[Bug] [semantic_splitter] semantic splitter creating empty splits , thus empty inputs which generate LLM invoke errors #394
Comments
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Hey @epie-godfred, Do you provide a llm to the semantic splitter in your config.yaml? You need to provide a llm: splitter: |
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Hey @DrProNoob I was using the UI and it seems there is no way of adding this particular configuration from the UI. $ knext project create --config_path example_config_comune.yaml after updating the example_config.yaml provided here into #------------project configuration start----------------#
openie_llm: &openie_llm
api_key: <openrouter_api_key>
base_url: https://openrouter.ai/api/v1
model: deepseek/deepseek-chat
type: maas
chat_llm: &chat_llm
api_key: <openrouter_api_key>
base_url: https://openrouter.ai/api/v1
model: deepseek/deepseek-chat
type: maas
vectorize_model: &vectorize_model
api_key: <openai_api_key>
base_url: https://api.openai.com/v1
model: text-embedding-3-small
type: openai
vector_dimensions: 1024
vectorizer: *vectorize_model
log:
level: INFO
project:
biz_scene: default
host_addr: http://127.0.0.1:8887
id: "1"
language: en
namespace: KAGBologna
#------------project configuration end----------------#
#------------kag-builder configuration start----------------#
kag_builder_pipeline:
chain:
type: unstructured_builder_chain # kag.builder.default_chain.DefaultUnstructuredBuilderChain
extractor:
type: schema_free_extractor # kag.builder.component.extractor.schema_free_extractor.SchemaFreeExtractor
llm: *openie_llm
ner_prompt:
type: default_ner # kag.builder.prompt.default.ner.OpenIENERPrompt
std_prompt:
type: default_std # kag.builder.prompt.default.std.OpenIEEntitystandardizationdPrompt
triple_prompt:
type: default_triple # kag.builder.prompt.default.triple.OpenIETriplePrompt
reader:
type: dict_reader # kag.builder.component.reader.dict_reader.DictReader
post_processor:
type: kag_post_processor # kag.builder.component.postprocessor.kag_postprocessor.KAGPostProcessor
splitter:
type: semantic_splitter #length_splitter # kag.builder.component.splitter.length_splitter.LengthSplitter
llm: *chat_llm
split_length: 100000
window_length: 0
vectorizer:
type: batch_vectorizer # kag.builder.component.vectorizer.batch_vectorizer.BatchVectorizer
vectorize_model: *vectorize_model
writer:
type: kg_writer # kag.builder.component.writer.kg_writer.KGWriter
num_threads_per_chain: 1
num_chains: 16
scanner:
type: 2wiki_dataset_scanner # kag.builder.component.scanner.dataset_scanner.MusiqueCorpusScanner
#------------kag-builder configuration end----------------#
#------------kag-solver configuration start----------------#
search_api: &search_api
type: openspg_search_api #kag.solver.tools.search_api.impl.openspg_search_api.OpenSPGSearchAPI
graph_api: &graph_api
type: openspg_graph_api #kag.solver.tools.graph_api.impl.openspg_graph_api.OpenSPGGraphApi
exact_kg_retriever: &exact_kg_retriever
type: default_exact_kg_retriever # kag.solver.retriever.impl.default_exact_kg_retriever.DefaultExactKgRetriever
el_num: 5
llm_client: *chat_llm
search_api: *search_api
graph_api: *graph_api
fuzzy_kg_retriever: &fuzzy_kg_retriever
type: default_fuzzy_kg_retriever # kag.solver.retriever.impl.default_fuzzy_kg_retriever.DefaultFuzzyKgRetriever
el_num: 5
vectorize_model: *vectorize_model
llm_client: *chat_llm
search_api: *search_api
graph_api: *graph_api
chunk_retriever: &chunk_retriever
type: default_chunk_retriever # kag.solver.retriever.impl.default_fuzzy_kg_retriever.DefaultFuzzyKgRetriever
llm_client: *chat_llm
recall_num: 10
rerank_topk: 10
kag_solver_pipeline:
memory:
type: default_memory # kag.solver.implementation.default_memory.DefaultMemory
llm_client: *chat_llm
max_iterations: 3
reasoner:
type: default_reasoner # kag.solver.implementation.default_reasoner.DefaultReasoner
llm_client: *chat_llm
