Serveur MCP askdocs

askdocs

A question-answering service over a document corpus that cites its sources: FastAPI with streamed answers (SSE), retrieval through an MCP server (cMCP's crag-mcp, backed by cRAG), a local LLM served by llama.cpp, and an evaluation that blocks regressions in CI.

Status: complete. The service, a 45-case evaluation with a CI gate, OpenTelemetry tracing, a one-command Docker Compose stack, a load test and an AWS guide (prepared, not yet run on AWS). Every number here comes from a script, with its hardware and date.

docs/architecture.md explains the design: why retrieval goes through MCP (measured: about 0.4 ms per question, below the noise against an in-process call), and why the service refuses with 503 instead of queueing.

How an answer is made

POST /ask ──► crag_search over MCP (stdio) ──► passages [1..k]
                 │ nothing clears the relevance gate?
                 └──► "I don't know" — the model is never called
          ──► prompt: numbered sources + rules ──► LLM (streamed)
          ──► SSE: sources, token…, done {citations, refused, usage, timings}

• Citations are checked, not trusted. done.citations lists the source numbers the answer uses; done.invalid_citations lists numbers that point at no source (a fabrication the eval counts).
• Refusing is a first-class answer. Off-topic questions are refused in the retrieval step, without paying for a generation; when passages exist but do not contain the answer, the model is told to reply with one fixed sentence, and done.refused reports it.
• Failures are bounded. Per-chunk read timeout on the model stream; retries (exponential backoff) only before the first token reaches the client; one reconnect if the MCP server dies; at most ASKDOCS_MAX_CONCURRENT answers in flight, then 503 with Retry-After.
• Incomplete is not short. A model stream that ends without [DONE] or a finish reason, or carries an error chunk, is an error, never a truncated "answer"; an answer cut by max_tokens has done.truncated.
• Documents are data. Sources and the question sit between per-request random fences that their text cannot close, so a document cannot forge a source header or an instruction.
• A client that disconnects stops the generation, in both modes: the SSE stream is closed at once; a JSON request (?stream=false) polls for the disconnect and cancels. Either way the upstream stream closes and llama.cpp cancels the task (tested with raw ASGI disconnects, and checked against a real llama.cpp server).
• Bounded input. Request bodies over 16 KB get 413 before they are read into memory, on Content-Length and on the bytes actually received (chunked uploads); questions over 2000 characters get 422.

Quick start

With Docker, one command. It pulls the models, builds cRAG and crag-mcp from source, builds the index, and serves the API on 127.0.0.1:8000:

docker compose up -d                                         # CPU (slow: ~14 s an answer)
RENDER_GID=$(getent group render | cut -d: -f3) \
  docker compose -f compose.yaml -f compose.rocm.yaml up -d  # AMD GPU (~0.7 s)
docker compose -f compose.yaml -f compose.cuda.yaml up -d    # NVIDIA GPU

To answer with a GGUF file of your own (a fine-tuned model, say), add compose.local-model.yaml with ASKDOCS_LLM_GGUF=/abs/path/model.gguf.

The first start takes a few minutes, and later starts reuse the volumes. The ROCm image of llama.cpp is 22.9 GB. docs/deploy-aws.md runs the same stack on one EC2 GPU instance, reached through Session Manager with no open port.

With the service up, scripts/demo.sh walks through it in four scenes (the recording above): a cited answer streamed, a two-document question, a refusal made before the model is called, and the latency the service measured meanwhile.

Without Docker:

# 1. The models: an embedder in Ollama, an LLM in llama.cpp.
ollama pull mxbai-embed-large
# 4 slots (= ASKDOCS_MAX_CONCURRENT), 4096 tokens each: -c is for all slots together.
llama-server -hf Qwen/Qwen2.5-3B-Instruct-GGUF:Q4_K_M --port 8081 -ngl 99 -c 16384 -np 4

# 2. The demo corpus: cMCP + cRAG documentation at pinned commits, indexed by cRAG.
CRAG=/path/to/cRAG/crag scripts/build_corpus.sh

# 3. The service.
python -m venv .venv && .venv/bin/pip install -e '.[dev]'
cp .env.example .env            # set ASKDOCS_MCP_COMMAND to your crag-mcp build
.venv/bin/askdocs --port 8000

curl -N localhost:8000/ask -H 'content-type: application/json' \
     -d '{"question": "How is the requestState sealed in cMCP?"}'

event: sources
data: {"sources": [{"n": 1, "source": "cMCP/CHANGELOG.md", "text": "...", "cosine": 0.683, ...}, ...]}

event: token
data: {"text": "The requestState is sealed with HMAC-SHA256 "}
...
event: done
data: {"answer": "...", "citations": [2, 3], "invalid_citations": [], "refused": false,
       "usage": {"prompt_tokens": 1594, "completion_tokens": 101},
       "timings": {"retrieval_ms": ..., "ttft_ms": ..., "generation_ms": ..., "total_ms": ...}}

POST /ask?stream=false returns the same as one JSON envelope ({"success", "data", "error"}); GET /healthz reports load. done and error also carry trace_id, and done carries cost_usd.

