infiniflow
[Bug]: Fail to bind LLM used by RAPTOR
Is there an existing issue for the same bug?
- [X] I have checked the existing issues.
RAGFlow workspace code commit ID
8939206531d8b994fd10565d4056b24ea599c1c5
RAGFlow image version
v0.15.0-slim
Other environment information
Linux Ubuntu 5.15.167.4-microsoft-standard-WSL2
Actual behavior
I use version 0.15.0-slim with local Ollama for embedding (snowflake-arctic-embed2) and LLM (qwen2.5:14b) with success for parse.
If I activate RAPTOR for a knowledge base, I get an error and Fail parsing :
[ERROR]Fail to bind LLM used by RAPTOR: 3 vs. 4
[ERROR]handle_task got exception, please check log
logs:
2024-12-19 10:38:25,090 INFO 32 task_consumer_0 reported heartbeat: {"name": "task_consumer_0", "now": "2024-12-19T10:38:25.090195", "boot_at": "2024-12-19T09:11:49.795794"
, "pending": 1, "lag": 0, "done": 4, "failed": 1, "current": {"id": "927bb7e4bdec11efb92d0242ac120006", "doc_id": "af0101f6bde911ef8f240242ac120006", "from_page": 100000000, "t
o_page": 100000000, "retry_count": 0, "kb_id": "4576b942bde911efb5920242ac120006", "parser_id": "paper", "parser_config": {"auto_keywords": 0, "auto_questions": 0, "raptor": {"
use_raptor": true, "prompt": "Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n {cluster_content}\n
The above is the content you need to summarize.", "max_token": 256, "threshold": 0.1, "max_cluster": 64, "random_seed": 0}}, "name": "MNRAS_67P_ModelSAP_stae1290.pdf", "type":
"pdf", "location": "MNRAS_67P_ModelSAP_stae1290.pdf", "size": 6289538, "tenant_id": "ee69c9acbde111efbe830242ac120006", "language": "English", "embd_id": "snowflake-arctic-embe
d2:latest@Ollama", "pagerank": 0, "img2txt_id": "llama3.2-vision:latest@Ollama", "asr_id": "", "llm_id": "qwen2.5:14b@Ollama", "update_time": 1734600923929, "task_type": "rapto
r"}}
2024-12-19 10:38:28,448 INFO 32 HTTP Request: POST http://host.docker.internal:11434/api/chat "HTTP/1.1 200 OK"
2024-12-19 10:38:28,488 INFO 32 HTTP Request: POST http://host.docker.internal:11434/api/embeddings "HTTP/1.1 200 OK"
2024-12-19 10:38:28,512 ERROR 32 summarize got exception
Traceback (most recent call last):
File "/ragflow/rag/raptor.py", line 92, in summarize
chunks.append((cnt, self._embedding_encode(cnt)))
File "/ragflow/rag/raptor.py", line 48, in _embedding_encode
response = get_embed_cache(self._embd_model.llm_name, txt)
File "/ragflow/graphrag/utils.py", line 104, in get_embed_cache
return np.array(json.loads(bin.decode("utf-8")))
AttributeError: 'str' object has no attribute 'decode'. Did you mean: 'encode'?
2024-12-19 10:38:28,515 INFO 32 set_progress(927bb7e4bdec11efb92d0242ac120006), progress: -1, progress_msg: Page(100000001~100000001): [ERROR]Fail to bind LLM used by RAPTO
R: 3 vs. 4
2024-12-19 10:38:28,545 ERROR 32 Fail to bind LLM used by RAPTOR: 3 vs. 4
Traceback (most recent call last):
File "/ragflow/rag/svr/task_executor.py", line 438, in do_handle_task
chunks, token_count, vector_size = run_raptor(task, chat_model, embedding_model, progress_callback)
File "/ragflow/rag/svr/task_executor.py", line 370, in run_raptor
chunks = raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
File "/ragflow/rag/raptor.py", line 132, in __call__
assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters)
AssertionError: 3 vs. 4
My Ollama local LLM I'm using for RAPTOR isn't compatible ? or other bug / problem ?
Thank's for your great work.
Expected behavior
No response
Steps to reproduce
I can't find any information about RAPTOR LLM in the documentation and the problem is identical for each document added.
