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feat(ingestion): add bounded, benchmark-aligned embedding execution

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Why:
- Plan 001 Phase 4 needs batched, concurrency-bounded embedding wired into
  the inline upload path, with process-wide capacity/timeout/chunk-limit
  guards (ADR-0017).
- The BM25 analyzer and dense-model config are ported from the `emet`
  evaluation lab, which benchmarked them against the real Farsi corpus
  (bm25-fa-norm-stop; nomic-embed-text-v2-moe at 768-dim; text-embedding-3-large
  at native 3072-dim), closing open items in ADR-0001/ADR-0005.

Changes:
- New: embedding ports, orchestration (embed_chunks), request-bounds
  helpers, and dense/sparse adapters (analyzers.py, bm25.py,
  openai_compatible.py).
- upload.py now parses/chunks/embeds inline behind INGESTION_MAX_CONCURRENCY
  (503), INGESTION_TIMEOUT_SECONDS (504), and the chunk-count ceiling (413);
  every failure path still writes a terminal job row.
- Lifespan builds and warms both dense embedders at startup (fail-soft) and
  creates the sparse embedder and concurrency semaphore.
- httpx moves from dev to main dependencies (adapters use it directly).

Impact:
- Qdrant point upserts are still Phase 5 -- chunks_indexed stays 0.
- New EMBEDDING_* env vars documented in .env.example; safe defaults.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
Ali Zarin Kolah
2026-08-19 17:13:32 +03:30
parent aa6d595424
commit 5c0a5938f8
33 changed files with 2455 additions and 536 deletions
Download Patch File Download Diff File Expand all files Collapse all files

39
.env.example
View File

@@ -45,6 +45,45 @@ INGESTION_EMBED_CONCURRENCY=4
QDRANT_URL=http://127.0.0.1:6343
QDRANT_API_KEY=
# Dense embedders (ADR-0001). Both speak an OpenAI-compatible /embeddings
# endpoint, so one adapter serves both. Models and endpoints are the ones the
# `emet` evaluation lab benchmarked as winners on the Farsi corpus.
#
# dense_nomic runs behind Ollama's OpenAI-compat shim, which accepts any
# non-empty API key. KEEP_ALIVE holds the model resident: a cold load of
# nomic-embed-text-v2-moe takes >150s, well past INGESTION_TIMEOUT_SECONDS,
# so an idle-then-upload would otherwise 504.
EMBEDDING_NOMIC_BASE_URL=http://192.168.10.10:11435/v1
EMBEDDING_NOMIC_MODEL=nomic-embed-text-v2-moe
EMBEDDING_NOMIC_API_KEY=sk-not-set
EMBEDDING_NOMIC_KEEP_ALIVE=30m
EMBEDDING_NOMIC_TIMEOUT_SECONDS=30.0
# Empty = emet parity. The model card specifies `search_document: ` (ADR-0004),
# but the benchmark ran without it and the prefix shifts the vector a lot
# (cosine 0.57 on identical text) — so if you set this, the query side must
# send `search_query: ` to match, or retrieval gets worse rather than better.
EMBEDDING_NOMIC_DOCUMENT_PREFIX=
# Leave DIMENSIONS empty for text-embedding-3-large's native 3072, which is
# what was benchmarked. Setting it truncates via Matryoshka and is a
# re-embedding migration, not a config tweak.
EMBEDDING_OPENAI_BASE_URL=https://api.openai.com/v1
EMBEDDING_OPENAI_MODEL=text-embedding-3-large
EMBEDDING_OPENAI_API_KEY=
EMBEDDING_OPENAI_DIMENSIONS=
EMBEDDING_OPENAI_DOCUMENT_PREFIX=
EMBEDDING_OPENAI_TIMEOUT_SECONDS=30.0
# Sparse BM25 (ADR-0001, ADR-0005): the benchmarked `bm25-fa-norm-stop`.
# k/b saturation is applied client-side; IDF comes from Qdrant's
# modifier="idf" on the sparse vector field. AVG_LEN is the average document
# length in analyzer tokens — emet's placeholder, worth recalibrating from
# real corpus statistics.
EMBEDDING_SPARSE_ANALYZER=fa_norm_stop
EMBEDDING_SPARSE_K=1.2
EMBEDDING_SPARSE_B=0.75
EMBEDDING_SPARSE_AVG_LEN=256.0
# Parsing and chunking (ADR-0018).
# max_chunk_tokens is nomic-embed-text-v2-moe's sequence length; text past it
# is silently truncated by the model, so the cap is enforced before embedding.

20
CLAUDE.md
View File

@@ -7,11 +7,21 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
This repo is ADR-driven and early in implementation. Working today: the FastAPI
app factory and lifespan wiring (`src/bootstrap/`), `/healthz` and `/readyz`,
structlog config, Postgres/MinIO/Qdrant clients (`src/infrastructure/`), five
SQLAlchemy models with one Alembic migration, and document parsing plus
fixed-size chunking (`src/application/ingestion/`). Not built yet: API-key auth,
`POST /v1/files` (so `/v1` currently exposes no routes), repositories, embedding
adapters, Qdrant collection bootstrap, and `src/agent/`. That maps to plan 001
Phase 1 done, Phase 2 partly done, and the parsing half of Phase 5.
SQLAlchemy models with one Alembic migration, document parsing plus fixed-size
chunking (`src/application/ingestion/`), API-key auth, `POST`/`GET /v1/files`
with durable two-phase job creation (`src/application/files/`), and bounded
inline embedding: `dense_nomic`/`dense_openai` adapters over an
OpenAI-compatible HTTP client and a `bm25-fa-norm-stop` sparse adapter
(`src/infrastructure/embedding/`), wired into the upload path behind
`INGESTION_MAX_CONCURRENCY` (`503`), `INGESTION_TIMEOUT_SECONDS` (`504`), and
the chunk-count ceiling (`413`). The embedding configuration is **ported from
the `emet` evaluation lab** (`~/code/talie/emet`), which benchmarked these
models and analyzers on the real Farsi corpus — the analyzer and BM25 weights
are verified token-for-token against it, so treat them as a measured artifact
and re-benchmark rather than tune them in place (ADR-0005). Not built yet: Qdrant collection bootstrap,
Qdrant point upserts (so uploaded chunks are parsed/embedded but not yet
searchable), and `src/agent/`. That maps to plan 001 Phases 1-4 done, Phase 5
not started.
Architecture decisions live in `docs/adr/` (18 ADRs plus the 0000 template;
0001–0004 are `Accepted` — 0004 amended by 0018; 0014 is `Superseded by 0017`;

42
docs/adr/0001-ingestion-pipeline-and-collection-schema.md
View File

@@ -49,7 +49,7 @@ One collection, e.g. `chunks`, shared by all tenants and domains.
| Name | Type | Purpose | Notes |
|---|---|---|---|
| `dense_nomic` | dense vector | primary semantic similarity (multilingual, incl. Persian) | `nomic-embed-text-v2-moe`, 768-dim ([0004](0004-docx-csv-chunking-strategy.md)) |
| `dense_openai` | dense vector | second semantic signal | OpenAI large embedding model (e.g. `text-embedding-3-large`), dimension per OpenAI's `dimensions` param (TBD — full 3072 vs. a truncated size) |
| `dense_openai` | dense vector | second semantic signal | `text-embedding-3-large` at its **native 3072 dimensions** — the `dimensions` param is deliberately left unset (see below) |
| `sparse` | sparse vector | lexical/keyword-sensitive retrieval | `bm25-fa-norm-stop` — Qdrant FastEmbed's BM25 sparse encoder configured for Persian (stopword removal + normalization), not a separately trained model |
| `late_interaction` | multivector | reserved for late-interaction rerank ([0003](0003-agent-hybrid-retrieval.md)) | `jina-colbert-v2` ([0005](0005-reranking-model-and-sparse-analyzer-selection.md)), `comparator: max_sim`, `hnsw_config: m=0` (rerank-only, never independently ANN-searched), stored **on disk** |
@@ -61,6 +61,35 @@ dense/sparse query latency. Two dense vectors are provisioned deliberately —
`dense_nomic` and `dense_openai` are two independent semantic signals, both
prefetched and fused at query time (ADR-0003), not a primary/fallback pair.
#### Dense model endpoints and dimensions (resolved by the `emet` benchmark)
Both dense models are reached over the **same OpenAI-compatible
`/embeddings` API**, so one adapter
(`src/infrastructure/embedding/openai_compatible.py`) serves both named
vectors with different configuration:
| Named vector | Model | Endpoint | Dimensions |
|---|---|---|---|
| `dense_nomic` | `nomic-embed-text-v2-moe` | self-hosted Ollama OpenAI-compat shim | **768** (verified against the live endpoint) |
| `dense_openai` | `text-embedding-3-large` | OpenAI hosted API | **3072** (native; `dimensions` unset) |
`dense_openai`'s dimension was previously listed as an open dependency. It is
now pinned to the native 3072, because that is the configuration the `emet`
lab benchmarked — it never passed a `dimensions` argument. Setting it later
would truncate via Matryoshka and is a **re-embedding migration, not a config
tweak**, exactly as the negative consequence below warns.
Two operational notes about the self-hosted embedder, both learned by
measurement rather than assumption:
- **Cold load exceeds 150s**, far beyond `INGESTION_TIMEOUT_SECONDS`, so an
idle-then-upload would return `504`. Mitigated on both ends: Ollama's
`keep_alive` keeps the model resident, and the FastAPI lifespan warms each
dense embedder at startup (fail-soft — a down embedder must not block boot).
- **Once warm it is fast**: ~0.30s for one input and ~0.34s for a batch of 16.
Batching is therefore nearly free, which is what keeps ADR-0017's inline
ingestion viable.
### Multitenancy / indexing config
- HNSW: `m: 0` (disable the global index) + `payload_m: 16`, per Qdrant's
@@ -212,9 +241,14 @@ them — see ADR-0002 for how reorder/insert/delete operations keep
ingestion time and both are queried at retrieval time — roughly double
the dense embedding cost/latency of a single-dense-vector design, plus an
external network dependency on OpenAI's API in the ingestion path.
- `dense_openai`'s exact output dimension is still an open dependency that
should be pinned before ingestion is implemented — changing it later is a
re-embedding migration, not a config tweak.
- ~~`dense_openai`'s exact output dimension is still an open dependency~~ —
**resolved**: pinned to the native 3072 (see "Dense model endpoints and
dimensions" above). The warning still stands for any future change:
re-dimensioning is a re-embedding migration, not a config tweak.
- The `sparse` vector must be created with `modifier="idf"`. The client
computes only BM25's term-frequency saturation; without that modifier
Qdrant applies no IDF at all and lexical retrieval silently degrades
(ADR-0005).
- `jina-colbert-v2` ([0005](0005-reranking-model-and-sparse-analyzer-selection.md))
adds a hard GPU dependency to ingestion (not just query time, since the
document-side multivector is computed here) and its commercial license is

17
docs/adr/0004-docx-csv-chunking-strategy.md
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@@ -62,6 +62,23 @@ from its model card: 768-dim output, Matryoshka-truncatable down to 256;
every embedded string — `search_document: ` at ingestion time, `search_query: `
on the agent's query side (ADR-0003).
> **Amendment — the task prefix is currently not applied.** The `emet`
> benchmark that selected this model ran *without* any prefix: its Ollama
> deployment's template is a bare `{{ .Prompt }}` passthrough that injects
> nothing, which was verified directly against the running endpoint. The
> prefix is not cosmetic — embedding the same Persian text with and without
> `search_document: ` yields a cosine of only **0.5741** — so applying it at
> ingest while the query side omits `search_query: ` would make retrieval
> *worse* than using neither.
>
> Implementation therefore defaults `EMBEDDING_NOMIC_DOCUMENT_PREFIX` to
> empty, matching the measured configuration, and exposes it as config so the
> prefixed variant is a one-line experiment rather than a code change. The
> model card remains the reason to expect prefixing to help; what is missing
> is evidence on *this* corpus. Turning it on is a paired change — ingest and
> query must move together — and should be settled by an emet run that
> measures the pair, not by an unmeasured edit here.
## Decision
### Parsing order: structural extraction before chunking

