### What problem does this PR solve?
Add fault-tolerant mechanism to RAPTOR.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
GraghRAG handle cancel gracefully. #10997.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Add get_uuid, download_img and hash_str2int into misc_utils.py
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
- rename rmSpace to remove_redundant_spaces
- move clean_markdown_block to common module
- add unit tests for remove_redundant_spaces and clean_markdown_block
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Add fault-tolerant mechanism to GraphRAG. #10406.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Since `description_list` was a tuple containing a single element (which
was the actual list of descriptions), `len(description_list)` was always
**1**.
The subsequent check:
`if len(description_list) <= 12:` always evaluated to `True` (since $1
\le 12$), even if the inner list contained more than 12 descriptions.
This prevented the necessary summarization logic from running for long
lists.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix broken imports
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: jinhai <haijin.chn@gmail.com>
### What problem does this PR solve?
### Type of change
- [x] Refactoring
- [x] Performance Improvement
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
### What problem does this PR solve?
This revision performed a comprehensive check on LightRAG to ensure the
correctness of its implementation. It **did not involve** Entity
Resolution and Community Reports Generation. There is an example using
default entity types and the General chunking method, which shows good
results in both time and effectiveness. Moreover, response caching is
enabled for resuming failed tasks.
[The-Necklace.pdf](https://github.com/user-attachments/files/22042432/The-Necklace.pdf)
After:
```bash
Begin at:
Fri, 29 Aug 2025 16:48:03 GMT
Duration:
222.31 s
Progress:
16:48:04 Task has been received.
16:48:06 Page(1~7): Start to parse.
16:48:06 Page(1~7): OCR started
16:48:08 Page(1~7): OCR finished (1.89s)
16:48:11 Page(1~7): Layout analysis (3.72s)
16:48:11 Page(1~7): Table analysis (0.00s)
16:48:11 Page(1~7): Text merged (0.00s)
16:48:11 Page(1~7): Finish parsing.
16:48:12 Page(1~7): Generate 7 chunks
16:48:12 Page(1~7): Embedding chunks (0.29s)
16:48:12 Page(1~7): Indexing done (0.04s). Task done (7.84s)
16:48:17 Start processing for f421fb06849e11f0bdd32724b93a52b2: She had no dresses, no je...
16:48:17 Start processing for f421fb06849e11f0bdd32724b93a52b2: Her husband, already half...
16:48:17 Start processing for f421fb06849e11f0bdd32724b93a52b2: And this life lasted ten ...
16:48:17 Start processing for f421fb06849e11f0bdd32724b93a52b2: Then she asked, hesitatin...
16:49:30 Completed processing for f421fb06849e11f0bdd32724b93a52b2: She had no dresses, no je... after 1 gleanings, 21985 tokens.
16:49:30 Entities extraction of chunk 3 1/7 done, 12 nodes, 13 edges, 21985 tokens.
16:49:40 Completed processing for f421fb06849e11f0bdd32724b93a52b2: Finally, she replied, hes... after 1 gleanings, 22584 tokens.
16:49:40 Entities extraction of chunk 5 2/7 done, 19 nodes, 19 edges, 22584 tokens.
16:50:02 Completed processing for f421fb06849e11f0bdd32724b93a52b2: Then she asked, hesitatin... after 1 gleanings, 24610 tokens.
16:50:02 Entities extraction of chunk 0 3/7 done, 16 nodes, 28 edges, 24610 tokens.
16:50:03 Completed processing for f421fb06849e11f0bdd32724b93a52b2: And this life lasted ten ... after 1 gleanings, 24031 tokens.
16:50:04 Entities extraction of chunk 1 4/7 done, 24 nodes, 22 edges, 24031 tokens.
16:50:14 Completed processing for f421fb06849e11f0bdd32724b93a52b2: So they begged the jewell... after 1 gleanings, 24635 tokens.
16:50:14 Entities extraction of chunk 6 5/7 done, 27 nodes, 26 edges, 24635 tokens.
16:50:29 Completed processing for f421fb06849e11f0bdd32724b93a52b2: Her husband, already half... after 1 gleanings, 25758 tokens.
16:50:29 Entities extraction of chunk 2 6/7 done, 25 nodes, 35 edges, 25758 tokens.
16:51:35 Completed processing for f421fb06849e11f0bdd32724b93a52b2: The Necklace By Guy de Ma... after 1 gleanings, 27491 tokens.
16:51:35 Entities extraction of chunk 4 7/7 done, 39 nodes, 37 edges, 27491 tokens.
16:51:35 Entities and relationships extraction done, 147 nodes, 177 edges, 171094 tokens, 198.58s.