Additional information
No response
For 3/51 documents I always get a RAPTOR error, but if I relaunch in UI, keeping the chunks already calculated (not clear existing chunks), it manages to finish and the document goes to success status. unfortunately, as I've launched about twenty simultaneous parse operations, I can't find the console logs for sure.
For 1/51 it's not working (Rapport 2005_I.pdf)
Progress:
Page(0~12): reused previous task's chunks.
Page(12~24): reused previous task's chunks.
Page(24~36): reused previous task's chunks.
Page(36~48): reused previous task's chunks.
Page(48~60): reused previous task's chunks.
Page(60~72): reused previous task's chunks.
Page(72~84): reused previous task's chunks.
Page(84~96): reused previous task's chunks.
Page(96~108): reused previous task's chunks.
Page(108~117): reused previous task's chunks.
Start to do RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval).
Task has been received.
Page(100000001~100000001): Cluster one layer: 320 -> 7
Page(100000001~100000001): Cluster one layer: 7 -> 4
Page(100000001~100000001): [ERROR]Fail to bind LLM used by RAPTOR: 2 vs. 3
[ERROR]handle_task got exception, please check log
log console
2024-12-22 10:34:52,402 INFO 43360 HTTP Request: POST http://host.docker.internal:11434/api/chat "HTTP/1.1 200 OK"
2024-12-22 10:34:52,450 INFO 43360 HTTP Request: POST http://host.docker.internal:11434/api/embeddings "HTTP/1.1 200 OK"
2024-12-22 10:34:52,467 ERROR 43360 summarize got exception
Traceback (most recent call last):
File "/ragflow/rag/raptor.py", line 92, in summarize
chunks.append((cnt, self._embedding_encode(cnt)))
File "/ragflow/rag/raptor.py", line 49, in _embedding_encode
if response:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
2024-12-22 10:34:52,470 INFO 43360 set_progress(994223dac04711efaefd0242ac120006), progress: -1, progress_msg: Page(100000001~100000001): [ERROR]Fail to bind LLM used by RAPTOR: 2 vs. 3
2024-12-22 10:34:52,488 ERROR 43360 Fail to bind LLM used by RAPTOR: 2 vs. 3
Traceback (most recent call last):
File "/ragflow/rag/svr/task_executor.py", line 438, in do_handle_task
chunks, token_count, vector_size = run_raptor(task, chat_model, embedding_model, progress_callback)
File "/ragflow/rag/svr/task_executor.py", line 370, in run_raptor
chunks = raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
File "/ragflow/rag/raptor.py", line 132, in __call__
assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters)
AssertionError: 2 vs. 3
2024-12-22 10:34:52,491 INFO 43360 set_progress(994223dac04711efaefd0242ac120006), progress: -1, progress_msg: [ERROR]handle_task got exception, please check log
2024-12-22 10:34:52,507 ERROR 43360 handle_task got exception for task {"id": "994223dac04711efaefd0242ac120006", "doc_id": "5a65db7cbfa011efbd950242ac120006", "from_page": 100000000, "to_page": 100000000, "retry_count": 0, "kb_id": "d5f8d862bf9f11efb7f40242ac120006", "parser_id": "book", "parser_config": {"auto_keywords": 0, "auto_questions": 0, "raptor": {"use_raptor": true, "prompt": "Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n {cluster_content}\nThe above is the content you need to summarize.", "max_token": 256, "threshold": 0.1, "max_cluster": 64, "random_seed": 0}}, "name": "Rapport 2005_I.pdf", "type": "pdf", "location": "Rapport 2005_I.pdf", "size": 15740366, "tenant_id": "ee69c9acbde111efbe830242ac120006", "language": "English", "embd_id": "snowflake-arctic-embed2:latest@Ollama", "pagerank": 0, "img2txt_id": "llama3.2-vision:latest@Ollama", "asr_id": "", "llm_id": "qwen2.5:14b@Ollama", "update_time": 1734859921824, "task_type": "raptor"}
Traceback (most recent call last):
File "/ragflow/rag/svr/task_executor.py", line 511, in handle_task
do_handle_task(task)
File "/ragflow/rag/svr/task_executor.py", line 438, in do_handle_task
chunks, token_count, vector_size = run_raptor(task, chat_model, embedding_model, progress_callback)