lf_planner:
type: default_lf_planner # kag.solver.plan.default_lf_planner.DefaultLFPlanner
llm_client: *chat_llm
vectorize_model: *vectorize_model
lf_executor:
type: default_lf_executor # kag.solver.execute.default_lf_executor.DefaultLFExecutor
llm_client: *chat_llm
force_chunk_retriever: true
exact_kg_retriever: *exact_kg_retriever
fuzzy_kg_retriever: *fuzzy_kg_retriever
chunk_retriever: *chunk_retriever
merger:
type: default_lf_sub_query_res_merger # kag.solver.execute.default_sub_query_merger.DefaultLFSubQueryResMerger
vectorize_model: *vectorize_model
chunk_retriever: *chunk_retriever
generator:
type: default_generator # kag.solver.implementation.default_generator.DefaultGenerator
llm_client: *chat_llm
generate_prompt:
type: default_resp_generator # kag.solver.prompt.default.resp_generator.RespGenerator
reflector:
type: default_reflector # kag.solver.implementation.default_reflector.DefaultReflector
llm_client: *chat_llm
#------------kag-solver configuration end----------------#I changed the splitter config from splitter:
type: length_splitter # kag.builder.component.splitter.length_splitter.LengthSplitter
split_length: 100000
window_length: 0to splitter:
type: semantic_splitter #length_splitter # kag.builder.component.splitter.length_splitter.LengthSplitter
llm: *chat_llm
split_length: 100000
window_length: 0still getting the same error : 2025-03-05 15:25:49(172.24.0.5): Task scheduling completed. cost:11 ms !
2025-03-05 15:25:49(172.24.0.5): Lock released successfully!
2025-03-05 15:25:49(172.24.0.5): Splitter task trace log:
pemja.core.PythonException: <class 'RuntimeError'>: LLM invoke exception, info: Expecting value: line 1 column 1 (char 0)
llm input:
{
"instruction": "
Please understand the content of the text in the input field, recognize the structure and components of the text, and determine the segmentation points according to the semantic theme, dividing it into several non-overlapping sections. If the article has recognizable structural information such as chapters, please divide it according to the top-level structure.
Please return the results according to the schema definition, including summaries and starting points of the sections. The format must be a JSON string. Please follow the examples given in the example field.",
"schema": {
"Section Summary": "A brief summary of the section text",
"Section Starting Point": "The starting point of the section in the original text, limited to about 20 characters. This segmentation point will be used to split the original text, so it must be found in the original text!"
},
"input": "",
"example": [
{
"input": "Jay Chou (Jay Chou), born on January 18, 1979, in Xinbei City, Taiwan Province, originally from Yongchun County, Fujian Province, is a Mandopop male singer, musician, actor, director, screenwriter, and a graduate of Tamkang Senior High School.
In 2000, recommended by Yang Junrong, Jay Chou started singing his own compositions.",
"output": [
{
"Section Summary": "Personal Introduction",
"Section Starting Point": "Jay Chou (Jay Chou), born on January 18"
},
{
"Section Summary": "Career Start",
"Section Starting Point": "
In 2000, recommended by Yang Junrong"
}
]
},
{
"input": "Hangzhou Flexible Employment Personnel Housing Provident Fund Management Measures (Trial)
To expand the benefits of the housing provident fund system and support flexible employment personnel to solve housing problems, according to the State Council's 'Housing Provident Fund Management Regulations', 'Zhejiang Province Housing Provident Fund Regulations' and the relevant provisions and requirements of the Ministry of Housing and Urban-Rural Development and the Zhejiang Provincial Department of Housing and Urban-Rural Development on flexible employment personnel participating in the housing provident fund system, combined with the actual situation in Hangzhou, this method is formulated.