68
docs/adr/0005-reranking-model-and-sparse-analyzer-selection.md
View File

@@ -2,9 +2,10 @@
## Status
Proposed — the fusion/rerank *shape* and reranker model are decided; the
final BM25 analyzer and the commercial license status of the reranker are
still open per the follow-up items below.
Proposed — the fusion/rerank *shape*, the reranker model, and (as of the
`emet` benchmark, see "Benchmark outcome" below) the **BM25 analyzer** are
decided. The commercial license status of the reranker remains open per the
follow-up items below.
## Context
@@ -100,6 +101,50 @@ entirely to the analyzer stage, not the ranking formula:
consistent with Farsi's high density of function words (ezafe particles,
prepositions, common verbs) adding TF/IDF noise if left in.
### 3a. Benchmark outcome: `bm25-fa-norm-stop` confirmed, and where the BM25 math runs
The `emet` evaluation lab (`~/code/talie/emet`) ran the four-variant
comparison above against the real Farsi corpus and confirmed
**`bm25-fa-norm-stop`** as the winner. It is the only sparse variant promoted
into emet's hybrid matrix (`emet/hybrid.yaml`). This closes follow-up item 4
below.
The winning analyzer is a specific, reproducible artifact, ported into
`src/infrastructure/embedding/analyzers.py` and verified token-for-token
against emet's implementation. Its details are load-bearing:
- Unicode **NFC** (not NFKC), then ZWNJ → space, then Persian/Arabic-Indic
digits → ASCII, then `ي→ی ك→ک ة→ه ؤ→و إ→ا أ→ا`.
- Tokenizer `[^\W_]+`, which **keeps digits**. This matters for an insurance
corpus: policy numbers, dates, and amounts are exactly the terms lexical
retrieval should match, and the digit folding above means a query in ASCII
digits matches a document authored in Persian ones.
- A 51-entry stopword set (40 Persian/Arabic + 11 English, the corpus being
mixed-script). Deliberately not a full `hazm` list.
- No stemming — `fa_norm_stem` was the losing arm.
**The BM25 formula is split across two systems, deliberately.** The client
applies term-frequency saturation, including the `k`/`b` document-length
normalization; **IDF is supplied by Qdrant** via `modifier="idf"` on the
sparse vector field, computed from collection-wide statistics rather than
from a fixed client-side corpus.
That split is a correctness trap worth stating plainly: a `chunks` collection
created *without* `modifier="idf"` will score these vectors as saturated term
frequencies with no IDF weighting at all — no error, no warning, just
materially worse lexical retrieval. The collection bootstrap must set it.
Document and query encoding are asymmetric in exactly one term: documents
carry the `b` length normalization, queries do not (standard BM25 practice).
Both sides must therefore encode through the same implementation, which is
why the sparse port carries a `query` flag rather than leaving retrieval to
grow a second, silently divergent encoder.
Term → sparse-index mapping is `blake2b(token, digest_size=8) % (2**31 - 1)`,
a pure hash with no vocabulary table, so it needs no shared state and stays
identical across processes and between ingest and query time. Changing the
hash orphans every stored sparse vector: that is a re-ingestion, not a deploy.
### 4. BM25 parameters: keep `k=1.2`, `b=0.75`; tune analyzer, not formula
These are standard, well-validated defaults (Trotman, Puurula & Burgess,
@@ -159,8 +204,21 @@ comparison and `b` sweep in the follow-ups below.
3. Run an ablation: single dense model + sparse + rerank vs. the current
dual-dense-model + sparse + rerank setup, on real Farsi queries, to
justify (or drop) the second dense vector (`dense_openai`).
4. Compare `bm25-fa-norm-stop` vs. `bm25-fa-norm-stem` in isolation to
determine whether gains come from stopword removal, stemming, or both.
4. ~~Compare `bm25-fa-norm-stop` vs. `bm25-fa-norm-stem` in isolation~~ —
**done**, see "Benchmark outcome" above. `fa_norm_stop` won; stemming was
not adopted.
5. Sweep BM25 `b` (e.g. 0.5–0.9) for the winning analyzer, since document
length varies significantly across the corpus (short chat messages vs.
long articles) and `0.75` is a generic default, not corpus-tuned.
6. **Recalibrate `avg_len`.** The client-side `b` term needs an average
document length in *analyzer tokens*. The ported value (256.0) is emet's
own placeholder, and emet measured it over short Q&A records rather than
this service's ~400-token chunks, so it is very likely miscalibrated here.
Exposed as `EMBEDDING_SPARSE_AVG_LEN` so it can be corrected from real
corpus statistics without a code change.
7. **Re-benchmark the analyzer with diacritic stripping.** `fa_norm_stop`
does not remove harakat or tatweel, so `ســلام` and `سلام` are distinct
terms. `src/application/ingestion/normalization.py` already strips both
for chunk *content*; extending that to the analyzer is plausibly an
improvement but would deviate from the measured configuration, so it
belongs in an emet run rather than an unmeasured edit.

10
docs/plans/001-ingestion-vertical-slice.md
View File

@@ -247,6 +247,16 @@ code and a terminal job row.
### Phase 5: Ingestion execution and Qdrant Chunk/Point CRUD
> **Carried forward from Phase 4 — the `chunks` collection must create the
> `sparse` vector with `modifier="idf"`.** The BM25 adapter computes only
> term-frequency saturation client-side; IDF comes from Qdrant's
> collection-wide statistics. Omit the modifier and there is no error and no
> warning — sparse scoring silently loses its IDF term and lexical retrieval
> degrades. See ADR-0005, "Benchmark outcome".
>
> Collection creation must also use the pinned dimensions from ADR-0001:
> `dense_nomic` 768, `dense_openai` 3072.
1. Implement the ingestion service called by the route, using the
application-lifetime database, MinIO, Qdrant, model, and logging clients.
2. Validate the persisted records before fetching the MinIO object.

2
pyproject.toml
View File

@@ -9,6 +9,7 @@ dependencies = [
"anyio>=4.11.0",
"asyncpg>=0.31.0",
"fastapi[standard]==0.141.1",
"httpx>=0.28.1",
"langgraph>=1.2.10",
"minio>=7.2.20",
"openpyxl>=3.1.5",
@@ -23,7 +24,6 @@ dependencies = [
[dependency-groups]
dev = [
"asgi-lifespan>=2.1.0",
"httpx>=0.28.1",
"pytest>=8.3.5",
"pytest-asyncio>=0.25.3",
"pytest-cov>=6.0.0",

25
src/api/errors.py
View File

@@ -17,10 +17,22 @@ from src.application.auth.errors import (
TenantInactiveError,
)
from src.application.files.errors import FileTooLargeError, InvalidUploadError
from src.application.ingestion.errors import DocumentParseError, UnsupportedSourceTypeError
from src.application.ingestion.errors import (
ChunkLimitExceededError,
DocumentParseError,
EmbedderError,
IngestionAtCapacityError,
IngestionTimeoutError,
UnsupportedSourceTypeError,
)
logger = structlog.get_logger(__name__)
# A fixed backoff hint, not a computed retry budget: ADR-0017 rejects a
# request outright at capacity rather than queueing it, so there is no
# in-process estimate of when a slot will free up to report instead.
_CAPACITY_RETRY_AFTER_SECONDS = 1
# (exception type, status code, stable error code)
_MAPPING: tuple[tuple[type[Exception], int, str], ...] = (
(InvalidApiKeyError, status.HTTP_401_UNAUTHORIZED, "invalid_api_key"),
@@ -30,6 +42,9 @@ _MAPPING: tuple[tuple[type[Exception], int, str], ...] = (
(DocumentParseError, status.HTTP_400_BAD_REQUEST, "validation_error"),
(UnsupportedSourceTypeError, status.HTTP_415_UNSUPPORTED_MEDIA_TYPE, "unsupported_media_type"),
(FileTooLargeError, status.HTTP_413_CONTENT_TOO_LARGE, "payload_too_large"),
(ChunkLimitExceededError, status.HTTP_413_CONTENT_TOO_LARGE, "payload_too_large"),
(EmbedderError, status.HTTP_502_BAD_GATEWAY, "embedder_error"),
(IngestionTimeoutError, status.HTTP_504_GATEWAY_TIMEOUT, "ingestion_timeout"),
)
@@ -66,6 +81,14 @@ def register_exception_handlers(app: FastAPI) -> None:
app.add_exception_handler(exc_type, _handler)
@app.exception_handler(IngestionAtCapacityError)
def _capacity_handler(request: Request, exc: IngestionAtCapacityError) -> JSONResponse:
return JSONResponse(
status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
content=_envelope("ingestion_at_capacity", str(exc), _request_id(request)),
headers={"Retry-After": str(_CAPACITY_RETRY_AFTER_SECONDS)},
)
@app.exception_handler(RequestValidationError)
def _validation_handler(request: Request, exc: RequestValidationError) -> JSONResponse:
return JSONResponse(

22
src/api/routers/files.py
View File

@@ -5,9 +5,10 @@ touch MinIO/Qdrant, or otherwise carry ingestion business logic (ADR-0015).
"""
import uuid
from collections.abc import Sequence
from typing import Annotated
from anyio import CapacityLimiter
from anyio import CapacityLimiter, Semaphore
from fastapi import APIRouter, Depends, Form, HTTPException, Response, UploadFile, status
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
@@ -16,12 +17,16 @@ from src.api.schemas.files import FileStatusResponse, FileUploadResponse
from src.application.auth.context import AuthContext
from src.application.files.status import get_file_status
from src.application.files.upload import upload_source_file
from src.application.ports.embedding import DenseEmbedder, SparseEmbedder
from src.application.ports.object_storage import ObjectStorage
from src.bootstrap.dependencies import (
get_dense_embedders,
get_ingestion_concurrency_limiter,
get_ingestion_limiter,
get_object_storage,
get_sessionmaker,
get_settings,
get_sparse_embedder,
)
from src.config import Settings
@@ -32,6 +37,9 @@ _SessionmakerDep = Annotated[async_sessionmaker[AsyncSession], Depends(get_sessi
_ObjectStorageDep = Annotated[ObjectStorage, Depends(get_object_storage)]
_SettingsDep = Annotated[Settings, Depends(get_settings)]
_IngestionLimiterDep = Annotated[CapacityLimiter, Depends(get_ingestion_limiter)]
_ConcurrencyLimiterDep = Annotated[Semaphore, Depends(get_ingestion_concurrency_limiter)]
_DenseEmbeddersDep = Annotated[Sequence[DenseEmbedder], Depends(get_dense_embedders)]
_SparseEmbedderDep = Annotated[SparseEmbedder, Depends(get_sparse_embedder)]
@router.post("", status_code=status.HTTP_201_CREATED)
@@ -44,6 +52,9 @@ async def upload_file(
storage: _ObjectStorageDep,
settings: _SettingsDep,
limiter: _IngestionLimiterDep,
concurrency_limiter: _ConcurrencyLimiterDep,
dense_embedders: _DenseEmbeddersDep,
sparse_embedder: _SparseEmbedderDep,
) -> FileUploadResponse:
data = await file.read()
result = await upload_source_file(
@@ -53,9 +64,12 @@ async def upload_file(
domain=domain,
filename=file.filename or "",
data=data,
max_upload_size_bytes=settings.ingestion.max_upload_size_bytes,
chunking_strategy=settings.chunking.strategy,
validation_limiter=limiter,
ingestion_settings=settings.ingestion,
chunking_settings=settings.chunking,
thread_limiter=limiter,
concurrency_limiter=concurrency_limiter,
dense_embedders=dense_embedders,
sparse_embedder=sparse_embedder,
)
if not result.is_new_attempt:
response.status_code = status.HTTP_200_OK

