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19 days ago
glama

MCP RAG Server

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.

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// key findings
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
🔐
You'll be asked for 1 credential: POSTGRES_PASSWORD
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// required environment variables
This server reads these from process.env. You'll be asked to provide them before it can run.
configPROCESSED_DIR./data/processed
configSOURCE_DIR./data/source
configEMBEDDING_MODELintfloat/multilingual-e5-large
configEMBEDDING_PREFIX_QUERY検索クエリ用: - ユーザーの検索クエリに自動で追加
configEMBEDDING_PREFIX_EMBEDDING"passage: "
configPOSTGRES_HOST
configPOSTGRES_PORT
configPOSTGRES_USER
🔐 secretPOSTGRES_PASSWORDdocker run --name postgres-pgvector -e =password -p 5432:5432 -d pgvector/pgvector:pg17
configPOSTGRES_DB
configEMBEDDING_DIM
// full audit trail
The full breakdown of what we checked, the deductions that landed, the network hosts, the dependency advisories, and concrete fix guidance is available to verified publishers.
// improvement guidance — verified publishers only
We have 1 concrete improvement we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
[![M8ven Score](https://m8ven.ai/badge/mcp/karaage0703-mcp-rag-server-1j8x4x)](https://m8ven.ai/mcp/karaage0703-mcp-rag-server-1j8x4x)
commit: d407ed5817a1d36f5e94289d2868677876b4758c
code hash: 4c73c4c2843f3268bb7dd45673b8b717b1f597a7211d0c37952e2291cc553dc7
verified: 6/17/2026, 12:43:06 PM
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