initial commit
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13
.gitignore
vendored
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13
.gitignore
vendored
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# Python-generated files
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__pycache__/
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*.py[oc]
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build/
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dist/
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wheels/
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*.egg-info
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# Virtual environments
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.venv
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# Enviroment variables
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.env
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1
.python-version
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1
.python-version
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3.13
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58
docs/adr/0000-adr-template.md
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58
docs/adr/0000-adr-template.md
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# 0000. ADR process and template
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## Status
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Accepted
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## Context
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We are about to make several architecture decisions about how the Qdrant
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vector store is structured and used (ingestion, CRUD, and agent retrieval).
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`docs/adr/README.md` currently has no convention for recording these
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decisions. We need a lightweight, consistent format so future contributors
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can understand *why* the system is built the way it is, not just *what* it
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does.
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## Decision
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We will record architecturally significant decisions as Architecture Decision
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Records (ADRs) under `docs/adr/`, using this convention:
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- **Filename**: `NNNN-short-title.md`, zero-padded, monotonically increasing
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(`0000`, `0001`, `0002`, ...). The number is permanent once assigned.
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- **Sections**, in this order:
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1. Title (`# NNNN. Title`)
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2. Status — one of `Proposed`, `Accepted`, `Superseded by NNNN`
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3. Context — the problem, constraints, and forces at play
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4. Decision — what we're doing, stated directly
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5. Consequences — `Positive` and `Negative` subsections
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6. Alternatives Considered — options we rejected and why
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- **Immutability**: once an ADR is `Accepted`, it is not edited to reflect a
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changed decision. A changed decision gets a *new* ADR that sets the old
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one's status to `Superseded by NNNN` and links back to it. Small
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clarifications/typo fixes are fine to edit in place.
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- **Scope**: only decisions with real architectural weight (data model,
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external system boundaries, protocols, cross-cutting concerns) get an ADR.
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Routine implementation choices do not.
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|
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## Consequences
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|
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### Positive
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- Future contributors (and agents) can trace *why* the Qdrant schema, CRUD
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API, and retrieval pipeline look the way they do without re-deriving it
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from code.
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- Decisions that turn out wrong are corrected via a visible trail
|
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(superseding ADRs) instead of silently rewritten history.
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### Negative
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- Adds minor overhead — anyone making a significant architectural change must
|
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also write or update an ADR.
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|
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## Alternatives Considered
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- **No formal record, rely on commit messages/PR descriptions**: rejected —
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these are hard to discover later and don't survive squash-merges or
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history rewrites.
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- **Full MADR template (with explicit "Decision Drivers" and scored option
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comparison tables)**: rejected as too heavyweight for this project's current
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size; we can adopt more structure later if needed via a new ADR.
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237
docs/adr/0001-ingestion-pipeline-and-collection-schema.md
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docs/adr/0001-ingestion-pipeline-and-collection-schema.md
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# 0001. Ingestion pipeline and Qdrant collection schema
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## Status
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Accepted
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|
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## Context
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FastAPI needs to accept uploaded documents (`.docx`, `.csv`), preprocess them,
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split them into chunks, embed the chunks, and store them in Qdrant. This
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collection is shared by two other pipelines that will be defined in later
|
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ADRs: direct CRUD/search over chunks ([0002](0002-chunk-crud-and-search-api.md))
|
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and hybrid retrieval for an AI agent
|
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([0003](0003-agent-hybrid-retrieval.md)). The schema decided here — vector
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configuration and payload fields — is load-bearing for both, so it needs to
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be right before either is built on top of it.
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|
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Two Qdrant behaviors make this schema decision urgent rather than deferrable:
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|
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- **Sparse vectors cannot be added to an existing unnamed-dense-vector
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collection** — doing so requires recreating the collection. If we start
|
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dense-only and add sparse/late-interaction vectors later, that's a
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disruptive migration.
|
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- **Co-locating large late-interaction (multivector) representations with
|
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dense vectors in the same segment degrades *all* queries**, including
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plain dense search, unless the multivector is stored on disk.
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We've decided (see project discussion) that:
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- Qdrant runs self-hosted via Docker.
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- Tenancy is a **single shared collection**, partitioned by payload, not
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collection-per-tenant — Qdrant's own multitenancy guidance is explicit that
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per-tenant collections "do not scale past a few hundred and waste
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resources," while payload partitioning scales to 10k+ tenants.
|
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- The dense embedding model is now decided — `nomic-embed-text-v2-moe`, see
|
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[0004](0004-docx-csv-chunking-strategy.md). Sparse and late-interaction
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models are **not yet chosen**; those remain swappable via FastEmbed-
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compatible config.
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- The exact payload field list beyond the baseline below is still open for
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discussion; this ADR proposes a starting schema, not a final one.
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|
||||
## Decision
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|
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### Collection
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One collection, e.g. `chunks`, shared by all tenants and domains.
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### Vectors (named vectors, defined at creation time)
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| Name | Type | Purpose | Notes |
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|---|---|---|---|
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| `dense_nomic` | dense vector | primary semantic similarity (multilingual, incl. Persian) | `nomic-embed-text-v2-moe`, 768-dim ([0004](0004-docx-csv-chunking-strategy.md)) |
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| `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) |
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| `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 |
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| `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** |
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|
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All four are defined **now**, even though `late_interaction` won't be
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populated until the agent retrieval work in ADR-0003 lands, specifically to
|
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avoid the forced-recreation problem described above. `late_interaction` is
|
||||
configured with on-disk storage so its larger footprint doesn't degrade the
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dense/sparse query latency. Two dense vectors are provisioned deliberately —
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`dense_nomic` and `dense_openai` are two independent semantic signals, both
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||||
prefetched and fused at query time (ADR-0003), not a primary/fallback pair.
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||||
|
||||
### Multitenancy / indexing config
|
||||
|
||||
- HNSW: `m: 0` (disable the global index) + `payload_m: 16`, per Qdrant's
|
||||
multitenant collection guidance.
|
||||
- Payload index on `tenant_id`: keyword index with `is_tenant: true` — this
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||||
co-locates a tenant's vectors on disk for sequential reads.
|
||||
- Payload index on `domain`: keyword index (secondary partition dimension
|
||||
within a tenant).
|
||||
- Payload index on `file_id`: keyword index, used by CRUD lookups in
|
||||
ADR-0002 (e.g. "delete all chunks belonging to this file").
|
||||
- Payload index on `order_id`: **float** index (Qdrant's `order_by` and
|
||||
`Range` filter conditions only support numeric/datetime payloads, not
|
||||
keyword/string — see the `order_id` format note below). Used for
|
||||
`order_by` when listing/scrolling a file's chunks in sequence (ADR-0002)
|
||||
and for previous/next lookups.
|
||||
- Payload index on `previous_chunk_id` / `next_chunk_id`: keyword index,
|
||||
used for O(1) adjacency retrieval (see below).
|
||||
|
||||
### Payload schema
|
||||
|
||||
This schema is now decided for the fields below. Additional document-context
|
||||
fields (e.g. page/row position, section heading, effective/expiration dates
|
||||
for insurance policy documents) are deliberately **deferred to a future ADR**
|
||||
that will accompany the docx/csv chunking-strategy work — that decision
|
||||
involves format-specific tradeoffs not yet made.