1. This method applies to the voluntary deposit, use, and management of the housing provident fund for flexible employment personnel within the administrative region of this city.
2. The flexible employment personnel referred to in this method are those who are within the administrative region of this city, aged 16 and above, and males under 60 and females under 55, with full civil capacity, and employed in a flexible manner such as part-time, self-employed, or in new forms of employment.
3. Flexible employment personnel applying to deposit the housing provident fund should apply to the Hangzhou Housing Provident Fund Management Center (hereinafter referred to as the Provident Fund Center) for deposit registration procedures and set up personal accounts.",
"output": [
{
"Section Summary": "Background and Basis for Formulating the Management Measures",
"Section Starting Point": "To expand the benefits of the housing provident fund system"
},
{
"Section Summary": "Scope of Application of the Management Measures",
"Section Starting Point": "1. This method applies to the voluntary deposit"
},
{
"Section Summary": "Definition of Flexible Employment Personnel",
"Section Starting Point": "2. The flexible employment personnel referred to in this method"
},
{
"Section Summary": "Procedures for Flexible Employment Personnel to Register for Deposit",
"Section Starting Point": "3. Flexible employment personnel applying to deposit the housing provident fund"
}
]
}
]
}
llm output:
at /openspg_venv/lib/python3.8/site-packages/kag/bridge/spg_server_bridge.run_component(spg_server_bridge.py:48)
at /openspg_venv/lib/python3.8/site-packages/kag/interface/builder/base.invoke(base.py:114)
at /openspg_venv/lib/python3.8/site-packages/kag/builder/component/splitter/semantic_splitter._invoke(semantic_splitter.py:157)
at /openspg_venv/lib/python3.8/site-packages/kag/builder/component/splitter/semantic_splitter.semantic_chunk(semantic_splitter.py:119)
at /openspg_venv/lib/python3.8/site-packages/kag/interface/common/llm_client.invoke(llm_client.py:117)
at pemja.core.PythonInterpreter.invokeMethod(Native Method)
at pemja.core.PythonInterpreter.invokeMethod(PythonInterpreter.java:118)
at com.antgroup.openspg.common.util.pemja.PemjaUtils.invoke(PemjaUtils.java:33)
at com.antgroup.openspg.server.core.scheduler.service.task.async.builder.KagSplitterAsyncTask$SplitterTaskCallable.splitterChunk(KagSplitterAsyncTask.java:217)
at com.antgroup.openspg.server.core.scheduler.service.task.async.builder.KagSplitterAsyncTask$SplitterTaskCallable.call(KagSplitterAsyncTask.java:179)
at com.antgroup.openspg.server.core.scheduler.service.task.async.builder.KagSplitterAsyncTask$SplitterTaskCallable.call(KagSplitterAsyncTask.java:142)
at com.antgroup.openspg.server.core.scheduler.service.common.MemoryTaskServer.executeTask(MemoryTaskServer.java:67)
at com.antgroup.openspg.server.core.scheduler.service.common.MemoryTaskServer.lambda$submit$0(MemoryTaskServer.java:56)
at java.util.concurrent.CompletableFuture$AsyncSupply.run(CompletableFuture.java:1604)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:750)
2025-03-05 15:25:49(172.24.0.5): Splitter task status is ERROR
2025-03-05 15:25:49(172.24.0.5): The asynchronous task has been created! resource:builder/project_4/instance_13/74_kagSplitterAsyncTask.kag
2025-03-05 15:25:49(172.24.0.5): Lock preempted successfully!
2025-03-05 15:24:49(172.24.0.5): Task scheduling completed. cost:9 ms !
2025-03-05 15:24:49(172.24.0.5): Lock released successfully!
2025-03-05 15:24:49(172.24.0.5): Splitter task trace log:
>> 15:24:32: Split chunk(Sommario)
>> 15:24:32: Split chunk(Documento Unico di Programmazione 2024-2026) successfully. chunk size:2
>> 15:24:28: Split chunk(Documento Unico di Programmazione 2024-2026)
>> 15:24:28: Invoke the split operator
>> 15:24:28: Start split document!
