Enables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.
Claim it to get a verified publisher badge, a free copy of our full audit findings, and direct contact for any high-priority issues we find.
Install from
M8ven verifies MCPs across every public registry — install directly from whichever one you prefer.
process.env. You'll be asked to provide them before it can run.PROCESSED_DIR— ./data/processedSOURCE_DIR— ./data/sourceEMBEDDING_MODEL— intfloat/multilingual-e5-largeEMBEDDING_PREFIX_QUERY— 検索クエリ用: - ユーザーの検索クエリに自動で追加EMBEDDING_PREFIX_EMBEDDING— "passage: "POSTGRES_HOSTPOSTGRES_PORTPOSTGRES_USERPOSTGRES_PASSWORD— docker run --name postgres-pgvector -e =password -p 5432:5432 -d pgvector/pgvector:pg17POSTGRES_DBEMBEDDING_DIM[](https://m8ven.ai/mcp/karaage0703-mcp-rag-server-1j8x4x)