|
||||
|
||||
| Field | Type | Purpose |
|
||||
|---|---|---|
|
||||
| `tenant_id` | keyword, `is_tenant: true` index | tenant isolation |
|
||||
| `domain` | keyword | logical partition within a tenant (e.g. `fire`, `car` for insurance lines) |
|
||||
| `file_id` | keyword | groups chunks back to their source file (formerly referred to as `document_id` — standardized on `file_id`) |
|
||||
| `chunk_id` | keyword | stable identifier for a single chunk |
|
||||
| `content_type` | keyword | classification of the chunk's content; exact value set (e.g. `paragraph`, `table_row`, `heading`) to be finalized alongside the chunking-strategy ADR |
|
||||
| `source_filename` | keyword | original uploaded filename |
|
||||
| `source_type` | keyword (`docx` \| `csv`) | which parser produced this chunk |
|
||||
| `order_id` | float (see below) | chunk's *display* position within the file; mutable so the backend can reorder/insert chunks |
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||||
| `chunk_index` | integer | chunk's *original ingestion* ordinal — immutable, used to derive the deterministic point ID below (kept separate from `order_id` precisely because `order_id` can change) |
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||||
| `previous_chunk_id` | keyword, nullable | `chunk_id` of the preceding chunk in display order (`null` for the first chunk in a file) — O(1) adjacency pointer for context-window expansion in ADR-0003 |
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||||
| `next_chunk_id` | keyword, nullable | `chunk_id` of the following chunk in display order (`null` for the last chunk in a file) — same purpose as `previous_chunk_id` |
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||||
| `content` | text (full-text indexed) | the chunk text itself, also used for keyword search in ADR-0002 |
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||||
| `is_active` | boolean | soft-delete / visibility flag — inactive chunks are excluded from CRUD listing and agent retrieval but retained for audit |
|
||||
| `deleted_at` | datetime, nullable | set when `is_active` transitions to `false`; distinguishes "deactivated" from "never active" |
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||||
| `created_at` | datetime | ingestion timestamp |
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||||
| `updated_at` | datetime | last modification timestamp |
|
||||
| `created_by` | keyword | user/service that created the chunk |
|
||||
| `updated_by` | keyword | user/service that last modified the chunk |
|
||||
| `version` | integer | optimistic-concurrency counter, used in ADR-0002 |
|
||||
| `content_hash` | keyword | hash of the chunk's raw text; lets re-ingestion detect unchanged content and skip re-embedding it |
|
||||
| `embedding_model_version` | keyword | identifies which embedding model(s) produced this chunk's vectors; needed to know which chunks require re-embedding after a future model swap |
|
||||
|
||||
#### `order_id` format
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||||
|
||||
`order_id` is a **fractional float key** (e.g. `1.0`, `2.0`, `3.0`, ...),
|
||||
not a plain sequential integer and not a lexicographic string. We are not
|
||||
implementing chunk insertion/reordering yet, but choosing this format now
|
||||
means that when that feature is added, inserting a chunk between two
|
||||
existing ones (e.g. assigning it `1.5`, then `1.25` for a subsequent insert
|
||||
in the same gap) only touches that one chunk's payload — it never requires
|
||||
renumbering every subsequent chunk, the same benefit a sortable string would
|
||||
give.
|
||||
|
||||
A string key was considered first but **does not work in Qdrant**: the
|
||||
`Range` filter condition (`gt`/`gte`/`lt`/`lte`) only supports float/integer
|
||||
payloads (datetime gets its own separate `DatetimeRange` condition), and
|
||||
`order_by` on scroll requires a payload index that supports `Range`
|
||||
filtering — so `order_by` is likewise limited to numeric/datetime fields.
|
||||
A keyword/string `order_id` could only be sorted client-side after fetching
|
||||
every chunk for a file, and couldn't support a targeted previous/next query
|
||||
at all. A float key gets native `Range`/`order_by` support instead.
|
||||
|
||||
**Known limitation**: repeatedly inserting into the exact same gap (~50+
|
||||
times between the same two neighbors) runs into floating-point precision
|
||||
limits. Mitigate with an occasional rebalance job that respaces a file's
|
||||
`order_id` values (e.g. back to `1000, 2000, 3000, ...`); this is standard
|
||||
for any fractional-indexing scheme and not expected to be hit in normal use.
|
||||
|
||||
#### Previous/next chunk retrieval
|
||||
|
||||
Two ways to get a chunk's neighbors, both viable given this schema:
|
||||
|
||||
1. **Pointer fields (primary, recommended for the agent path)**: read
|
||||
`previous_chunk_id`/`next_chunk_id` off the retrieved chunk and fetch
|
||||
those chunk IDs in a single batch "retrieve points by ID" call. This is
|
||||
the intended pattern for ADR-0003's context-window expansion, since it
|
||||
runs on every retrieved chunk and a single batch-get is cheaper than a
|
||||
filtered query per chunk.
|
||||
2. **Range query (fallback, only viable because `order_id` is numeric)**:
|
||||
filter `file_id = X` + `order_id < current` (Range `lt`) + `order_by desc`
|
||||
+ `limit 1` for the previous chunk; mirror with `gt`/ascending for the
|
||||
next chunk. Useful if the pointer fields are ever missing/stale, or for
|
||||
ad-hoc debugging.
|
||||
|
||||
Pointer fields are only as correct as the mutation logic that maintains
|
||||
them — see ADR-0002 for how reorder/insert/delete operations keep
|
||||
`previous_chunk_id`/`next_chunk_id` in sync.
|
||||
|
||||
### Ingestion flow
|
||||
|
||||
1. FastAPI upload endpoint receives a `.docx` or `.csv` file.
|
||||
2. Parse: `python-docx` for `.docx`, `pandas` for `.csv`.
|
||||
3. Preprocess: clean/normalize extracted text.
|
||||
4. Chunk: split into chunks (size/overlap are tunable config, not fixed by
|
||||
this ADR).
|
||||
5. Embed each chunk into all four vectors: `dense_nomic`, `dense_openai`,
|
||||
`sparse`, and `late_interaction`. `dense_nomic` uses
|
||||
`nomic-embed-text-v2-moe` with the chunk text prefixed
|
||||
`search_document: ` ([0004](0004-docx-csv-chunking-strategy.md));
|
||||
`dense_openai` uses the OpenAI large embedding model; `sparse` uses
|
||||
`bm25-fa-norm-stop`; `late_interaction` uses `jina-colbert-v2`
|
||||
([0005](0005-reranking-model-and-sparse-analyzer-selection.md)) — its
|
||||
document-side multivector is computed and stored at ingestion time here,
|
||||
while the query-side multivector is computed per-query in ADR-0003 for
|
||||
the MAX_SIM rerank comparison.
|
||||
6. Assign a **deterministic point ID** — UUIDv5 derived from
|
||||
`file_id` + `chunk_index` (the immutable ingestion ordinal, not the
|
||||
mutable `order_id`) — so re-ingesting the same file upserts existing
|
||||
chunks instead of creating duplicates, and re-ordering chunks later never
|
||||
changes their IDs. Initialize `order_id` from `chunk_index` at ingestion
|
||||
time (e.g. `chunk_index` 0, 1, 2 → `order_id` `1.0`, `2.0`, `3.0`), and set
|
||||
`previous_chunk_id`/`next_chunk_id` to each chunk's immediate ingestion-order
|
||||
neighbor (`null` at the two ends of the file).
|
||||
7. Batch upsert into Qdrant: 64–256 points per request, 2–4 parallel upload
|
||||
streams, per Qdrant's bulk-upload guidance.
