mirror of https://github.com/OpenSPG/KAG
docs(examples): finish README.md for the examples directory (#210)
* add data introduction for example supplychain * add schema modeling for example supplychain * add kg construction for example supplychain * add kg query for example supplychain * finish README_cn.md for the supplychain example * finish README.md for the supplychain example * add README.md for the riskmining example * add README.md for the medicine example * update README.md of hotpotqa, 2wiki and musique * add README_cn.md for hotpotqa, 2wiki and musique * update README.md of csqa * add README_cn.md for csqa * update link targets to 0.6 version of the docs * add README.md for baike * reformat Python code in examples * add README_cn.md for examples * finish README.md for examples * fix typo in README.md for examples
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xionghuaidong
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# Example
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## Create project
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Create your new knext project from knext cli tool.
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* Edit your config file like example.cfg
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```python
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[project]
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project_name = KagDemo
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description = A knext demo project showcasing KAG features.
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namespace = KagDemo
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project_dir = kagdemo
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host_addr = http://localhost:8887
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# KAG Examples
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[vectorizer]
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type = openai
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model = bge-m3
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api_key = EMPTY
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base_url = http://127.0.0.1:11434/v1
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vector_dimensions = 1024
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[English](./README.md) |
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[简体中文](./README_cn.md)
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[llm]
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type = ollama
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base_url = http://localhost:11434/api/generate
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model = llama3.1
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## 1. Precondition
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[prompt]
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biz_scene = default
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language = zh
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Please refer to [Quick Start](https://openspg.yuque.com/ndx6g9/cwh47i/rs7gr8g4s538b1n7) to install KAG and its dependency OpenSPG server, and learn about using KAG in developer mode.
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[indexer]
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with_semantic = False
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similarity_threshold = 0.8
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## 2. Create a knowledge base
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[retriever]
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with_semantic = False
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pagerank_threshold = 0.9
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match_threshold = 0.8
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top_k = 10
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### 2.1 Create the project
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[log]
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level = INFO
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#### Step 1: Enter the examples directory
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```bash
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cd kag/examples
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```
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* Run command:
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```sh
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kag project create --config_path example.cfg
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#### Step 2: Edit project configuration
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```bash
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vim ./example_config.yaml
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```
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* Put your source data in {project_dir}/builder/data (in the example kagdemo, we put a pdf file in the data directory.)
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* Edit and your schema file.(**Optional**)
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* Edit the schema in {project_dir}/schema/{project_name}.schema
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* Run command in work directory {project_dir}:
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```sh
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```yaml
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#------------project configuration start----------------#
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openie_llm: &openie_llm
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api_key: key
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base_url: https://api.deepseek.com
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model: deepseek-chat
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type: maas
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chat_llm: &chat_llm
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api_key: key
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base_url: https://api.deepseek.com
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model: deepseek-chat
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type: maas
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vectorize_model: &vectorize_model
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api_key: key
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base_url: https://api.siliconflow.cn/v1/
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model: BAAI/bge-m3
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type: openai
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vector_dimensions: 1024
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vectorizer: *vectorize_model
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log:
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level: INFO
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project:
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biz_scene: default
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host_addr: http://127.0.0.1:8887
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id: "1"
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language: en
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namespace: TwoWikiTest
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#------------project configuration end----------------#
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#------------kag-builder configuration start----------------#
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kag_builder_pipeline:
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chain:
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type: unstructured_builder_chain # kag.builder.default_chain.DefaultUnstructuredBuilderChain
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extractor:
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type: schema_free_extractor # kag.builder.component.extractor.schema_free_extractor.SchemaFreeExtractor