File "/ragflow/rag/svr/task_executor.py", line 370, in run_raptor
chunks = raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
File "/ragflow/rag/raptor.py", line 132, in __call__
assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters)
AssertionError: 2 vs. 3
2024-12-22 10:35:01,784 INFO 43360 task_consumer_0 reported heartbeat: {"name": "task_consumer_0", "now": "2024-12-22T10:35:01.784164", "boot_at": "2024-12-21T23:47:57.830717", "pending": 0, "lag": 0, "done": 48, "failed": 4, "current": null}
2024-12-22 10:35:04,238 INFO 23 172.18.0.6 - - [22/Dec/2024 10:35:04] "GET /v1/document/list?kb_id=d5f8d862bf9f11efb7f40242ac120006&keywords=&page_size=100&page=1 HTTP/1.1" 200 -
2024-12-22 10:35:19,396 INFO 23 172.18.0.6 - - [22/Dec/2024 10:35:19] "GET /v1/document/list?kb_id=d5f8d862bf9f11efb7f40242ac120006&keywords=&page_size=100&page=1 HTTP/1.1" 200 -
2024-12-22 10:35:31,815 INFO 43360 task_consumer_0 reported heartbeat: {"name": "task_consumer_0", "now": "2024-12-22T10:35:31.815294", "boot_at": "2024-12-21T23:47:57.830717", "pending": 0, "lag": 0, "done": 48, "failed": 4, "current": null}
Thanks for your great work
@KevinHuSh Hi, I'm still having this issue in 0.15.1. When I use Ollama (llama3.1), but also when I use Together.ai LLM (llama 3.3) it has the same issue
@KevinHuSh This is still a bug for me in 0.15.1 using either Ollama or Together.ai as an inference server.
2025-01-20 13:31:37,682 INFO 17 HTTP Request: POST http://ollamainference:11434/v1/chat/completions "HTTP/1.1 200 OK" 2025-01-20 13:31:38,446 ERROR 17 LLMBundle.encode can't update token usage for e39eb6dccce011ef965e0242ac120006/EMBEDDING used_tokens: 322 2025-01-20 13:31:39,179 ERROR 17 LLMBundle.encode can't update token usage for e39eb6dccce011ef965e0242ac120006/EMBEDDING used_tokens: 322 2025-01-20 13:31:39,467 INFO 17 HTTP Request: POST http://ollamainference:11434/v1/chat/completions "HTTP/1.1 200 OK" 2025-01-20 13:31:40,348 ERROR 17 LLMBundle.encode can't update token usage for e39eb6dccce011ef965e0242ac120006/EMBEDDING used_tokens: 359 2025-01-20 13:31:41,174 ERROR 17 LLMBundle.encode can't update token usage for e39eb6dccce011ef965e0242ac120006/EMBEDDING used_tokens: 359 2025-01-20 13:31:43,019 INFO 17 HTTP Request: POST http://ollamainference:11434/v1/chat/completions "HTTP/1.1 200 OK" 2025-01-20 13:31:44,021 ERROR 17 LLMBundle.encode can't update token usage for e39eb6dccce011ef965e0242ac120006/EMBEDDING used_tokens: 411 2025-01-20 13:31:44,369 INFO 17 HTTP Request: POST http://ollamainference:11434/v1/chat/completions "HTTP/1.1 500 Internal Server Error" 2025-01-20 13:31:44,370 ERROR 17 summarize got exception Traceback (most recent call last): File "/ragflow/rag/raptor.py", line 82, in summarize cnt = self._chat("You're a helpful assistant.", File "/ragflow/rag/raptor.py", line 43, in _chat raise Exception(response) Exception: ERROR: Error code: 500 - {'error': {'message': 'POST predict: Post "http://127.0.0.1:37507/completion": EOF', 'type': 'api_error', 'param': None, 'code': None}} 2025-01-20 13:31:44,478 INFO 17 HTTP Request: POST http://ollamainference:11434/v1/chat/completions "HTTP/1.1 500 Internal Server Error" 2025-01-20 13:31:44,478 INFO 17 HTTP Request: POST http://ollamainference:11434/v1/chat/completions "HTTP/1.1 500 Internal Server Error" 2025-01-20 13:31:44,479 INFO 17 HTTP Request: POST http://ollamainference:11434/v1/chat/completions "HTTP/1.1 500 Internal Server Error" 2025-01-20 13:31:44,480 ERROR 17 summarize got exception Traceback (most recent call last): File "/ragflow/rag/raptor.py", line 82, in summarize cnt = self._chat("You're a helpful assistant.", File "/ragflow/rag/raptor.py", line 43, in _chat raise Exception(response) Exception: ERROR: Error code: 500 - {'error': {'message': 'an error was encountered while running the model: unexpected EOF', 'type': 'api_error', 