126
src/application/files/upload.py
View File

@@ -5,21 +5,27 @@ one request-scoped session, because the request is two units of work
(ADR-0012, ADR-0017):
txn A (short): source_files [+ ingestion_jobs(status='running')], commit
no txn: store bytes in MinIO
no txn: store bytes in MinIO, parse/chunk (threads),
embed dense+sparse (bounded/batched)
txn B (short): ingestion_jobs -> succeeded/failed, append event, commit
No Postgres session is open during the MinIO write. A storage failure between
No Postgres session is open during phase 2. A failure at any point between
txn A and txn B still leaves a durable, inspectable `failed` job — never a
job stuck in `running`.
job stuck in `running`. The whole request additionally holds one of
`INGESTION_MAX_CONCURRENCY` process-wide slots (`503` when exhausted) and
phase 2 is bounded by `INGESTION_TIMEOUT_SECONDS` (`504`) (ADR-0017, plan 001
Phase 4).
Parsing/chunking/Qdrant indexing are Phase 4/5 work, not implemented here:
this phase stores bytes only, so a successful job reports `chunks_indexed=0`.
Qdrant point upserts are Phase 5 work, not implemented here: this phase
parses, chunks, and embeds, so a successful job still reports
`chunks_indexed=0` — nothing is searchable yet.
"""
import uuid
from collections.abc import Sequence
import structlog
from anyio import CapacityLimiter, to_thread
from anyio import CapacityLimiter, Semaphore, fail_after, to_thread
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
from src.application.auth.context import AuthContext
@@ -27,7 +33,22 @@ from src.application.files.errors import InvalidUploadError
from src.application.files.models import UploadResult
from src.application.files.storage_keys import source_file_object_key
from src.application.files.validation import validate_and_hash_upload
from src.application.ingestion import (
ChunkTooLargeError,
DocumentParseError,
UnsupportedSourceTypeError,
parse_and_chunk_document,
)
from src.application.ingestion.bounds import acquire_ingestion_slot, enforce_chunk_limit
from src.application.ingestion.embedding import embed_chunks
from src.application.ingestion.errors import (
ChunkLimitExceededError,
EmbedderError,
IngestionTimeoutError,
)
from src.application.ports.embedding import DenseEmbedder, SparseEmbedder
from src.application.ports.object_storage import ObjectStorage
from src.config import ChunkingSettings, IngestionSettings
from src.infrastructure.postgres.repositories import ingestion_jobs as jobs_repo
from src.infrastructure.postgres.repositories import source_files as source_files_repo
@@ -71,9 +92,12 @@ async def upload_source_file(
domain: str,
filename: str,
data: bytes,
max_upload_size_bytes: int,
chunking_strategy: str,
validation_limiter: CapacityLimiter,
ingestion_settings: IngestionSettings,
chunking_settings: ChunkingSettings,
thread_limiter: CapacityLimiter,
concurrency_limiter: Semaphore,
dense_embedders: Sequence[DenseEmbedder],
sparse_embedder: SparseEmbedder,
) -> UploadResult:
domain = domain.strip()
if not domain:
@@ -81,11 +105,12 @@ async def upload_source_file(
validated = await to_thread.run_sync(
lambda: validate_and_hash_upload(
filename=filename, data=data, max_size_bytes=max_upload_size_bytes
filename=filename, data=data, max_size_bytes=ingestion_settings.max_upload_size_bytes
),
limiter=validation_limiter,
limiter=thread_limiter,
)
async with acquire_ingestion_slot(concurrency_limiter):
async with sessionmaker() as session:
existing = await source_files_repo.find_active_by_content_hash(
session,
@@ -140,7 +165,7 @@ async def upload_source_file(
tenant_id=auth.tenant_id,
source_file_id=source_file_id,
requested_by_api_key_id=auth.api_key_id,
chunking_strategy=chunking_strategy,
chunking_strategy=chunking_settings.strategy,
)
jobs_repo.append_event(
session,
@@ -153,9 +178,14 @@ async def upload_source_file(
await session.commit()
ingestion_job_id = job.id
# Phase 2: no Postgres session open across this work (ADR-0017).
# Phase 2: no Postgres session open across this work (ADR-0017),
# bounded end-to-end by INGESTION_TIMEOUT_SECONDS.
try:
await storage.put_object(key=object_key, data=data, content_type=validated.content_type)
with fail_after(ingestion_settings.timeout_seconds):
try:
await storage.put_object(
key=object_key, data=data, content_type=validated.content_type
)
except Exception as exc:
logger.warning(
"files.upload.storage_failed",
@@ -172,6 +202,71 @@ async def upload_source_file(
)
raise
try:
chunks = await parse_and_chunk_document(
data,
source_type=validated.source_type,
file_id=source_file_id,
settings=chunking_settings,
limiter=thread_limiter,
)
except (DocumentParseError, UnsupportedSourceTypeError, ChunkTooLargeError) as exc:
await _mark_job_failed(
sessionmaker,
tenant_id=auth.tenant_id,
ingestion_job_id=ingestion_job_id,
error_code="parse_failed",
error_message=str(exc),
)
raise
try:
enforce_chunk_limit(chunks, max_chunks=ingestion_settings.max_chunks_per_file)
except ChunkLimitExceededError as exc:
await _mark_job_failed(
sessionmaker,
tenant_id=auth.tenant_id,
ingestion_job_id=ingestion_job_id,
error_code="chunk_limit_exceeded",
error_message=str(exc),
)
raise
try:
embedded = await embed_chunks(
chunks,
dense_embedders=dense_embedders,
sparse_embedder=sparse_embedder,
settings=ingestion_settings,
thread_limiter=thread_limiter,
)
except EmbedderError as exc:
await _mark_job_failed(
sessionmaker,
tenant_id=auth.tenant_id,
ingestion_job_id=ingestion_job_id,
error_code="embedding_failed",
error_message=str(exc),
)
raise
except TimeoutError:
logger.warning(
"files.upload.timeout",
tenant_id=str(auth.tenant_id),
file_id=str(source_file_id),
ingestion_job_id=str(ingestion_job_id),
)
await _mark_job_failed(
sessionmaker,
tenant_id=auth.tenant_id,
ingestion_job_id=ingestion_job_id,
error_code="timeout",
error_message=f"ingestion exceeded {ingestion_settings.timeout_seconds}s",
)
raise IngestionTimeoutError(
f"ingestion exceeded {ingestion_settings.timeout_seconds}s"
) from None
async with sessionmaker() as session:
await jobs_repo.mark_terminal(
session,
@@ -186,7 +281,8 @@ async def upload_source_file(
ingestion_job_id=ingestion_job_id,
level="info",
stage="completed",
message="object stored; parsing/embedding/indexing not yet implemented",
message="chunks parsed and embedded; Qdrant indexing not yet implemented",
details={"chunks_parsed": len(chunks), "chunks_embedded": len(embedded)},
)
await session.commit()

48
src/application/ingestion/bounds.py Normal file
View File

@@ -0,0 +1,48 @@
"""Request bounds for inline ingestion (ADR-0017).
Three independent bounds, each mapping to its own status code: the chunk
ceiling (`413`, checked before embedding starts), process-wide concurrency
(`503` + `Retry-After`, rejected rather than queued), and the work-phase
deadline (`504`, and the caller must still write a terminal job status).
"""
from collections.abc import AsyncIterator, Sequence
from contextlib import asynccontextmanager
from anyio import Semaphore, WouldBlock
from src.application.ingestion.errors import ChunkLimitExceededError, IngestionAtCapacityError
from src.application.ingestion.models import Chunk
def enforce_chunk_limit(chunks: Sequence[Chunk], *, max_chunks: int) -> None:
"""Raise `ChunkLimitExceededError` if `chunks` exceeds `max_chunks`.
Call this immediately after parsing/chunking and before any embedding
call — the ceiling must be discovered up front, not mid-batch.
"""
if len(chunks) > max_chunks:
raise ChunkLimitExceededError(
f"document produced {len(chunks)} chunks, over the {max_chunks}-chunk limit"
)
@asynccontextmanager
async def acquire_ingestion_slot(limiter: Semaphore) -> AsyncIterator[None]:
"""Hold one of `INGESTION_MAX_CONCURRENCY` process-wide slots for the block.
`limiter` is an `anyio.Semaphore` created once in the lifespan. Rejects
immediately with `IngestionAtCapacityError` when the process is already at
capacity, rather than queueing the request behind an unbounded wait
(ADR-0017) — the semaphore's own async `acquire()` would do the latter.
"""
try:
limiter.acquire_nowait()
except WouldBlock:
raise IngestionAtCapacityError(
"ingestion is at capacity; retry after the configured backoff"
) from None
try:
yield
finally:
limiter.release()

112
src/application/ingestion/embedding.py Normal file
View File

@@ -0,0 +1,112 @@
"""The one caller-facing entry point for embedding chunks (ADR-0001, ADR-0017).
`embed_chunks` is the only version of this step callers should reach for: it
owns batching, the `embed_concurrency` semaphore bounding in-flight dense
batches, and the `anyio.to_thread.run_sync` + `CapacityLimiter` offload for
the blocking BM25 pipeline. Composing these correctly at every call site is
exactly the obligation a deep module absorbs once (see CLAUDE.md's "prefer
deep modules").
Per-provider text shaping — task prefixes, `keep_alive`, request payload —
belongs to the adapters in `src/infrastructure/embedding/`, not here. This
module knows only that an embedder turns texts into vectors.
"""
import asyncio
from collections.abc import Sequence
from functools import partial
from anyio import CapacityLimiter, to_thread
from src.application.ingestion.errors import EmbedderError
from src.application.ingestion.models import Chunk, EmbeddedChunk
from src.application.ports.embedding import DenseEmbedder, SparseEmbedder
from src.config import IngestionSettings
def _batches(texts: Sequence[str], size: int) -> list[Sequence[str]]:
return [texts[i : i + size] for i in range(0, len(texts), size)]
async def _embed_dense_bounded(
embedder: DenseEmbedder,
batch: Sequence[str],
*,
semaphore: asyncio.Semaphore,
) -> list[list[float]]:
async with semaphore:
try:
return await embedder.embed_batch(batch)
except Exception as exc:
raise EmbedderError(f"{embedder.name} embedding batch failed: {exc}") from exc
async def _embed_dense_all(
embedder: DenseEmbedder,
texts: Sequence[str],
*,
batch_size: int,
semaphore: asyncio.Semaphore,
) -> list[list[float]]:
batches = _batches(texts, batch_size)
results = await asyncio.gather(
*(_embed_dense_bounded(embedder, batch, semaphore=semaphore) for batch in batches)
)
return [vector for batch_result in results for vector in batch_result]
def _embed_sparse_sync(embedder: SparseEmbedder, texts: Sequence[str]):
try:
return embedder.embed_batch(texts)
except Exception as exc:
raise EmbedderError(f"{embedder.name} embedding batch failed: {exc}") from exc
async def embed_chunks(
chunks: Sequence[Chunk],
*,
dense_embedders: Sequence[DenseEmbedder],
sparse_embedder: SparseEmbedder,
settings: IngestionSettings,
thread_limiter: CapacityLimiter,
) -> list[EmbeddedChunk]:
"""Embed every chunk into all dense vectors plus the sparse vector.
Dense embedders run concurrently with each other; each one's batches are
concurrent among themselves too, bounded by one `embed_concurrency`
semaphore shared across all dense embedders (ADR-0017: the limit exists
for both providers' rate limits and the self-hosted server's capacity —
not a per-provider budget). The sparse (BM25) pass is blocking and runs
once, off the event loop.
Raises `EmbedderError` (502) if any embedder call fails.
"""
if not chunks:
return []
texts = [chunk.content for chunk in chunks]
semaphore = asyncio.Semaphore(settings.embed_concurrency)
dense_task = asyncio.gather(
*(
_embed_dense_all(
embedder, texts, batch_size=settings.embed_batch_size, semaphore=semaphore
)
for embedder in dense_embedders
)
)
sparse_task = to_thread.run_sync(
partial(_embed_sparse_sync, sparse_embedder, texts), limiter=thread_limiter
)
dense_results, sparse_vectors = await asyncio.gather(dense_task, sparse_task)
dense_by_name = {
embedder.name: vectors
for embedder, vectors in zip(dense_embedders, dense_results, strict=True)
}
embedded: list[EmbeddedChunk] = []
for index, chunk in enumerate(chunks):
dense = {name: vectors[index] for name, vectors in dense_by_name.items()}
embedded.append(EmbeddedChunk(chunk=chunk, dense=dense, sparse=sparse_vectors[index]))
return embedded

