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fix: rag tool embeddings config

Browse Source 
* 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
2025-11-24 16:51:28 -05:00
committed by GitHub
parent 9c84475691
commit a928cde6ee
46 changed files with 1850 additions and 291 deletions
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8
docs/en/concepts/knowledge.mdx
View File

@@ -388,8 +388,8 @@ crew = Crew(
agents=[sales_agent, tech_agent, support_agent],
tasks=[...],
embedder={ # Fallback embedder for agents without their own
"provider": "google",
"config": {"model": "text-embedding-004"}
"provider": "google-generativeai",
"config": {"model_name": "gemini-embedding-001"}
}
)
@@ -629,9 +629,9 @@ agent = Agent(
backstory="Expert researcher",
knowledge_sources=[knowledge_source],
embedder={
"provider": "google",
"provider": "google-generativeai",
"config": {
"model": "models/text-embedding-004",
"model_name": "gemini-embedding-001",
"api_key": "your-google-key"
}
}

18
docs/en/concepts/memory.mdx
View File

@@ -341,7 +341,7 @@ crew = Crew(
embedder={
"provider": "openai",
"config": {
"model": "text-embedding-3-small" # or "text-embedding-3-large"
"model_name": "text-embedding-3-small" # or "text-embedding-3-large"
}
}
)
@@ -353,7 +353,7 @@ crew = Crew(
"provider": "openai",
"config": {
"api_key": "your-openai-api-key", # Optional: override env var
"model": "text-embedding-3-large",
"model_name": "text-embedding-3-large",
"dimensions": 1536, # Optional: reduce dimensions for smaller storage
"organization_id": "your-org-id" # Optional: for organization accounts
}
@@ -375,7 +375,7 @@ crew = Crew(
"api_base": "https://your-resource.openai.azure.com/",
"api_type": "azure",
"api_version": "2023-05-15",
"model": "text-embedding-3-small",
"model_name": "text-embedding-3-small",
"deployment_id": "your-deployment-name" # Azure deployment name
}
}
@@ -390,10 +390,10 @@ Use Google's text embedding models for integration with Google Cloud services.
crew = Crew(
memory=True,
embedder={
"provider": "google",
"provider": "google-generativeai",
"config": {
"api_key": "your-google-api-key",
"model": "text-embedding-004" # or "text-embedding-preview-0409"
"model_name": "gemini-embedding-001" # or "text-embedding-005", "text-multilingual-embedding-002"
}
}
)
@@ -461,7 +461,7 @@ crew = Crew(
"provider": "cohere",
"config": {
"api_key": "your-cohere-api-key",
"model": "embed-english-v3.0" # or "embed-multilingual-v3.0"
"model_name": "embed-english-v3.0" # or "embed-multilingual-v3.0"
}
}
)
@@ -478,7 +478,7 @@ crew = Crew(
"provider": "voyageai",
"config": {
"api_key": "your-voyage-api-key",
"model": "voyage-large-2", # or "voyage-code-2" for code
"model": "voyage-3", # or "voyage-3-lite", "voyage-code-3"
"input_type": "document" # or "query"
}
}
@@ -912,10 +912,10 @@ crew = Crew(
crew = Crew(
memory=True,
embedder={
"provider": "google",
"provider": "google-generativeai",
"config": {
"api_key": "your-api-key",
"model": "text-embedding-004"
"model_name": "gemini-embedding-001"
}
}
)

