## Summary
This PR adds checkpoint/resume support for the GraphRAG
`extract_community` and `resolve_entities` stages.
The implementation stores successful intermediate results in the
document store so interrupted ingestion can resume without repeating
already-completed LLM work. Checkpoints are loaded before each stage,
reused when available, saved after successful batch/community
processing, and cleaned up after the stage completes successfully.
## Related Issue
Closes: #15518
## Change Type
- [x] Feature
- [x] Bug fix
- [x] Test
- [ ] Refactor
- [ ] Documentation
- [ ] Breaking change
## Real Behavior Proof
Validation commands run locally:
```bash
uv run python -m py_compile \
rag/graphrag/checkpoints.py \
rag/graphrag/general/community_reports_extractor.py \
rag/graphrag/entity_resolution.py \
rag/graphrag/general/index.py \
test/unit_test/rag/graphrag/test_checkpoints.py
```
Result:
```text
Passed
```
```bash
uv run pytest test/unit_test/rag/graphrag/test_checkpoints.py
```
Result:
```text
4 passed
```
```bash
uv run pytest \
test/unit_test/rag/graphrag/test_phase_markers.py \
test/unit_test/rag/graphrag/test_graphrag_utils.py \
test/unit_test/rag/graphrag/test_checkpoints.py
```
Result:
```text
95 passed
```
```bash
git diff --check
```
Result:
```text
Passed
```
## Checklist
- [x] Implemented checkpoint/resume support for `extract_community`.
- [x] Implemented checkpoint/resume support for `resolve_entities`.
- [x] Avoided touching unrelated API behavior.
- [x] Added unit tests for the new checkpoint helper logic.
- [x] Verified Python syntax compilation.
- [x] Ran related GraphRAG unit tests successfully.
- [x] Ran `git diff --check`.
- [ ] Ran full project test suite.
---------
Co-authored-by: Wang Qi <wangq8@outlook.com>
### What problem does this PR solve?
Python implementation of the Go-based model_provider API suite.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: bill <yibie_jingnian@163.com>
### What problem does this PR solve?
1. Enhance retry and timeout, and adjust the default timeout
2. NER: spacy do not batch chunks
3. extract _has_cancel_and_exit
4. enhance log messages
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
1. expose batch_chunk_token_size for configuration
2. retrieve chunks when build subgraph for the doc, not retreive all
docs chunks at the begining
3. get all chunks for a document, used to be hard coded 10000
4. delete not used method run_graphrag
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
Follow on: #14617
### What problem does this PR solve?
GraphRAG feature - Part 1 - add spacy to extract entity and relation
<img width="1621" height="1288" alt="image"
src="https://github.com/user-attachments/assets/aadeddad-94da-46c6-adad-9c3784181f61"
/>
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
This PR addresses three related GraphRAG reliability issues that
together allow long-running GraphRAG tasks (10+ hours of LLM extraction)
to be resumed after a crash or pause without re-doing completed work. It
builds on #14096 (per-doc subgraph cache) and extends the same idea to
the resolution and community-detection phases.
Fixes#14236.
## 1. Fix concurrent merge crash
Long GraphRAG runs would crash near the end of entity resolution with:
```
RuntimeError: dictionary keys changed during iteration
```
in `Extractor._merge_graph_nodes`. Two changes:
- `rag/graphrag/general/extractor.py`: snapshot `graph.neighbors(node1)`
via `list(...)` before iterating, so concurrent `add_edge` /
`remove_node` mutations on the shared `nx.Graph` cannot invalidate the
iterator. Also tracks each redirected neighbour in `node0_neighbors` so
a later merged node sharing the same external neighbour takes the
edge-merge branch instead of overwriting via `add_edge`.
- `rag/graphrag/entity_resolution.py`: serialize the merge step with a
dedicated `asyncio.Semaphore(1)`. `nx.Graph` is not thread-safe and
concurrent merges on overlapping neighbourhoods can produce incorrect
results even with the snapshot fix.