23
src/application/ingestion/errors.py
View File

@@ -39,3 +39,26 @@ class ChunkTooLargeError(IngestionError):
against is silent — `nomic-embed-text-v2-moe` truncates over-long input
without raising (ADR-0004).
"""
class EmbedderError(IngestionError):
"""A dense or sparse embedder call failed (transport error, non-2xx, or
a malformed response).
Maps to `502` per ADR-0017.
"""
class IngestionAtCapacityError(IngestionError):
"""`INGESTION_MAX_CONCURRENCY` in-process ingestions are already running.
Maps to `503` with `Retry-After`, not a queued wait (ADR-0017).
"""
class IngestionTimeoutError(IngestionError):
"""The work phase (parse/embed/upsert) exceeded `INGESTION_TIMEOUT_SECONDS`.
Maps to `504`. The caller must still write a terminal `failed` job status
before this propagates (ADR-0017).
"""

25
src/application/ingestion/models.py
View File

@@ -64,3 +64,28 @@ class Chunk(BaseModel):
next_chunk_id: uuid.UUID | None = None
token_count: int
character_count: int
class SparseVector(BaseModel):
"""A sparse (term-index -> weight) vector, Qdrant's `modifier="idf"` shape.
Kept free of the `qdrant_client` SDK (ADR-0015: ports carry no infra
imports) — `src/infrastructure/qdrant/` converts this to the SDK's own
`SparseVector` type at upsert time (Phase 5).
"""
indices: list[int]
values: list[float]
class EmbeddedChunk(BaseModel):
"""A chunk plus every vector it will be upserted with (ADR-0001).
`dense` is keyed by named-vector name (`dense_nomic`, `dense_openai`).
`late_interaction` is deliberately absent — not computed at ingest
(ADR-0017).
"""
chunk: Chunk
dense: dict[str, list[float]]
sparse: SparseVector

49
src/application/ports/embedding.py Normal file
View File

@@ -0,0 +1,49 @@
"""Embedding ports (ADR-0001, ADR-0017).
`src/infrastructure/embedding/` holds the production adapters; tests use
scripted fakes (ADR-0016). Application code depends on these Protocols, not
on `httpx`/provider SDKs directly.
"""
from collections.abc import Sequence
from typing import Protocol
from src.application.ingestion.models import SparseVector
class DenseEmbedder(Protocol):
"""One named dense vector's embedding client (`dense_nomic`/`dense_openai`).
`embed_batch` is a single batched network call — callers own concurrency
bounding (ADR-0017's `embed_concurrency` semaphore), not this Protocol.
"""
name: str
async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
"""Return one vector per input text, same order. Raises `EmbedderError`
(see `src/application/ingestion/errors.py`) on transport/response
failure.
"""
...
class SparseEmbedder(Protocol):
"""The `sparse` (BM25) vector's embedding client.
Blocking/CPU-bound (ADR-0017): callers offload it via
`anyio.to_thread.run_sync` with the ingestion `CapacityLimiter`, not call
it directly from an `async def`.
"""
name: str
def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
"""Return one sparse vector per input text, same order.
`query=True` selects the query-side weighting, which omits document
length normalization. Ingestion always passes `False`; the flag exists
so retrieval (ADR-0003) encodes queries through this same port rather
than growing a second, silently divergent implementation.
"""
...

20
src/bootstrap/dependencies.py
View File

@@ -1,12 +1,13 @@
from collections.abc import AsyncIterator
from collections.abc import AsyncIterator, Sequence
from dataclasses import dataclass
from anyio import CapacityLimiter
from anyio import CapacityLimiter, Semaphore
from fastapi import Request
from minio import Minio
from qdrant_client import AsyncQdrantClient
from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, async_sessionmaker
from src.application.ports.embedding import DenseEmbedder, SparseEmbedder
from src.application.ports.object_storage import ObjectStorage
from src.config import Settings
@@ -20,6 +21,9 @@ class AppResources:
qdrant_client: AsyncQdrantClient
object_storage: ObjectStorage
ingestion_limiter: CapacityLimiter
dense_embedders: Sequence[DenseEmbedder]
sparse_embedder: SparseEmbedder
ingestion_concurrency_limiter: Semaphore
def _resources(request: Request) -> AppResources:
@@ -46,6 +50,18 @@ def get_ingestion_limiter(request: Request) -> CapacityLimiter:
return _resources(request).ingestion_limiter
def get_dense_embedders(request: Request) -> Sequence[DenseEmbedder]:
return _resources(request).dense_embedders
def get_sparse_embedder(request: Request) -> SparseEmbedder:
return _resources(request).sparse_embedder
def get_ingestion_concurrency_limiter(request: Request) -> Semaphore:
return _resources(request).ingestion_concurrency_limiter
def get_sessionmaker(request: Request) -> async_sessionmaker[AsyncSession]:
"""The session *factory*, not a request-scoped session.

94
src/bootstrap/lifespan.py
View File

@@ -1,13 +1,20 @@
from collections.abc import AsyncIterator, Callable
from collections.abc import AsyncIterator, Callable, Sequence
from contextlib import AbstractAsyncContextManager, asynccontextmanager
import httpx
import structlog
from anyio import CapacityLimiter, to_thread
from anyio import CapacityLimiter, Semaphore, to_thread
from fastapi import FastAPI
from src.application.ingestion import get_encoder
from src.application.ports.embedding import DenseEmbedder
from src.bootstrap.dependencies import AppResources
from src.config import Settings
from src.infrastructure.embedding.bm25 import Bm25SparseEmbedder
from src.infrastructure.embedding.openai_compatible import (
OpenAICompatibleEmbedder,
is_ollama_base_url,
)
from src.infrastructure.minio.client import create_client as create_minio_client
from src.infrastructure.minio.storage import MinioObjectStorage
from src.infrastructure.observability.logging import configure_logging
@@ -17,6 +24,34 @@ from src.infrastructure.qdrant.client import create_client as create_qdrant_clie
logger = structlog.get_logger(__name__)
def _auth_headers(api_key: str | None) -> dict[str, str]:
"""Bearer header, or none at all when no key is configured.
Sending an empty `Bearer ` is worse than sending nothing: some gateways
treat a malformed credential as an auth failure rather than as anonymous.
"""
return {"Authorization": f"Bearer {api_key}"} if api_key else {}
async def _warm_dense_embedders(embedders: Sequence[DenseEmbedder]) -> None:
"""Force each dense model to load before the first upload needs it.
Same rationale as the tiktoken warm-up above, but with the opposite
failure policy. A self-hosted embedder that has unloaded the model takes
minutes to serve its first request — longer than
`INGESTION_TIMEOUT_SECONDS` — so paying that once at boot keeps it off a
user's upload. Unlike the tokenizer this is best-effort: an embedder that
is merely *down* must not stop the process from booting and reporting its
own health, and `/readyz` is where that condition belongs.
"""
for embedder in embedders:
try:
await embedder.embed_batch(["warmup"])
logger.info("lifespan.embedder.warmed", embedder=embedder.name)
except Exception:
logger.warning("lifespan.embedder.warm_failed", embedder=embedder.name, exc_info=True)
def create_lifespan(
settings: Settings | None = None,
) -> Callable[[FastAPI], AbstractAsyncContextManager[None, bool | None]]:
@@ -44,6 +79,48 @@ def create_lifespan(
qdrant_client = create_qdrant_client(resolved_settings.qdrant)
logger.info("lifespan.qdrant.client.created")
nomic_settings = resolved_settings.embedding.nomic
nomic_http_client = httpx.AsyncClient(
base_url=nomic_settings.base_url,
timeout=nomic_settings.timeout_seconds,
headers=_auth_headers(nomic_settings.api_key),
)
openai_settings = resolved_settings.embedding.openai
openai_http_client = httpx.AsyncClient(
base_url=openai_settings.base_url,
timeout=openai_settings.timeout_seconds,
headers=_auth_headers(openai_settings.api_key),
)
dense_embedders = (
OpenAICompatibleEmbedder(
nomic_http_client,
name="dense_nomic",
model=nomic_settings.model,
document_prefix=nomic_settings.document_prefix,
keep_alive=(
nomic_settings.keep_alive
if is_ollama_base_url(nomic_settings.base_url)
else None
),
),
OpenAICompatibleEmbedder(
openai_http_client,
name="dense_openai",
model=openai_settings.model,
dimensions=openai_settings.dimensions,
document_prefix=openai_settings.document_prefix,
),
)
sparse_embedder = Bm25SparseEmbedder(resolved_settings.embedding.sparse)
logger.info("lifespan.embedders.created")
await _warm_dense_embedders(dense_embedders)
# Bounds how many ingestions run in this process at once (ADR-0017);
# a distinct resource from ingestion_limiter, which bounds threads
# spent on blocking work within a single ingestion.
ingestion_concurrency_limiter = Semaphore(resolved_settings.ingestion.max_concurrency)
# Bounds threads spent on blocking ingestion work (parsing, chunking,
# hashing, the sync minio SDK) so it cannot exhaust Starlette's own
# thread pool (ADR-0017).
@@ -60,6 +137,9 @@ def create_lifespan(
qdrant_client=qdrant_client,
object_storage=object_storage,
ingestion_limiter=ingestion_limiter,
dense_embedders=dense_embedders,
sparse_embedder=sparse_embedder,
ingestion_concurrency_limiter=ingestion_concurrency_limiter,
)
try:
@@ -75,4 +155,14 @@ def create_lifespan(
except Exception:
logger.exception("lifespan.qdrant.close.failed")
try:
await nomic_http_client.aclose()
except Exception:
logger.exception("lifespan.embedding.nomic_client.close.failed")
try:
await openai_http_client.aclose()
except Exception:
logger.exception("lifespan.embedding.openai_client.close.failed")
return lifespan

74
src/config.py
View File

@@ -87,6 +87,79 @@ class QdrantSettings(BaseSettings):
api_key: str | None = None
class NomicEmbeddingSettings(BaseSettings):
"""Self-hosted `nomic-embed-text-v2-moe` server (ADR-0001, 768-dim).
Talks the OpenAI-compatible `/embeddings` endpoint shape. The default
points at the Ollama host the `emet` benchmark used, whose OpenAI-compat
shim accepts any non-empty API key.
`document_prefix` is empty by design. ADR-0004 cites the model card's
`search_document: ` requirement, but emet's winning run used no prefix
(Ollama's template is a bare passthrough and injects none), and the
prefix is not cosmetic -- it moves the vector substantially. Turning it
on here obliges the query side to send `search_query: ` too (ADR-0003),
so it stays off until an emet run measures the pair together.
"""
model_config = SettingsConfigDict(env_prefix="EMBEDDING_NOMIC_", extra="ignore")
base_url: str = "http://192.168.10.10:11435/v1"
model: str = "nomic-embed-text-v2-moe"
api_key: str = "sk-not-set"
document_prefix: str = ""
keep_alive: str = "30m"
timeout_seconds: float = 30.0
class OpenaiEmbeddingSettings(BaseSettings):
"""OpenAI's hosted embedding API (ADR-0001's second dense signal).
`dimensions` is unset, so `text-embedding-3-large` returns its native
3072 dimensions -- the configuration emet benchmarked. Setting it would
truncate via Matryoshka and is a re-embedding migration, not a config
tweak.
"""
model_config = SettingsConfigDict(env_prefix="EMBEDDING_OPENAI_", extra="ignore")
base_url: str = "https://api.openai.com/v1"
model: str = "text-embedding-3-large"
api_key: str | None = None
dimensions: int | None = None
document_prefix: str = ""
timeout_seconds: float = 30.0
class SparseEmbeddingSettings(BaseSettings):
"""BM25 sparse-vector parameters (ADR-0001, ADR-0005).
`analyzer`, `k`, and `b` are the configuration emet benchmarked as
`bm25-fa-norm-stop`; changing them invalidates that result. `k`/`b`
saturation is applied client-side here, while IDF comes from Qdrant's
`modifier="idf"` sparse-vector config at query time.
`avg_len` is emet's placeholder average document length in analyzer
tokens, exposed as a setting so it can be recalibrated from real corpus
statistics without a code change.
"""
model_config = SettingsConfigDict(env_prefix="EMBEDDING_SPARSE_", extra="ignore")
analyzer: str = "fa_norm_stop"
k: float = 1.2
b: float = 0.75
avg_len: float = 256.0
class EmbeddingSettings(BaseSettings):
model_config = SettingsConfigDict(extra="ignore")
nomic: NomicEmbeddingSettings = Field(default_factory=NomicEmbeddingSettings)
openai: OpenaiEmbeddingSettings = Field(default_factory=OpenaiEmbeddingSettings)
sparse: SparseEmbeddingSettings = Field(default_factory=SparseEmbeddingSettings)
class AppLimitSettings(BaseSettings):
model_config = SettingsConfigDict(env_prefix="APP_", extra="ignore")
@@ -109,5 +182,6 @@ class Settings(BaseSettings):
ingestion: IngestionSettings = Field(default_factory=IngestionSettings)
chunking: ChunkingSettings = Field(default_factory=ChunkingSettings)
qdrant: QdrantSettings = Field(default_factory=QdrantSettings)
embedding: EmbeddingSettings = Field(default_factory=EmbeddingSettings)
app: AppLimitSettings = Field(default_factory=AppLimitSettings)
logging: LoggingSettings = Field(default_factory=LoggingSettings)

