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    A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.
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    MIT
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    Enables semantic search and interaction with Fuel Network documentation and Sway Language resources within AI-powered development environments. It provides high-quality technical context using a local vector database to assist with smart contract development and FuelVM queries.
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    A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.
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    MIT
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    Indexes local files (PDF, TXT, CSV, Markdown) with embeddings for semantic search. Provides both CLI and MCP server interfaces so Claude Desktop can search and read your local documents.
    MIT
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    A semantic search MCP server for YouTube transcripts using OpenAI embeddings and ChromaDB, enabling natural language queries to find conceptually similar content beyond keyword matching.
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    Enables stateless similarity search over pre-computed vector corpora using NMI and cosine fusion with entropy-calibrated weighting. Provides tools for ranking, scoring, outlier detection, and alpha calibration for AI agents.
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    Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
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    A personal memory MCP server that ingests AI agent conversation logs from multiple platforms into a searchable PostgreSQL+pgvector database, enabling cross-session recall of past reasoning and decisions.
    6
    MIT
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    A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
    13
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    Apache 2.0
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    A Model Context Protocol server for Chroma, enabling AI models to create collections and retrieve data using vector search, full text search, and metadata filtering.
    13
    Apache 2.0
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    Enables AI agents to manage and search data in Redis using natural language. Supports hashes, lists, sets, sorted sets, streams, JSON, and vector search.
    44
    MIT
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    The official Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis.
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    563
    MIT