528
docs/en/tools/ai-ml/ragtool.mdx
View File

@@ -77,7 +77,7 @@ The `RagTool` accepts the following parameters:
- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
- **adapter**: Optional. A custom adapter for the knowledge base. If not provided, a CrewAIRagAdapter will be used.
- **config**: Optional. Configuration for the underlying CrewAI RAG system.
- **config**: Optional. Configuration for the underlying CrewAI RAG system. Accepts a `RagToolConfig` TypedDict with optional `embedding_model` (ProviderSpec) and `vectordb` (VectorDbConfig) keys. All configuration values provided programmatically take precedence over environment variables.
## Adding Content
@@ -127,26 +127,528 @@ You can customize the behavior of the `RagTool` by providing a configuration dic
```python Code
from crewai_tools import RagTool
from crewai_tools.tools.rag import RagToolConfig, VectorDbConfig, ProviderSpec
# Create a RAG tool with custom configuration
config = {
"vectordb": {
"provider": "qdrant",
"config": {
"collection_name": "my-collection"
}
},
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small"
}
vectordb: VectorDbConfig = {
"provider": "qdrant",
"config": {
"collection_name": "my-collection"
}
}
embedding_model: ProviderSpec = {
"provider": "openai",
"config": {
"model_name": "text-embedding-3-small"
}
}
config: RagToolConfig = {
"vectordb": vectordb,
"embedding_model": embedding_model
}
rag_tool = RagTool(config=config, summarize=True)
```
## Embedding Model Configuration
The `embedding_model` parameter accepts a `crewai.rag.embeddings.types.ProviderSpec` dictionary with the structure:
```python
{
"provider": "provider-name", # Required
"config": { # Optional
# Provider-specific configuration
}
}
```
### Supported Providers
<AccordionGroup>
<Accordion title="OpenAI">
```python main.py
from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec
embedding_model: OpenAIProviderSpec = {
"provider": "openai",
"config": {
"api_key": "your-api-key",
"model_name": "text-embedding-ada-002",
"dimensions": 1536,
"organization_id": "your-org-id",
"api_base": "https://api.openai.com/v1",
"api_version": "v1",
"default_headers": {"Custom-Header": "value"}
}
}
```
**Config Options:**
- `api_key` (str): OpenAI API key
- `model_name` (str): Model to use. Default: `text-embedding-ada-002`. Options: `text-embedding-3-small`, `text-embedding-3-large`, `text-embedding-ada-002`
- `dimensions` (int): Number of dimensions for the embedding
- `organization_id` (str): OpenAI organization ID
- `api_base` (str): Custom API base URL
- `api_version` (str): API version
- `default_headers` (dict): Custom headers for API requests
**Environment Variables:**
- `OPENAI_API_KEY` or `EMBEDDINGS_OPENAI_API_KEY`: `api_key`
- `OPENAI_ORGANIZATION_ID` or `EMBEDDINGS_OPENAI_ORGANIZATION_ID`: `organization_id`
- `OPENAI_MODEL_NAME` or `EMBEDDINGS_OPENAI_MODEL_NAME`: `model_name`
- `OPENAI_API_BASE` or `EMBEDDINGS_OPENAI_API_BASE`: `api_base`
- `OPENAI_API_VERSION` or `EMBEDDINGS_OPENAI_API_VERSION`: `api_version`
- `OPENAI_DIMENSIONS` or `EMBEDDINGS_OPENAI_DIMENSIONS`: `dimensions`
</Accordion>
<Accordion title="Cohere">
```python main.py
from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec
embedding_model: CohereProviderSpec = {
"provider": "cohere",
"config": {
"api_key": "your-api-key",
"model_name": "embed-english-v3.0"
}
}
```
**Config Options:**
- `api_key` (str): Cohere API key
- `model_name` (str): Model to use. Default: `large`. Options: `embed-english-v3.0`, `embed-multilingual-v3.0`, `large`, `small`