0
src/infrastructure/embedding/__init__.py Normal file
View File

150
src/infrastructure/embedding/analyzers.py Normal file
View File

@@ -0,0 +1,150 @@
"""The `fa_norm_stop` BM25 analyzer (ADR-0001, ADR-0005).
Ported from the `emet` evaluation lab
(`src/chatbot_gh/adapters/sparse/analyzers.py`), which benchmarked four Farsi
analyzer variants on the real corpus and found `fa_norm_stop` the best
performer. This is a *measured* artifact: changing the normalization,
tokenization, or stopword list invalidates that result, so improvements belong
in a new emet benchmark run rather than in an edit here.
Deliberately independent of `src/application/ingestion/normalization.py`.
Those solve different problems: `normalize_persian_text` shapes chunk content
that gets cited back to the reader, so ADR-0018 has it preserve digits and
punctuation as authored. This module shapes index terms nobody ever sees, so
it folds digits and diacritics freely. Sharing one function between them would
let a display-motivated tweak silently perturb the benchmarked sparse index.
"""
import re
import unicodedata
# Several Arabic letterforms are visually indistinguishable from Latin ones in
# a monospace editor (alef from "l", heh from "o"), and literals render
# right-to-left, visually reordering the source line. `normalization.py` writes
# them as codepoints for that reason; this module follows the same convention.
_ZWNJ = 0x200C
_ARABIC_YEH = 0x064A
_ARABIC_KAF = 0x0643
_TEH_MARBUTA = 0x0629
_HAMZA_ON_WAW = 0x0624
_ALEF_HAMZA_BELOW = 0x0625
_ALEF_HAMZA_ABOVE = 0x0623
_PERSIAN_YEH = 0x06CC
_PERSIAN_KEHEH = 0x06A9
_HEH = 0x0647
_WAW = 0x0648
_ALEF = 0x0627
_SPACE = 0x0020
# Persian (U+06F0-U+06F9) and Arabic-Indic (U+0660-U+0669) digits both fold to
# ASCII, so the same number matches however it was authored.
_EASTERN_DIGITS = str.maketrans("۰۱۲۳۴۵۶۷۸۹٠١٢٣٤٥٦٧٨٩", "01234567890123456789")
# ZWNJ becomes a space (splitting compounds into separate terms) and the
# Arabic letterforms fold to their Persian equivalents. Every entry is a
# single codepoint mapping to a single codepoint over disjoint sources, so
# applying them in one pass is equivalent to emet's chained `str.replace`
# calls -- provided NFC runs first, since NFC is what composes the hamza
# forms this table then folds.
_FOLDING: dict[int, int] = {
_ZWNJ: _SPACE,
_ARABIC_YEH: _PERSIAN_YEH,
_ARABIC_KAF: _PERSIAN_KEHEH,
_TEH_MARBUTA: _HEH,
_HAMZA_ON_WAW: _WAW,
_ALEF_HAMZA_BELOW: _ALEF,
_ALEF_HAMZA_ABOVE: _ALEF,
}
_FOLDING.update(_EASTERN_DIGITS)
# Common Persian/Arabic stopwords (function words + FAQ noise), plus the
# English function words that appear in a mixed-script corpus. Kept small and
# explicit -- deliberately not a full hazm list. `_HEH_ALEF` is the plural
# suffix "ha"; written as a codepoint pair because both of its letters are
# Latin-confusable, which is exactly the case Ruff's RUF001 flags.
_HEH_ALEF = chr(_HEH) + chr(_ALEF)
_PERSIAN_STOPWORDS: frozenset[str] = frozenset(
{
"و",
"در",
"به",
"از",
"که",
"این",
"را",
"با",
"برای",
"آن",
"یک",
"است",
"شد",
"شده",
"می",
"های",
_HEH_ALEF,
"یا",
"تا",
"بر",
"اگر",
"هم",
"نیز",
"ولی",
"اما",
"چه",
"چون",
"روی",
"پس",
"پیش",
"هر",
"هیچ",
"بود",
"باشد",
"هست",
"نیست",
"کند",
"کرد",
"کردن",
"شود",
"the",
"a",
"an",
"of",
"to",
"and",
"in",
"on",
"for",
"is",
"are",
}
)
# Word characters minus underscore. Note this KEEPS digits: an insurance
# corpus is full of policy numbers, dates, and amounts, and those are exactly
# the tokens a lexical index should be able to match on.
_TOKEN_RE = re.compile(r"[^\W_]+", re.UNICODE)
FA_NORM_STOP = "fa_norm_stop"
def _normalize_fa(text: str) -> str:
return unicodedata.normalize("NFC", text).translate(_FOLDING)
def _tokenize_raw(text: str) -> list[str]:
return [m.group(0).lower() for m in _TOKEN_RE.finditer(text)]
def analyze(text: str, analyzer: str = FA_NORM_STOP) -> list[str]:
"""Tokenize `text` into sparse-index terms.
Only `fa_norm_stop` is implemented -- emet's other three variants
(`raw`, `fa_norm`, `fa_norm_stem`) lost the benchmark and exist there as
experiment arms, not as configurations this service should run.
"""
if analyzer != FA_NORM_STOP:
raise ValueError(f"Unknown analyzer '{analyzer}'")
tokens = _tokenize_raw(_normalize_fa(text))
return [token for token in tokens if token not in _PERSIAN_STOPWORDS]

92
src/infrastructure/embedding/bm25.py Normal file
View File

@@ -0,0 +1,92 @@
"""The `bm25-fa-norm-stop` sparse embedder (ADR-0001, ADR-0005).
Ported from the `emet` evaluation lab
(`src/chatbot_gh/adapters/sparse/bm25_embedder.py`), the configuration that
won its Farsi analyzer benchmark. Not Qdrant's hosted `Qdrant/bm25` FastEmbed
model, whose documented language support omits Farsi (ADR-0005).
**The BM25 work is split across two systems.** This adapter applies the
term-frequency saturation half client-side -- the `k` and `b` parameters,
including document-length normalization. IDF is *not* computed here: Qdrant
supplies it from collection-wide statistics when the sparse vector field is
created with `modifier="idf"`.
That split is load-bearing. A Qdrant collection created without
`modifier="idf"` will silently score these vectors as saturated term
frequencies with no IDF weighting at all -- no error, just materially worse
lexical retrieval. The collection bootstrap (plan 001 Phase 5) must set it.
"""
from collections import Counter
from collections.abc import Sequence
from hashlib import blake2b
from src.application.ingestion.models import SparseVector
from src.config import SparseEmbeddingSettings
from src.infrastructure.embedding.analyzers import analyze
# Qdrant sparse indices must be non-negative and fit a signed 32-bit int.
_INDEX_SPACE = 2**31 - 1
def _token_index(token: str) -> int:
"""Map a term to its sparse-vector index.
A hash rather than a vocabulary table, so the mapping needs no shared
state and stays identical across processes, restarts, and — critically —
between ingest-time and query-time encoding. `blake2b` rather than
`hash()`, which is PYTHONHASHSEED-salted and therefore differs per
process.
"""
digest = blake2b(token.encode("utf-8"), digest_size=8).digest()
return int.from_bytes(digest, "big") % _INDEX_SPACE
def text_to_sparse_vector(
text: str, *, settings: SparseEmbeddingSettings, query: bool = False
) -> SparseVector:
"""Encode one text as a BM25-saturated sparse vector (IDF applied by Qdrant).
Document and query sides differ in exactly one term: documents carry the
`b` length normalization, queries do not (standard BM25 practice — a
query's own length should not discount its terms).
"""
tokens = analyze(text, settings.analyzer)
if not tokens:
return SparseVector(indices=[], values=[])
frequencies = Counter(tokens)
doc_length = float(len(tokens))
k = settings.k
b = settings.b
indices: list[int] = []
values: list[float] = []
# Sorted so the emitted vector is deterministic for a given text, which
# keeps re-ingestion byte-stable and makes the output testable.
for token, freq in sorted(frequencies.items()):
if query:
weight = freq * (k + 1.0) / (freq + k)
else:
weight = freq * (k + 1.0) / (freq + k * (1.0 - b + b * doc_length / settings.avg_len))
indices.append(_token_index(token))
values.append(float(weight))
return SparseVector(indices=indices, values=values)
class Bm25SparseEmbedder:
"""A `SparseEmbedder` (see `src/application/ports/embedding.py`).
Pure CPU work with no network calls, so it is blocking: callers offload it
via `anyio.to_thread.run_sync` with the ingestion `CapacityLimiter`
(ADR-0017), never awaiting it directly on the event loop.
"""
name = "sparse"
def __init__(self, settings: SparseEmbeddingSettings) -> None:
self._settings = settings
def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
return [text_to_sparse_vector(text, settings=self._settings, query=query) for text in texts]

81
src/infrastructure/embedding/openai_compatible.py Normal file
View File

@@ -0,0 +1,81 @@
"""Dense embedding adapter for OpenAI-compatible `/embeddings` endpoints.
Backs both `dense_nomic` (self-hosted `nomic-embed-text-v2-moe` behind
Ollama's OpenAI-compatible shim) and `dense_openai` (OpenAI's hosted API) —
both speak the same request/response shape, so one adapter serves both named
vectors with different config (ADR-0001).
Uses `httpx` directly rather than the `openai` SDK. The `emet` benchmark this
configuration comes from uses the SDK, but it is a synchronous batch tool;
ADR-0017 requires async, semaphore-bounded batches here, and the request shape
is small enough that the SDK earns nothing.
`httpx.AsyncClient` is application-lifetime (ADR-0012): built once in the
FastAPI lifespan and passed in, never constructed per call.
"""
from collections.abc import Sequence
from urllib.parse import urlparse
import httpx
# Ollama's default port, plus the alternate the benchmarked deployment uses.
_OLLAMA_PORTS = frozenset({11434, 11435})
def is_ollama_base_url(base_url: str) -> bool:
"""Whether `base_url` looks like an Ollama OpenAI-compatible endpoint.
Ollama unloads an idle model, and reloading `nomic-embed-text-v2-moe`
costs well over two minutes — longer than `INGESTION_TIMEOUT_SECONDS`, so
a cold upload would 504. `keep_alive` is how the model is kept resident,
and it is an Ollama extension, hence the sniffing.
"""
parsed = urlparse(base_url)
host = parsed.hostname or ""
return parsed.port in _OLLAMA_PORTS or "ollama" in host.lower()
class OpenAICompatibleEmbedder:
"""A `DenseEmbedder` (see `src/application/ports/embedding.py`) over one
OpenAI-compatible `/embeddings` endpoint.
"""
def __init__(
self,
client: httpx.AsyncClient,
*,
name: str,
model: str,
dimensions: int | None = None,
document_prefix: str = "",
keep_alive: str | None = None,
) -> None:
self.name = name
self._client = client
self._model = model
self._dimensions = dimensions
self._document_prefix = document_prefix
self._keep_alive = keep_alive
async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
inputs = (
[f"{self._document_prefix}{text}" for text in texts]
if self._document_prefix
else list(texts)
)
payload: dict[str, object] = {"model": self._model, "input": inputs}
if self._dimensions is not None:
payload["dimensions"] = self._dimensions
if self._keep_alive is not None:
payload["keep_alive"] = self._keep_alive
response = await self._client.post("/embeddings", json=payload)
response.raise_for_status()
body = response.json()
# Sort by `index` rather than trusting response order: the contract
# guarantees the field, not the ordering, and a silently permuted
# batch would attach every vector to the wrong chunk.
data = sorted(body["data"], key=lambda item: item["index"])
return [item["embedding"] for item in data]

