# Enterprise Internal Knowledge Base: Production-Ready RAG + MCP [Health: Active] **Category:** 🧠 Knowledge & Memory **Repository:** https://github.com/kimsb2429/internal-knowledge-base **GitHub Stars:** 0 **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/enterprise-internal-knowledge-base-production-ready-rag-mcp ## Description Production-ready RAG + MCP demo: eval-in-CI merge gate, Langfuse traces, structure-aware chunking. ## Claude Desktop Quick Installation Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low): ```json "mcpServers": { "enterprise-internal-knowledge-base-production-ready-rag-mcp": { "command": "npx", "args": ["-y","enterprise-internal-knowledge-base-production-ready-rag-mcp"] } } ``` ## Documentation & README # Enterprise Internal Knowledge Base — Production-Ready RAG + MCP A public Retrieval-Augmented Generation pipeline exposed as an MCP server. Sample content from Veterans Affairs education manuals. The repo implements evaluation, observability, and structure-aware ingestion. Cost/latency tuning, tenant-level access control, and other production concerns are discussed in the article linked below. **📖 Full writeup in *Towards AI*:** [Enterprise Internal Knowledge Base RAG MCP: POC-to-Production](https://medium.com/towards-artificial-intelligence/poc-to-production-rag-af49476f4ddc) --- ## Why this exists RAG demos tend to focus on the quality of the retrieval pipeline, without recognizing that production RAG fails on the next ten steps: prompt or model changes that pass code review but tank answer quality, cost and latency drift that cannot be traced to specific queries, cross-tenant leakage that only surfaces in audit. This repo shows what catching them looks like in practice. The corpus is public (VA Education manuals — 238 documents, 9,000+ chunks) so anyone can clone, run, and adapt the pipeline. --- ## Quickstart ```bash git clone https://github.com/kimsb2429/internal-knowledge-base cd internal-knowledge-base # 1. Start Postgres + pgvector docker compose up -d # 2. Python env + dependencies python3 -m venv .venv && source .venv/bin/activate pip install -r requirements.txt # 3. Restore corpus fixture (~2 min — 238 docs + 9k chunks pre-embedded) docker exec -i ikb_pgvector pg_restore -U ikb -d ikb < evals/fixture_v1.dump # 4. Smoke-test the MCP server python scripts/test_mcp_server.py # 7/7 tests pass # 5. Start the MCP server (stdio transport) python scripts/mcp_server.py ``` ### Consuming from Claude Desktop Add to `~/Library/Application Support/Claude/claude_desktop_config.json`: ```json { "mcpServers": { "ikb": { "command": "python", "args": ["/absolute/path/to/internal-knowledge-base/scripts/mcp_server.py"] } } } ``` Then ask Claude things like *"What RPO handles GI Bill claims in Texas?"* — the MCP server returns ranked chunks with citations. --- ## Architecture **Ingestion (one-time per corpus):** ```mermaid graph LR A[KnowVA crawler
HTML + PDF] --> B[Source-specific
preprocessor] B --> C[Structure-aware
chunker] C --> D[mxbai-embed-large
local, 1024-dim] D --> E[(pgvector)] F[Anthropic Contextual
Retrieval] -.-> E E -.-> F style E fill:#e1f5fe ``` **Query (per MCP tool call):** ```mermaid graph LR A[Claude Desktop
MCP client] --> B[FastMCP server] B --> C[pgvector top-K] C --> D[Reranker
mxbai or FlashRank] D --> E[Claude Sonnet
generation] E --> A E --> F[Langfuse trace] style F fill:#fff9c4 ``` **Stack:** - **Vector store:** Postgres + pgvector (Docker, port 5433); `content_tsv` GIN index for hybrid-ready - **Embeddings:** mxbai-embed-large (1024 dims, local via sentence-transformers) — $0 API cost - **Reranker:** mxbai-rerank-base-v2 (full eval) / FlashRank MiniLM (CI fast mode, 22M ONNX, ~2s/query) - **Generation:** Claude Sonnet - **MCP server:** FastMCP 3.2.4 — Tools (`query`), Resources (`document://{source_id}`), Prompts (`cite_from_chunks`) - **Observability:** Langfuse Cloud, per-trace public sharing - **Eval:** DeepEval + 110-query golden set + GitHub Actions merge gate --- ## Eval scores Full 110-question golden set, contextualized chunks + reranker: | Metric | Score | |---|---| | Faithfulness | 0.95 | | Answer Relevance | 0.91 | | Context Precision | 0.61 | | Context Recall | 0.52 | | Context Relevance | 0.56 | 🔗 **[Live Langfuse trace](https://us.cloud.langfuse.com/project/cmo0wah7a00pfad071nk6x84c/traces/a574193bbff7d5438f7fae9e27f4bb83)** (public, no login). **Notable result:** Anthropic's Contextual Retrieval pattern produced modest lift on top of reranking (+4.8pp AnsRel, +4.1pp CtxPrec) at this scale — well short of the +35% recall their published numbers suggested. Reported as found; juiced numbers would defeat the point. --- ## Eval-in-CI as a merge gate Every PR runs the golden set in fast mode (FlashRank reranker, ~3-4 min wall, $0.30 in Sonnet calls) against a fixture DB. PRs that regress more than ±5pp on top1/topk/keyword_recall, or +10pp on `idk_rate`, are blocked. **Forever-artifact:** [PR #5](https://github.com/kimsb2429/internal-knowledge-base/pull/5) — a deliberate failing-then-passing PR. Red CI catches a 20pp top1 regression; green CI confirms the fix. The Actions tab is the proof. Workflow: [`.github/workflows/eval-gate.yml`](https://github.com/kimsb2429/internal-knowledge-base/blob/HEAD/.github/workflows/eval-gate.yml). --- ## What this repo doesn't cover A few production-shape items are seams, not implementations: - **Multi-tenant scoping** — `auth_context` parameter present on every MCP tool, typed, currently unused (labels the SSO/ACL seam) - **Ingestion concurrency** — single-threaded chunker + embedder; production would use a modulus-distributed worker pool - **Hybrid search wiring** — `content_tsv` GIN index is live; BM25 + RRF fusion at query time stays a post-launch addition The writeup linked above covers these topics. --- ## Repo layout ``` docs/ Research, evidence base, deep-dives data/ Crawled corpus + golden query set scripts/ crawl_knowva.py eGain v11 API crawler enrich_metadata.py Headings, ACL, authority tier, content_category knowva_preprocess.py Source-specific HTML normalization chunk_documents.py Structure-aware splitter (preserves table colspan/rowspan) embed_and_store.py mxbai-embed-large → pgvector contextualize_chunks.py Anthropic Batches API for Contextual Retrieval rerank.py mxbai-rerank + FlashRank retrieve.py / generate.py RAG path mcp_server.py FastMCP exposure run_eval.py / score_eval.py / check_regression.py Eval harness + CI gate evals/ Fixture DB dump + baseline JSON .github/workflows/ eval-gate.yml — merge-gate workflow ``` --- ## Reproducing from raw corpus (~30 min) Each script is idempotent and resume-safe. ```bash python scripts/crawl_knowva.py # Crawl raw HTML (skip if data/knowva_manuals/articles/ exists) python scripts/enrich_metadata.py # Add headings, ACL, authority tier python scripts/knowva_preprocess.py # Normalize HTML quirks python scripts/chunk_documents.py # Structure-aware split python scripts/embed_and_store.py # mxbai → pgvector python scripts/contextualize_chunks.py # Anthropic Batches API (~$12, optional but recommended) ``` Then `python scripts/run_eval.py --fast` to verify the eval baseline reproduces. --- ## Further reading - **Full demo writeup**: [Enterprise Internal Knowledge Base RAG MCP: POC-to-Production](https://medium.com/towards-artificial-intelligence/poc-to-production-rag-af49476f4ddc) (*Towards AI*, Medium) - [`docs/2026-04-11-engineering-rag-evidence-and-howtos.md`](https://github.com/kimsb2429/internal-knowledge-base/blob/HEAD/docs/2026-04-11-engineering-rag-evidence-and-howtos.md) — engineering analysis, evidence base, Zero-to-MCP plan - [`docs/2026-04-12-rag-pipeline-buy-vs-build.md`](https://github.com/kimsb2429/internal-knowledge-base/blob/HEAD/docs/2026-04-12-rag-pipeline-buy-vs-build.md) — buy-vs-build map per pipeline stage - [`docs/deep-dive/2026-04-16-docs-vs-code-rag-adjudication.md`](https://github.com/kimsb2429/internal-knowledge-base/blob/HEAD/docs/deep-dive/2026-04-16-docs-vs-code-rag-adjudication.md) — when unified RAG stops working --- ## License MIT — see [LICENSE](https://github.com/kimsb2429/internal-knowledge-base/blob/HEAD/LICENSE).