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81 lines
2.7 KiB
Markdown
81 lines
2.7 KiB
Markdown
# WeaviateVectorSearchTool
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## Description
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This tool is specifically crafted for conducting semantic searches within docs within a Weaviate vector database. Use this tool to find semantically similar docs to a given query.
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Weaviate is a vector database that is used to store and query vector embeddings. You can follow their docs here: https://weaviate.io/developers/wcs/connect
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## Installation
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Install the crewai_tools package by executing the following command in your terminal:
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```shell
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uv pip install 'crewai[tools]'
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```
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## Example
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To utilize the WeaviateVectorSearchTool for different use cases, follow these examples:
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```python
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from crewai_tools import WeaviateVectorSearchTool
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# To enable the tool to search any website the agent comes across or learns about during its operation
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tool = WeaviateVectorSearchTool(
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collection_name='example_collections',
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limit=3,
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weaviate_cluster_url="https://your-weaviate-cluster-url.com",
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weaviate_api_key="your-weaviate-api-key",
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)
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# or
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# Setup custom model for vectorizer and generative model
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tool = WeaviateVectorSearchTool(
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collection_name='example_collections',
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limit=3,
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vectorizer=Configure.Vectorizer.text2vec_openai(model="nomic-embed-text"),
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generative_model=Configure.Generative.openai(model="gpt-4o-mini"),
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weaviate_cluster_url="https://your-weaviate-cluster-url.com",
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weaviate_api_key="your-weaviate-api-key",
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)
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# Adding the tool to an agent
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rag_agent = Agent(
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name="rag_agent",
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role="You are a helpful assistant that can answer questions with the help of the WeaviateVectorSearchTool.",
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llm="gpt-4o-mini",
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tools=[tool],
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)
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```
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## Arguments
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- `collection_name` : The name of the collection to search within. (Required)
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- `weaviate_cluster_url` : The URL of the Weaviate cluster. (Required)
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- `weaviate_api_key` : The API key for the Weaviate cluster. (Required)
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- `limit` : The number of results to return. (Optional)
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- `vectorizer` : The vectorizer to use. (Optional)
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- `generative_model` : The generative model to use. (Optional)
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Preloading the Weaviate database with documents:
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```python
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from crewai_tools import WeaviateVectorSearchTool
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# Use before hooks to generate the documents and add them to the Weaviate database. Follow the weaviate docs: https://weaviate.io/developers/wcs/connect
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test_docs = client.collections.get("example_collections")
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docs_to_load = os.listdir("knowledge")
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with test_docs.batch.dynamic() as batch:
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for d in docs_to_load:
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with open(os.path.join("knowledge", d), "r") as f:
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content = f.read()
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batch.add_object(
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{
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"content": content,
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"year": d.split("_")[0],
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}
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)
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tool = WeaviateVectorSearchTool(collection_name='example_collections', limit=3)
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```
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