|
||||
|
||||
## Consequences
|
||||
|
||||
### Positive
|
||||
- Defining all four named vectors up front avoids a future forced
|
||||
collection recreation when late-interaction rerank is added in ADR-0003.
|
||||
- Payload-partitioned multitenancy scales to large tenant counts without
|
||||
per-tenant collection sprawl, and shares infrastructure/config across
|
||||
ADR-0002 and ADR-0003.
|
||||
- Deterministic point IDs make ingestion idempotent — safe to re-run on the
|
||||
same document.
|
||||
- Numeric `order_id` is natively supported by Qdrant's `Range`/`order_by`,
|
||||
enabling both ordered listing (ADR-0002) and previous/next chunk lookups
|
||||
(ADR-0003's context-window expansion) without client-side sorting.
|
||||
|
||||
### Negative
|
||||
- The collection carries four named vectors and every chunk is embedded
|
||||
into all of them at ingestion (including `late_interaction` via
|
||||
`jina-colbert-v2`), rather than only the vectors actually queried at
|
||||
launch — more ingestion-time compute than a leaner initial cut.
|
||||
- Document-context payload fields (page/row position, section heading,
|
||||
effective/expiration dates, etc.) are still deferred to the
|
||||
chunking-strategy ADR; adding them later means an additive payload
|
||||
migration, though it won't touch the fields already decided here.
|
||||
- Two dense vectors means every chunk is embedded twice (nomic + OpenAI) at
|
||||
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.
|
||||
- `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
|
||||
still unconfirmed.
|
||||
- `previous_chunk_id`/`next_chunk_id` are denormalized pointers — every
|
||||
reorder/insert/delete must update the affected neighbors' payloads too
|
||||
(see ADR-0002), or the pointers go stale. Fractional float `order_id` also
|
||||
needs an (infrequent) rebalance job as a long-term maintenance task.
|
||||
|
||||
## Alternatives Considered
|
||||
|
||||
- **Collection per domain**: rejected. Domains would need materially
|
||||
different vector configs or strict data-boundary requirements to justify
|
||||
this; neither applies here, and it fragments tenant-scaling benefits.
|
||||
- **Collection per tenant**: rejected outright per Qdrant's own guidance —
|
||||
doesn't scale past a few hundred tenants.
|
||||
- **Start dense-only, add sparse/late-interaction later**: rejected — Qdrant
|
||||
requires recreating the collection to add sparse vectors to an unnamed
|
||||
dense-only collection, which is a disruptive migration we can avoid by
|
||||
deciding the full vector shape now.
|
||||
129
docs/adr/0002-chunk-crud-and-search-api.md
Normal file
129
docs/adr/0002-chunk-crud-and-search-api.md
Normal file
@@ -0,0 +1,129 @@
|
||||
# 0002. Chunk CRUD and payload/keyword search API
|
||||
|
||||
## Status
|
||||
|
||||
Accepted
|
||||
|
||||
## Context
|
||||
|
||||
Beyond bulk ingestion ([0001](0001-ingestion-pipeline-and-collection-schema.md)),
|
||||
we need FastAPI endpoints for direct, fine-grained management of individual
|
||||
chunks: create a single chunk, update a chunk's content/vectors/payload,
|
||||
delete chunks (by ID or in bulk by filter), and list/search chunks by keyword
|
||||
and payload metadata (tenant, domain, file, etc.) — independent of the
|
||||
semantic hybrid retrieval used by the AI agent (ADR-0003).
|
||||
|
||||
This pipeline operates on the same collection and payload schema defined in
|
||||
ADR-0001, and Qdrant's Points API already provides the primitives needed —
|
||||
there's no need for a custom data layer on top.
|
||||
|
||||
## Decision
|
||||
|
||||
Expose a thin FastAPI layer directly over Qdrant's Points API, operating on
|
||||
the `chunks` collection and payload schema from ADR-0001:
|
||||
|
||||
| Operation | FastAPI endpoint (indicative) | Qdrant primitive |
|
||||
|---|---|---|
|
||||
| Create a chunk | `POST /chunks` | upsert (single point) |
|
||||
| Update a chunk's vectors/content | `PUT /chunks/{chunk_id}` | upsert (`update_only` mode) |
|
||||
| Partially update payload | `PATCH /chunks/{chunk_id}/payload` | `set_payload` / `overwrite_payload` |
|
||||
| Delete one chunk | `DELETE /chunks/{chunk_id}` | delete by ID |
|
||||
| Delete many chunks | `DELETE /chunks?file_id=...` | delete by filter |
|
||||
| List/paginate chunks, in order | `GET /chunks?file_id=...` | `scroll` with filter + pagination, `order_by: order_id` |
|
||||
| Reorder/insert a chunk | `PATCH /chunks/{chunk_id}/order` | `set_payload` on `order_id` only |
|
||||
| Count chunks | `GET /chunks/count` | `count` |
|
||||
| Keyword search | `GET /chunks/search?q=...` | full-text payload index on `content` (match, not semantic) |
|
||||
| Bulk multi-op edits | `POST /chunks/batch` | Qdrant `points/batch` |
|
||||
|
||||
### Ordering and reordering
|
||||
|
||||
Chunks belonging to a `file_id` are listed via `scroll` ordered by the
|
||||
mutable `order_id` payload field (ADR-0001) — this is what lets the frontend
|
||||
display and let a backend user modify chunk order. Because `order_id` is a
|
||||
fractional float key rather than a sequential integer position, moving or
|
||||
inserting a chunk only requires assigning it a new value between its two new
|
||||
neighbors — no renumbering of siblings, and no effect on the chunk's stable
|
||||
point ID (which is derived from the immutable `chunk_index`, not `order_id`).
|
||||
|
||||
`PATCH /chunks/{chunk_id}/order` does more than set one field, though: since
|
||||
ADR-0001 also maintains `previous_chunk_id`/`next_chunk_id` pointers for O(1)
|
||||
adjacency lookups, a single reorder touches **up to four chunks** in one
|
||||
`points/batch` call:
|
||||
1. The moved chunk: new `order_id`, new `previous_chunk_id`/`next_chunk_id`.
|
||||
2. Its old neighbors: re-link their `previous_chunk_id`/`next_chunk_id` to
|
||||
skip over the moved chunk.
|
||||
3. Its new neighbors: re-link them to point at the moved chunk.
|
||||
|
||||
The same fix-up applies to insert (new chunk) and delete (soft-deleted chunk
|
||||
— see below) operations: any operation that changes a chunk's position or
|
||||
removes it from the sequence must atomically update the neighbors' pointers
|
||||
in the same `points/batch` request, or `previous_chunk_id`/`next_chunk_id`
|
||||
go stale.
|
||||
|
||||
### Keyword search is not semantic search
|
||||
|
||||
`GET /chunks/search` matches against the full-text payload index on
|
||||
`content` (and structured filters on `tenant_id`, `domain`, `file_id`,
|
||||
etc.) — it is filter/match-based keyword search, not embedding-based
|
||||
retrieval. This is deliberately distinct from the hybrid dense+sparse
|
||||
retrieval used by the AI agent in ADR-0003; the two "search" concepts serve
|
||||
different callers (a human/admin managing chunks vs. an agent retrieving
|
||||
context) and should not be conflated in the API or in future discussion.