'param': None, 'code': None}} 2025-01-20 13:31:44,481 ERROR 17 summarize got exception Traceback (most recent call last): File "/ragflow/rag/raptor.py", line 82, in summarize cnt = self._chat("You're a helpful assistant.", File "/ragflow/rag/raptor.py", line 43, in _chat raise Exception(response) Exception: ERROR: Error code: 500 - {'error': {'message': 'an error was encountered while running the model: unexpected EOF', 'type': 'api_error', 'param': None, 'code': None}} 2025-01-20 13:31:44,481 ERROR 17 summarize got exception Traceback (most recent call last): File "/ragflow/rag/raptor.py", line 82, in summarize cnt = self._chat("You're a helpful assistant.", File "/ragflow/rag/raptor.py", line 43, in _chat raise Exception(response) Exception: ERROR: Error code: 500 - {'error': {'message': 'an error was encountered while running the model: unexpected EOF', 'type': 'api_error', 'param': None, 'code': None}} 2025-01-20 13:31:45,010 ERROR 17 LLMBundle.encode can't update token usage for e39eb6dccce011ef965e0242ac120006/EMBEDDING used_tokens: 411 2025-01-20 13:31:45,016 INFO 17 set_progress(a0892a42d73211ef9bad0242ac120006), progress: -1, progress_msg: 13:31:45 Page(100000001~100000001): [ERROR]Fail to bind LLM used by RAPTOR: 11 vs. 15 2025-01-20 13:31:45,022 ERROR 17 Fail to bind LLM used by RAPTOR: 11 vs. 15 Traceback (most recent call last): File "/ragflow/rag/svr/task_executor.py", line 440, in do_handle_task chunks, token_count, vector_size = run_raptor(task, chat_model, embedding_model, progress_callback) File "/ragflow/rag/svr/task_executor.py", line 372, in run_raptor chunks = raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback) File "/ragflow/rag/raptor.py", line 132, in call assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters) AssertionError: 11 vs. 15 2025-01-20 13:31:45,025 INFO 17 set_progress(a0892a42d73211ef9bad0242ac120006), progress: -1, progress_msg: 13:31:45 [ERROR]handle_task got exception, please check log 2025-01-20 13:31:45,031 ERROR 17 handle_task got exception for task {"id": "a0892a42d73211ef9bad0242ac120006", "doc_id": "210ca866d1ce11ef92290242ac120006", "from_page": 100000000, "to_page": 100000000, "retry_count": 0, "kb_id": "e45d2d96d1cd11efb6c60242ac120006", "parser_id": "laws", "parser_config": {"auto_keywords": 10, "auto_questions": 0, "raptor": {"use_raptor": true, "prompt": "Please summarize the following paragraphs. Be careful and consistent with the numbering, do not make things up. Paragraphs as follows:\n {cluster_content}\nThe above is the content you need to summarize.", "max_token": 875, "threshold": 0.1, "max_cluster": 64, "random_seed": 0}, "layout_recognize": true, "task_page_size": 12, "pages": [[1, 1024]]}, "name": "Project-Coach-Acquisition-Term-Facility-Agreement-EXECUTED-05.09.2022_Redacted.pdf", "type": "pdf", "location": "Project-Coach-Acquisition-Term-Facility-Agreement-EXECUTED-05.09.2022_Redacted.pdf", "size": 902627, "tenant_id": "e39eb6dccce011ef965e0242ac120006", "language": "English", "embd_id": "BAAI/bge-large-en-v1.5@FastEmbed", "pagerank": 0, "img2txt_id": "", "asr_id": "", "llm_id": "llama3.1___OpenAI-API@OpenAI-API-Compatible", "update_time": 1737379791346, "task_type": "raptor"} Traceback (most recent call last): File "/ragflow/rag/svr/task_executor.py", line 513, in handle_task do_handle_task(task) File "/ragflow/rag/svr/task_executor.py", line 440, in do_handle_task chunks, token_count, vector_size = run_raptor(task, chat_model, embedding_model, progress_callback) File "/ragflow/rag/svr/task_executor.py", line 372, in run_raptor chunks = raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback) File "/ragflow/rag/raptor.py", line 132, in call assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters) AssertionError: 11 vs. 15
I encountered the same issue while using vLLM with both Qwen2.5-Coder and InternVL2.5-MPO models.