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llm: *openie_llm
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ner_prompt:
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type: default_ner # kag.builder.prompt.default.ner.OpenIENERPrompt
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std_prompt:
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type: default_std # kag.builder.prompt.default.std.OpenIEEntitystandardizationdPrompt
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triple_prompt:
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type: default_triple # kag.builder.prompt.default.triple.OpenIETriplePrompt
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reader:
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type: dict_reader # kag.builder.component.reader.dict_reader.DictReader
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post_processor:
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type: kag_post_processor # kag.builder.component.postprocessor.kag_postprocessor.KAGPostProcessor
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similarity_threshold: 0.9
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splitter:
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type: length_splitter # kag.builder.component.splitter.length_splitter.LengthSplitter
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split_length: 100000
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window_length: 0
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vectorizer:
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type: batch_vectorizer # kag.builder.component.vectorizer.batch_vectorizer.BatchVectorizer
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vectorize_model: *vectorize_model
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writer:
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type: kg_writer # kag.builder.component.writer.kg_writer.KGWriter
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num_threads_per_chain: 1
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num_chains: 16
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scanner:
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type: 2wiki_dataset_scanner # kag.builder.component.scanner.dataset_scanner.MusiqueCorpusScanner
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#------------kag-builder configuration end----------------#
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#------------kag-solver configuration start----------------#
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search_api: &search_api
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type: openspg_search_api #kag.solver.tools.search_api.impl.openspg_search_api.OpenSPGSearchAPI
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graph_api: &graph_api
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type: openspg_graph_api #kag.solver.tools.graph_api.impl.openspg_graph_api.OpenSPGGraphApi
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exact_kg_retriever: &exact_kg_retriever
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type: default_exact_kg_retriever # kag.solver.retriever.impl.default_exact_kg_retriever.DefaultExactKgRetriever
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el_num: 5
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llm_client: *chat_llm
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search_api: *search_api
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graph_api: *graph_api
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fuzzy_kg_retriever: &fuzzy_kg_retriever
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type: default_fuzzy_kg_retriever # kag.solver.retriever.impl.default_fuzzy_kg_retriever.DefaultFuzzyKgRetriever
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el_num: 5
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vectorize_model: *vectorize_model
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llm_client: *chat_llm
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search_api: *search_api
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graph_api: *graph_api
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chunk_retriever: &chunk_retriever
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type: default_chunk_retriever # kag.solver.retriever.impl.default_fuzzy_kg_retriever.DefaultFuzzyKgRetriever
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llm_client: *chat_llm
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recall_num: 10
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rerank_topk: 10
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kag_solver_pipeline:
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memory:
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type: default_memory # kag.solver.implementation.default_memory.DefaultMemory
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llm_client: *chat_llm
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max_iterations: 3
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reasoner:
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type: default_reasoner # kag.solver.implementation.default_reasoner.DefaultReasoner
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llm_client: *chat_llm
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lf_planner:
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type: default_lf_planner # kag.solver.plan.default_lf_planner.DefaultLFPlanner
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llm_client: *chat_llm
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vectorize_model: *vectorize_model
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lf_executor:
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type: default_lf_executor # kag.solver.execute.default_lf_executor.DefaultLFExecutor
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llm_client: *chat_llm
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force_chunk_retriever: true
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exact_kg_retriever: *exact_kg_retriever
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fuzzy_kg_retriever: *fuzzy_kg_retriever
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chunk_retriever: *chunk_retriever
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merger:
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type: default_lf_sub_query_res_merger # kag.solver.execute.default_sub_query_merger.DefaultLFSubQueryResMerger
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vectorize_model: *vectorize_model
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chunk_retriever: *chunk_retriever
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generator:
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type: default_generator # kag.solver.implementation.default_generator.DefaultGenerator
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llm_client: *chat_llm
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generate_prompt:
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type: resp_simple # kag/examples/2wiki/solver/prompt/resp_generator.py
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reflector:
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type: default_reflector # kag.solver.implementation.default_reflector.DefaultReflector
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llm_client: *chat_llm
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#------------kag-solver configuration end----------------#
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```
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Update the generative model configurations ``openie_llm`` and ``chat_llm`` and the representive model configuration ``vectorizer_model`` in the configuration file.
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You need to fill in correct ``api_key``s. If your model providers and model names are different from the default values, you also need to update ``base_url`` and ``model``.