16:51:35 Entities merging done, 0.01s.
16:51:35 Relationships merging done, 0.01s.
16:51:35 ignored 7 relations due to missing entities.
16:51:35 generated subgraph for doc f421fb06849e11f0bdd32724b93a52b2 in 198.68 seconds.
16:51:35 run_graphrag f421fb06849e11f0bdd32724b93a52b2 graphrag_task_lock acquired
16:51:35 set_graph removed 0 nodes and 0 edges from index in 0.00s.
16:51:35 Get embedding of nodes: 9/147
16:51:35 Get embedding of nodes: 109/147
16:51:37 Get embedding of edges: 9/170
16:51:37 Get embedding of edges: 109/170
16:51:40 set_graph converted graph change to 319 chunks in 4.21s.
16:51:40 Insert chunks: 4/319
16:51:40 Insert chunks: 104/319
16:51:40 Insert chunks: 204/319
16:51:40 Insert chunks: 304/319
16:51:40 set_graph added/updated 147 nodes and 170 edges from index in 0.53s.
16:51:40 merging subgraph for doc f421fb06849e11f0bdd32724b93a52b2 into the global graph done in 4.79 seconds.
16:51:40 Knowledge Graph done (204.29s)
```
Before:
```bash
Begin at:
Fri, 29 Aug 2025 17:00:47 GMT
processDuration:
173.38 s
Progress:
17:00:49 Task has been received.
17:00:51 Page(1~7): Start to parse.
17:00:51 Page(1~7): OCR started
17:00:53 Page(1~7): OCR finished (1.82s)
17:00:57 Page(1~7): Layout analysis (3.64s)
17:00:57 Page(1~7): Table analysis (0.00s)
17:00:57 Page(1~7): Text merged (0.00s)
17:00:57 Page(1~7): Finish parsing.
17:00:57 Page(1~7): Generate 7 chunks
17:00:57 Page(1~7): Embedding chunks (0.31s)
17:00:57 Page(1~7): Indexing done (0.03s). Task done (7.88s)
17:00:57 created task graphrag
17:01:00 Task has been received.
17:02:17 Entities extraction of chunk 1 1/7 done, 9 nodes, 9 edges, 10654 tokens.
17:02:31 Entities extraction of chunk 2 2/7 done, 12 nodes, 13 edges, 11066 tokens.
17:02:33 Entities extraction of chunk 4 3/7 done, 9 nodes, 10 edges, 10433 tokens.
17:02:42 Entities extraction of chunk 5 4/7 done, 11 nodes, 14 edges, 11290 tokens.
17:02:52 Entities extraction of chunk 6 5/7 done, 13 nodes, 15 edges, 11039 tokens.
17:02:55 Entities extraction of chunk 3 6/7 done, 14 nodes, 13 edges, 11466 tokens.
17:03:32 Entities extraction of chunk 0 7/7 done, 19 nodes, 18 edges, 13107 tokens.
17:03:32 Entities and relationships extraction done, 71 nodes, 89 edges, 79055 tokens, 149.66s.
17:03:32 Entities merging done, 0.01s.
17:03:32 Relationships merging done, 0.01s.
17:03:32 ignored 1 relations due to missing entities.
17:03:32 generated subgraph for doc b1d9d3b6848711f0aacd7ddc0714c4d3 in 149.69 seconds.
17:03:32 run_graphrag b1d9d3b6848711f0aacd7ddc0714c4d3 graphrag_task_lock acquired
17:03:32 set_graph removed 0 nodes and 0 edges from index in 0.00s.
17:03:32 Get embedding of nodes: 9/71
17:03:33 Get embedding of edges: 9/88
17:03:34 set_graph converted graph change to 161 chunks in 2.27s.
17:03:34 Insert chunks: 4/161
17:03:34 Insert chunks: 104/161
17:03:34 set_graph added/updated 71 nodes and 88 edges from index in 0.28s.
17:03:34 merging subgraph for doc b1d9d3b6848711f0aacd7ddc0714c4d3 into the global graph done in 2.60 seconds.
17:03:34 Knowledge Graph done (153.18s)
```
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
- [x] Performance Improvement
### What problem does this PR solve?
#9082#6365
<u> **WARNING: it's not compatible with the older version of `Agent`
module, which means that `Agent` from older versions can not work
anymore.**</u>
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
This PR investigates the cause of #7957.
TL;DR: Incorrect similarity calculations lead to too many candidates.
Since candidate selection involves interaction with the LLM, this causes
significant delays in the program.