10
tests/conftest.py
View File

@@ -12,12 +12,22 @@ from src.main import create_app
@pytest.fixture
def settings() -> Settings:
# Every external dependency points at a closed port so unit tests never
# reach real infrastructure. For the embedders this matters twice over:
# the real defaults are a colleague's Ollama box and OpenAI's paid API,
# and ADR-0016 forbids routine runs calling either. Connection-refused is
# immediate, so the lifespan's fail-soft warm-up costs nothing here --
# and these tests passing at all is what proves it is fail-soft.
return Settings(
postgres={"host": "127.0.0.1", "port": 1},
minio={"endpoint": "127.0.0.1:1"},
ingestion={"timeout_seconds": 1.0},
qdrant={"url": "http://127.0.0.1:1"},
app={"readiness_check_timeout_seconds": 0.5},
embedding={
"nomic": {"base_url": "http://127.0.0.1:1/v1", "timeout_seconds": 0.5},
"openai": {"base_url": "http://127.0.0.1:1/v1", "timeout_seconds": 0.5},
},
)

43
tests/fakes.py
View File

@@ -1,7 +1,11 @@
"""Hand-written fakes for narrow application-owned ports (ADR-0016)."""
import asyncio
from collections.abc import Sequence
from dataclasses import dataclass, field
from src.application.ingestion.models import SparseVector
@dataclass
class FakeObjectStorage:
@@ -15,3 +19,42 @@ class FakeObjectStorage:
self.fail_next = False
raise OSError("simulated object storage failure")
self.objects[key] = data
@dataclass
class FakeDenseEmbedder:
"""A scripted `DenseEmbedder`. Returns a fixed-dimension zero vector per
text by default; `fail_next` simulates one batch failure.
"""
name: str
dimensions: int = 4
calls: list[list[str]] = field(default_factory=list)
fail_next: bool = False
delay_seconds: float = 0.0
"""Simulates a slow provider call, e.g. to exercise timeout handling."""
async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
self.calls.append(list(texts))
if self.delay_seconds:
await asyncio.sleep(self.delay_seconds)
if self.fail_next:
self.fail_next = False
raise RuntimeError("simulated embedder failure")
return [[0.0] * self.dimensions for _ in texts]
@dataclass
class FakeSparseEmbedder:
"""A scripted `SparseEmbedder`. Returns an empty sparse vector per text."""
name: str = "sparse"
calls: list[list[str]] = field(default_factory=list)
fail_next: bool = False
def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
self.calls.append(list(texts))
if self.fail_next:
self.fail_next = False
raise RuntimeError("simulated embedder failure")
return [SparseVector(indices=[], values=[]) for _ in texts]

210
tests/integration/postgres/test_upload_service.py
View File

@@ -1,19 +1,23 @@
"""ADR-0017's two-transaction upload shape, exercised against real Postgres.
Object storage is faked (`FakeObjectStorage`) -- it's a port, not the thing
under test here. `MinioObjectStorage` itself is covered in
`tests/integration/minio/`.
Object storage and the embedders are faked (`tests/fakes.py`) -- they're
ports, not the thing under test here. `MinioObjectStorage` itself is covered
in `tests/integration/minio/`.
"""
import pytest
from anyio import CapacityLimiter
from anyio import CapacityLimiter, Semaphore
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
from src.application.auth.context import AuthContext
from src.application.files.models import UploadResult
from src.application.files.upload import upload_source_file
from src.application.ingestion.errors import IngestionAtCapacityError, IngestionTimeoutError
from src.application.ports.object_storage import ObjectStorage
from src.config import ChunkingSettings, IngestionSettings
from src.infrastructure.postgres.models.ingestion_job import IngestionJob
from tests.fakes import FakeObjectStorage
from tests.fakes import FakeDenseEmbedder, FakeObjectStorage, FakeSparseEmbedder
from tests.support.factories import create_api_key, create_tenant
pytestmark = [
@@ -25,6 +29,34 @@ pytestmark = [
_CSV_BYTES = b"name,value\nfirst,1\n"
async def _upload(
*,
sessionmaker: async_sessionmaker[AsyncSession],
storage: ObjectStorage,
auth: AuthContext,
domain: str = "general",
filename: str = "report.csv",
data: bytes = _CSV_BYTES,
) -> UploadResult:
return await upload_source_file(
sessionmaker=sessionmaker,
storage=storage,
auth=auth,
domain=domain,
filename=filename,
data=data,
ingestion_settings=IngestionSettings(),
chunking_settings=ChunkingSettings(),
thread_limiter=CapacityLimiter(2),
concurrency_limiter=Semaphore(2),
dense_embedders=[
FakeDenseEmbedder(name="dense_nomic"),
FakeDenseEmbedder(name="dense_openai"),
],
sparse_embedder=FakeSparseEmbedder(),
)
async def _auth_for(db_session: AsyncSession) -> AuthContext:
tenant = await create_tenant(db_session)
api_key, _ = await create_api_key(db_session, tenant=tenant)
@@ -48,17 +80,7 @@ async def test_upload_source_file_commits_running_job_before_storage_write(
auth = await _auth_for(db_session)
storage = FakeObjectStorage()
result = await upload_source_file(
sessionmaker=db_sessionmaker,
storage=storage,
auth=auth,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
max_upload_size_bytes=1_000_000,
chunking_strategy="fixed_size",
validation_limiter=CapacityLimiter(2),
)
result = await _upload(sessionmaker=db_sessionmaker, storage=storage, auth=auth)
assert result.status == "succeeded"
assert result.chunks_indexed == 0
@@ -77,28 +99,8 @@ async def test_upload_source_file_duplicate_hash_returns_existing_without_reinge
auth = await _auth_for(db_session)
storage = FakeObjectStorage()
first = await upload_source_file(
sessionmaker=db_sessionmaker,
storage=storage,
auth=auth,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
max_upload_size_bytes=1_000_000,
chunking_strategy="fixed_size",
validation_limiter=CapacityLimiter(2),
)
second = await upload_source_file(
sessionmaker=db_sessionmaker,
storage=storage,
auth=auth,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
max_upload_size_bytes=1_000_000,
chunking_strategy="fixed_size",
validation_limiter=CapacityLimiter(2),
)
first = await _upload(sessionmaker=db_sessionmaker, storage=storage, auth=auth)
second = await _upload(sessionmaker=db_sessionmaker, storage=storage, auth=auth)
assert second.file_id == first.file_id
assert second.ingestion_job_id == first.ingestion_job_id
@@ -117,17 +119,7 @@ async def test_upload_source_file_retries_after_failed_job(
failing_storage = FakeObjectStorage(fail_next=True)
with pytest.raises(OSError):
await upload_source_file(
sessionmaker=db_sessionmaker,
storage=failing_storage,
auth=auth,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
max_upload_size_bytes=1_000_000,
chunking_strategy="fixed_size",
validation_limiter=CapacityLimiter(2),
)
await _upload(sessionmaker=db_sessionmaker, storage=failing_storage, auth=auth)
async with db_sessionmaker() as verify_session:
jobs = (
@@ -142,17 +134,7 @@ async def test_upload_source_file_retries_after_failed_job(
assert len(jobs) == 1
assert jobs[0].status == "failed"
retry = await upload_source_file(
sessionmaker=db_sessionmaker,
storage=FakeObjectStorage(),
auth=auth,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
max_upload_size_bytes=1_000_000,
chunking_strategy="fixed_size",
validation_limiter=CapacityLimiter(2),
)
retry = await _upload(sessionmaker=db_sessionmaker, storage=FakeObjectStorage(), auth=auth)
assert retry.status == "succeeded"
assert retry.is_new_attempt
@@ -168,27 +150,91 @@ async def test_upload_source_file_is_tenant_isolated_for_identical_content(
auth_a = await _auth_for(db_session)
auth_b = await _auth_for(db_session)
result_a = await upload_source_file(
sessionmaker=db_sessionmaker,
storage=FakeObjectStorage(),
auth=auth_a,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
max_upload_size_bytes=1_000_000,
chunking_strategy="fixed_size",
validation_limiter=CapacityLimiter(2),
)
result_b = await upload_source_file(
sessionmaker=db_sessionmaker,
storage=FakeObjectStorage(),
auth=auth_b,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
max_upload_size_bytes=1_000_000,
chunking_strategy="fixed_size",
validation_limiter=CapacityLimiter(2),
)
result_a = await _upload(sessionmaker=db_sessionmaker, storage=FakeObjectStorage(), auth=auth_a)
result_b = await _upload(sessionmaker=db_sessionmaker, storage=FakeObjectStorage(), auth=auth_b)
assert result_a.file_id != result_b.file_id
async def test_upload_source_file_timeout_writes_failed_job_and_raises(
db_session: AsyncSession, db_sessionmaker: async_sessionmaker[AsyncSession]
) -> None:
"""A work phase that outruns `INGESTION_TIMEOUT_SECONDS` must still leave
a terminal `failed` job -- never one stuck in `running` (ADR-0017, plan
001 Phase 4).
"""
auth = await _auth_for(db_session)
slow_embedder = FakeDenseEmbedder(name="dense_nomic", delay_seconds=10)
with pytest.raises(IngestionTimeoutError):
await upload_source_file(
sessionmaker=db_sessionmaker,
storage=FakeObjectStorage(),
auth=auth,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
ingestion_settings=IngestionSettings(timeout_seconds=0.05),
chunking_settings=ChunkingSettings(),
thread_limiter=CapacityLimiter(2),
concurrency_limiter=Semaphore(2),
dense_embedders=[slow_embedder, FakeDenseEmbedder(name="dense_openai")],
sparse_embedder=FakeSparseEmbedder(),
)
async with db_sessionmaker() as verify_session:
jobs = (
(
await verify_session.execute(
select(IngestionJob).where(IngestionJob.tenant_id == auth.tenant_id)
)
)
.scalars()
.all()
)
assert len(jobs) == 1
assert jobs[0].status == "failed"
assert jobs[0].error_code == "timeout"
async def test_upload_source_file_at_capacity_rejects_before_any_job_row(
db_session: AsyncSession, db_sessionmaker: async_sessionmaker[AsyncSession]
) -> None:
"""`INGESTION_MAX_CONCURRENCY` rejects outright rather than queuing
(ADR-0017) -- and does so before txn A, so a rejected request leaves no
job row behind at all.
"""
auth = await _auth_for(db_session)
concurrency_limiter = Semaphore(1)
concurrency_limiter.acquire_nowait() # simulate the one slot already in use
with pytest.raises(IngestionAtCapacityError):
await upload_source_file(
sessionmaker=db_sessionmaker,
storage=FakeObjectStorage(),
auth=auth,
domain="general",
filename="report.csv",
data=_CSV_BYTES,
ingestion_settings=IngestionSettings(),
chunking_settings=ChunkingSettings(),
thread_limiter=CapacityLimiter(2),
concurrency_limiter=concurrency_limiter,
dense_embedders=[
FakeDenseEmbedder(name="dense_nomic"),
FakeDenseEmbedder(name="dense_openai"),
],
sparse_embedder=FakeSparseEmbedder(),
)
async with db_sessionmaker() as verify_session:
jobs = (
(
await verify_session.execute(
select(IngestionJob).where(IngestionJob.tenant_id == auth.tenant_id)
)
)
.scalars()
.all()
)
assert jobs == []