API

POST /ask	body {"question": str (1–2000 chars), "k": int 1–20 (optional)}; SSE by default, JSON with ?stream=false
SSE events	sources → token* → done, or error {stage: retrieval|generation|internal, message, partial?}; a stream always ends with one of the two
Errors	413 body over 16 KB · 422 bad input · 503 too many in flight · 502 (JSON mode) when retrieval or the model fails, 500 on an internal error; details stay in the server log
GET /healthz	{"status", "in_flight", "max_concurrent"}
GET /stats	answers, refusals, errors by stage, and P50/P95 of each stage, tokens and cost over the last 1000 answers

Configuration

Environment variables (or .env), all prefixed ASKDOCS_; see .env.example and src/askdocs/config.py, where every limit carries its reason.

Variable	Default
LLM_PROVIDER	llamacpp	llamacpp or openai (any OpenAI-compatible API; needs LLM_MODEL and LLM_API_KEY)
LLM_BASE_URL	per provider	http://localhost:8081/v1 / https://api.openai.com/v1
LLM_MAX_TOKENS, LLM_TEMPERATURE	512, 0.0	temperature 0 keeps evals reproducible
LLM_READ_TIMEOUT_S, LLM_MAX_RETRIES	60, 2	timeout per streamed chunk, not per answer
MCP_COMMAND, MCP_DB	crag-mcp, data/docs.db	the MCP server and its index
EMBED_MODEL, EMBED_URL	mxbai-embed-large, Ollama	must match the model the index was built with
TOP_K, MAX_CONCURRENT	5, 4
WARMUP	true	one background retrieval at startup, so the embedder is loaded before the first question
LLM_PRICE_INPUT_PER_MTOK, LLM_PRICE_OUTPUT_PER_MTOK	0, 0	USD per million tokens, for cost_usd; 0 for a local model
OTEL_ENDPOINT, OTEL_SERVICE_NAME	unset, askdocs	OTLP/HTTP collector, e.g. http://localhost:4318; needs pip install '.[otel]'

Observability

Each answer is one OpenTelemetry trace: askdocs.answer with retrieval (and the MCP SDK's crag_search call under it) and generation (tokens, finish reason, a first_token event), under FastAPI's own POST /ask span. Off by default; with no endpoint set the API is a no-op.

Measured over the 45 eval questions, one at a time (P50 / P95):

Stage	ms
retrieval (MCP round trip, embedding included)	14 / 18
time to first token	449 / 567
answer, end to end	683 / 1152

Per answer	P50 / P95
prompt tokens	2009 / 2492
completion tokens	33 / 89

Generation is about 97% of an answer, and the prompt is about 60 times the answer, so with a hosted model top_k drives the cost. Tracing adds about 94 µs per answer. docs/observability.md has the trace tree, why the spans are not made "current" across the token stream, the cold-start finding, and how the numbers were measured.

Tests

.venv/bin/pytest -q                      # hermetic: fake LLM, fake MCP server over real stdio
ASKDOCS_INTEGRATION=1 .venv/bin/pytest -m integration   # the real stack, running

The retriever is tested against a stand-in MCP server (tests/fake_crag_mcp.py) spoken to over real stdio, including a crash mid-session and the reconnect after it.

Evaluation

evals/cases.jsonl holds 45 labelled questions about the demo corpus: 30 factual, 5 that need two sources, 6 in the corpus's domain that it does not answer, 4 off-topic. Each answerable case lists the files the answer lives in and the key facts it must contain.

python evals/run.py --phase answer    # answering model running (llama.cpp :8081)
python evals/run.py --phase judge     # then the judge model (:8082) grades that run
python evals/agreement.py             # judge vs a second grader on a sample

Two phases because 16 GB of RAM did not hold both models plus a desktop (the kernel OOM-killed the answering model); one process can do both with --phase all.

What is measured, and why apart:

• Retrieval recall@k: is the right file among the passages? Scored without the model, so a retrieval miss is not blamed on generation.
• Key-fact recall: does the answer contain the labelled facts? A refusal of an answerable question scores 0.
• Refusals: right for the 10 unanswerable/off-topic cases, wrong for the 35 others.
• Faithfulness: the answer is split into sentences; for each, a judge model (Qwen2.5-7B, not the 3B that wrote the answer) is asked one yes/no question per cited passage. An uncited sentence is unsupported.