## 2. Don't wipe partial graph on pause
Previously the pause / cancel UI path called
`settings.docStoreConn.delete({"knowledge_graph_kwd": [...]}, ...)`,
destroying every subgraph, entity, relation, and graph row.
Re-triggering then started GraphRAG from scratch even though #14096 had
already added `load_subgraph_from_store`.
After main was merged in (which deleted `api/apps/kb_app.py` per
#14394), the pause path now lives on the new REST surface `DELETE
/v1/datasets/<id>/<index_type>`:
- `api/apps/services/dataset_api_service.py`: `delete_index` accepts a
`wipe: bool = True` parameter. When `False` the doc-store rows and
GraphRAG phase markers are left intact and only the running task is
cancelled. Default preserves historical behaviour.
- `api/apps/restful_apis/dataset_api.py`: parses `?wipe=false|0|no|off`
from the query string and forwards it.
- `web/src/utils/api.ts` + `web/src/services/knowledge-service.ts`:
`unbindPipelineTask` appends `?wipe=false` when explicitly false.
- The GraphRAG pause action in
`web/src/pages/dataset/dataset/generate-button/hook.ts` passes `wipe:
false` for `KnowledgeGraph`; raptor is unchanged.
**UX impact:** the pause icon next to a running GraphRAG task no longer
wipes graph data. The only path that still wipes is the explicit Delete
action in `GenerateLogButton` (trash icon behind a confirmation modal).
## 3. Phase-completion markers (`rag/graphrag/phase_markers.py`)
A small Redis-backed marker layer at
`graphrag:phase:{kb_id}:{resolution_done|community_done}` (7-day TTL).
`run_graphrag_for_kb` consults the markers on entry and skips phases
that already completed in a prior run. Markers are cleared automatically
when:
- new docs are merged into the graph (which invalidates prior resolution
and community results),
- `delete_index` wipes the graph, or
- `delete_knowledge_graph` is called.
Redis failures never block a run -- markers are an optimization, not a
gate.
## 4. Idempotent community detection
`extract_community` previously did `delete-then-insert` on
`community_report` rows; a crash mid-insert left the dataset with no
reports. Now report IDs are derived deterministically from `(kb_id,
community.title)`, the existing report IDs are snapshotted before
insert, new rows are written, then only stale rows are pruned. A failure
at any step leaves either the prior or the new report set intact --
never a partial mix.
## 5. Tunable doc-store insert pipeline
The GraphRAG insert loop in `rag/graphrag/utils.py` and the
`community_report` insert in `rag/graphrag/general/index.py` were both
hardcoded to `es_bulk_size = 4` and ran strictly sequentially. On a real
KB this meant 1077 chunks took ~21 minutes for a 100-chunk slice -- pure
round-trip overhead.
- New `insert_chunks_bounded()` helper in `rag/graphrag/utils.py`
batches inserts via a bounded `asyncio.Semaphore`. Same retry / timeout
semantics as the prior loop.
- Defaults: 64 docs per batch, 4 batches in flight (matches the regular
ingest pipeline in `document_service.py`). Tunable per-deployment via
`GRAPHRAG_INSERT_BULK_SIZE` and `GRAPHRAG_INSERT_CONCURRENCY`.
- Both `set_graph` and `extract_community` now use the helper.
This dropped the same 1077-chunk insert from minutes to seconds in local
testing without measurable extra pressure on Infinity (total in-flight
docs ≤ `BULK_SIZE × CONCURRENCY` = 256 by default).
## Tests
- `test/unit_test/rag/graphrag/test_merge_graph_nodes.py` (3 tests):
dense neighbourhood merge, neighbour-snapshot regression, concurrent
serialized merges.
- `test/unit_test/rag/graphrag/test_phase_markers.py` (4 tests): set/has
round-trip, kb-scoped clear, no-op on empty input, graceful Redis
failure.