|
||||
|
||||
### Delete is soft by default
|
||||
|
||||
`DELETE /chunks/{chunk_id}` and `DELETE /chunks?file_id=...` set
|
||||
`is_active: false` and `deleted_at` (via `set_payload`) rather than removing
|
||||
the point from Qdrant — consistent with ADR-0001's soft-delete fields. This
|
||||
keeps deleted chunks available for audit and lets `GET /chunks` and
|
||||
`GET /chunks/search` filter them out by default (`is_active: true` implied
|
||||
unless the caller explicitly asks to include inactive chunks). A hard delete
|
||||
(actual point removal, e.g. `points/delete`) is available separately for
|
||||
compliance-driven purges, not as the default CRUD behavior. Either way, the
|
||||
deleted chunk's `previous_chunk_id`/`next_chunk_id` neighbors are relinked to
|
||||
point at each other in the same `points/batch` call, so context-window
|
||||
expansion (ADR-0003) never walks into a deactivated or removed chunk.
|
||||
|
||||
### Tenant isolation
|
||||
|
||||
Every endpoint (read and write) has its `tenant_id` filter injected
|
||||
server-side from the authenticated request context — never accepted as
|
||||
client-supplied input in the request body or query string. This is
|
||||
non-negotiable: it's the same isolation boundary ADR-0001 relies on for
|
||||
multitenancy, and it must hold for every code path that touches the
|
||||
collection, not just ingestion.
|
||||
|
||||
### Concurrency
|
||||
|
||||
Updates use the `version` payload field (from ADR-0001) together with
|
||||
Qdrant's `update_filter`, giving an optimistic-concurrency-style guard
|
||||
against races between a concurrent ingestion re-run (ADR-0001) and a manual
|
||||
edit through this API.
|
||||
|
||||
## Consequences
|
||||
|
||||
### Positive
|
||||
- Reuses the exact collection/schema from ADR-0001 — no parallel data model
|
||||
to keep in sync.
|
||||
- Thin mapping to Qdrant primitives keeps the API predictable and easy to
|
||||
extend as new filter/sort needs arise.
|
||||
- Clear separation from ADR-0003's semantic search avoids API consumers
|
||||
confusing "find chunks matching this filter" with "retrieve chunks relevant
|
||||
to this query."
|
||||
|
||||
### Negative
|
||||
- Keyword search here is limited to what Qdrant's full-text payload index
|
||||
supports — no stemming/synonym handling beyond what that index offers.
|
||||
- Optimistic concurrency via `version` requires every writer (ingestion and
|
||||
this API) to consistently read-check-write; a writer that skips this can
|
||||
silently clobber concurrent edits.
|
||||
|
||||
## Alternatives Considered
|
||||
|
||||
- **Separate keyword-search infrastructure (e.g. Elasticsearch)**: rejected —
|
||||
Qdrant's native full-text payload index already covers filter-style keyword
|
||||
search on `content`, and adding a second search system would duplicate data
|
||||
and infrastructure for no clear benefit at current scale.
|
||||
- **Client-supplied `tenant_id` in request body**: rejected — trusting
|
||||
client input for the isolation boundary is a direct multitenancy security
|
||||
risk; it must come from server-side auth context.
|
||||
123
docs/adr/0003-agent-hybrid-retrieval.md
Normal file
123
docs/adr/0003-agent-hybrid-retrieval.md
Normal file
@@ -0,0 +1,123 @@
|
||||
# 0003. Agent hybrid retrieval (dense + dense + sparse + late-interaction rerank)
|
||||
|
||||
## Status
|
||||
|
||||
Accepted
|
||||
|
||||
## Context
|
||||
|
||||
The AI agent needs to retrieve the most relevant chunks for a query against
|
||||
the `chunks` collection defined in ADR-0001, which already provisions four
|
||||
named vectors — `dense_nomic`, `dense_openai`, `sparse`, and
|
||||
`late_interaction` — for exactly this purpose. The goal is higher relevance
|
||||
than single-dense-vector search by combining two independent semantic
|
||||
signals with lexical (sparse) retrieval, then refining the result with a
|
||||
late-interaction (ColBERT-style) rerank.
|
||||
|
||||
Qdrant's Query API supports doing this as a single server-side request via
|
||||
`prefetch` stages, fusion, and a final rerank stage — avoiding a client-side
|
||||
fan-out of multiple separate queries. It also explicitly cautions that
|
||||
hybrid search/reranking can *mask* underlying embedding-model quality issues
|
||||
rather than fix them, so it shouldn't be adopted without a baseline
|
||||
comparison.
|
||||
|
||||
Both dense models are now fixed: `dense_nomic` uses `nomic-embed-text-v2-moe`
|
||||
([0004](0004-docx-csv-chunking-strategy.md)), which requires a
|
||||
`search_query: ` task prefix on the embedded query text (mirroring
|
||||
`search_document: ` on the ingestion side); `dense_openai` uses the OpenAI
|
||||
large embedding model, no prefix convention needed. `sparse` uses
|
||||
`bm25-fa-norm-stop` (ADR-0001). The late-interaction/reranker model is now
|
||||
decided — `jina-colbert-v2` — see [0005](0005-reranking-model-and-sparse-analyzer-selection.md)
|
||||
for the model comparison, the GPU dependency it introduces, and its
|
||||
unresolved commercial-license status.
|
||||
|
||||
## Decision
|
||||
|
||||
### Query shape
|
||||
|
||||
A single Qdrant Query API request per agent query, structured as:
|
||||
|
||||
1. **Prefetch 1 — dense ANN**: search the `dense_nomic` vector, top ~100–200
|
||||
candidates.
|
||||
2. **Prefetch 2 — dense ANN**: search the `dense_openai` vector, top ~100–200
|
||||
candidates.
|
||||
3. **Prefetch 3 — sparse ANN**: search the `sparse` vector, top ~100–200
|
||||
candidates.
|
||||
4. **Fusion**: combine all three prefetch results via **RRF** (Reciprocal
|
||||
Rank Fusion) as the default — per Qdrant's own guidance, RRF "ignores
|
||||
score magnitude, a decent default to start with" and handles the
|
||||
incomparable score scales across two dense models and a sparse model.
|
||||
5. **Rerank**: apply late-interaction (`late_interaction` multivector,
|
||||
max-sim) reranking over the fused top-N (not the full collection) to
|
||||
produce the final top-k returned to the agent, using `jina-colbert-v2`
|
||||
([0005](0005-reranking-model-and-sparse-analyzer-selection.md)).
|
||||
|
||||
Every prefetch and the final query carries the same `tenant_id`/`domain`
|
||||
filter from ADR-0001's payload schema — identical isolation guarantee to
|
||||
ADR-0002.
|
||||
|
||||
### Context-window expansion
|
||||
|
||||
For each chunk returned by the fused/reranked query, the agent may expand
|
||||
its context by pulling the immediately preceding and following chunks before
|
||||
building the prompt. This uses the `previous_chunk_id`/`next_chunk_id`
|
||||
pointer fields from ADR-0001 — a single batch "retrieve points by ID" call
|
||||
per result chunk, not an additional filtered search. Since this runs on every
|
||||
returned chunk, it relies on those pointers being kept correct by ADR-0002's
|
||||
CRUD mutation logic (reorder/insert/delete).
|
||||
|
||||
### Bounding rerank cost
|
||||
|
||||
Late-interaction rerank is the most compute-expensive stage. It is applied
|
||||
only to the fusion's top-N output (e.g. top 50), never to the full
|
||||
candidate set or the full collection, to keep latency bounded.
