chatbot_v3/docs/adr/0011-python-structured-logging-with-structlog.md

0011. Python structured logging with structlog and request context

Status

Proposed

Context

The service needs application-level Python logging in addition to the durable Postgres records from ADR-0009 and the LLM/agent traces from ADR-0010.

Postgres audit tables answer durable business questions such as which tenant API key called an endpoint, which point mutation was requested, which ingestion job ran, and what usage ledger rows were produced. Langfuse answers LLM observability questions such as which graph node or prompt version produced an answer. Structured Python logs answer operational questions while the service is running:

• Which request failed and where?
• Which logs belong to one FastAPI request or LangGraph run?
• Which tenant, API key, thread, run, file, point, or ingestion job was involved?
• Which dependency was slow or unavailable?
• Which fallback path or retry was used?

FastAPI, LangGraph, SQLAlchemy, Qdrant, Langfuse, and HTTP clients can all emit logs from asynchronous code. Since async tasks can interleave on the same event loop, relying on process-global mutable variables is unsafe. The logging context must be request-scoped and safe across async task switching. Python contextvars, exposed through structlog.contextvars, provide this behavior.

The user has used a previous log.py based on logging, structlog, logging.config.dictConfig, ProcessorFormatter, JSON rendering, stdlib log capture, and manual ContextVar fields such as session_id and property_id. This service should keep the same core idea but adapt the field names to the current architecture:

• thread_id instead of session_id for LangGraph conversations, matching ADR-0007 and ADR-0008;
• tenant_id, tenant_slug, api_key_id, and actor_type from AuthContext;
• request_id from FastAPI middleware;
• run_id from graph_runs for chat runs;
• file_id, point_id, and ingestion_job_id for ingestion and point work;
• langfuse_trace_id when available for cross-navigation to ADR-0010 traces.

Decision

Use structlog as the application logging interface

Use structlog for application logs and integrate it with Python stdlib logging so framework/library logs are formatted consistently.

Application code imports loggers with:

import structlog

logger = structlog.get_logger(__name__)

Log events use stable event names and structured fields:

logger.info(
    "graph.run.completed",
    status="answered",
    duration_ms=duration_ms,
    retrieved_chunk_count=len(retrieved_chunks),
)

Do not build log messages by interpolating operational metadata into prose. Prefer fields over long strings because fields are queryable.

Emit JSON logs by default in production

Production logs are JSON on stdout so process managers, container runtimes, and log collectors can ingest them directly. Local development may use a colored console renderer controlled by configuration.

File logging is optional and mainly for local development. If enabled, it must use explicit rotation settings such as maxBytes and backupCount. Do not rely on a default RotatingFileHandler with no rotation parameters. In containerized production, stdout/stderr collection is preferred over writing logs/app.log inside the application container.

Configure stdlib and structlog together

The logging setup should happen once during process startup, before the FastAPI app begins serving requests.

Indicative configuration shape:

import logging
import logging.config
import sys

import structlog


def configure_logging(*, log_level: str, json_logs: bool) -> None:
    shared_processors = [
        structlog.contextvars.merge_contextvars,
        structlog.stdlib.add_log_level,
        structlog.stdlib.add_logger_name,
        structlog.processors.TimeStamper(fmt="iso", utc=True),
        structlog.processors.StackInfoRenderer(),
    ]

    structlog.configure(
        processors=[
            *shared_processors,
            structlog.processors.format_exc_info,
            structlog.stdlib.ProcessorFormatter.wrap_for_formatter,
        ],
        logger_factory=structlog.stdlib.LoggerFactory(),
        wrapper_class=structlog.stdlib.BoundLogger,
        cache_logger_on_first_use=True,
    )

    renderer = (
        structlog.processors.JSONRenderer()
        if json_logs
        else structlog.dev.ConsoleRenderer(colors=True)
    )

    logging.config.dictConfig(
        {
            "version": 1,
            "disable_existing_loggers": False,
            "formatters": {
                "default": {
                    "()": structlog.stdlib.ProcessorFormatter,
                    "processors": [
                        structlog.stdlib.ProcessorFormatter.remove_processors_meta,
                        renderer,
                    ],
                    "foreign_pre_chain": [
                        structlog.stdlib.ExtraAdder(),
                        *shared_processors,
                    ],
                },
            },
            "handlers": {
                "console": {
                    "class": "logging.StreamHandler",
                    "level": log_level,
                    "formatter": "default",
                    "stream": sys.stdout,
                },
            },
            "loggers": {
                "": {
                    "handlers": ["console"],
                    "level": log_level,
                    "propagate": False,
                },
                "uvicorn": {
                    "handlers": ["console"],
                    "level": log_level,
                    "propagate": False,
                },
                "uvicorn.access": {
                    "handlers": ["console"],
                    "level": log_level,
                    "propagate": False,
                },
                "sqlalchemy.engine": {
                    "handlers": ["console"],
                    "level": "WARNING",
                    "propagate": False,
                },
                "watchfiles": {
                    "handlers": ["console"],
                    "level": "INFO",
                    "propagate": False,
                },
            },
        }
    )

