From e0a7d3ec021138154b3526a5b8e5345791ce4f73 Mon Sep 17 00:00:00 2001 From: Ali Zarinkolah Date: Sun, 2 Aug 2026 15:52:46 +0330 Subject: [PATCH] initial commit --- .gitignore | 13 + .python-version | 1 + README.md | 0 docs/adr/0000-adr-template.md | 58 ++ ...ngestion-pipeline-and-collection-schema.md | 237 +++++ docs/adr/0002-chunk-crud-and-search-api.md | 129 +++ docs/adr/0003-agent-hybrid-retrieval.md | 123 +++ docs/adr/0004-docx-csv-chunking-strategy.md | 207 ++++ ...ing-model-and-sparse-analyzer-selection.md | 166 +++ docs/adr/README.md | 0 pyproject.toml | 47 + uv.lock | 978 ++++++++++++++++++ 12 files changed, 1959 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 README.md create mode 100644 docs/adr/0000-adr-template.md create mode 100644 docs/adr/0001-ingestion-pipeline-and-collection-schema.md create mode 100644 docs/adr/0002-chunk-crud-and-search-api.md create mode 100644 docs/adr/0003-agent-hybrid-retrieval.md create mode 100644 docs/adr/0004-docx-csv-chunking-strategy.md create mode 100644 docs/adr/0005-reranking-model-and-sparse-analyzer-selection.md create mode 100644 docs/adr/README.md create mode 100644 pyproject.toml create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a506ee8 --- /dev/null +++ b/.gitignore @@ -0,0 +1,13 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv + +# Enviroment variables +.env diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/docs/adr/0000-adr-template.md b/docs/adr/0000-adr-template.md new file mode 100644 index 0000000..e32201f --- /dev/null +++ b/docs/adr/0000-adr-template.md @@ -0,0 +1,58 @@ +# 0000. ADR process and template + +## Status + +Accepted + +## Context + +We are about to make several architecture decisions about how the Qdrant +vector store is structured and used (ingestion, CRUD, and agent retrieval). +`docs/adr/README.md` currently has no convention for recording these +decisions. We need a lightweight, consistent format so future contributors +can understand *why* the system is built the way it is, not just *what* it +does. + +## Decision + +We will record architecturally significant decisions as Architecture Decision +Records (ADRs) under `docs/adr/`, using this convention: + +- **Filename**: `NNNN-short-title.md`, zero-padded, monotonically increasing + (`0000`, `0001`, `0002`, ...). The number is permanent once assigned. +- **Sections**, in this order: + 1. Title (`# NNNN. Title`) + 2. Status — one of `Proposed`, `Accepted`, `Superseded by NNNN` + 3. Context — the problem, constraints, and forces at play + 4. Decision — what we're doing, stated directly + 5. Consequences — `Positive` and `Negative` subsections + 6. Alternatives Considered — options we rejected and why +- **Immutability**: once an ADR is `Accepted`, it is not edited to reflect a + changed decision. A changed decision gets a *new* ADR that sets the old + one's status to `Superseded by NNNN` and links back to it. Small + clarifications/typo fixes are fine to edit in place. +- **Scope**: only decisions with real architectural weight (data model, + external system boundaries, protocols, cross-cutting concerns) get an ADR. + Routine implementation choices do not. + +## Consequences + +### Positive +- Future contributors (and agents) can trace *why* the Qdrant schema, CRUD + API, and retrieval pipeline look the way they do without re-deriving it + from code. +- Decisions that turn out wrong are corrected via a visible trail + (superseding ADRs) instead of silently rewritten history. + +### Negative +- Adds minor overhead — anyone making a significant architectural change must + also write or update an ADR. + +## Alternatives Considered + +- **No formal record, rely on commit messages/PR descriptions**: rejected — + these are hard to discover later and don't survive squash-merges or + history rewrites. +- **Full MADR template (with explicit "Decision Drivers" and scored option + comparison tables)**: rejected as too heavyweight for this project's current + size; we can adopt more structure later if needed via a new ADR. diff --git a/docs/adr/0001-ingestion-pipeline-and-collection-schema.md b/docs/adr/0001-ingestion-pipeline-and-collection-schema.md new file mode 100644 index 0000000..aeb46e9 --- /dev/null +++ b/docs/adr/0001-ingestion-pipeline-and-collection-schema.md @@ -0,0 +1,237 @@ +# 0001. Ingestion pipeline and Qdrant collection schema + +## Status + +Accepted + +## Context + +FastAPI needs to accept uploaded documents (`.docx`, `.csv`), preprocess