78
tests/unit/application/test_bounds.py Normal file
View File

@@ -0,0 +1,78 @@
"""Request bounds for inline ingestion: chunk ceiling and the process-wide
concurrency gate (ADR-0017, plan 001 Phase 4).
"""
import uuid
import pytest
from anyio import Semaphore
from src.application.ingestion.bounds import acquire_ingestion_slot, enforce_chunk_limit
from src.application.ingestion.errors import ChunkLimitExceededError, IngestionAtCapacityError
from src.application.ingestion.models import Chunk, ContentType
pytestmark = pytest.mark.unit
def _chunk(index: int) -> Chunk:
return Chunk(
chunk_id=uuid.uuid4(),
chunk_index=index,
order_id=float(index + 1),
content="x",
content_type=ContentType.PARAGRAPH,
token_count=1,
character_count=1,
)
def test_enforce_chunk_limit_within_bound_does_not_raise() -> None:
enforce_chunk_limit([_chunk(0), _chunk(1)], max_chunks=2)
def test_enforce_chunk_limit_over_bound_raises() -> None:
with pytest.raises(ChunkLimitExceededError):
enforce_chunk_limit([_chunk(0), _chunk(1), _chunk(2)], max_chunks=2)
@pytest.mark.asyncio
async def test_acquire_ingestion_slot_allows_up_to_the_limit() -> None:
limiter = Semaphore(2)
async with acquire_ingestion_slot(limiter), acquire_ingestion_slot(limiter):
pass # two concurrent holders within a limit of two: no rejection
@pytest.mark.asyncio
async def test_acquire_ingestion_slot_rejects_beyond_the_limit() -> None:
limiter = Semaphore(1)
async with acquire_ingestion_slot(limiter):
with pytest.raises(IngestionAtCapacityError):
async with acquire_ingestion_slot(limiter):
pass
@pytest.mark.asyncio
async def test_acquire_ingestion_slot_releases_on_exit() -> None:
limiter = Semaphore(1)
async with acquire_ingestion_slot(limiter):
pass
# The slot from the first `async with` must be released by the time it
# exits, or every subsequent request would see permanent capacity loss.
async with acquire_ingestion_slot(limiter):
pass
@pytest.mark.asyncio
async def test_acquire_ingestion_slot_releases_after_body_raises() -> None:
limiter = Semaphore(1)
with pytest.raises(ValueError, match="boom"):
async with acquire_ingestion_slot(limiter):
raise ValueError("boom")
async with acquire_ingestion_slot(limiter):
pass

211
tests/unit/application/test_embedding.py Normal file
View File

@@ -0,0 +1,211 @@
"""Batching, concurrency bounding, and failure translation for `embed_chunks`
(ADR-0001, ADR-0017, plan 001 Phase 4).
"""
import asyncio
import uuid
from collections.abc import Sequence
from dataclasses import dataclass, field
import pytest
from anyio import CapacityLimiter
from src.application.ingestion.embedding import embed_chunks
from src.application.ingestion.errors import EmbedderError
from src.application.ingestion.models import Chunk, ContentType, SparseVector
from src.config import IngestionSettings
pytestmark = [pytest.mark.unit, pytest.mark.asyncio]
def _chunk(index: int, content: str = "hello world") -> Chunk:
return Chunk(
chunk_id=uuid.uuid4(),
chunk_index=index,
order_id=float(index + 1),
content=content,
content_type=ContentType.PARAGRAPH,
token_count=2,
character_count=len(content),
)
@dataclass
class _TrackingDenseEmbedder:
"""Records each batch's texts and the peak number of batches in flight
at once, to prove concurrency is bounded, not serial.
"""
name: str
dimensions: int = 3
batches: list[list[str]] = field(default_factory=list)
in_flight: int = 0
peak_in_flight: int = 0
async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
self.in_flight += 1
self.peak_in_flight = max(self.peak_in_flight, self.in_flight)
self.batches.append(list(texts))
await asyncio.sleep(0) # yield so overlapping calls can interleave
self.in_flight -= 1
return [[0.0] * self.dimensions for _ in texts]
@dataclass
class _FailingDenseEmbedder:
name: str
async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
raise RuntimeError("boom")
@dataclass
class _StubSparseEmbedder:
name: str = "sparse"
calls: list[list[str]] = field(default_factory=list)
def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
self.calls.append(list(texts))
return [SparseVector(indices=[i], values=[1.0]) for i in range(len(texts))]
@dataclass
class _FailingSparseEmbedder:
name: str = "sparse"
def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
raise RuntimeError("boom")
@pytest.fixture
def settings() -> IngestionSettings:
return IngestionSettings(embed_batch_size=2, embed_concurrency=2)
@pytest.fixture
def thread_limiter() -> CapacityLimiter:
return CapacityLimiter(4)
async def test_embed_chunks_empty_input_returns_empty(
settings: IngestionSettings, thread_limiter: CapacityLimiter
) -> None:
result = await embed_chunks(
[],
dense_embedders=[_TrackingDenseEmbedder(name="dense_nomic")],
sparse_embedder=_StubSparseEmbedder(),
settings=settings,
thread_limiter=thread_limiter,
)
assert result == []
async def test_embed_chunks_batches_before_parallelizing(
settings: IngestionSettings, thread_limiter: CapacityLimiter
) -> None:
chunks = [_chunk(i) for i in range(5)]
embedder = _TrackingDenseEmbedder(name="dense_nomic")
await embed_chunks(
chunks,
dense_embedders=[embedder],
sparse_embedder=_StubSparseEmbedder(),
settings=settings,
thread_limiter=thread_limiter,
)
# embed_batch_size=2 over 5 chunks -> 3 batches (2, 2, 1), never one call
# per chunk.
assert len(embedder.batches) == 3
assert [len(batch) for batch in embedder.batches] == [2, 2, 1]
async def test_embed_chunks_bounds_concurrency_by_embed_concurrency(
thread_limiter: CapacityLimiter,
) -> None:
settings = IngestionSettings(embed_batch_size=1, embed_concurrency=2)
chunks = [_chunk(i) for i in range(6)]
embedder = _TrackingDenseEmbedder(name="dense_nomic")
await embed_chunks(
chunks,
dense_embedders=[embedder],
sparse_embedder=_StubSparseEmbedder(),
settings=settings,
thread_limiter=thread_limiter,
)
assert embedder.peak_in_flight <= settings.embed_concurrency
assert embedder.peak_in_flight > 1 # proves it isn't serial either
async def test_embed_chunks_passes_chunk_text_through_unmodified(
settings: IngestionSettings, thread_limiter: CapacityLimiter
) -> None:
"""Text shaping (task prefixes, `keep_alive`) is the adapter's job, not
this module's -- see `src/infrastructure/embedding/openai_compatible.py`.
Orchestration here must stay provider-agnostic.
"""
chunks = [_chunk(0, content="salam")]
nomic = _TrackingDenseEmbedder(name="dense_nomic")
openai = _TrackingDenseEmbedder(name="dense_openai")
await embed_chunks(
chunks,
dense_embedders=[nomic, openai],
sparse_embedder=_StubSparseEmbedder(),
settings=settings,
thread_limiter=thread_limiter,
)
assert nomic.batches[0] == ["salam"]
assert openai.batches[0] == ["salam"]
async def test_embed_chunks_assembles_dense_and_sparse_per_chunk_in_order(
settings: IngestionSettings, thread_limiter: CapacityLimiter
) -> None:
chunks = [_chunk(0, "a"), _chunk(1, "b")]
nomic = _TrackingDenseEmbedder(name="dense_nomic", dimensions=3)
result = await embed_chunks(
chunks,
dense_embedders=[nomic],
sparse_embedder=_StubSparseEmbedder(),
settings=settings,
thread_limiter=thread_limiter,
)
assert [item.chunk.chunk_id for item in result] == [c.chunk_id for c in chunks]
assert all(len(item.dense["dense_nomic"]) == 3 for item in result)
assert all("sparse" not in item.dense for item in result)
async def test_embed_chunks_dense_failure_raises_embedder_error(
settings: IngestionSettings, thread_limiter: CapacityLimiter
) -> None:
chunks = [_chunk(0)]
with pytest.raises(EmbedderError):
await embed_chunks(
chunks,
dense_embedders=[_FailingDenseEmbedder(name="dense_nomic")],
sparse_embedder=_StubSparseEmbedder(),
settings=settings,
thread_limiter=thread_limiter,
)
async def test_embed_chunks_sparse_failure_raises_embedder_error(
settings: IngestionSettings, thread_limiter: CapacityLimiter
) -> None:
chunks = [_chunk(0)]
with pytest.raises(EmbedderError):
await embed_chunks(
chunks,
dense_embedders=[_TrackingDenseEmbedder(name="dense_nomic")],
sparse_embedder=_FailingSparseEmbedder(),
settings=settings,
thread_limiter=thread_limiter,
)

31
tests/unit/bootstrap/test_lifespan.py
View File

@@ -2,7 +2,9 @@ import pytest
from fastapi import FastAPI
from httpx import AsyncClient
from src.bootstrap.lifespan import _warm_dense_embedders
from src.config import Settings
from tests.fakes import FakeDenseEmbedder
pytestmark = [pytest.mark.unit, pytest.mark.asyncio]
@@ -17,3 +19,32 @@ async def test_lifespan_binds_resources_to_app_state(app: FastAPI, client: Async
resources = app.state.resources
assert isinstance(resources.settings, Settings)
assert resources.minio_client is not None
assert len(resources.dense_embedders) == 2
assert {e.name for e in resources.dense_embedders} == {"dense_nomic", "dense_openai"}
assert resources.sparse_embedder.name == "sparse"
assert resources.ingestion_concurrency_limiter is not None
async def test_warm_dense_embedders_calls_every_embedder() -> None:
"""Pays the model-load cost at boot instead of on a user's first upload:
a cold nomic load outruns INGESTION_TIMEOUT_SECONDS entirely.
"""
embedders = [FakeDenseEmbedder(name="dense_nomic"), FakeDenseEmbedder(name="dense_openai")]
await _warm_dense_embedders(embedders)
assert all(e.calls for e in embedders)
async def test_warm_dense_embedders_survives_an_unreachable_embedder() -> None:
"""A down embedder must not stop the process booting -- otherwise the
service cannot come up to report its own health. `/readyz` owns that
signal, not startup.
"""
failing = FakeDenseEmbedder(name="dense_nomic", fail_next=True)
healthy = FakeDenseEmbedder(name="dense_openai")
await _warm_dense_embedders([failing, healthy])
# The failure is swallowed *and* does not abort the remaining warm-ups.
assert healthy.calls