**Environment Variables:**
- `COHERE_API_KEY` or `EMBEDDINGS_COHERE_API_KEY`: `api_key`
- `EMBEDDINGS_COHERE_MODEL_NAME`: `model_name`
</Accordion>
<Accordion title="VoyageAI">
```python main.py
from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec
embedding_model: VoyageAIProviderSpec = {
"provider": "voyageai",
"config": {
"api_key": "your-api-key",
"model": "voyage-3",
"input_type": "document",
"truncation": True,
"output_dtype": "float32",
"output_dimension": 1024,
"max_retries": 3,
"timeout": 60.0
}
}
```
**Config Options:**
- `api_key` (str): VoyageAI API key
- `model` (str): Model to use. Default: `voyage-2`. Options: `voyage-3`, `voyage-3-lite`, `voyage-code-3`, `voyage-large-2`
- `input_type` (str): Type of input. Options: `document` (for storage), `query` (for search)
- `truncation` (bool): Whether to truncate inputs that exceed max length. Default: `True`
- `output_dtype` (str): Output data type
- `output_dimension` (int): Dimension of output embeddings
- `max_retries` (int): Maximum number of retry attempts. Default: `0`
- `timeout` (float): Request timeout in seconds
**Environment Variables:**
- `VOYAGEAI_API_KEY` or `EMBEDDINGS_VOYAGEAI_API_KEY`: `api_key`
- `VOYAGEAI_MODEL` or `EMBEDDINGS_VOYAGEAI_MODEL`: `model`
- `VOYAGEAI_INPUT_TYPE` or `EMBEDDINGS_VOYAGEAI_INPUT_TYPE`: `input_type`
- `VOYAGEAI_TRUNCATION` or `EMBEDDINGS_VOYAGEAI_TRUNCATION`: `truncation`
- `VOYAGEAI_OUTPUT_DTYPE` or `EMBEDDINGS_VOYAGEAI_OUTPUT_DTYPE`: `output_dtype`
- `VOYAGEAI_OUTPUT_DIMENSION` or `EMBEDDINGS_VOYAGEAI_OUTPUT_DIMENSION`: `output_dimension`
- `VOYAGEAI_MAX_RETRIES` or `EMBEDDINGS_VOYAGEAI_MAX_RETRIES`: `max_retries`
- `VOYAGEAI_TIMEOUT` or `EMBEDDINGS_VOYAGEAI_TIMEOUT`: `timeout`
</Accordion>
<Accordion title="Ollama">
```python main.py
from crewai.rag.embeddings.providers.ollama.types import OllamaProviderSpec
embedding_model: OllamaProviderSpec = {
"provider": "ollama",
"config": {
"model_name": "llama2",
"url": "http://localhost:11434/api/embeddings"
}
}
```
**Config Options:**
- `model_name` (str): Ollama model name (e.g., `llama2`, `mistral`, `nomic-embed-text`)
- `url` (str): Ollama API endpoint URL. Default: `http://localhost:11434/api/embeddings`
**Environment Variables:**
- `OLLAMA_MODEL` or `EMBEDDINGS_OLLAMA_MODEL`: `model_name`
- `OLLAMA_URL` or `EMBEDDINGS_OLLAMA_URL`: `url`
</Accordion>
<Accordion title="Amazon Bedrock">
```python main.py
from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
embedding_model: BedrockProviderSpec = {
"provider": "amazon-bedrock",
"config": {
"model_name": "amazon.titan-embed-text-v2:0",
"session": boto3_session
}
}
```
**Config Options:**
- `model_name` (str): Bedrock model ID. Default: `amazon.titan-embed-text-v1`. Options: `amazon.titan-embed-text-v1`, `amazon.titan-embed-text-v2:0`, `cohere.embed-english-v3`, `cohere.embed-multilingual-v3`
- `session` (Any): Boto3 session object for AWS authentication
**Environment Variables:**
- `AWS_ACCESS_KEY_ID`: AWS access key
- `AWS_SECRET_ACCESS_KEY`: AWS secret key
- `AWS_REGION`: AWS region (e.g., `us-east-1`)
</Accordion>
<Accordion title="Azure OpenAI">
```python main.py
from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec
embedding_model: AzureProviderSpec = {
"provider": "azure",
"config": {
"deployment_id": "your-deployment-id",
"api_key": "your-api-key",
"api_base": "https://your-resource.openai.azure.com",
"api_version": "2024-02-01",
"model_name": "text-embedding-ada-002",
"api_type": "azure"
}
}
```
**Config Options:**
- `deployment_id` (str): **Required** - Azure OpenAI deployment ID