What this PR does:
1. **Fix similarity calculation**:
When processing a 64 pages government document, the corrected similarity
calculation reduces the number of candidates from over 100,000 to around
16,000. With a default batch size of 100 pairs per LLM call, this fix
reduces unnecessary LLM interactions from over 1,000 calls to around
160, a roughly 10x improvement.
2. **Add concurrency and timeout limits**:
Up to 5 entity types are processed in "parallel", each with a 180-second
timeout. These limits may be configurable in future updates.
3. **Improve logging**:
The candidate resolution process now reports progress in real time.
4. **Mitigates potential concurrency risks**
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
### What problem does this PR solve?
Support passing the attribute check when the upstream has already made
sure it.
### Type of change
- [X] Performance Improvement
### What problem does this PR solve?
Close#7879
I checked the current master code, the kb_parser_config is join from
knowledge table, so I think should be some edge cases due to history
data
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Info of whether applying graph resolution and community extraction is
storage in `task["kb_parser_config"]`. However, previous code get
`graphrag_conf` from `task["parser_config"]`, making `with_resolution`
and `with_community` are always false.
### 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?
When you removed any document in a knowledge base using knowledge graph,
the graph's `removed_kwd` is set to "Y".
However, in the function `graphrag.utils.get_gaph`, `rebuild_graph`
method is passed and directly return `None` while `removed_kwd=Y`,
making residual part of the graph abandoned (but old entity data still
exist in db).
Besides, infinity instance actually pass deleting graph components'
`source_id` when removing document. It may cause wrong graph after
rebuild.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
当前graphrag的LOOP_PROMPT，会导致模型输出Y之后，继续补充了实体和关系，比较浪费时间。参照[graph
rag](https://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/extract_graph.py)最新的代码，修改了LOOP_PROMPT，经过验证，修改后可以稳定的输出Y停止。
Currently, GraphRAG’s LOOP_PROMPT causes the model to keep appending
entities and relationships even after outputting “Y,” which wastes time.
Referring to the latest code in
[graphRAG](https://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/extract_graph.py),
I modified the LOOP_PROMPT, and after verification the updated prompt
reliably outputs “Y” and stops.
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [x] Performance Improvement
- [ ] Other (please describe):
Co-authored-by: liuzhenghua-jk <liuzhenghua-jk@360shuke.com>
### What problem does this PR solve?
Some models force thinking, resulting in the absence of the think tag in
the returned content
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Problem Description
Multiple files in the RAGFlow project contain closure trap issues when
using lambda functions with `trio.open_nursery()`. This problem causes
concurrent tasks created in loops to reference the same variable,
resulting in all tasks processing the same data (the data from the last
iteration) rather than each task processing its corresponding data from
the loop.
## Issue Details
When using a `lambda` to create a closure function and passing it to
`nursery.start_soon()` within a loop, the lambda function captures a
reference to the loop variable rather than its value. For example:
```python
# Problematic code
async with trio.open_nursery() as nursery:
for d in docs:
nursery.start_soon(lambda: doc_keyword_extraction(chat_mdl, d, topn))
```
In this pattern, when concurrent tasks begin execution, `d` has already
become the value after the loop ends (typically the last element),
causing all tasks to use the same data.
## Fix Solution
Changed the way concurrent tasks are created with `nursery.start_soon()`
by leveraging Trio's API design to directly pass the function and its
arguments separately:
```python
# Fixed code
async with trio.open_nursery() as nursery:
for d in docs:
nursery.start_soon(doc_keyword_extraction, chat_mdl, d, topn)
```
This way, each task uses the parameter values at the time of the
function call, rather than references captured through closures.
## Fixed Files
Fixed closure traps in the following files:
1. `rag/svr/task_executor.py`: 3 fixes, involving document keyword
extraction, question generation, and tag processing
2. `rag/raptor.py`: 1 fix, involving document summarization
3. `graphrag/utils.py`: 2 fixes, involving graph node and edge
processing
4. `graphrag/entity_resolution.py`: 2 fixes, involving entity resolution
and graph node merging
5. `graphrag/general/mind_map_extractor.py`: 2 fixes, involving document
processing
6. `graphrag/general/extractor.py`: 3 fixes, involving content
processing and graph node/edge merging
7. `graphrag/general/community_reports_extractor.py`: 1 fix, involving
community report extraction
## Potential Impact
This fix resolves a serious concurrency issue that could have caused:
- Data processing errors (processing duplicate data)
- Performance degradation (all tasks working on the same data)
- Inconsistent results (some data not being processed)
After the fix, all concurrent tasks should correctly process their
respective data, improving system correctness and reliability.
