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+# WeaviateVectorSearchTool
+
+## Description
+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.
+
+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
+
+## Installation
+Install the crewai_tools package by executing the following command in your terminal:
+
+```shell
+uv pip install 'crewai[tools]'
+```
+
+## Example
+To utilize the WeaviateVectorSearchTool for different use cases, follow these examples:
+
+```python
+from crewai_tools import WeaviateVectorSearchTool
+
+# To enable the tool to search any website the agent comes across or learns about during its operation
+tool = WeaviateVectorSearchTool(
+    collection_name='example_collections',
+    limit=3,
+    weaviate_cluster_url="https://your-weaviate-cluster-url.com",
+    weaviate_api_key="your-weaviate-api-key",
+)
+
+# or 
+
+# Setup custom model for vectorizer and generative model
+tool = WeaviateVectorSearchTool(
+    collection_name='example_collections',
+    limit=3,
+    vectorizer=Configure.Vectorizer.text2vec_openai(model="nomic-embed-text"),
+    generative_model=Configure.Generative.openai(model="gpt-4o-mini"),
+    weaviate_cluster_url="https://your-weaviate-cluster-url.com",
+    weaviate_api_key="your-weaviate-api-key",
+)
+
+# 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 WeaviateVectorSearchTool.",
+    llm="gpt-4o-mini",
+    tools=[tool],
+)
+```
+
+## Arguments
+- `collection_name` : The name of the collection to search within. (Required)
+- `weaviate_cluster_url` : The URL of the Weaviate cluster. (Required)
+- `weaviate_api_key` : The API key for the Weaviate cluster. (Required)
+- `limit` : The number of results to return. (Optional)
+- `vectorizer` : The vectorizer to use. (Optional)
+- `generative_model` : The generative model to use. (Optional)
+
+Preloading the Weaviate database with documents:
+
+```python
+from crewai_tools import WeaviateVectorSearchTool
+
+# Use before hooks to generate the documents and add them to the Weaviate database. Follow the weaviate docs: https://weaviate.io/developers/wcs/connect
+test_docs = client.collections.get("example_collections")
+
+
+docs_to_load = os.listdir("knowledge")
+with test_docs.batch.dynamic() as batch:
+    for d in docs_to_load:
+        with open(os.path.join("knowledge", d), "r") as f:
+            content = f.read()
+        batch.add_object(
+            {
+                "content": content,
+                "year": d.split("_")[0],
+            }
+        )
+tool = WeaviateVectorSearchTool(collection_name='example_collections', limit=3)
+
+```