The WeaviateVectorSearchTool
is designed to search a Weaviate vector database for semantically similar documents.
WeaviateVectorSearchTool
is specifically crafted for conducting semantic searches within documents stored in a Weaviate vector database. This tool allows you to find semantically similar documents to a given query, leveraging the power of vector embeddings for more accurate and contextually relevant search results.
Weaviate is a vector database that stores and queries vector embeddings, enabling semantic search capabilities.
WeaviateVectorSearchTool
, follow these steps:
crewai[tools]
and weaviate-client
packages are installed in your Python environment.OPENAI_API_KEY
.WeaviateVectorSearchTool
accepts the following parameters:
3
.text2vec_openai
with the nomic-embed-text
model.gpt-4o
.WeaviateVectorSearchTool
with a CrewAI agent:
WeaviateVectorSearchTool
provides a powerful way to search for semantically similar documents in a Weaviate vector database. By leveraging vector embeddings, it enables more accurate and contextually relevant search results compared to traditional keyword-based searches. This tool is particularly useful for applications that require finding information based on meaning rather than exact matches.