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#### Step 3: Create the project (i.e. knowledge base in product mode)
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```bash
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knext project create --config_path ./example_config.yaml
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```
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#### Step 4: Initial contents of the directory
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After creating the project, a directory with the same name as the ``namespace`` field in the ``project`` configuration (e.g., ``TwoWikiTest`` in this example) will be created under the ``kag/examples`` directory, and the KAG framework project code will be initialized.
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Users can modify one or more of the following files to complete the customization of business-specific knowledge graph construction and reasoning-based question answering.
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```text
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.
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├── builder
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│ ├── __init__.py
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│ ├── data
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│ │ └── __init__.py
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│ ├── indexer.py
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│ └── prompt
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│ └── __init__.py
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├── kag_config.yaml
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├── reasoner
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│ └── __init__.py
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├── schema
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│ ├── TwoWikiTest.schema
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│ └── __init__.py
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└── solver
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├── __init__.py
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├── data
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│ └── __init__.py
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└── prompt
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└── __init__.py
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```
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### 2.2 Update the project (Optional)
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If there are configuration changes, you can refer to this section to update the configuration information to the server.
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#### Step 1: Enter the project directory
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```bash
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cd kag/examples/TwoWikiTest
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```
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#### Step 2: Edit project configuration
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**Note**: The embedding vectors generated by different representation models can vary significantly. It is recommended not to update the ``vectorizer_model`` configuration after the project is created. If you need to update the ``vectorizer_model`` configuration, please create a new project.
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```bash
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vim ./kag_config.yaml
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```
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#### Step 3: Run the update command
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After editing the project configuration, use the ``knext project update`` command to update the local configuration information to the OpenSPG server.
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```bash
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knext project update --proj_path .
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```
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## 3. Import documents
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### Step 1: Enter the project directory
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```bash
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cd kag/examples/TwoWikiTest
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```
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### Step 2: Retrieve corpus data
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The test corpus data for the 2wiki dataset is located at ``kag/examples/2wiki/builder/data/2wiki_corpus.json``, containing 6,119 documents and 1,000 question-answer pairs. To quickly complete the entire process, there is also a ``2wiki_corpus_sub.json`` file in the same directory, which contains only 7 documents. We will use this smaller dataset as an example for the experiment.
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Copy it to the directory with the same name as the ``TwoWikiTest`` project:
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```bash
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cp ../2wiki/builder/data/2wiki_sub_corpus.json builder/data
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```
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### Step 3: Edit the schema (Optional)
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Edit the schema file ``schema/TwoWikiTest.schema``. For an introduction of OpenSPG schema, please refer to [Declarative Schema](https://openspg.yuque.com/ndx6g9/cwh47i/fiq6zum3qtzr7cne).
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### Step 4: Commit the schema to OpenSPG server
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```bash
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knext schema commit
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```
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* **Define your BuilderChain**:
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* In the {project_dir}/builder/indexer.py, define your BuilderChain by extend the BuilderChainABC class and override the build method. For example:
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### Step 5: Execute the build task
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Define the build task in the file ``builder/indexer.py``:
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```python
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import os
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from kag.builder.component.reader import DocxReader, PDFReader
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from kag.builder.component.splitter import LengthSplitter, OutlineSplitter
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from knext.builder.builder_chain_abc import BuilderChainABC
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from kag.builder.component.extractor import KAGExtractor
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from kag.builder.component.writer import KGWriter
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from kag.solver.logic.solver_pipeline import SolverPipeline
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import logging
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from kag.common.env import init_kag_config