2025-03-05 15:24:49(172.24.0.5): Splitter task status is RUNNING
2025-03-05 15:24:49(172.24.0.5): The asynchronous task has been created! resource:builder/project_4/instance_13/74_kagSplitterAsyncTask.kag
2025-03-05 15:24:49(172.24.0.5): Lock preempted successfully! |
|
Hey @epie-godfred, could you change the log to debug and share the output. |
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During the Done initialize project config with host addr http://127.0.0.1:8887 and project_id 1
Done init config from server: set KAG_DEBUG_DUMP_CONFIG=1 to dump config
2025-03-06 16:51:32,264 [] [] [http-nio-8887-exec-8] ERROR c.a.o.s.a.h.s.HttpBizTemplate - execute http biz callback unknown error
pemja.core.PythonException: <class 'pyhocon.exceptions.ConfigMissingException'>: No configuration setting found for key llm
at /openspg_venv/lib/python3.8/site-packages/kag/bridge/spg_server_bridge.run_component(spg_server_bridge.py:45)
at /openspg_venv/lib/python3.8/site-packages/kag/common/registry/registrable.from_config(registrable.py:813)
at /openspg_venv/lib/python3.8/site-packages/kag/common/registry/registrable.from_config(registrable.py:763)
at /openspg_venv/lib/python3.8/site-packages/kag/common/registry/registrable.from_config(registrable.py:813)
at /openspg_venv/lib/python3.8/site-packages/kag/common/registry/registrable.from_config(registrable.py:791)
at /openspg_venv/lib/python3.8/site-packages/kag/common/registry/registrable.create_kwargs(registrable.py:249)
at /openspg_venv/lib/python3.8/site-packages/kag/common/registry/registrable.pop_and_construct_arg(registrable.py:281)
at /openspg_venv/lib/python3.8/site-packages/pyhocon/config_tree.pop(config_tree.py:274)
at /openspg_venv/lib/python3.8/site-packages/pyhocon/config_tree.get(config_tree.py:236)
at /openspg_venv/lib/python3.8/site-packages/pyhocon/config_tree._get(config_tree.py:176)
at pemja.core.PythonInterpreter.invokeMethod(Native Method)
at pemja.core.PythonInterpreter.invokeMethod(PythonInterpreter.java:118)
at com.antgroup.openspg.common.util.pemja.PemjaUtils.invoke(PemjaUtils.java:33)
at com.antgroup.openspgapp.api.http.server.builder.BuilderJobController.paragraphSplit(BuilderJobController.java:731)
at com.antgroup.openspgapp.api.http.server.builder.BuilderJobController$12.action(BuilderJobController.java:708)
at com.antgroup.openspgapp.api.http.server.builder.BuilderJobController$12.action(BuilderJobController.java:676)
at com.antgroup.openspg.server.api.http.server.HttpBizTemplate.execute2(HttpBizTemplate.java:78)
at com.antgroup.openspgapp.api.http.server.builder.BuilderJobController.splitPreview(BuilderJobController.java:675)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.springframework.web.method.support.InvocableHandlerMethod.doInvoke(InvocableHandlerMethod.java:205)
at org.springframework.web.method.support.InvocableHandlerMethod.invokeForRequest(InvocableHandlerMethod.java:150)
at org.springframework.web.servlet.mvc.method.annotation.ServletInvocableHandlerMethod.invokeAndHandle(ServletInvocableHandlerMethod.java:117)
at org.springframework.web.servlet.mvc.method.annotation.RequestMappingHandlerAdapter.invokeHandlerMethod(RequestMappingHandlerAdapter.java:895)
at org.springframework.web.servlet.mvc.method.annotation.RequestMappingHandlerAdapter.handleInternal(RequestMappingHandlerAdapter.java:808)
at org.springframework.web.servlet.mvc.method.AbstractHandlerMethodAdapter.handle(AbstractHandlerMethodAdapter.java:87)
at org.springframework.web.servlet.DispatcherServlet.doDispatch(DispatcherServlet.java:1071)
at org.springframework.web.servlet.DispatcherServlet.doService(DispatcherServlet.java:964)
at org.springframework.web.servlet.FrameworkServlet.processRequest(FrameworkServlet.java:1006)
at org.springframework.web.servlet.FrameworkServlet.doPost(FrameworkServlet.java:909)
at javax.servlet.http.HttpServlet.service(HttpServlet.java:696)
at org.springframework.web.servlet.FrameworkServlet.service(FrameworkServlet.java:883)
at javax.servlet.http.HttpServlet.service(HttpServlet.java:779)