|
||||
|
||||
### Evaluate before enabling in production
|
||||
|
||||
Before turning this on for real traffic, run an evaluation comparing hybrid
|
||||
+ rerank against a dense-only baseline on representative queries. Qdrant's
|
||||
own guidance warns against adopting hybrid search prematurely, since it can
|
||||
paper over embedding-model quality problems instead of addressing them. If
|
||||
the baseline is already good, the added complexity/cost of this pipeline
|
||||
should be justified with evidence, not assumed.
|
||||
|
||||
## Consequences
|
||||
|
||||
### Positive
|
||||
- Single round-trip server-side fusion + rerank — lower latency than
|
||||
orchestrating multiple queries and fusing client-side in FastAPI.
|
||||
- Directly reuses the vector schema and tenant isolation already established
|
||||
in ADR-0001/0002 — no divergent data model for the agent path.
|
||||
- Bounding rerank to the fused top-N keeps the expensive late-interaction
|
||||
step's cost predictable regardless of collection size.
|
||||
|
||||
### Negative
|
||||
- Late-interaction multivectors are storage/compute heavy; with
|
||||
`jina-colbert-v2` chosen ([0005](0005-reranking-model-and-sparse-analyzer-selection.md))
|
||||
this stage now also requires GPU capacity (`flash_attn`/CUDA), not just
|
||||
the Docker deployment ADR-0001 assumed.
|
||||
- RRF is score-magnitude-agnostic by design — if a use case later needs
|
||||
absolute score information (e.g. a relevance threshold), a different
|
||||
fusion method (DBSF or a custom `FormulaQuery`) would need to be
|
||||
evaluated separately.
|
||||
- Adds a mandatory evaluation step before production rollout, rather than
|
||||
shipping hybrid search immediately.
|
||||
- Three prefetch stages instead of two (two dense + one sparse) means every
|
||||
agent query embeds the question with both `dense_nomic` and `dense_openai`
|
||||
— an extra embedding call and an external OpenAI API dependency on the
|
||||
query's critical path, not just at ingestion time.
|
||||
|
||||
## Alternatives Considered
|
||||
|
||||
- **Client-side fan-out** (separate dense and sparse queries against Qdrant,
|
||||
manual RRF fusion in FastAPI): rejected — more round trips and latency
|
||||
than Qdrant's native server-side Query API fusion, for no functional
|
||||
benefit.
|
||||
- **Cross-encoder reranking via FastEmbed instead of late-interaction**: not
|
||||
adopted as the initial decision, since ADR-0001 already reserves a
|
||||
multivector field for late-interaction rerank; noted as a possible
|
||||
*additional* future stage rather than a replacement.
|
||||
- **DBSF or FormulaQuery as the default fusion method**: rejected as the
|
||||
starting point — RRF is simpler and Qdrant's recommended default; DBSF/
|
||||
FormulaQuery remain available as a later optimization if empirical
|
||||
evaluation shows RRF underperforming.
|
||||
207
docs/adr/0004-docx-csv-chunking-strategy.md
Normal file
207
docs/adr/0004-docx-csv-chunking-strategy.md
Normal file
@@ -0,0 +1,207 @@
|
||||
# 0004. Document parsing and chunking strategy (docx / xlsx / csv)
|
||||
|
||||
## Status
|
||||
|
||||
Accepted
|
||||
|
||||
## Context
|
||||
|
||||
ADR-0001 deferred two things to "when we start the docx/csv chunking work":
|
||||
the exact `content_type` value set, and any document-context payload fields.
|
||||
That work starts now. This ADR also fixes the dense embedding model, which
|
||||
ADR-0001 left as TBD — it directly constrains chunk sizing.
|
||||
|
||||
A representative sample of real source files (`new_asia_csvs/`, insurance
|
||||
documents from Bimeh Asia) was inspected directly (`python-docx`/`openpyxl`)
|
||||
before deciding anything, rather than assuming a shape. Findings:
|
||||
|
||||
- **No `.csv` files exist in practice** — the tree is entirely `.xlsx`
|
||||
(plus two legacy `.doc` binaries `python-docx` cannot open at all).
|
||||
- **Spreadsheets** come in two shapes: clean two-column Q&A tables
|
||||
(`q`/`a` or `سوال`/`پاسخ`), and directory/contact tables (branches, phone
|
||||
numbers) with a merged title row, a blank separator row, and merged cells
|
||||
(`openpyxl` only stores a merged range's value in its top-left cell — the
|
||||
rest read as empty). Several files carry a dead second sheet.
|
||||
- **Word documents** have no consistent shape at all: some are flowing prose
|
||||
with real `Heading` styles (rare), most are flowing prose with **no
|
||||
heading styles**, structure only implied by text (`"1- ... پاسخ: ..."`);
|
||||
one file is a single 35×5 table with zero body paragraphs; one file
|
||||
alternates literal "سوال:"/"پاسخ:" paragraphs; two files contain embedded
|
||||
images with no alt text.
|
||||
|
||||
Given this, no single chunking algorithm can be applied uniformly — table
|
||||
content and flowing prose need different treatment, and even flowing prose
|
||||
varies in whether headings can be trusted as section boundaries.
|
||||
|
||||
The user ran an offline comparison of five chunking strategies against this
|
||||
data and found **semantic-aware chunking** produced the highest retrieval
|
||||
accuracy, with **fixed-size chunking** (paired with the previous/next
|
||||
pointer fields from ADR-0001) a viable, simpler runner-up.
|
||||
|
||||
Embedding model: **`nomic-embed-text-v2-moe`** (dense only — sparse and
|
||||
late-interaction models remain TBD per ADR-0001/0003). Relevant constraints
|
||||
from its model card: 768-dim output, Matryoshka-truncatable down to 256;
|
||||
**512-token max sequence length**; requires a task-instruction prefix on
|
||||
every embedded string — `search_document: ` at ingestion time, `search_query: `
|
||||
on the agent's query side (ADR-0003).
|
||||
|
||||
## Decision
|
||||
|
||||
### Parsing order: structural extraction before chunking
|
||||
|
||||
Every source file is first decomposed into **structural units** — table
|
||||
rows, Q&A pairs, prose blocks — before any chunking algorithm runs. A
|
||||
chunking algorithm never sees a whole document as undifferentiated text;
|
||||
it only runs on the prose-block units, because table rows and Q&A pairs are
|
||||
already atomic and splitting them would break their meaning.
|
||||
|
||||
1. **Walk the docx body in document order** (`doc.element.body` children,
|
||||
not `doc.paragraphs`/`doc.tables` separately), so tables interleaved with
|
||||
paragraphs keep their position, and nested tables inside cells are
|
||||
handled recursively. This matters for the "bad design" case the user
|
||||
flagged — a table cell containing an entire sub-document — which is
|
||||
handled as: recurse into the nested table's rows first; if a single cell
|
||||
still contains multiple paragraphs of unstructured prose, treat that
|
||||
cell's text as its own prose block and run the chunker on it, rather
|
||||
than emitting the whole oversized cell as one chunk.
|
||||
2. **Detect table rows** → one structural unit per row, columns joined as
|
||||
`"{header}: {value}"` pairs (mirrors the xlsx handling below). Header
|
||||
detection: the first row whose cells are mostly short, unique, non-empty
|
||||
strings; title rows (merged, single populated cell) and blank separator
|
||||
rows are skipped, not treated as headers.
|
||||
3. **Detect Q&A pattern** → one structural unit per question/answer pair,
|
||||
via heuristics: literal "سوال"/"پاسخ" (or `q`/`a`) paragraph pairing, or
|
||||
a leading enumerator (`"1-"`, `"2-"`) followed by a "پاسخ:"-prefixed
|
||||
paragraph. This is intentionally a heuristic, not an LLM classification
|
||||
step — it's cheap, deterministic, and the sample above showed the pattern
|
||||
is simple enough (leading marker + adjacent paragraph) not to need model
|
||||
inference per document.