Notes:

• Use the logger name sqlalchemy.engine, not sqlalchemy.engin.
• SQL statement logging is too noisy and can leak values; keep it WARNING by default in production and enable INFO/DEBUG only in controlled debugging.
• structlog.stdlib.ExtraAdder() keeps useful fields from stdlib log records.
• structlog.contextvars.merge_contextvars ensures request-bound fields appear on both structlog and stdlib logs processed through the formatter.

Bind request context with contextvars

At FastAPI ingress, clear stale context, bind request identifiers, and return the request id to callers. This makes it possible to select all logs from one request or one graph run even when async tasks interleave.

Indicative middleware:

from time import perf_counter
from uuid import uuid4

import structlog
from fastapi import Request
from starlette.types import ASGIApp

REQUEST_ID_HEADER = "X-Request-ID"


async def logging_context_middleware(request: Request, call_next: ASGIApp):
    structlog.contextvars.clear_contextvars()

    request_id = request.headers.get(REQUEST_ID_HEADER) or str(uuid4())
    route = request.scope.get("route")
    path_template = getattr(route, "path", request.url.path)

    structlog.contextvars.bind_contextvars(
        request_id=request_id,
        method=request.method,
        path_template=path_template,
    )

    logger = structlog.get_logger("app.http")
    started = perf_counter()
    logger.info("request.started")

    try:
        response = await call_next(request)
    except Exception:
        logger.exception(
            "request.failed",
            duration_ms=round((perf_counter() - started) * 1000, 2),
        )
        raise

    response.headers[REQUEST_ID_HEADER] = request_id
    logger.info(
        "request.completed",
        status_code=response.status_code,
        duration_ms=round((perf_counter() - started) * 1000, 2),
    )
    return response

After API-key authentication succeeds, the auth dependency or route handler binds trusted tenant/auth fields:

structlog.contextvars.bind_contextvars(
    tenant_id=str(auth.tenant_id),
    tenant_slug=auth.tenant_slug,
    api_key_id=str(auth.api_key_id),
    actor_type=auth.actor_type,
)

Route handlers bind route-specific fields when they become known:

structlog.contextvars.bind_contextvars(
    external_user_id=body.user_id,
    thread_id=thread_id,
    run_id=str(run_id),
)

Use these canonical context keys:

Field	Source	Notes
request_id	FastAPI middleware	Primary log correlation id; also appears in ADR-0008/0009 records.
tenant_id	AuthContext	Trusted server-side tenant id; never request body/query.
tenant_slug	AuthContext	Useful for filtering; avoid if contractual policy treats it as sensitive.
api_key_id	AuthContext	Non-secret id only. Never log raw API keys or auth headers.
actor_type	AuthContext	backend, admin, or worker.
external_user_id	Main backend	May be high-cardinality; acceptable in logs, not metrics labels.
thread_id	REST path	LangGraph thread id.
run_id	graph_runs.id	One chat run.
ingestion_job_id	ingestion_jobs.id	File ingestion correlation.
file_id	source_files.id	Source-file correlation.
point_id	Qdrant point id	Point mutation/read correlation.
langfuse_trace_id	Langfuse	Cross-link to ADR-0010 trace when available.

Use structlog.contextvars.clear_contextvars() at request/task ingress to avoid leaking a previous request's context into reused workers.

Bind context explicitly for jobs and background work

Context variables work across normal async task switching, but background jobs, worker processes, scheduled jobs, and threadpool work should bind context at their own entry point from durable identifiers.

Examples:

• ingestion worker binds tenant_id, ingestion_job_id, file_id, and request_id if inherited from the upload request;
• LangGraph run execution binds thread_id, run_id, tenant_id, and external_user_id before invoking the graph;
• point batch workers bind tenant_id, api_request_log_id, and operation metadata before processing each batch.

If code crosses a boundary where contextvars may not propagate automatically, pass the identifiers explicitly and bind them again at the boundary.