- `api_key` (str): Azure OpenAI API key
- `api_base` (str): Azure OpenAI resource endpoint
- `api_version` (str): API version. Example: `2024-02-01`
- `model_name` (str): Model name. Default: `text-embedding-ada-002`
- `api_type` (str): API type. Default: `azure`
- `dimensions` (int): Output dimensions
- `default_headers` (dict): Custom headers
**Environment Variables:**
- `AZURE_OPENAI_API_KEY` or `EMBEDDINGS_AZURE_API_KEY`: `api_key`
- `AZURE_OPENAI_ENDPOINT` or `EMBEDDINGS_AZURE_API_BASE`: `api_base`
- `EMBEDDINGS_AZURE_DEPLOYMENT_ID`: `deployment_id`
- `EMBEDDINGS_AZURE_API_VERSION`: `api_version`
- `EMBEDDINGS_AZURE_MODEL_NAME`: `model_name`
- `EMBEDDINGS_AZURE_API_TYPE`: `api_type`
- `EMBEDDINGS_AZURE_DIMENSIONS`: `dimensions`
</Accordion>
<Accordion title="Google Generative AI">
```python main.py
from crewai.rag.embeddings.providers.google.types import GenerativeAiProviderSpec
embedding_model: GenerativeAiProviderSpec = {
"provider": "google-generativeai",
"config": {
"api_key": "your-api-key",
"model_name": "gemini-embedding-001",
"task_type": "RETRIEVAL_DOCUMENT"
}
}
```
**Config Options:**
- `api_key` (str): Google AI API key
- `model_name` (str): Model name. Default: `gemini-embedding-001`. Options: `gemini-embedding-001`, `text-embedding-005`, `text-multilingual-embedding-002`
- `task_type` (str): Task type for embeddings. Default: `RETRIEVAL_DOCUMENT`. Options: `RETRIEVAL_DOCUMENT`, `RETRIEVAL_QUERY`
**Environment Variables:**
- `GOOGLE_API_KEY`, `GEMINI_API_KEY`, or `EMBEDDINGS_GOOGLE_API_KEY`: `api_key`
- `EMBEDDINGS_GOOGLE_GENERATIVE_AI_MODEL_NAME`: `model_name`
- `EMBEDDINGS_GOOGLE_GENERATIVE_AI_TASK_TYPE`: `task_type`
</Accordion>
<Accordion title="Google Vertex AI">
```python main.py
from crewai.rag.embeddings.providers.google.types import VertexAIProviderSpec
embedding_model: VertexAIProviderSpec = {
"provider": "google-vertex",
"config": {
"model_name": "text-embedding-004",
"project_id": "your-project-id",
"region": "us-central1",
"api_key": "your-api-key"
}
}
```
**Config Options:**
- `model_name` (str): Model name. Default: `textembedding-gecko`. Options: `text-embedding-004`, `textembedding-gecko`, `textembedding-gecko-multilingual`
- `project_id` (str): Google Cloud project ID. Default: `cloud-large-language-models`
- `region` (str): Google Cloud region. Default: `us-central1`
- `api_key` (str): API key for authentication
**Environment Variables:**
- `GOOGLE_APPLICATION_CREDENTIALS`: Path to service account JSON file
- `GOOGLE_CLOUD_PROJECT` or `EMBEDDINGS_GOOGLE_VERTEX_PROJECT_ID`: `project_id`
- `EMBEDDINGS_GOOGLE_VERTEX_MODEL_NAME`: `model_name`
- `EMBEDDINGS_GOOGLE_VERTEX_REGION`: `region`
- `EMBEDDINGS_GOOGLE_VERTEX_API_KEY`: `api_key`
</Accordion>
<Accordion title="Jina AI">
```python main.py
from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec
embedding_model: JinaProviderSpec = {
"provider": "jina",
"config": {
"api_key": "your-api-key",
"model_name": "jina-embeddings-v3"
}
}
```
**Config Options:**
- `api_key` (str): Jina AI API key
- `model_name` (str): Model name. Default: `jina-embeddings-v2-base-en`. Options: `jina-embeddings-v3`, `jina-embeddings-v2-base-en`, `jina-embeddings-v2-small-en`
**Environment Variables:**
- `JINA_API_KEY` or `EMBEDDINGS_JINA_API_KEY`: `api_key`
- `EMBEDDINGS_JINA_MODEL_NAME`: `model_name`
</Accordion>
<Accordion title="HuggingFace">
```python main.py
from crewai.rag.embeddings.providers.huggingface.types import HuggingFaceProviderSpec