|
||||
4. **Everything else** → flowing prose blocks, segmented by heading styles
|
||||
where present (`Heading 1`/`2`), or the whole remaining run of paragraphs
|
||||
as one block where no heading styles exist.
|
||||
|
||||
### Chunking the prose blocks
|
||||
|
||||
Two supported strategies, selectable by config, **semantic-aware as the
|
||||
default**:
|
||||
|
||||
- **Semantic-aware chunking** (default): split each prose block into
|
||||
sentences, embed each sentence, and break where adjacent-sentence
|
||||
similarity drops below a percentile threshold — grouping semantically
|
||||
coherent runs of sentences into a chunk. Chosen as default per the user's
|
||||
own offline accuracy comparison.
|
||||
- **Fixed-size chunking** (config alternative): token-count-based splitting
|
||||
with overlap. Simpler and cheaper (no embedding pass needed just to
|
||||
decide boundaries), and viable specifically *because* ADR-0001 already
|
||||
gives every chunk `previous_chunk_id`/`next_chunk_id` pointers — a fixed
|
||||
chunk that cuts a thought in half can still be expanded with its
|
||||
neighbors at retrieval time (ADR-0003).
|
||||
|
||||
Both strategies share one hard constraint: **no chunk's embedded text may
|
||||
exceed `nomic-embed-text-v2-moe`'s 512-token sequence length** — text beyond
|
||||
that is silently truncated by the model, not an error, so chunk size must
|
||||
be bounded well under 512 tokens regardless of which strategy is active.
|
||||
|
||||
Table-row and Q&A structural units bypass this chunker entirely — they are
|
||||
already-sized, already-atomic chunks (a row or a Q&A pair is rarely close
|
||||
to 512 tokens; if one is, it's truncated the same way, but this is not the
|
||||
common case per the sample).
|
||||
|
||||
### Spreadsheet (xlsx / csv) handling
|
||||
|
||||
Row = chunk, matching the table-row handling above:
|
||||
|
||||
- Real header row detected (skipping merged title rows and blank separator
|
||||
rows); each cell rendered as `"{column_header}: {cell_value}"`.
|
||||
- Merged cells forward-filled — a merged range's value is copied to every
|
||||
row in that range before chunking, so each row-chunk is self-contained
|
||||
and doesn't silently lose data that `openpyxl` only attaches to the
|
||||
range's top-left cell.
|
||||
- Sheets with no non-empty data rows (the dead second-sheet pattern seen in
|
||||
several sample files) are skipped, not ingested as empty chunks.
|
||||
- `.csv` is handled identically via `pandas`, even though none exist in the
|
||||
current sample — the row → chunk mapping is the same regardless of
|
||||
container format.
|
||||
|
||||
### Embedding model and prefixes
|
||||
|
||||
`nomic-embed-text-v2-moe` is the dense embedding model for ADR-0001's
|
||||
`dense` vector (768-dim, Matryoshka-truncatable to 256 if storage/latency
|
||||
later requires it — not adopted now, full 768 is the default). Every string
|
||||
sent to the model **must** carry its task prefix: `search_document: ` when
|
||||
embedding a chunk at ingestion time, `search_query: ` when embedding the
|
||||
agent's query (ADR-0003) — omitting or mismatching the prefix degrades
|
||||
retrieval quality per the model's own documentation. This is a pipeline
|
||||
invariant, not a per-call decision.
|
||||
|
||||
### Images: LLM extraction now, self-hosted OCR later
|
||||
|
||||
Embedded docx images are sent to the ChatGPT (vision) API at ingestion time
|
||||
to extract a text description/transcription, which becomes its own chunk
|
||||
(`content_type: image_caption`) positioned in document order via the same
|
||||
`previous_chunk_id`/`next_chunk_id` mechanism as any other chunk — not
|
||||
appended silently into a neighboring text chunk. The extraction call is
|
||||
swappable behind a small interface so it can be replaced with a self-hosted
|
||||
OCR/captioning model later without changing the chunk/payload shape.
|
||||
|
||||
### Legacy `.doc` files
|
||||
|
||||
The two legacy binary `.doc` files in the sample cannot be opened by
|
||||
`python-docx`. They are converted to `.docx` via headless LibreOffice
|
||||
(`soffice --headless --convert-to docx`) as a pipeline pre-processing step
|
||||
before the normal docx structural-extraction path runs — not a separate
|
||||
parser.
|
||||
|
||||
### `content_type` value set (finalized)
|
||||
|
||||
| Value | Meaning |
|
||||
|---|---|
|
||||
| `paragraph` | a chunk produced by the prose chunker (semantic or fixed-size) |
|
||||
| `table_row` | one row from a docx table or spreadsheet |
|
||||
| `qa_pair` | one detected question/answer pair |
|
||||
| `image_caption` | text extracted from an embedded image |
|
||||
|
||||
## Consequences
|
||||
|
||||
### Positive
|
||||
- Structural extraction before chunking means table rows and Q&A pairs —
|
||||
the cleanest, highest-signal content in the sample — are never mangled by
|
||||
a generic text chunker.
|
||||
- Semantic-aware default matches the user's own measured accuracy result
|
||||
rather than a generic best practice; fixed-size remains available where
|
||||
its lower cost matters, without losing adjacency thanks to ADR-0001's
|
||||
pointer fields.
|
||||
- Fixes the dense embedding model (previously TBD in ADR-0001), unblocking
|
||||
actual implementation of ingestion and agent retrieval.
|
||||
- Legacy `.doc` and image content are handled instead of silently dropped
|
||||
or erroring the whole file.
|
||||
|
||||
### Negative
|
||||
- Heuristic table/Q&A/prose detection will misclassify some future document
|
||||
that doesn't match the patterns in this sample; it will need tuning as
|
||||
new document batches are onboarded, not just this one.
|
||||
- Semantic chunking adds an embedding pass at chunk-boundary-decision time,
|
||||
separate from the final chunk-embedding pass — extra ingestion latency
|
||||
and cost versus fixed-size alone.
|
||||
- ChatGPT API image extraction is an external network dependency in the
|
||||
ingestion path (cost, latency, availability) until the self-hosted OCR
|
||||
replacement lands.
|
||||
- `nomic-embed-text-v2-moe`'s 512-token limit is a hard ceiling on chunk
|
||||
size for both strategies; any future switch to a model with a shorter
|
||||
limit would require re-tuning chunk-size config, though not the pipeline
|
||||
shape.
|
||||
|
||||
## Alternatives Considered
|
||||
|
||||
- **LLM-based document classification** (send each document to an LLM to
|
||||
decide table/Q&A/prose shape and boundaries): rejected as the default —
|
||||
the sample showed simple heuristics suffice, and per-document LLM calls
|
||||
add cost/latency/non-determinism the heuristics avoid. Not ruled out as a
|
||||
future fallback for prose blocks heuristics can't confidently segment.
|
||||
Not needed for this sample; noted as reference only for future data.
|
||||
- **Uniform fixed-size chunking everywhere** (no structural pre-extraction):
|
||||
rejected — would split table rows and Q&A pairs mid-content, destroying
|
||||
the cleanest signal in the source data.
