = dict(provider_config)
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if embedding_function is not None:
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kwargs["embedding_function"] = embedding_function
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return ChromaDBConfig(**kwargs)
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if provider == "qdrant":
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from crewai.rag.qdrant.config import QdrantConfig
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config_kwargs = {}
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if embedding_function:
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config_kwargs["embedding_function"] = embedding_function
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kwargs = dict(provider_config)
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if embedding_function is not None:
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kwargs["embedding_function"] = embedding_function
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return QdrantConfig(**kwargs)
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config_kwargs.update(provider_config)
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return QdrantConfig(**config_kwargs)
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return None
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raise ValueError(f"Unhandled provider: {provider}")
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def add(
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self,
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32
lib/crewai-tools/src/crewai_tools/tools/rag/types.py
Normal file
32
lib/crewai-tools/src/crewai_tools/tools/rag/types.py
Normal file
@@ -0,0 +1,32 @@
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"""Type definitions for RAG tool configuration."""
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from typing import Any, Literal
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from crewai.rag.embeddings.types import ProviderSpec
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from typing_extensions import TypedDict
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class VectorDbConfig(TypedDict):
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"""Configuration for vector database provider.
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Attributes:
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provider: RAG provider literal.
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config: RAG configuration options.
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"""
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provider: Literal["chromadb", "qdrant"]
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config: dict[str, Any]
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class RagToolConfig(TypedDict, total=False):
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"""Configuration accepted by RAG tools.
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Supports embedding model and vector database configuration.
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Attributes:
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embedding_model: Embedding model configuration accepted by RAG tools.
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vectordb: Vector database configuration accepted by RAG tools.
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"""
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embedding_model: ProviderSpec
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vectordb: VectorDbConfig
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