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from kag.common.registry import import_modules_from_path
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from kag.builder.runner import BuilderChainRunner
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logger = logging.getLogger(__name__)
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file_path = os.path.dirname(__file__)
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suffix_mapping = {
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"docx": DocxReader,
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"pdf": PDFReader
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}
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def buildKB(file_path):
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from kag.common.conf import KAG_CONFIG
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class KagDemoBuildChain(BuilderChainABC):
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runner = BuilderChainRunner.from_config(
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KAG_CONFIG.all_config["kag_builder_pipeline"]
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)
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runner.invoke(file_path)
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def build(self, **kwargs):
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file_path = kwargs.get("file_path","a.docx")
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suffix = file_path.split(".")[-1]
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reader = suffix_mapping[suffix]()
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if reader is None:
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raise NotImplementedError
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length_splitter = LengthSplitter(split_length=8000)
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outline_splitter = OutlineSplitter()
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project_id = int(os.getenv("KAG_PROJECT_ID"))
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extractor = KAGExtractor(project_id=project_id)
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writer = KGWriter()
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chain = reader >> length_splitter >> outline_splitter >> extractor >> writer
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return chain
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|
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def buildKG(test_file,**kwargs):
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chain = KagDemoBuildChain(file_path = test_file)
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chain.invoke(test_file, max_workers=10)
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logger.info(f"\n\nbuildKB successfully for {file_path}\n\n")
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|
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if __name__ == "__main__":
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init_kag_config(os.path.join(file_path,"../../../tests/builder/component/test_config.cfg"))
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test_docx = os.path.join(file_path,"../../../tests/builder/data/test_docx.docx")
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test_pdf = os.path.join(file_path,"../../../tests/builder/data/KnowledgeGraphTutorialSub.pdf")
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buildKG(test_pdf)
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```
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* Build Knowledge Graph by run:
|
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```sh
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python {project_dir}/builder/indexer.py
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import_modules_from_path(".")
|
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dir_path = os.path.dirname(__file__)
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# Set file_path to the path of the corpus file prepared earlier
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file_path = os.path.join(dir_path, "data/2wiki_sub_corpus.json")
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|
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buildKB(file_path)
|
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```
|
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|
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* Question Answer(QA):
|
||||
* Define SolverPipeline in {project_dir}/solver/evalFor{project_name}.py, for example:
|
||||
Run the ``indexer.py`` script to complete the knowledge graph construction for unstructured data.
|
||||
|
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```bash
|
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cd builder
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python indexer.py
|
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```
|
||||
|
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After the build script is started, a checkpoint directory for the task will be generated in the current working directory, recording the checkpoints and statistical information of the build process.
|
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|
||||
```text
|
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ckpt
|
||||
├── chain
|
||||
├── extractor
|
||||
├── kag_checkpoint_0_1.ckpt
|
||||
├── postprocessor
|
||||
├── reader
|
||||
└── splitter
|
||||
```
|
||||
|
||||
You can view the extraction task statistics, such as how many nodes/edges were extracted from each document, using the following command:
|
||||
|
||||
```bash
|
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less ckpt/kag_checkpoint_0_1.ckpt
|
||||
```
|
||||
|
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To see how many document entries were successfully written to the graph database, use the following command:
|
||||
|
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```bash
|
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wc -l ckpt/kag_checkpoint_0_1.ckpt
|
||||
```
|
||||
|
||||
The KAG framework provides checkpoint-based resumption functionality. If the task is interrupted due to a program error or other external factors (e.g., insufficient LLM invocation credits), you can rerun ``indexer.py``. KAG will automatically load the checkpoint file and reuse the existing results.
|
||||
|
||||
### Step 6: Inspect the constructed knowledge graph
|
||||
|
||||
Currently, OpenSPG-KAG provides the [Knowledge Exploration](https://openspg.yuque.com/ndx6g9/cwh47i/mzq74eaynm4rqx4b) capability in product mode, along with the corresponding API documentation [HTTP API Reference](https://openspg.yuque.com/ndx6g9/cwh47i/qvbgge62p7argtd2).
|
||||
|
||||
|
||||
|
||||
## 4. Reasoning-based question answering
|
||||
|
||||
### Step 1: Enter the project directory
|
||||
|
||||
```bash
|
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cd kag/examples/TwoWikiTest
|
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```
|
||||
|
||||
### Step 2: Retrieve QA data
|
||||
|
||||
The question-answer data corresponding to the ``2wiki_sub_corpus.json`` dataset is located at ``kag/examples/2wiki/solver/data/2wiki_qa_sub.json``, containing two question-answer pairs. Copy it to the directory with the same name as the ``TwoWikiTest`` project.