|
||||
- **Self-hosted OCR/captioning from day one**: rejected for the initial
|
||||
cut — ChatGPT API gets image extraction working now without standing up
|
||||
and tuning a model first; revisit once volume/cost justifies it.
|
||||
166
docs/adr/0005-reranking-model-and-sparse-analyzer-selection.md
Normal file
166
docs/adr/0005-reranking-model-and-sparse-analyzer-selection.md
Normal file
@@ -0,0 +1,166 @@
|
||||
# 0005. Reranking model and sparse (BM25) analyzer selection for the Farsi corpus
|
||||
|
||||
## 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.
|
||||
|
||||
## Context
|
||||
|
||||
ADR-0003 fixed the retrieval *shape* (dense_nomic + dense_openai + sparse
|
||||
prefetch → RRF fusion → late-interaction rerank) but deliberately left two
|
||||
things open: which model powers `late_interaction`, and the sparse-side
|
||||
analyzer detail behind `bm25-fa-norm-stop` (ADR-0001). This ADR resolves
|
||||
those, driven by the corpus being **Farsi (Persian)** — a morphologically
|
||||
rich, low-resource language for most public embedding/rerank benchmarks and
|
||||
for Qdrant's own hosted tooling.
|
||||
|
||||
Two things specific to Farsi drove this investigation rather than picking a
|
||||
generic default:
|
||||
|
||||
- Qdrant's hosted `Qdrant/bm25` FastEmbed model's documented supported-
|
||||
language list does not include Farsi (`fa`) stemming — using it as-is
|
||||
would silently apply no stemming rule, or the wrong one, for this corpus.
|
||||
- Farsi's morphology (verb conjugation, ezafe constructions, high-frequency
|
||||
function words) makes pure BM25/keyword signals noisier than in English,
|
||||
which raised the bar on how load-bearing the rerank stage needs to be —
|
||||
treated here as close to mandatory for quality, not optional.
|
||||
|
||||
## Decision
|
||||
|
||||
### 1. Fusion: RRF by default, weighted RRF as a fallback
|
||||
|
||||
Confirms ADR-0003's fusion stage combines all three prefetch results
|
||||
(`dense_nomic`, `dense_openai`, `sparse`) via **RRF** by default. If one
|
||||
signal (typically sparse, on Farsi text) is observed to dominate the fused
|
||||
ranking unexpectedly, **weighted RRF** is the fallback — not a switch to a
|
||||
different fusion algorithm. This refines, not replaces, ADR-0003's fusion
|
||||
decision.
|
||||
|
||||
### 2. Late-interaction reranker: jina-colbert-v2
|
||||
|
||||
Two multilingual late-interaction (ColBERT-style) options were evaluated
|
||||
against the requirement of confirmed Farsi support:
|
||||
|
||||
| Model | Farsi support | License | Local hosting |
|
||||
|---|---|---|---|
|
||||
| **jina-colbert-v2** | Confirmed — `fa` explicitly listed among 89 supported languages | Conflicting: HF repo metadata says `cc-by-4.0`; Jina's own announcement states CC BY-NC-4.0 (non-commercial), commercial use via paid API/AWS/Azure only | Possible via PyLate/RAGatouille; requires `flash_attn` (CUDA GPU effectively required); reported loading issues via generic `transformers.AutoModel` |
|
||||
| **LFM2-ColBERT-350M** | Not supported (8 languages: en, ar, zh, fr, de, ja, ko, es) | — | — |
|
||||
| BGE-M3 (alternative, not adopted) | Strong Farsi performance in the FaMTEB benchmark | Apache-2.0 — unambiguous, commercial-friendly | CPU-runnable, no `flash_attn` dependency |
|
||||
|
||||
**jina-colbert-v2 is adopted** as the `late_interaction` reranker: it has
|
||||
explicitly confirmed Farsi support among its 89 languages, which is the
|
||||
primary requirement for this corpus. This means:
|
||||
|
||||
- A **CUDA GPU is now a required dependency** for the rerank stage
|
||||
(`flash_attn`), not optional infrastructure — this changes the self-hosted
|
||||
Docker deployment assumption from ADR-0001/0003, which had not committed
|
||||
to GPU hosting.
|
||||
- The **commercial license is unresolved** — HF repo metadata states
|
||||
`cc-by-4.0` while Jina's own announcement states CC BY-NC-4.0
|
||||
(non-commercial, with commercial use only via Jina's paid API/AWS/Azure
|
||||
offering). This must be confirmed directly with Jina **before** this
|
||||
project ships commercially on a self-hosted jina-colbert-v2 model; if
|
||||
Jina confirms the non-commercial reading, self-hosting it commercially is
|
||||
not an option and the fallback is Jina's paid hosted API or BGE-M3 as a
|
||||
substitute reranker (same `late_interaction` vector shape, no schema
|
||||
change needed either way).
|
||||
- Loading it outside PyLate/RAGatouille (e.g. generic
|
||||
`transformers.AutoModel`) has reported issues — plan to load it through
|
||||
one of those two libraries, not a raw `transformers` call.
|
||||
|
||||
`late_interaction` continues to use `hnsw_config: m=0` (per ADR-0001,
|
||||
now made explicit: HNSW indexing is disabled for this vector because it is
|
||||
used only for reranking already-fetched candidates via MAX_SIM, never for
|
||||
independent ANN search — the recommended pattern for rerank-only
|
||||
multivectors) plus on-disk storage.
|
||||
|
||||
### 3. Sparse retrieval: custom BM25 pipeline, not Qdrant's hosted model
|
||||
|
||||
Confirms ADR-0001's choice: the project's own BM25 pipeline (Farsi
|
||||
normalization/stopword/stemming computed outside Qdrant Cloud Inference) is
|
||||
used instead of Qdrant's hosted `Qdrant/bm25` FastEmbed model, specifically
|
||||
because that hosted model's documented language list omits Farsi. The
|
||||
sparse vector is uploaded with `modifier="idf"`, the standard BM25 sparse
|
||||
vector configuration.
|
||||
|
||||
Four analyzer variants were benchmarked, all sharing identical BM25 scoring
|
||||
parameters (`k=1.2`, `b=0.75`) so any accuracy difference is attributable
|
||||
entirely to the analyzer stage, not the ranking formula:
|
||||
|
||||
| Analyzer | Description |
|
||||
|---|---|
|
||||
| `bm25-raw` | no normalization |
|
||||
| `bm25-fa-norm` | Farsi normalization only |
|
||||
| `bm25-fa-norm-stop` | normalization + stopword removal — **current best performer** |
|
||||
| `bm25-fa-norm-stem` | normalization + stemming |
|
||||
|
||||
`bm25-fa-norm-stop` is confirmed as the sparse analyzer named in ADR-0001,
|
||||
consistent with Farsi's high density of function words (ezafe particles,
|
||||
prepositions, common verbs) adding TF/IDF noise if left in.
|
||||
|
||||
### 4. BM25 parameters: keep `k=1.2`, `b=0.75`; tune analyzer, not formula
|
||||
|
||||
These are standard, well-validated defaults (Trotman, Puurula & Burgess,
|
||||
2014) and are not the source of the observed analyzer-variant accuracy
|
||||
differences — so formula tuning is deprioritized in favor of the analyzer
|
||||
comparison and `b` sweep in the follow-ups below.
|
||||
|
||||
## Consequences
|
||||
|
||||
### Positive
|
||||
- Resolves ADR-0003's two deferred decisions (reranker model, sparse
|
||||
analyzer detail) with a corpus-specific rationale instead of a generic
|
||||
default.