embedding_model: HuggingFaceProviderSpec = {
"provider": "huggingface",
"config": {
"url": "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
}
}
```
**Config Options:**
- `url` (str): Full URL to HuggingFace inference API endpoint
**Environment Variables:**
- `HUGGINGFACE_URL` or `EMBEDDINGS_HUGGINGFACE_URL`: `url`
</Accordion>
<Accordion title="Instructor">
```python main.py
from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec
embedding_model: InstructorProviderSpec = {
"provider": "instructor",
"config": {
"model_name": "hkunlp/instructor-xl",
"device": "cuda",
"instruction": "Represent the document"
}
}
```
**Config Options:**
- `model_name` (str): HuggingFace model ID. Default: `hkunlp/instructor-base`. Options: `hkunlp/instructor-xl`, `hkunlp/instructor-large`, `hkunlp/instructor-base`
- `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`, `mps`
- `instruction` (str): Instruction prefix for embeddings
**Environment Variables:**
- `EMBEDDINGS_INSTRUCTOR_MODEL_NAME`: `model_name`
- `EMBEDDINGS_INSTRUCTOR_DEVICE`: `device`
- `EMBEDDINGS_INSTRUCTOR_INSTRUCTION`: `instruction`
</Accordion>
<Accordion title="Sentence Transformer">
```python main.py
from crewai.rag.embeddings.providers.sentence_transformer.types import SentenceTransformerProviderSpec
embedding_model: SentenceTransformerProviderSpec = {
"provider": "sentence-transformer",
"config": {
"model_name": "all-mpnet-base-v2",
"device": "cuda",
"normalize_embeddings": True
}
}
```
**Config Options:**
- `model_name` (str): Sentence Transformers model name. Default: `all-MiniLM-L6-v2`. Options: `all-mpnet-base-v2`, `all-MiniLM-L6-v2`, `paraphrase-multilingual-MiniLM-L12-v2`
- `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`, `mps`
- `normalize_embeddings` (bool): Whether to normalize embeddings. Default: `False`
**Environment Variables:**
- `EMBEDDINGS_SENTENCE_TRANSFORMER_MODEL_NAME`: `model_name`
- `EMBEDDINGS_SENTENCE_TRANSFORMER_DEVICE`: `device`
- `EMBEDDINGS_SENTENCE_TRANSFORMER_NORMALIZE_EMBEDDINGS`: `normalize_embeddings`
</Accordion>
<Accordion title="ONNX">
```python main.py
from crewai.rag.embeddings.providers.onnx.types import ONNXProviderSpec
embedding_model: ONNXProviderSpec = {
"provider": "onnx",
"config": {
"preferred_providers": ["CUDAExecutionProvider", "CPUExecutionProvider"]
}
}
```
**Config Options:**
- `preferred_providers` (list[str]): List of ONNX execution providers in order of preference
**Environment Variables:**
- `EMBEDDINGS_ONNX_PREFERRED_PROVIDERS`: `preferred_providers` (comma-separated list)
</Accordion>
<Accordion title="OpenCLIP">
```python main.py
from crewai.rag.embeddings.providers.openclip.types import OpenCLIPProviderSpec
embedding_model: OpenCLIPProviderSpec = {
"provider": "openclip",
"config": {
"model_name": "ViT-B-32",
"checkpoint": "laion2b_s34b_b79k",
"device": "cuda"
}
}
```
**Config Options:**
- `model_name` (str): OpenCLIP model architecture. Default: `ViT-B-32`. Options: `ViT-B-32`, `ViT-B-16`, `ViT-L-14`
- `checkpoint` (str): Pretrained checkpoint name. Default: `laion2b_s34b_b79k`. Options: `laion2b_s34b_b79k`, `laion400m_e32`, `openai`
- `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`
**Environment Variables:**
- `EMBEDDINGS_OPENCLIP_MODEL_NAME`: `model_name`
- `EMBEDDINGS_OPENCLIP_CHECKPOINT`: `checkpoint`
- `EMBEDDINGS_OPENCLIP_DEVICE`: `device`
</Accordion>
<Accordion title="Text2Vec">
```python main.py
from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec
embedding_model: Text2VecProviderSpec = {
"provider": "text2vec",
"config": {
