Knowledge Graph Generation Error, extracted 0 node and 0 edge

[Yeon1007]

When generating the knowledge graph, why can't the node and edge be extracted from each chunk, whereas they could be extracted from the same chunk and the same LLM before? After checking the logs, the reason was that the assistant's results were the same regardless of which model was used. only the "(", What influenced this result, and how should it be resolved? @Magicbook1108 @dosu

Log: {
2026-03-11 15:10:25
"role": "assistant",
2026-03-11 15:10:25
"content": "("
2026-03-11 15:10:25
},
2026-03-11 15:10:25
{
:
log shown: MANY entities and relationships were missed in the last extraction. Please find only the missing entities and relationships from previous text.\n\n---Remember Steps---\n\n1. Identify all entities. For each identified entity, extract the following information:\n- entity_name: Name of the entity, use same language as input text. If English, capitalized the name\n- entity_type: One of the following types:
2026-03-11 15:49:06
2026-03-11 15:49:06,451 INFO 44 set_progress(8c55c35a1d1d11f1a4689db95c36ddbe), progress: 0.6, progress_msg: 15:49:06 Entities extraction of chunk 38 39/39 done, 0 nodes, 0 edges, 39449 tokens.
2026-03-11 15:49:06
2026-03-11 15:49:06,485 INFO 44 set_progress(8c55c35a1d1d11f1a4689db95c36ddbe), progress: None, progress_msg: 15:49:06 Entities and relationships extraction done, 0 nodes, 0 edges, 1029591 tokens, 527.63s.

[dosubot[bot]]

This is a confirmed bug in RAGFlow. The LLM returning only "(" happens because RAGFlow passes an empty gen_conf={} to the LLM during entity extraction, which means max_tokens is not set. This causes your LLM provider to use its default output token limit, which truncates the response before the extraction can complete.

Since the regex parser expects tuples in the format (entity, type, description, ...), a response of just "(" has no closing parenthesis and produces 0 nodes/0 edges.

Fix:
Manually edit these files to add a max_tokens parameter:

• rag/graphrag/general/graph_extractor.py
• rag/graphrag/light/graph_extractor.py

Change gen_conf={} to gen_conf={"max_tokens": 4096} in the LLM call sections.

Alternative: If you're using a custom LLM provider/API, you can increase the default output token limit on the provider side instead.

This explains why it worked before with the same model - your LLM provider's default settings may have changed, or you might be using a different API endpoint with different defaults.

To reply, just mention @dosu.

---

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[dosubot[bot]]

A few ways to verify:

1. Enable httpx debug logging (most direct method - shows exact API payload):
Add this to your Python code or enable it via environment:

import logging
logging.getLogger("httpx").setLevel(logging.DEBUG)

2. Enable RAGFlow debug logging:
Edit docker/.env and add:

LOG_LEVELS=root=DEBUG

Then restart your containers.

3. Check the logs:

docker logs -f ragflow-server

Or check the ./ragflow-logs/ directory on your host.

4. Quickest verification - just run knowledge graph extraction again. If it was returning only "(" before and now returns proper entities/relationships (non-zero nodes/edges), your fix is working. The functional difference should be immediately obvious in the extraction results.

To reply, just mention @dosu.

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[Yeon1007]

i am checking the log, what keywords should i search for in logs? coz i set max_tokens to 4096 but when i search 4096 in the log there is no results @dosu

[dosubot[bot]]

RAGFlow doesn't actually log gen_conf or max_tokens values anywhere - that's why you can't find "4096" in the logs.

The best way to verify your fix is working is to look at the extraction results. Search for:

• "Entities extraction of chunk" - shows nodes/edges count per chunk
• "nodes" and "edges" - the extraction summary

If your fix worked, you should see non-zero nodes and edges in these log messages instead of the 0 nodes, 0 edges you saw before. That's the real indicator that max_tokens is being properly set and the LLM is returning complete responses.

If you want to see the actual API parameters being sent, you'd need to enable httpx debug logging as I mentioned earlier, or add your own logging statement in the code to print the gen_conf value.

To reply, just mention @dosu.

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[Yeon1007]

i made sure the change the code, rebuilt the image and ran the new image successfully, but when i tried to generate the knowledge graph it still got 0 nodes and 0 edges? what is the issue? any other reasons causing this? @dosu

[dosubot[bot]]

There are several other possible causes:

1. Cached responses - RAGFlow has a Redis cache with 24-hour TTL. If a previous failed extraction is cached, it may return empty results. Try clearing the Redis cache or waiting 24 hours.

2. Entity type filtering - RAGFlow only accepts entities of these types: "organization", "person", "geo", "event", "category". If your documents contain different entity types, they're silently discarded.

3. LLM model issues - Some models don't follow the required structured output format or don't work well with certain languages. Try a different LLM model.

4. Chunking method - Table chunking method skips knowledge graph extraction entirely. Make sure you're using General or another chunking method.

5. Check the actual LLM response - Can you look at the log for what the LLM is actually returning now? Is it still just "(" or is it returning a longer response that's being rejected for format reasons?

To help narrow this down: What model are you using, and what language are your documents in?

To reply, just mention @dosu.

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