0
tests/unit/infrastructure/__init__.py Normal file
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0
tests/unit/infrastructure/embedding/__init__.py Normal file
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170
tests/unit/infrastructure/embedding/test_bm25.py Normal file
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@@ -0,0 +1,170 @@
"""The `bm25-fa-norm-stop` sparse embedder (ADR-0001, ADR-0005).
These assert the analyzer/weighting behaviour benchmarked in the `emet`
evaluation lab. A change that makes one of these fail is a change that
invalidates that benchmark, not just a failing test.
"""
import pytest
from src.config import SparseEmbeddingSettings
from src.infrastructure.embedding.analyzers import analyze
from src.infrastructure.embedding.bm25 import Bm25SparseEmbedder, _token_index
pytestmark = pytest.mark.unit
@pytest.fixture
def settings() -> SparseEmbeddingSettings:
return SparseEmbeddingSettings()
@pytest.fixture
def embedder(settings: SparseEmbeddingSettings) -> Bm25SparseEmbedder:
return Bm25SparseEmbedder(settings)
# --- analyzer ------------------------------------------------------------
def test_analyze_keeps_digits_as_tokens() -> None:
"""Policy numbers, dates, and amounts are exactly what a lexical index
should match on. An earlier implementation dropped every digit.
"""
tokens = analyze("بیمه‌نامه شماره ۱۲۳۴۵ صادر شد")
assert "12345" in tokens
def test_analyze_folds_eastern_digits_to_ascii() -> None:
"""The same number must match however it was authored."""
assert analyze("۹۹۸۸۷۷") == analyze("٩٩٨٨٧٧") == analyze("998877")
def test_analyze_splits_on_zwnj() -> None:
"""ZWNJ joins compounds visually but they are separate index terms."""
assert analyze("آتش‌سوزی") == ["آتش", "سوزی"]
def test_analyze_folds_arabic_letterforms_to_persian() -> None:
# Arabic kaf (U+0643) vs. Persian keheh (U+06A9): the same word typed on
# two different keyboards must produce the same term.
assert analyze("كتاب") == analyze("کتاب")
def test_analyze_drops_persian_stopwords() -> None:
assert analyze("این کتاب و آن مداد") == analyze("کتاب مداد")
def test_analyze_drops_english_stopwords() -> None:
"""The corpus is mixed-script, so the list carries English too."""
assert analyze("the policy is valid") == ["policy", "valid"]
def test_analyze_lowercases_latin() -> None:
assert analyze("POLICY Number") == ["policy", "number"]
def test_analyze_rejects_unknown_analyzer() -> None:
with pytest.raises(ValueError, match="Unknown analyzer"):
analyze("متن", "fa_norm_stem")
# --- token indexing ------------------------------------------------------
def test_token_index_is_stable_across_calls() -> None:
assert _token_index("کتاب") == _token_index("کتاب")
def test_token_index_is_pinned_to_known_values() -> None:
"""A golden test. These indices are baked into every stored sparse vector,
so changing the hash silently orphans the whole index -- a re-ingestion,
not a deploy. Ingest-time and query-time encoding must agree forever.
"""
assert _token_index("کتاب") == 1701064151
assert _token_index("policy") == 741331709
assert _token_index("12345") == 1232178634
def test_token_index_fits_signed_int32() -> None:
for token in ("کتاب", "policy", "12345", "بیمه", "x" * 200):
assert 0 <= _token_index(token) < 2**31 - 1
def test_token_index_distinguishes_different_tokens() -> None:
assert _token_index("کتاب") != _token_index("مداد")
# --- vector construction -------------------------------------------------
def test_embed_batch_returns_one_vector_per_text(embedder: Bm25SparseEmbedder) -> None:
assert len(embedder.embed_batch(["سلام دنیا", "یک تست دیگر"])) == 2
def test_embed_batch_empty_text_returns_empty_vector(embedder: Bm25SparseEmbedder) -> None:
(vector,) = embedder.embed_batch([""])
assert vector.indices == []
assert vector.values == []
def test_embed_batch_all_stopwords_returns_empty_vector(embedder: Bm25SparseEmbedder) -> None:
(vector,) = embedder.embed_batch(["و در به از که"])
assert vector.indices == []
def test_embed_batch_emits_tokens_in_sorted_order(embedder: Bm25SparseEmbedder) -> None:
"""Deterministic output keeps re-ingestion byte-stable."""
text = "مداد کتاب دفتر"
expected = [_token_index(token) for token in sorted(analyze(text))]
(vector,) = embedder.embed_batch([text])
assert vector.indices == expected
def test_embed_batch_applies_bm25_saturation_not_raw_counts(
embedder: Bm25SparseEmbedder,
) -> None:
"""Weight must be sublinear in term frequency: tripling a term must not
triple its weight, which is the whole point of the `k` parameter.
"""
(once,) = embedder.embed_batch(["کتاب"])
(thrice,) = embedder.embed_batch(["کتاب کتاب کتاب"])
assert thrice.values[0] > once.values[0]
assert thrice.values[0] < 3 * once.values[0]
def test_embed_batch_query_side_omits_length_normalization(
embedder: Bm25SparseEmbedder, settings: SparseEmbeddingSettings
) -> None:
text = "کتاب مداد دفتر خودکار"
(document,) = embedder.embed_batch([text], query=False)
(query,) = embedder.embed_batch([text], query=True)
assert document.indices == query.indices # same terms, same hashing
assert document.values != query.values
k = settings.k
assert query.values[0] == pytest.approx(1.0 * (k + 1.0) / (1.0 + k))
def test_embed_batch_short_document_outweighs_long_one(
embedder: Bm25SparseEmbedder,
) -> None:
"""The `b` term discounts a term appearing in a longer document."""
(short,) = embedder.embed_batch(["کتاب"])
(long,) = embedder.embed_batch(["کتاب " + " ".join(f"واژه{i}" for i in range(200))])
short_weight = short.values[short.indices.index(_token_index("کتاب"))]
long_weight = long.values[long.indices.index(_token_index("کتاب"))]
assert short_weight > long_weight
def test_embed_batch_respects_configured_parameters() -> None:
"""k/b/avg_len come from settings, so they can be retuned without a code
change -- and so a retune is visibly a config decision.
"""
default = Bm25SparseEmbedder(SparseEmbeddingSettings())
tuned = Bm25SparseEmbedder(SparseEmbeddingSettings(k=2.5, b=0.2, avg_len=64.0))
text = "کتاب کتاب مداد"
assert default.embed_batch([text])[0].values != tuned.embed_batch([text])[0].values

170
tests/unit/infrastructure/embedding/test_openai_compatible.py Normal file
View File

@@ -0,0 +1,170 @@
"""`OpenAICompatibleEmbedder` against a mocked `/embeddings` endpoint.
Backs both `dense_nomic` and `dense_openai` (ADR-0001) -- `httpx.MockTransport`
stands in for the real self-hosted/OpenAI server so this stays a unit test
with no network dependency.
"""
import json
from collections.abc import Callable
import httpx
import pytest
from src.infrastructure.embedding.openai_compatible import (
OpenAICompatibleEmbedder,
is_ollama_base_url,
)
pytestmark = pytest.mark.unit
def _client(handler: Callable[[httpx.Request], httpx.Response]) -> httpx.AsyncClient:
return httpx.AsyncClient(transport=httpx.MockTransport(handler), base_url="http://embedder")
@pytest.mark.asyncio
async def test_embed_batch_returns_vectors_in_input_order() -> None:
def handler(request: httpx.Request) -> httpx.Response:
# Respond out of order to prove the adapter re-sorts by `index`.
return httpx.Response(
200,
json={
"data": [
{"index": 1, "embedding": [0.2, 0.2]},
{"index": 0, "embedding": [0.1, 0.1]},
],
"model": "test-model",
},
)
embedder = OpenAICompatibleEmbedder(_client(handler), name="dense_nomic", model="test-model")
vectors = await embedder.embed_batch(["first", "second"])
assert vectors == [[0.1, 0.1], [0.2, 0.2]]
@pytest.mark.asyncio
async def test_embed_batch_sends_model_and_input() -> None:
captured: dict[str, object] = {}
def handler(request: httpx.Request) -> httpx.Response:
captured.update(json.loads(request.read()))
return httpx.Response(
200, json={"data": [{"index": 0, "embedding": [0.0]}], "model": "test-model"}
)
embedder = OpenAICompatibleEmbedder(_client(handler), name="dense_openai", model="test-model")
await embedder.embed_batch(["only text"])
assert captured["model"] == "test-model"
assert captured["input"] == ["only text"]
assert "dimensions" not in captured
@pytest.mark.asyncio
async def test_embed_batch_sends_dimensions_when_configured() -> None:
captured: dict[str, object] = {}
def handler(request: httpx.Request) -> httpx.Response:
captured.update(json.loads(request.read()))
return httpx.Response(
200, json={"data": [{"index": 0, "embedding": [0.0] * 256}], "model": "test-model"}
)
embedder = OpenAICompatibleEmbedder(
_client(handler), name="dense_openai", model="test-model", dimensions=256
)
await embedder.embed_batch(["only text"])
assert captured["dimensions"] == 256
@pytest.mark.asyncio
async def test_embed_batch_raises_on_non_2xx_response() -> None:
def handler(request: httpx.Request) -> httpx.Response:
return httpx.Response(500, json={"error": "boom"})
embedder = OpenAICompatibleEmbedder(_client(handler), name="dense_nomic", model="test-model")
with pytest.raises(httpx.HTTPStatusError):
await embedder.embed_batch(["text"])
@pytest.mark.asyncio
async def test_embed_batch_sends_no_prefix_by_default() -> None:
"""emet parity: the benchmarked run used no task prefix (ADR-0004)."""
captured: dict[str, object] = {}
def handler(request: httpx.Request) -> httpx.Response:
captured.update(json.loads(request.read()))
return httpx.Response(200, json={"data": [{"index": 0, "embedding": [0.0]}], "model": "m"})
embedder = OpenAICompatibleEmbedder(_client(handler), name="dense_nomic", model="m")
await embedder.embed_batch(["سلام"])
assert captured["input"] == ["سلام"]
@pytest.mark.asyncio
async def test_embed_batch_applies_document_prefix_when_configured() -> None:
captured: dict[str, object] = {}
def handler(request: httpx.Request) -> httpx.Response:
captured.update(json.loads(request.read()))
return httpx.Response(200, json={"data": [{"index": 0, "embedding": [0.0]}], "model": "m"})
embedder = OpenAICompatibleEmbedder(
_client(handler), name="dense_nomic", model="m", document_prefix="search_document: "
)
await embedder.embed_batch(["سلام"])
assert captured["input"] == ["search_document: سلام"]
@pytest.mark.asyncio
async def test_embed_batch_sends_keep_alive_when_configured() -> None:
"""Keeps an Ollama-hosted model resident; a cold load outruns the
ingestion timeout entirely.
"""
captured: dict[str, object] = {}
def handler(request: httpx.Request) -> httpx.Response:
captured.update(json.loads(request.read()))
return httpx.Response(200, json={"data": [{"index": 0, "embedding": [0.0]}], "model": "m"})
embedder = OpenAICompatibleEmbedder(
_client(handler), name="dense_nomic", model="m", keep_alive="30m"
)
await embedder.embed_batch(["text"])
assert captured["keep_alive"] == "30m"
@pytest.mark.asyncio
async def test_embed_batch_omits_keep_alive_when_not_configured() -> None:
"""OpenAI would reject an unknown field, so it must not be sent there."""
captured: dict[str, object] = {}
def handler(request: httpx.Request) -> httpx.Response:
captured.update(json.loads(request.read()))
return httpx.Response(200, json={"data": [{"index": 0, "embedding": [0.0]}], "model": "m"})
embedder = OpenAICompatibleEmbedder(_client(handler), name="dense_openai", model="m")
await embedder.embed_batch(["text"])
assert "keep_alive" not in captured
@pytest.mark.parametrize(
("base_url", "expected"),
[
("http://192.168.10.10:11435/v1", True),
("http://127.0.0.1:11434/v1", True),
("http://ollama.internal/v1", True),
("https://api.openai.com/v1", False),
("http://127.0.0.1:8081/v1", False),
],
)
def test_is_ollama_base_url(base_url: str, expected: bool) -> None:
assert is_ollama_base_url(base_url) is expected

747
uv.lock generated
View File

@@ -221,50 +221,117 @@ wheels = [
[[package]]
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version = "3.4.9"
version = "3.5.1"
source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/e5/3f/143b048436775b0f76ac3eec145c019e8173ccc2885c8f20319b996d5e83/charset_normalizer-3.5.1.tar.gz", hash = "sha256:6117b84ea48435e5356dc737f5121485c30920ba43375fa7b434fd753df0eac3", size = 171764, upload-time = "2026-08-15T08:20:44.807Z" }
wheels = [
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