|
||||
- jina-colbert-v2 is the only evaluated option with explicitly confirmed
|
||||
Farsi support, directly matching this project's primary requirement.
|
||||
- Isolating BM25 formula parameters from analyzer choice gives a clean,
|
||||
defensible experimental basis for the `bm25-fa-norm-stop` selection.
|
||||
|
||||
### Negative
|
||||
- Introduces a hard GPU dependency (`flash_attn`/CUDA) for the rerank stage
|
||||
that ADR-0001/0003 hadn't assumed — self-hosted deployment now needs GPU
|
||||
capacity, not just Docker on commodity hardware.
|
||||
- Commercial license status is unresolved; shipping this commercially on a
|
||||
self-hosted jina-colbert-v2 model without confirming licensing with Jina
|
||||
is a legal risk, not just a technical one.
|
||||
- Loading path is constrained to PyLate/RAGatouille due to reported
|
||||
`transformers.AutoModel` issues — an extra library dependency and less
|
||||
flexibility than a standard `transformers` load would give.
|
||||
- `bm25-fa-norm-stop` is provisional until compared directly against
|
||||
`bm25-fa-norm-stem` — Persian stemming can over-collapse distinct words
|
||||
(irregular verb conjugation, Arabic-loanword plurals), so the current
|
||||
"best performer" result could shift.
|
||||
|
||||
## Alternatives Considered
|
||||
|
||||
- **BGE-M3 as the reranker**: not adopted — Apache-2.0 license and
|
||||
CPU-runnability are attractive and it remains the fallback if
|
||||
jina-colbert-v2's license is confirmed non-commercial, but jina-colbert-v2
|
||||
was prioritized for its explicit Farsi support.
|
||||
- **LFM2-ColBERT-350M**: rejected outright — no Farsi support among its 8
|
||||
supported languages.
|
||||
- **Qdrant's hosted `Qdrant/bm25` FastEmbed model**: rejected — its
|
||||
documented language support does not include Farsi stemming; using it
|
||||
would risk silently wrong or absent stemming for this corpus.
|
||||
- **Treating "RRF vs. rerank" as either/or**: rejected — RRF alone can't
|
||||
resolve disagreement between two distinct dense embedding spaces plus a
|
||||
noisy Farsi BM25 signal, so the two-stage prefetch-fusion-then-rerank
|
||||
pipeline from ADR-0003 is kept, not replaced with either component alone.
|
||||
|
||||
## Follow-up / Open Items
|
||||
|
||||
1. Confirm jina-colbert-v2's commercial license status directly with Jina
|
||||
before commercial deployment; fall back to Jina's paid hosted API or
|
||||
BGE-M3 if the non-commercial reading is confirmed.
|
||||
2. Provision GPU capacity for self-hosted jina-colbert-v2 (`flash_attn`
|
||||
requires CUDA) as part of the deployment plan, not an afterthought.
|
||||
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.
|
||||
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.
|
||||
0
docs/adr/README.md
Normal file
0
docs/adr/README.md
Normal file
47
pyproject.toml
Normal file
47
pyproject.toml
Normal file
@@ -0,0 +1,47 @@
|
||||
[project]
|
||||
name = "new-chatbot"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.13"
|
||||
dependencies = [
|
||||
"fastapi[standard]==0.141.1",
|
||||
"pydantic-settings>=2.14.2",
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"ruff>=0.16.1",
|
||||
"ty>=0.0.65",
|
||||
]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
target-version = "py313"
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = [
|
||||
# pycodestyle
|
||||
"E",
|
||||
# pyflakes
|
||||
"F",
|
||||
# isort
|
||||
"I",
|
||||
# pyupgrade
|
||||
"UP",
|
||||
# flake8-bugbear
|
||||
"B",
|
||||
# flake8-comprehensions
|
||||
"C4",
|
||||
# flake8-simplify
|
||||
"SIM",
|
||||
# Ruff-specific rules
|
||||
"RUF",
|
||||
]
|
||||
|
||||
[tool.ruff.format]
|
||||
quote-style = "double"
|
||||
indent-style = "space"
|
||||
|
||||
[tool.ty.environment]
|
||||
python-version = "3.13"
|
||||
978
uv.lock
generated
Normal file
978
uv.lock
generated
Normal file
@@ -0,0 +1,978 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.13"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.14'",
|
||||
"python_full_version < '3.14'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-doc"
|
||||
version = "0.0.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = "sha256:c7e58ce09192557605d8bbd92836d7e1d520ac9580096042c0bfd197efacf1bb", size = 10758, upload-time = "2026-07-28T13:50:58.129Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/3e/30/e900b21425a860e195f32e37657aa1f7c7f2b1bfb26f03ca209b90933c06/annotated_doc-0.0.5-py3-none-any.whl", hash = "sha256:117bac03a25ede5df5440e855b32d556049ca169ead221505badf432fed4b101", size = 5302, upload-time = "2026-07-28T13:50:57.239Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.8.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.14.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/61/cc/a381afa6efea9f496eff839d4a6a1aed3bfafc7b3ab4b0d1b243a12573dd/anyio-4.14.2.tar.gz", hash = "sha256:cfa139f3ed1a23ee8f88a145ddb5ac7605b8bbfd8592baacd7ce3d8bb4313c7f", size = 260176, upload-time = "2026-07-12T20:29:07.082Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/da/35/f2287558c17e29fafc8ef3daf819bb9834061cfa43bff8014f7df7f63bdc/anyio-4.14.2-py3-none-any.whl", hash = "sha256:9f505dda5ac9f0c8309b5e8bd445a8c2bf7246f3ce950121e45ea15bc41d1494", size = 125813, upload-time = "2026-07-12T20:29:05.763Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2026.7.22"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a3/c2/24167ea9858356b47a87a50d39908bfdb72ceeefe0041586e704e5376b3a/certifi-2026.7.22.tar.gz", hash = "sha256:741e2c3b351ddf169a738da9f2c048608ff7f2c5cc02f1ebc6b118bb090d5d55", size = 138112, upload-time = "2026-07-22T03:35:12.644Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0b/a7/71ac2cff56fec219ed242bb11b8efb69fcc4bec75db06fb7bfe35de520e6/certifi-2026.7.22-py3-none-any.whl", hash = "sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775", size = 136983, upload-time = "2026-07-22T03:35:11.276Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.4.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/76/d4/81420972a676e8ffea40450d8c8c92943e7218a78fe9b64359836cc9876b/click-8.4.2.tar.gz", hash = "sha256:9a6cea6e60b17ebe0a44c5cc636d94f09bd66142c1cd7d8b4cd731c4917a15f6", size = 338000, upload-time = "2026-06-24T17:45:15.148Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fb/e2/79c688af8b210d232694e31e59da9f6ec747bae31c3f5946e4e9b98860d5/click-8.4.2-py3-none-any.whl", hash = "sha256:e6f9f66136c816745b9d65817da91d61d957fb16e02e4dcd0552553c5a197b76", size = 119243, upload-time = "2026-06-24T17:45:13.73Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "detect-installer"
|
||||
version = "0.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5f/ce/6897d812825e9d4c53e3c7112726e800cc5231b013b2223bf64f653ff362/detect_installer-0.1.0.tar.gz", hash = "sha256:00ad7ba0a36e3cf7d08a40d3643011746dbc112597c7d475cc91c416710ca4e7", size = 3049, upload-time = "2026-02-23T10:40:22.567Z" }
|
||||
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Reference in New Issue
Block a user