at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:227)
at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162)
at org.apache.tomcat.websocket.server.WsFilter.doFilter(WsFilter.java:53)
at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189)
at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162)
at com.antgroup.openspgapp.api.http.server.filter.AclFilter.doFilter(AclFilter.java:137)
at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189)
at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162)
at org.springframework.web.filter.RequestContextFilter.doFilterInternal(RequestContextFilter.java:100)
at org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.java:117)
at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189)
at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162)
at org.springframework.web.filter.FormContentFilter.doFilterInternal(FormContentFilter.java:93)
at org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.java:117)
at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189)
at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162)
at org.springframework.boot.actuate.metrics.web.servlet.WebMvcMetricsFilter.doFilterInternal(WebMvcMetricsFilter.java:96)
at org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.java:117)
at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189)
at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162)
at com.alipay.sofa.tracer.plugins.springmvc.SpringMvcSofaTracerFilter.doFilter(SpringMvcSofaTracerFilter.java:87)
at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189)
at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162)
at org.springframework.web.filter.CharacterEncodingFilter.doFilterInternal(CharacterEncodingFilter.java:201)
at org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.java:117)
at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189)
at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162)
at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:177)
at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:97)
at org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:541)
at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:135)
at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:92)
at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:78)
at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:360)
at org.apache.coyote.http11.Http11Processor.service(Http11Processor.java:399)
at org.apache.coyote.AbstractProcessorLight.process(AbstractProcessorLight.java:65)
at org.apache.coyote.AbstractProtocol$ConnectionHandler.process(AbstractProtocol.java:891)
at org.apache.tomcat.util.net.NioEndpoint$SocketProcessor.doRun(NioEndpoint.java:1784)
at org.apache.tomcat.util.net.SocketProcessorBase.run(SocketProcessorBase.java:49)
at org.apache.tomcat.util.threads.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1191)
at org.apache.tomcat.util.threads.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:659)
at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
at java.lang.Thread.run(Thread.java:750)
the project configuration file is the same as yerterday : #------------project configuration start----------------#
openie_llm: &openie_llm
api_key: <openrouter_api_key>
base_url: https://openrouter.ai/api/v1
model: deepseek/deepseek-chat
type: maas
chat_llm: &chat_llm
api_key: <openrouter_api_key>
base_url: https://openrouter.ai/api/v1
model: deepseek/deepseek-chat
type: maas
vectorize_model: &vectorize_model
api_key: <openai_api_key>
base_url: https://api.openai.com/v1
model: text-embedding-3-small
type: openai
vector_dimensions: 1024
vectorizer: *vectorize_model
log:
level: DEBUG
project:
biz_scene: default
host_addr: http://127.0.0.1:8887
id: "1"
language: en
namespace: KAGBologna1
#------------project configuration end----------------#
#------------kag-builder configuration start----------------#
kag_builder_pipeline:
chain:
type: unstructured_builder_chain # kag.builder.default_chain.DefaultUnstructuredBuilderChain
extractor:
