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Add MongoDB, RabbitMQ ingest, evals, and deploy config - #1
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LangGraph rejected the tools because they had no docstrings, and the MCP entry point still imported FastMCP, which mcp 2.x removed. Pin the stack and cover both paths so CI installs the same dependencies the agent uses. Co-authored-by: naveecode <naveecode@users.noreply.github.com>
Ingest splits text, stores a hashed embedding, and ranks matches by shared tokens with cosine as the tiebreaker. MONGO_URL selects MongoDB; without it the API keeps working in memory. Atlas $vectorSearch is used when MONGO_VECTOR_INDEX is set and the local ranker is the fallback. Co-authored-by: naveecode <naveecode@users.noreply.github.com>
POST /docs publishes to the ingest queue when RABBITMQ_URL is set and the worker stores chunks after a failure budget of three attempts. Poison messages skip retries. Without a broker the API still ingests inline and drops cached answers that the new document would change. Co-authored-by: naveecode <naveecode@users.noreply.github.com>
Golden questions score tool choice, exact calculator answers, and document hits. Empty input, oversized questions, and prompt-injection phrasing are refused before a tool runs. python -m app.evals exits non-zero on a miss. Co-authored-by: naveecode <naveecode@users.noreply.github.com>
The image now has a healthcheck. CI builds it and exercises /health, ingest, and /ask. render.yaml and fly.toml run the API and the ingest worker. Co-authored-by: naveecode <naveecode@users.noreply.github.com>
The README now covers the environment variables, ingest retries, Atlas vector search, and how to deploy the API and worker on Render or Fly. Co-authored-by: naveecode <naveecode@users.noreply.github.com>
naveecode
marked this pull request as ready for review
October 14, 2026 13:04
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Summary
create_react_agent, and the server uses MCP 2'sMCPServer(theFastMCPimport was removed). Tests drive a scripted ReAct loop and call the registered tools.MONGO_URLselects MongoDB; otherwise the API stays in memory. Chat turns are saved on/ask. Atlas$vectorSearchruns whenMONGO_VECTOR_INDEXis set, and the local ranker is the fallback.POST /docspublishes to RabbitMQ whenRABBITMQ_URLis set. The worker tries each message three times, then publishes it toingest.dlq. Without a broker, ingest stays inline and drops cached answers the new document would change.python -m app.evalsscores tool choice, golden answers, and guardrails on the offline router. Empty input, oversized questions, and prompt-injection phrasing are refused before a tool runs./health, ingest, and/ask.render.yamlandfly.tomlrun the API and the worker.Verification
pytest -q: 30 passedpython -m app.evals: 6 passed, 0 faileddocker buildsucceeded, and a container smoke test covered/health,/docs,/ask, and/chats. The image healthcheck reported healthy.docker compose configvalidates the API, worker, Redis, MongoDB, and RabbitMQ stack.Still open