PDF Search

GraphRAG

Enhance your document search experience with a potent combination of Neo4j graph database and Qdrant vector database. Uncover semantic connections and expand structural context by seamlessly following relationships.

RAG Docs

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

RAG Documentation

Experience advanced knowledge access with seamless integration of Qdrant vector search and documentation retrieval in Model Context Protocol (MCP). Unlock context-aware responses and enable semantic querying for a richer user experience.

Qdrant with OpenAI Embeddings

Unlock the potential of AI applications by seamlessly integrating them with Qdrant vector databases through the innovative Model Context Protocol (MCP). This cutting-edge protocol leverages OpenAI embeddings to empower semantic search capabilities, facilitating contextual document retrieval and enhancing knowledge base query processes.

PDF Reader

Enhance your PDF content management with a cutting-edge Model Context Protocol (MCP) that efficiently extracts and manages text, images, and offers OCR services. Benefit from high-performance caching for seamless operations.

Journal RAG

Easily search and retrieve personal notes and reflections from your markdown journal using advanced vector database technology, enhancing the way you recall past memories, ideas, and events.

Rust Local RAG

Discover a cutting-edge Model Context Protocol (MCP) offering swift local document access and management. Leveraging Rust for unparalleled PDF handling capabilities and semantic search powered by Ollama embeddings, this protocol efficiently indexes PDF files within designated folders. Enjoy rapid document retrieval sans reliance on external solutions.

Pinecone Vector DB

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

Qdrant Knowledge Graph

Enhance your applications with seamless integration of a knowledge graph and advanced semantic search features. Streamline the storage, retrieval, and querying processes for structured data, enabling context-aware functionalities.

Qdrant Docs Rag

Efficiently capture and retrieve real-time contextual information using vector-based search with Qdrant technology.

QDrant RagDocs

Employs Qdrant vector database and embeddings for enhanced semantic search and documentation organization, facilitating Retrieval-Augmented Generation within the Model Context Protocol (MCP) framework.

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.

RAG Documentation Search

Enhances document search with semantic vectors for contextually relevant results from specified document repositories.

PDF Reader

Unlock the capability to access and retrieve information from both secured and unsecured PDF documents with the Model Context Protocol (MCP). This protocol empowers users to analyze documents, index content, and extract data seamlessly.

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.