"model_name": "shibing624/text2vec-base-multilingual"
}
}
```
**Config Options:**
- `model_name` (str): Text2Vec model name from HuggingFace. Default: `shibing624/text2vec-base-chinese`. Options: `shibing624/text2vec-base-multilingual`, `shibing624/text2vec-base-chinese`
**Environment Variables:**
- `EMBEDDINGS_TEXT2VEC_MODEL_NAME`: `model_name`
</Accordion>
<Accordion title="Roboflow">
```python main.py
from crewai.rag.embeddings.providers.roboflow.types import RoboflowProviderSpec
embedding_model: RoboflowProviderSpec = {
"provider": "roboflow",
"config": {
"api_key": "your-api-key",
"api_url": "https://infer.roboflow.com"
}
}
```
**Config Options:**
- `api_key` (str): Roboflow API key. Default: `""` (empty string)
- `api_url` (str): Roboflow inference API URL. Default: `https://infer.roboflow.com`
**Environment Variables:**
- `ROBOFLOW_API_KEY` or `EMBEDDINGS_ROBOFLOW_API_KEY`: `api_key`
- `ROBOFLOW_API_URL` or `EMBEDDINGS_ROBOFLOW_API_URL`: `api_url`
</Accordion>
<Accordion title="WatsonX (IBM)">
```python main.py
from crewai.rag.embeddings.providers.ibm.types import WatsonXProviderSpec
embedding_model: WatsonXProviderSpec = {
"provider": "watsonx",
"config": {
"model_id": "ibm/slate-125m-english-rtrvr",
"url": "https://us-south.ml.cloud.ibm.com",
"api_key": "your-api-key",
"project_id": "your-project-id",
"batch_size": 100,
"concurrency_limit": 10,
"persistent_connection": True
}
}
```
**Config Options:**
- `model_id` (str): WatsonX model identifier
- `url` (str): WatsonX API endpoint
- `api_key` (str): IBM Cloud API key
- `project_id` (str): WatsonX project ID
- `space_id` (str): WatsonX space ID (alternative to project_id)
- `batch_size` (int): Batch size for embeddings. Default: `100`
- `concurrency_limit` (int): Maximum concurrent requests. Default: `10`
- `persistent_connection` (bool): Use persistent connections. Default: `True`
- Plus 20+ additional authentication and configuration options
**Environment Variables:**
- `WATSONX_API_KEY` or `EMBEDDINGS_WATSONX_API_KEY`: `api_key`
- `WATSONX_URL` or `EMBEDDINGS_WATSONX_URL`: `url`
- `WATSONX_PROJECT_ID` or `EMBEDDINGS_WATSONX_PROJECT_ID`: `project_id`
- `EMBEDDINGS_WATSONX_MODEL_ID`: `model_id`
- `EMBEDDINGS_WATSONX_SPACE_ID`: `space_id`
- `EMBEDDINGS_WATSONX_BATCH_SIZE`: `batch_size`
- `EMBEDDINGS_WATSONX_CONCURRENCY_LIMIT`: `concurrency_limit`
- `EMBEDDINGS_WATSONX_PERSISTENT_CONNECTION`: `persistent_connection`
</Accordion>
<Accordion title="Custom">
```python main.py
from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec
class MyEmbeddingFunction(EmbeddingFunction):
def __call__(self, input):
# Your custom embedding logic
return embeddings
embedding_model: CustomProviderSpec = {
"provider": "custom",
"config": {
"embedding_callable": MyEmbeddingFunction
}
}
```
**Config Options:**
- `embedding_callable` (type[EmbeddingFunction]): Custom embedding function class
**Note:** Custom embedding functions must implement the `EmbeddingFunction` protocol defined in `crewai.rag.core.base_embeddings_callable`. The `__call__` method should accept input data and return embeddings as a list of numpy arrays (or compatible format that will be normalized). The returned embeddings are automatically normalized and validated.
</Accordion>
</AccordionGroup>
### Notes
- All config fields are optional unless marked as **Required**
- API keys can typically be provided via environment variables instead of config
- Default values are shown where applicable
## Conclusion
The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.