-
`test/testcases/test_web_api/test_dataset_management/test_dataset_sdk_routes_unit.py`:
new `test_delete_index_wipe_flag_unit` covers `wipe=false` for both
GraphRAG and raptor on the new REST route, and confirms the default
still wipes and clears phase markers.
## Compatibility
- Backward compatible: tasks queued before this change behave
identically (default `wipe=true`, no markers expected).
- No schema/migration changes; all new state lives in Redis.
- New optional REST query param `wipe` on `DELETE
/v1/datasets/<id>/<index_type>`.
- New optional env vars `GRAPHRAG_INSERT_BULK_SIZE` and
`GRAPHRAG_INSERT_CONCURRENCY`; defaults preserve safe behaviour.
## Example of resume
Screenshot below shows a test resuming knowledge graph generation after
applying the concurrency fix and re-deploying.
<img width="521" height="677" alt="image"
src="https://github.com/user-attachments/assets/9ef0d405-cbb3-420d-a1a1-e51f3e7e9b7a"
/>
### Type of change
- [X] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### What problem does this PR solve?
Addresses review feedback on #14074 (Checkpoint mechanism for
long-running workflow jobs, issue #12494).
**Changes based on @yuzhichang's review:**
1. **Renamed `checkpoint_service.py` → `task_checkpoint.py`** as
suggested.
2. **Replaced Redis with direct docEngine queries** as suggested — the
subgraph already gets persisted to the doc store by
`generate_subgraph()`, so we just query for it instead of maintaining a
separate checkpoint in Redis. This is simpler, has no extra dependency,
and uses a single source of truth.
**Changes based on CodeRabbit review:**
3. **Fixed `source_id` query format mismatch** — subgraphs are stored
with `source_id: [doc_id]` (list), but the original query used
`source_id: doc_id` (string). Now follows the same pattern as
`does_graph_contains()` in `rag/graphrag/utils.py`: filter by
`knowledge_graph_kwd` only, then match `source_id` in Python. This
avoids ambiguity across Elasticsearch / Infinity / OceanBase backends.
### Changes
| File | Change |
|---|---|
| `api/db/services/task_checkpoint.py` (new) |
`load_subgraph_from_store()` and `has_raptor_chunks()` — docEngine-based
checkpoint queries |
| `rag/graphrag/general/index.py` | `build_one()` calls
`load_subgraph_from_store()` before running LLM extraction |
| `rag/svr/task_executor.py` | RAPTOR per-doc loop calls
`has_raptor_chunks()` before processing |
| `test/unit_test/rag/graphrag/test_checkpoint_resume.py` (new) | 10
unit tests covering subgraph loading, source_id filtering, edge cases |
### How it works
- **GraphRAG:** Before running expensive LLM entity/relation extraction
for a doc, checks the doc store for an existing subgraph (saved by a
previous interrupted run). If found, loads it directly and skips LLM
calls.
- **RAPTOR:** Before processing a doc, checks if RAPTOR chunks
(`raptor_kwd="raptor"`) already exist for it. If yes, skips.
### Testing
- 10 new unit tests — all passing
- Full existing suite: 617 passed
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
GraphRAG _async_chat.
### Type of change
- [x] Refactoring
- [x] Performance Improvement
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Refactor**
* Unified chat calls to an async invocation across extractors, improving
timeout handling and ensuring task IDs propagate reliably.
* **Tests**
* Added and expanded unit tests and mocks to cover extractor behavior,
timeout scenarios, and safe test-package imports, reducing regression
risk.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
### What problem does this PR solve?
Fix graphrag extractor chat response parsing and skip truncated cache
values
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Add id for table tenant_llm and apply in LLMBundle.
### Type of change
- [x] Refactoring
---------
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
Co-authored-by: Liu An <asiro@qq.com>
### What problem does this PR solve?
Fix error when extracting the graph.
A string is expected, but a tuple was provided.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