them, +split them into chunks, embed the chunks, and store them in Qdrant. This +collection is shared by two other pipelines that will be defined in later +ADRs: direct CRUD/search over chunks ([0002](0002-chunk-crud-and-search-api.md)) +and hybrid retrieval for an AI agent +([0003](0003-agent-hybrid-retrieval.md)). The schema decided here — vector +configuration and payload fields — is load-bearing for both, so it needs to +be right before either is built on top of it. + +Two Qdrant behaviors make this schema decision urgent rather than deferrable: + +- **Sparse vectors cannot be added to an existing unnamed-dense-vector + collection** — doing so requires recreating the collection. If we start + dense-only and add sparse/late-interaction vectors later, that's a + disruptive migration. +- **Co-locating large late-interaction (multivector) representations with + dense vectors in the same segment degrades *all* queries**, including + plain dense search, unless the multivector is stored on disk. + +We've decided (see project discussion) that: +- Qdrant runs self-hosted via Docker. +- Tenancy is a **single shared collection**, partitioned by payload, not + collection-per-tenant — Qdrant's own multitenancy guidance is explicit that + per-tenant collections "do not scale past a few hundred and waste + resources," while payload partitioning scales to 10k+ tenants. +- The dense embedding model is now decided — `nomic-embed-text-v2-moe`, see + [0004](0004-docx-csv-chunking-strategy.md). Sparse and late-interaction + models are **not yet chosen**; those remain swappable via FastEmbed- + compatible config. +- The exact payload field list beyond the baseline below is still open for + discussion; this ADR proposes a starting schema, not a final one. + +## Decision + +### Collection + +One collection, e.g. `chunks`, shared by all tenants and domains. + +### Vectors (named vectors, defined at creation time) + +| 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) | +| `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** | + +All four are defined **now**, even though `late_interaction` won't be +populated until the agent retrieval work in ADR-0003 lands, specifically to +avoid the forced-recreation problem described above. `late_interaction` is +configured with on-disk storage so its larger footprint doesn't degrade the +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. + +### 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 + 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 | +| `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) | +| `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 | +| `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` | +| `content` | text (full-text indexed) | the chunk text itself, also used for keyword search in ADR-0002 | +| `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" | +| `created_at` | datetime | ingestion timestamp | +| `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 + +`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. diff --git a/docs/adr/0002-chunk-crud-and-search-api.md b/docs/adr/0002-chunk-crud-and-search-api.md new file mode 100644 index 0000000..2283362 --- /dev/null +++ b/docs/adr/0002-chunk-crud-and-search-api.md @@ -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. diff --git a/docs/adr/0003-agent-hybrid-retrieval.md b/docs/adr/0003-agent-hybrid-retrieval.md new file mode 100644 index 0000000..d0320cb --- /dev/null +++ b/docs/adr/0003-agent-hybrid-retrieval.md @@ -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. diff --git a/docs/adr/0004-docx-csv-chunking-strategy.md b/docs/adr/0004-docx-csv-chunking-strategy.md new file mode 100644 index 0000000..79daa22 --- /dev/null +++ b/docs/adr/0004-docx-csv-chunking-strategy.md @@ -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. diff --git a/docs/adr/0005-reranking-model-and-sparse-analyzer-selection.md b/docs/adr/0005-reranking-model-and-sparse-analyzer-selection.md new file mode 100644 index 0000000..66a19ee --- /dev/null +++ b/docs/adr/0005-reranking-model-and-sparse-analyzer-selection.md @@ -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. diff --git a/docs/adr/README.md b/docs/adr/README.md new file mode 100644 index 0000000..e69de29 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..e614c01 --- /dev/null +++ b/pyproject.toml @@ -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", + # 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