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+# CouchbaseFTSVectorSearchTool
+## Description
+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 
+
+This tool is specifically crafted for performing semantic search using Couchbase. Use this tool to find semantically similar docs to a given query.
+
+## Installation
+Install the crewai_tools package by executing the following command in your terminal:
+
+```shell
+uv pip install 'crewai[tools]'
+```
+
+## Setup
+Before instantiating the tool, you need a Couchbase cluster. 
+- Create a cluster on [Couchbase Capella](https://docs.couchbase.com/cloud/get-started/create-account.html), Couchbase's cloud database solution.
+- Create a [local Couchbase server](https://docs.couchbase.com/server/current/getting-started/start-here.html). 
+
+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.
+
+Follow the docs below to create a vector search index on Couchbase.
+- [Create a vector search index on Couchbase Capella.](https://docs.couchbase.com/cloud/vector-search/create-vector-search-index-ui.html)
+- [Create a vector search index on your local Couchbase server.](https://docs.couchbase.com/server/current/vector-search/create-vector-search-index-ui.html)
+
+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.
+
+## Example
+To utilize the CouchbaseFTSVectorSearchTool for different use cases, follow these examples:
+
+```python
+from crewai_tools import CouchbaseFTSVectorSearchTool
+
+# Instantiate a Couchbase Cluster object from the Couchbase SDK
+
+tool = CouchbaseFTSVectorSearchTool(
+    cluster=cluster,
+    collection_name="collection",
+    scope_name="scope",
+    bucket_name="bucket",
+    index_name="index",
+    embedding_function=embed_fn
+)
+
+# Adding the tool to an agent
+rag_agent = Agent(
+    name="rag_agent",
+    role="You are a helpful assistant that can answer questions with the help of the CouchbaseFTSVectorSearchTool.",
+    llm="gpt-4o-mini",
+    tools=[tool],
+)
+```
+
+## Arguments
+- `cluster`: An initialized Couchbase `Cluster` instance. 
+- `bucket_name`: The name of the Couchbase bucket. 
+- `scope_name`: The name of the scope within the bucket. 
+- `collection_name`: The name of the collection within the scope. 
+- `index_name`: The name of the search index (vector index). 
+- `embedding_function`: A function that takes a string and returns its embedding (list of floats). 
+- `embedding_key`: Name of the field in the search index storing the vector. (Optional, defaults to 'embedding')
+- `scoped_index`: Whether the index is scoped (True) or cluster-level (False). (Optional, defaults to True)
+- `limit`: The maximum number of search results to return. (Optional, defaults to 3)