6
docs/en/tools/database-data/mysqltool.mdx
View File

@@ -58,10 +58,10 @@ tool = MySQLSearchTool(
),
),
embedder=dict(
provider="google",
provider="google-generativeai",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/database-data/pgsearchtool.mdx
View File

@@ -71,10 +71,10 @@ tool = PGSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/file-document/jsonsearchtool.mdx
View File

@@ -64,10 +64,10 @@ tool = JSONSearchTool(
},
},
"embedding_model": {
"provider": "google", # or openai, ollama, ...
"provider": "google-generativeai", # or openai, ollama, ...
"config": {
"model": "models/embedding-001",
"task_type": "retrieval_document",
"model_name": "gemini-embedding-001",
"task_type": "RETRIEVAL_DOCUMENT",
# Further customization options can be added here.
},
},

8
docs/en/tools/file-document/pdfsearchtool.mdx
View File

@@ -63,15 +63,15 @@ tool = PDFSearchTool(
"config": {
# Model identifier for the chosen provider. "model" will be auto-mapped to "model_name" internally.
"model": "text-embedding-3-small",
# Optional: API key. If omitted, the tool will use provider-specific env vars when available
# (e.g., OPENAI_API_KEY for provider="openai").
# Optional: API key. If omitted, the tool will use provider-specific env vars
# (e.g., OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY for OpenAI).
# "api_key": "sk-...",
# Provider-specific examples:
# --- Google Generative AI ---
# (Set provider="google-generativeai" above)
# "model": "models/embedding-001",
# "task_type": "retrieval_document",
# "model_name": "gemini-embedding-001",
# "task_type": "RETRIEVAL_DOCUMENT",
# "title": "Embeddings",
# --- Cohere ---

4
docs/en/tools/file-document/txtsearchtool.mdx
View File

@@ -66,9 +66,9 @@ tool = TXTSearchTool(
"provider": "openai", # or google-generativeai, cohere, ollama, ...
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...", # optional if env var is set
# "api_key": "sk-...", # optional if env var is set (e.g., OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY)
# Provider examples:
# Google → model: "models/embedding-001", task_type: "retrieval_document"
# Google → model_name: "gemini-embedding-001", task_type: "RETRIEVAL_DOCUMENT"
# Cohere → model: "embed-english-v3.0"
# Ollama → model: "nomic-embed-text"
},

6
docs/en/tools/search-research/codedocssearchtool.mdx
View File

@@ -73,10 +73,10 @@ tool = CodeDocsSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/search-research/githubsearchtool.mdx
View File

@@ -75,10 +75,10 @@ tool = GithubSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/search-research/websitesearchtool.mdx
View File

@@ -66,10 +66,10 @@ tool = WebsiteSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/search-research/youtubechannelsearchtool.mdx
View File

@@ -106,10 +106,10 @@ youtube_channel_tool = YoutubeChannelSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/search-research/youtubevideosearchtool.mdx
View File

@@ -108,10 +108,10 @@ youtube_search_tool = YoutubeVideoSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),