2025-02-14 03:32:08,751 INFO 16 HTTP Request: POST http://host.docker.internal:20009/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-14 03:32:08,789 INFO 16 HTTP Request: POST http://host.docker.internal:20010/v1/embeddings "HTTP/1.1 200 OK"
2025-02-14 03:32:08,806 ERROR 16 summarize got exception
Traceback (most recent call last):
File "/ragflow/rag/raptor.py", line 92, in summarize
chunks.append((cnt, self._embedding_encode(cnt)))
File "/ragflow/rag/raptor.py", line 49, in _embedding_encode
if response:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
2025-02-14 03:32:13,899 INFO 16 HTTP Request: POST http://host.docker.internal:20009/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-14 03:32:13,900 INFO 16 HTTP Request: POST http://host.docker.internal:20009/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-14 03:32:13,901 INFO 16 HTTP Request: POST http://host.docker.internal:20009/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-14 03:32:13,976 INFO 16 HTTP Request: POST http://host.docker.internal:20010/v1/embeddings "HTTP/1.1 200 OK"
2025-02-14 03:32:13,998 ERROR 16 summarize got exception
Traceback (most recent call last):
File "/ragflow/rag/raptor.py", line 92, in summarize
chunks.append((cnt, self._embedding_encode(cnt)))
File "/ragflow/rag/raptor.py", line 49, in _embedding_encode
if response:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
2025-02-14 03:32:14,057 INFO 16 HTTP Request: POST http://host.docker.internal:20010/v1/embeddings "HTTP/1.1 200 OK"
2025-02-14 03:32:14,062 INFO 16 HTTP Request: POST http://host.docker.internal:20010/v1/embeddings "HTTP/1.1 200 OK"
2025-02-14 03:32:14,094 ERROR 16 summarize got exception
Traceback (most recent call last):
File "/ragflow/rag/raptor.py", line 92, in summarize
chunks.append((cnt, self._embedding_encode(cnt)))
File "/ragflow/rag/raptor.py", line 49, in _embedding_encode
if response:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
2025-02-14 03:32:14,161 INFO 16 HTTP Request: POST http://host.docker.internal:20010/v1/embeddings "HTTP/1.1 200 OK"
2025-02-14 03:32:14,198 INFO 16 set_progress(8463e5f6ea0a11efb27a0242ac1a0006), progress: -1, progress_msg: 03:32:14 [ERROR]Fail to bind LLM used by RAPTOR: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
2025-02-14 03:32:14,205 ERROR 16 Fail to bind LLM used by RAPTOR: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
Traceback (most recent call last):
File "/ragflow/rag/svr/task_executor.py", line 499, in do_handle_task
chunks, token_count = run_raptor(task, chat_model, embedding_model, vector_size, progress_callback)
File "/ragflow/rag/svr/task_executor.py", line 406, in run_raptor
chunks = raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
File "/ragflow/rag/raptor.py", line 134, in __call__
raise th.result()
File "/ragflow/rag/raptor.py", line 92, in summarize
chunks.append((cnt, self._embedding_encode(cnt)))
File "/ragflow/rag/raptor.py", line 49, in _embedding_encode
if response:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
2025-02-14 03:32:14,217 INFO 16 set_progress(8463e5f6ea0a11efb27a0242ac1a0006), progress: -1, progress_msg: 03:32:14 [ERROR][Exception]: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