Baseline (evals/results/report.md; Qwen2.5-3B answering, top-k 5; bootstrap 95% intervals over cases):

Metric	Mean [95% CI]	n
Retrieval recall@1 / @3 / @5	0.61 / 0.94 / 0.99	35
Context recall (right file among the 5 passages the model got)	0.94 [0.87, 1.00]	35
Key-fact recall	0.87 [0.76, 0.96]	35
... factual / multi-source	0.92 / 0.60 [0.30, 0.90]	30 / 5
Wrong refusals	0.03 [0.00, 0.09]	35
Correct refusals (unanswerable + off-topic)	1.00	10
Faithfulness (judged sentences supported)	0.60 [0.45, 0.75]	34

What it says:

• Retrieval is not the bottleneck. The right file is in the top 5 for 34 of 35 questions, but first only 61% of the time, so the prompt has to make the model read past source [1].
• Faithfulness is the weak point. The two failure modes found reading the verdicts: citing a neighbouring passage instead of the one holding the fact, and copying a passage as a list with no citations at all. The answer is often right and badly attributed.
• The judge is usable, not perfect. On a 25-sentence sample graded independently against the full cited passages it agreed on 23 (Cohen's kappa 0.80): it rejected one sentence supported by a paraphrase and accepted one that garbled its passage. At this sample size the errors do not lean either way. The second grader was a stronger model (Claude Opus 5.5, recorded as reference_by), not a person: a check of the judge, not ground truth.
• Single runs are noisy. At temperature 0, four answer runs gave key-fact recall 0.77, 0.80, 0.81 and 0.87: the prompt fence is random per request and llama.cpp batches slots, so the text changes. Retrieval was identical every time. A difference of ~0.1 between two runs is not a regression.
• The corpus contradicts itself once: crag-mcp's README says schema errors are JSON-RPC -32602, a dogfood report says they are isError: true. Case f16 follows the README.

The gate

python evals/gate.py check                    # what CI runs
python evals/gate.py compare evals/results NEW_RUN_DIR
python evals/gate.py noise RUN_DIR RUN_DIR ... --write

CI has no GPU and no models, so it cannot rerun the eval. It checks the committed results instead: they must cover exactly the current cases.jsonl (a case added or edited without a rerun fails) and clear the floors in evals/gate.json.

The regression test runs where the models are. It pairs the two runs case by case and calls a drop a regression only if both

1. the paired bootstrap interval of (new − base) excludes zero, and
2. the drop is larger than the run-to-run noise: the largest gap between any two runs of the same code (4 runs: key facts 0.10, wrong refusals 0.06, faithfulness 0.05, retrieval 0).

The second condition is not decoration. Comparing the baseline with a rerun of the same code, the paired test alone flagged a key-fact "regression" of −0.10 with an interval of [−0.20, −0.01]. Run noise is not case-sampling noise, and a bootstrap over cases does not see it.

And it catches a real one: the same eval with ASKDOCS_TOP_K=1 (the model sees one passage instead of five) is flagged on context recall −0.33, key facts −0.27, wrong refusals +0.31 and out-of-range citations +0.24, and fails the floors. Retrieval recall@k does not move in that run, because it measures the retriever at k=10, not what reached the model; that is why context recall exists.

Floors: each is the baseline's 95% interval bound moved by the noise, rounded outward to 0.05, so the committed baseline passes and a run as bad as the noisy end of the baseline still does. One exception, recorded in the file: correct refusals (n=10, never failed) would get a degenerate floor of 1.0 and get 0.9.

Load test

Locust in a closed loop over the eval questions (loadtest/run.sh), with a limit of 4 on 4 llama.cpp slots (docs/load-test.md):

users	answers/s	ttft P50 / P95 ms	answer P50 / P95 ms	rejected (503)	failed
1	1.34	440 / 580	690 / 1700	0%	0
4	1.78	540 / 1100	2100 / 4300	0%	0
16	1.81	550 / 1500	2100 / 4000	87%	0

• A configuration bug it caught. The first run failed 68 of 104 answers at 4 users: llama.cpp's -c is the context of all slots together, so 8192 was too small. It is now -c 16384 -np 4.
• The service is prefill-bound. Each answer needs about 0.46 s of GPU just to read its 2000-token prompt, so throughput stops near 1.8 answers/s.
• The limit must equal the slots. With a limit of 8 on 4 slots, throughput stayed the same and time to first token went from 0.5 s to 2.6 s, as the extra answers queued unseen inside llama.cpp.

Limitations

KNOWN_LIMITATIONS.md lists where askdocs fails or is untested, with the numbers: faithfulness at 0.60, no authentication, one GPU's worth of throughput, an AWS deploy that has not been run.

License

MIT