LanceDB

dbt

Facilitates seamless integration between dbt resources and conversational interfaces, allowing for streamlined CLI command execution, effortless exploration of model details, and efficient data manipulation within the Semantic Layer.

Milvus Vector Database

Enhance your search capabilities by linking with the Milvus vector database, facilitating vector search, full-text search, and versatile queries. Ideal for boosting semantic search and streamlining knowledge retrieval processes.

Legion Database

Facilitates the seamless interaction and administration of diverse database platforms including PostgreSQL, MySQL, SQL Server, and BigQuery to support tasks such as data analytics, business insights, and database investigation.

RAG Docs

Enhances information retrieval through semantic search functionality and a vector database (Qdrant), facilitating streamlined access to extensive document repositories.

LlamaCloud

Explore controlled vector indexes for information retrieval purposes.

ChromaDB

Enhance your natural language processing and information retrieval projects with seamless integration of advanced capabilities from ChromaDB vector database. Experience optimized semantic document search, storage, and retrieval functionalities for enhanced efficiency.

FalkorDB

Unleash the power of conversational search on graph databases through the transformation of inquiries into FalkorDB commands. Ideal for exploring relationships within knowledge graphs, recommendation engines, and network datasets.

Pinecone Vector DB

Utilize Pinecone's vector databases to enhance semantic search capabilities and RAG functionality.

VikingDB

Unlock the power of data storage and retrieval with VikingDB vector database solution.

Better Qdrant

Enhance your AI systems with seamless integration to the Qdrant vector database for advanced semantic search functions using diverse embedding services. Streamline document handling and similarity assessments directly within the chat interface for an enhanced user experience.

Milvus (Vector Database)

Enhance the connectivity between artificial intelligence platforms and the Milvus vector database to streamline semantic search, text retrieval, and hybrid data retrieval processes. This integration facilitates swift and accurate similarity matching and data extraction from vector datasets.

OpenSearch

Effortlessly connect to OpenSearch databases for seamless querying, log analysis, document retrieval, and structured data retrieval within the conversation interface without interruption.

Qdrant Vector Database

Enhance your data retrieval with seamless semantic search features by leveraging the Model Context Protocol's integration with the Qdrant vector database. This cutting-edge protocol supports storage and access to data through various embedding sources, offering flexibility in deployment through Docker or local setups.

RAGDocs (Vector Documentation Search)

Unlock the ability to search and retrieve semantic documentation effortlessly through vector databases. Get URL extraction, source oversight, and index queuing, paired with diverse embedding providers such as Ollama and OpenAI.

LanceDB Vector Search

Unlock powerful vector search functions by leveraging LanceDB and Ollama's embedding model to conduct similarity searches within document collections seamlessly, eliminating the need for context switching.