2025-02-14 03:32:14,229 ERROR 16 handle_task got exception for task {"id": "8463e5f6ea0a11efb27a0242ac1a0006", "doc_id": "bd5ecfbcea0811efa89e0242ac1a0006", "from_page": 100000000, "to_page": 100000000, "retry_count": 0, "kb_id": "a71e9372ea0811efb49d0242ac1a0006", "parser_id": "naive", "parser_config": {"auto_keywords": 0, "auto_questions": 0, "raptor": {"use_raptor": true, "prompt": "\u8acb\u4f9d\u7167\u4e0b\u5217\u8981\u6c42\u5c0d\u4ee5\u4e0b\u5167\u5bb9\u9032\u884cRAPTOR\u6f14\u7b97\u6cd5\u8655\u7406\uff1a\n1. \u56b4\u683c\u4fdd\u7559\u6240\u6709\u95dc\u9375\u6578\u64da\uff0c\u78ba\u4fdd\u6578\u5b57\u5b8c\u5168\u6b63\u78ba\u3002\n2. \u7dad\u6301\u539f\u6587\u6838\u5fc3\u610f\u7fa9\uff0c\u8acb\u52ff\u66f4\u52d5\u3002\n3. \u8acb\u52ff\u65b0\u589e\u4efb\u4f55\u539f\u6587\u672a\u63d0\u53ca\u7684\u8cc7\u8a0a\u3002\n\n\u5167\u5bb9\u5982\u4e0b\uff1a\n{cluster_content}", "max_token": 256, "threshold": 0.1, "max_cluster": 64, "random_seed": 0}, "graphrag": {"use_graphrag": false}, "chunk_token_num": 128, "delimiter": "\\n!?;\u3002\uff1b\uff01\uff1f", "layout_recognize": "DeepDOC", "html4excel": true}, "name": "adrv9008-1-w-9008-2-w-9009-w-hardware-reference-manual-ug-1295.pdf", "type": "pdf", "location": "adrv9008-1-w-9008-2-w-9009-w-hardware-reference-manual-ug-1295.pdf", "size": 3347239, "tenant_id": "3b764974e9d311efa4ee0242ac1a0006", "language": "English", "embd_id": "gte-Qwen2-7B-instruct___OpenAI-API@OpenAI-API-Compatible", "pagerank": 0, "kb_parser_config": {"auto_keywords": 0, "auto_questions": 0, "raptor": {"use_raptor": true, "prompt": "\u8acb\u4f9d\u7167\u4e0b\u5217\u8981\u6c42\u5c0d\u4ee5\u4e0b\u5167\u5bb9\u9032\u884cRAPTOR\u6f14\u7b97\u6cd5\u8655\u7406\uff1a\n1. \u56b4\u683c\u4fdd\u7559\u6240\u6709\u95dc\u9375\u6578\u64da\uff0c\u78ba\u4fdd\u6578\u5b57\u5b8c\u5168\u6b63\u78ba\u3002\n2. \u7dad\u6301\u539f\u6587\u6838\u5fc3\u610f\u7fa9\uff0c\u8acb\u52ff\u66f4\u52d5\u3002\n3. \u8acb\u52ff\u65b0\u589e\u4efb\u4f55\u539f\u6587\u672a\u63d0\u53ca\u7684\u8cc7\u8a0a\u3002\n\n\u5167\u5bb9\u5982\u4e0b\uff1a\n{cluster_content}", "max_token": 256, "threshold": 0.1, "max_cluster": 64, "random_seed": 0}, "graphrag": {"use_graphrag": false}, "chunk_token_num": 128, "delimiter": "\\n!?;\u3002\uff1b\uff01\uff1f", "layout_recognize": "DeepDOC", "html4excel": true}, "img2txt_id": "InternVL2.5-MPO___OpenAI-API@OpenAI-API-Compatible", "asr_id": "", "llm_id": "InternVL2.5-MPO___OpenAI-API@OpenAI-API-Compatible", "update_time": 1739451636352, "task_type": "raptor"}
Traceback (most recent call last):
File "/ragflow/rag/svr/task_executor.py", line 626, in handle_task
do_handle_task(task)
File "/ragflow/rag/svr/task_executor.py", line 499, in do_handle_task
chunks, token_count = run_raptor(task, chat_model, embedding_model, vector_size, progress_callback)
File "/ragflow/rag/svr/task_executor.py", line 406, in run_raptor
chunks = raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
File "/ragflow/rag/raptor.py", line 134, in __call__
raise th.result()
File "/ragflow/rag/raptor.py", line 92, in summarize
chunks.append((cnt, self._embedding_encode(cnt)))
File "/ragflow/rag/raptor.py", line 49, in _embedding_encode
if response:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