type: schema_free_extractor # kag.builder.component.extractor.schema_free_extractor.SchemaFreeExtractor
llm: *openie_llm
ner_prompt:
type: default_ner # kag.builder.prompt.default.ner.OpenIENERPrompt
std_prompt:
type: default_std # kag.builder.prompt.default.std.OpenIEEntitystandardizationdPrompt
triple_prompt:
type: default_triple # kag.builder.prompt.default.triple.OpenIETriplePrompt
reader:
type: dict_reader # kag.builder.component.reader.dict_reader.DictReader
post_processor:
type: kag_post_processor # kag.builder.component.postprocessor.kag_postprocessor.KAGPostProcessor
splitter:
type: semantic_splitter #length_splitter # kag.builder.component.splitter.length_splitter.LengthSplitter
llm: *chat_llm
split_length: 100000
window_length: 0
vectorizer:
type: batch_vectorizer # kag.builder.component.vectorizer.batch_vectorizer.BatchVectorizer
vectorize_model: *vectorize_model
writer:
type: kg_writer # kag.builder.component.writer.kg_writer.KGWriter
num_threads_per_chain: 1
num_chains: 16
scanner:
type: 2wiki_dataset_scanner # kag.builder.component.scanner.dataset_scanner.MusiqueCorpusScanner
#------------kag-builder configuration end----------------#
#------------kag-solver configuration start----------------#
search_api: &search_api
type: openspg_search_api #kag.solver.tools.search_api.impl.openspg_search_api.OpenSPGSearchAPI
graph_api: &graph_api
type: openspg_graph_api #kag.solver.tools.graph_api.impl.openspg_graph_api.OpenSPGGraphApi
exact_kg_retriever: &exact_kg_retriever
type: default_exact_kg_retriever # kag.solver.retriever.impl.default_exact_kg_retriever.DefaultExactKgRetriever
el_num: 5
llm_client: *chat_llm
search_api: *search_api
graph_api: *graph_api
fuzzy_kg_retriever: &fuzzy_kg_retriever
type: default_fuzzy_kg_retriever # kag.solver.retriever.impl.default_fuzzy_kg_retriever.DefaultFuzzyKgRetriever
el_num: 5
vectorize_model: *vectorize_model
llm_client: *chat_llm
search_api: *search_api
graph_api: *graph_api
chunk_retriever: &chunk_retriever
type: default_chunk_retriever # kag.solver.retriever.impl.default_fuzzy_kg_retriever.DefaultFuzzyKgRetriever
llm_client: *chat_llm
recall_num: 10
rerank_topk: 10
kag_solver_pipeline:
memory:
type: default_memory # kag.solver.implementation.default_memory.DefaultMemory
llm_client: *chat_llm
max_iterations: 3
reasoner:
type: default_reasoner # kag.solver.implementation.default_reasoner.DefaultReasoner
llm_client: *chat_llm
lf_planner:
type: default_lf_planner # kag.solver.plan.default_lf_planner.DefaultLFPlanner
llm_client: *chat_llm
vectorize_model: *vectorize_model
lf_executor:
type: default_lf_executor # kag.solver.execute.default_lf_executor.DefaultLFExecutor
llm_client: *chat_llm
force_chunk_retriever: true
exact_kg_retriever: *exact_kg_retriever
fuzzy_kg_retriever: *fuzzy_kg_retriever
chunk_retriever: *chunk_retriever
merger:
type: default_lf_sub_query_res_merger # kag.solver.execute.default_sub_query_merger.DefaultLFSubQueryResMerger
vectorize_model: *vectorize_model
chunk_retriever: *chunk_retriever
generator:
type: default_generator # kag.solver.implementation.default_generator.DefaultGenerator
llm_client: *chat_llm
generate_prompt:
type: default_resp_generator # kag.solver.prompt.default.resp_generator.RespGenerator
reflector:
type: default_reflector # kag.solver.implementation.default_reflector.DefaultReflector
llm_client: *chat_llm
#------------kag-solver configuration end----------------#
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Operating system information
Other
What happened
I'm currently running KAG using docker with the following docker-compose file
with the following configurations for the models
Case when the input is not empty :
Another similar error
markdown document to be added to knowledge base attached.
I tried changing the
split_lengthparameter from default 20000 to 100000 but still get the same error. What can I do to resolve this?How to reproduce
Steps to reproduce the error:
split_legthprovidedAre you willing to submit PR?
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