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62 lines
3.1 KiB
Markdown
62 lines
3.1 KiB
Markdown
# CouchbaseFTSVectorSearchTool
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## Description
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Couchbase is a NoSQL database with vector search capabilities. Users can store and query vector embeddings. You can learn more about Couchbase vector search here: https://docs.couchbase.com/cloud/vector-search/vector-search.html
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This tool is specifically crafted for performing semantic search using Couchbase. Use this tool to find semantically similar docs to a given query.
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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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## Setup
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Before instantiating the tool, you need a Couchbase cluster.
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- Create a cluster on [Couchbase Capella](https://docs.couchbase.com/cloud/get-started/create-account.html), Couchbase's cloud database solution.
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- Create a [local Couchbase server](https://docs.couchbase.com/server/current/getting-started/start-here.html).
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You will need to create a bucket, scope and collection on the cluster. Then, [follow this guide](https://docs.couchbase.com/python-sdk/current/hello-world/start-using-sdk.html) to create a Couchbase Cluster object and load documents into your collection.
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Follow the docs below to create a vector search index on Couchbase.
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- [Create a vector search index on Couchbase Capella.](https://docs.couchbase.com/cloud/vector-search/create-vector-search-index-ui.html)
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- [Create a vector search index on your local Couchbase server.](https://docs.couchbase.com/server/current/vector-search/create-vector-search-index-ui.html)
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Ensure that the `Dimension` field in the index matches the embedding model. For example, OpenAI's `text-embedding-3-small` model has an embedding dimension of 1536 dimensions, and so the `Dimension` field must be 1536 in the index.
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## Example
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To utilize the CouchbaseFTSVectorSearchTool for different use cases, follow these examples:
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```python
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from crewai_tools import CouchbaseFTSVectorSearchTool
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# Instantiate a Couchbase Cluster object from the Couchbase SDK
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tool = CouchbaseFTSVectorSearchTool(
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cluster=cluster,
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collection_name="collection",
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scope_name="scope",
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bucket_name="bucket",
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index_name="index",
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embedding_function=embed_fn
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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 CouchbaseFTSVectorSearchTool.",
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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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- `cluster`: An initialized Couchbase `Cluster` instance.
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- `bucket_name`: The name of the Couchbase bucket.
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- `scope_name`: The name of the scope within the bucket.
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- `collection_name`: The name of the collection within the scope.
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- `index_name`: The name of the search index (vector index).
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- `embedding_function`: A function that takes a string and returns its embedding (list of floats).
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- `embedding_key`: Name of the field in the search index storing the vector. (Optional, defaults to 'embedding')
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- `scoped_index`: Whether the index is scoped (True) or cluster-level (False). (Optional, defaults to True)
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- `limit`: The maximum number of search results to return. (Optional, defaults to 3) |