[Issue]: <title> Warning: Error decoding faulty json, attempting repair

[240839785]

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Describe the issue

I am encountering a JSON decoding error when using the glm-4-flash tool to generate knowledge graphs. The error message is as follows:

Steps to reproduce

No response

GraphRAG Config Used

# Paste your config here

encoding_model: cl100k_base
skip_workflows: []
llm:
api_key: $$$
type: openai_chat # or azure_openai_chat
model: glm-4-flash
model_supports_json: true # recommended if this is available for your model.
max_tokens: 4000
request_timeout: 600.0
api_base: https://open.bigmodel.cn/api/paas/v4

api_version: 2024-02-15-preview

organization: <organization_id>

deployment_name: <azure_model_deployment_name>

tokens_per_minute: 150_000 # set a leaky bucket throttle

requests_per_minute: 10_000 # set a leaky bucket throttle

max_retries: 10

max_retry_wait: 10.0

sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times

concurrent_requests: 5 # the number of parallel inflight requests that may be made

temperature: 0 # temperature for sampling

top_p: 1 # top-p sampling

n: 1 # Number of completions to generate

parallelization:
stagger: 0.3

num_threads: 50 # the number of threads to use for parallel processing

async_mode: threaded # or asyncio

embeddings:

parallelization: override the global parallelization settings for embeddings

async_mode: threaded # or asyncio

target: required # or all

batch_size: 16 # the number of documents to send in a single request

batch_max_tokens: 8191 # the maximum number of tokens to send in a single request

llm:
api_key: $$$
type: openai_embedding # or azure_openai_embedding
#model: ChristianAzinn/mxbai-embed-large-v1-gguf
model: embedding-2
api_base: https://open.bigmodel.cn/api/paas/v4
# api_version: 2024-02-15-preview
# organization: <organization_id>
# deployment_name: <azure_model_deployment_name>
# tokens_per_minute: 150_000 # set a leaky bucket throttle
# requests_per_minute: 10_000 # set a leaky bucket throttle
# max_retries: 10
# max_retry_wait: 10.0
# sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
concurrent_requests: 5 # the number of parallel inflight requests that may be made

chunks:
size: 600
overlap: 150
group_by_columns: [id] # by default, we don't allow chunks to cross documents

input:
type: file # or blob
file_type: text # or csv
base_dir: "input"
file_encoding: utf-8
file_pattern: ".*\.txt$"

cache:
type: file # or blob
base_dir: "cache"

connection_string: <azure_blob_storage_connection_string>

container_name: <azure_blob_storage_container_name>

storage:
type: file # or blob
base_dir: "output/${timestamp}/artifacts"

connection_string: <azure_blob_storage_connection_string>

container_name: <azure_blob_storage_container_name>

reporting:
type: file # or console, blob
base_dir: "output/${timestamp}/reports"

connection_string: <azure_blob_storage_connection_string>

container_name: <azure_blob_storage_container_name>

entity_extraction:

strategy: fully override the entity extraction strategy.

type: one of graph_intelligence, graph_intelligence_json and nltk

llm: override the global llm settings for this task

parallelization: override the global parallelization settings for this task

async_mode: override the global async_mode settings for this task

prompt: "prompts/entity_extraction.txt"
entity_types: [organization,person,geo,event]
max_gleanings: 1

summarize_descriptions:

llm: override the global llm settings for this task

parallelization: override the global parallelization settings for this task

async_mode: override the global async_mode settings for this task

prompt: "prompts/summarize_descriptions.txt"
max_length: 500

claim_extraction:

llm: override the global llm settings for this task

parallelization: override the global parallelization settings for this task

async_mode: override the global async_mode settings for this task

enabled: true
prompt: "prompts/claim_extraction.txt"
description: "Any claims or facts that could be relevant to information discovery."
max_gleanings: 1

community_reports:

llm: override the global llm settings for this task

parallelization: override the global parallelization settings for this task

async_mode: override the global async_mode settings for this task

prompt: "prompts/community_report.txt"
max_length: 2000
max_input_length: 8000

cluster_graph:
max_cluster_size: 10

embed_graph:
enabled: false # if true, will generate node2vec embeddings for nodes

num_walks: 10

walk_length: 40

window_size: 2

iterations: 3

random_seed: 597832

umap:
enabled: false # if true, will generate UMAP embeddings for nodes

snapshots:
graphml: false
raw_entities: false
top_level_nodes: false

local_search:

text_unit_prop: 0.5

community_prop: 0.1

conversation_history_max_turns: 5

top_k_mapped_entities: 10

top_k_relationships: 10

llm_temperature: 0 # temperature for sampling

llm_top_p: 1 # top-p sampling

llm_n: 1 # Number of completions to generate

max_tokens: 12000

global_search:

llm_temperature: 0 # temperature for sampling

llm_top_p: 1 # top-p sampling

llm_n: 1 # Number of completions to generate

max_tokens: 12000

data_max_tokens: 12000

map_max_tokens: 1000

reduce_max_tokens: 2000

concurrency: 32

Logs and screenshots

09:49:42,176 graphrag.llm.openai.utils INFO Warning: Error decoding faulty json, attempting repair
09:49:42,177 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "create_community_report" with 0 retries took 27.0612059480045. input_tokens=3804, output_tokens=908
09:49:45,152 httpx INFO HTTP Request: POST https://open.bigmodel.cn/api/paas/v4/chat/completions "HTTP/1.1 200 OK"
09:49:45,154 graphrag.llm.openai.utils INFO Warning: Error decoding faulty json, attempting repair
09:49:45,154 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "create_community_report" with 0 retries took 30.045606669002154. input_tokens=4367, output_tokens=725
09:49:49,146 httpx INFO HTTP Request: POST https://open.bigmodel.cn/api/paas/v4/chat/completions "HTTP/1.1 200 OK"
09:49:49,151 graphrag.llm.openai.utils INFO Warning: Error decoding faulty json, attempting repair
09:49:49,152 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "create_community_report" with 0 retries took 18.514149362999888. input_tokens=3017, output_tokens=740
09:49:49,876 httpx INFO HTTP Request: POST https://open.bigmodel.cn/api/paas/v4/chat/completions "HTTP/1.1 200 OK"

Additional Information

• GraphRAG Version:
• Operating System:
• Python Version:
• Related Issues:

[240839785]

Despite the JSON decoding error warnings, all workflows completed successfully. The final output indicates that the knowledge graph generation was successful.

[madlogos]

Refer to this post #575. It works for me.

@awaescher

Small update: I found out that my model always returned nosense like this:

python -m graphrag.query --root ./myfolder --method global "What are the main topics"
"The main topic is 'What are the main topics'"

I found out that my local Ollama instance (0.3.0) seemed to ignore the system prompt and I got it working by manually stitching together the two prompts into one:

File: /graphrag/query/structured_search/global_search/search.py , method: _map_response_single_batch

#search_messages = [
#  {"role": "system", "content": search_prompt},
#  {"role": "user", "content": query},
#]
search_messages = [ {"role": "user", "content": search_prompt + "\n\n### USER QUESTION ### \n\n" + query}