Log levels and event naming

Use log levels consistently:

Level	Use
DEBUG	Local diagnostics, disabled by default in production.
INFO	Normal lifecycle events: request started/completed, graph run completed, ingestion job completed.
WARNING	Recoverable anomalies: fallback prompt used, retry scheduled, insufficient retrieval before clarification/escalation.
ERROR	Failed operations requiring attention: unhandled exception, dependency outage, ingestion failure.

Do not log expected user behavior at ERROR. For example, a user asking an ambiguous question that leads to clarification is an INFO event; a retriever being unavailable is an ERROR event.

Use stable dot-separated event names:

• request.started
• request.completed
• request.failed
• auth.succeeded
• auth.failed
• graph.run.started
• graph.run.completed
• graph.run.escalated
• retrieval.completed
• retrieval.insufficient
• llm.call.completed
• llm.call.failed
• ingestion.job.started
• ingestion.job.completed
• point.mutation.completed

Do not include dynamic values in logger names or event names. Put dynamic values in structured fields.

Security and privacy rules

Logs must not contain secrets or raw sensitive payloads.

Never log:

• plaintext API keys;
• Authorization headers;
• database URLs or provider credentials;
• raw uploaded file contents;
• full retrieved chunks by default;
• raw user messages, raw prompts, or raw model outputs by default;
• embeddings or vectors.

Prefer:

• ids (request_id, thread_id, run_id, file_id, point_id);
• hashes (input_message_hash, output_message_hash, content_sha256);
• counts, sizes, durations, and status codes;
• short redacted summaries only when useful and allowed by tenant policy.

The same redaction policy used for ADR-0009 llm_call_payloads and ADR-0010 Langfuse tracing should guide log redaction. Logging should be safe even when log aggregation has broader access than the application database.

Relationship to Postgres and Langfuse

Structured logs complement but do not replace ADR-0009 and ADR-0010.

Question	System of record
What happened operationally inside this process?	Structured logs.
Which API key called which endpoint and what durable side effect occurred?	Postgres audit tables from ADR-0009.
Which graph node, prompt version, retrieved chunks, and model calls produced an answer?	Langfuse traces from ADR-0010.
What should be used for tenant billing and compliance reports?	Postgres llm_calls, llm_pricing, api_request_logs, and audit tables.
What should be used for interactive debugging of one LLM answer?	Langfuse trace, linked from logs/Postgres by ids.

Logs may contain request_id, run_id, and langfuse_trace_id so engineers can navigate across all three systems.

Consequences

Positive

• Logs become queryable by request_id, thread_id, run_id, tenant_id, file_id, and ingestion_job_id.
• Contextvars prevent async task interleaving from mixing request context.
• Stdlib/framework logs and application logs share one JSON structure.
• Production logs are compatible with common log collectors and container runtimes.
• Logs, Postgres audit rows, and Langfuse traces can be correlated without duplicating each system's purpose.

Negative

• Logging setup is more complex than plain logging.basicConfig().
• Developers must learn to use structured fields instead of prose-only log messages.
• Context must be rebound at worker/background-task boundaries.
• Too much logging can increase cost and leak sensitive data if redaction rules are not followed.
• JSON logs are less pleasant locally unless a console renderer is enabled for development.

Alternatives Considered

• Use Python stdlib logging only: rejected. Stdlib logging can work, but structlog gives cleaner structured context, contextvars integration, and consistent event dictionaries across application and framework logs.
• Use manual ContextVar fields only: rejected as the default. Manual context variables work, but structlog.contextvars.bind_contextvars() and merge_contextvars provide a standard way to bind arbitrary request fields without maintaining one ContextVar per field. Manual ContextVars may still be used for special cases.
• Use session_id as the primary chat correlation field: rejected for this service. ADR-0007 standardizes on thread_id, which maps to LangGraph threads and Langfuse sessions. If the main backend calls the same concept a session, it is translated to thread_id at this service boundary.
• Write only to logs/app.log: rejected for production. File logging is useful locally, but stdout JSON is the better default for deployed services.
• Use Langfuse for all observability: rejected. Langfuse is excellent for LLM/agent traces, prompt versions, scores, and evals, but it is not a replacement for process logs covering FastAPI middleware, auth, SQLAlchemy, Qdrant calls, worker lifecycle, and non-LLM failures.
• Use Postgres audit tables as logs: rejected. ADR-0009 tables are durable business/audit records. They should not receive high-volume operational debug logs.