|
||||
|
||||
```bash
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cp ../2wiki/solver/data/2wiki_qa_sub.json solver/data
|
||||
```
|
||||
|
||||
### Step 3: Edit the QA script
|
||||
|
||||
```bash
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||||
vim ./solver/qa.py
|
||||
```
|
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|
||||
Paste the following content into ``qa.py`` (or you can directly copy the question-answer script from the built-in 2wiki project).
|
||||
|
||||
```python
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
|
||||
from kag.common.env import init_kag_config
|
||||
from tqdm import tqdm
|
||||
|
||||
from kag.common.benchmarks.evaluate import Evaluate
|
||||
from kag.solver.logic.solver_pipeline import SolverPipeline
|
||||
from kag.common.conf import KAG_CONFIG
|
||||
from kag.common.registry import import_modules_from_path
|
||||
|
||||
from kag.common.checkpointer import CheckpointerManager
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class KagDemo:
|
||||
|
||||
class EvaFor2wiki:
|
||||
"""
|
||||
init for kag client
|
||||
"""
|
||||
|
|
@ -129,44 +369,150 @@ Create your new knext project from knext cli tool.
|
|||
def __init__(self):
|
||||
pass
|
||||
|
||||
def qa(self, query):
|
||||
resp = SolverPipeline()
|
||||
answer, trace_log = resp.run(query)
|
||||
"""
|
||||
qa from knowledge base,
|
||||
"""
|
||||
|
||||
return answer,trace_log
|
||||
def qa(self, query):
|
||||
resp = SolverPipeline.from_config(KAG_CONFIG.all_config["kag_solver_pipeline"])
|
||||
answer, traceLog = resp.run(query)
|
||||
|
||||
logger.info(f"\n\nso the answer for '{query}' is: {answer}\n\n")
|
||||
return answer, traceLog
|
||||
|
||||
"""
|
||||
parallel qa from knowledge base
|
||||
and getBenchmarks(em, f1, answer_similarity)
|
||||
|
||||
"""
|
||||
|
||||
def parallelQaAndEvaluate(
|
||||
self, qaFilePath, resFilePath, threadNum=1, upperLimit=10
|
||||
):
|
||||
ckpt = CheckpointerManager.get_checkpointer(
|
||||
{"type": "zodb", "ckpt_dir": "ckpt"}
|
||||
)
|
||||
|
||||
def process_sample(data):
|
||||
try:
|
||||
sample_idx, sample = data
|
||||
sample_id = sample["_id"]
|
||||
question = sample["question"]
|
||||
gold = sample["answer"]
|
||||
if question in ckpt:
|
||||
print(f"found existing answer to question: {question}")
|
||||
prediction, traceLog = ckpt.read_from_ckpt(question)
|
||||
else:
|
||||
prediction, traceLog = self.qa(question)
|
||||
ckpt.write_to_ckpt(question, (prediction, traceLog))
|
||||
|
||||
evalObj = Evaluate()
|
||||
metrics = evalObj.getBenchMark([prediction], [gold])
|
||||
return sample_idx, sample_id, prediction, metrics, traceLog
|
||||
except Exception as e:
|
||||
import traceback
|
||||
|
||||
logger.warning(
|
||||
f"process sample failed with error:{traceback.print_exc()}\nfor: {data}"
|
||||
)
|
||||
return None
|
||||
|
||||
qaList = json.load(open(qaFilePath, "r"))
|
||||
total_metrics = {
|
||||
"em": 0.0,
|
||||
"f1": 0.0,
|
||||
"answer_similarity": 0.0,
|
||||
"processNum": 0,
|
||||
}
|
||||
with ThreadPoolExecutor(max_workers=threadNum) as executor:
|
||||
futures = [
|
||||
executor.submit(process_sample, (sample_idx, sample))
|
||||
for sample_idx, sample in enumerate(qaList[:upperLimit])
|
||||
]
|
||||
for future in tqdm(
|
||||
as_completed(futures),
|
||||
total=len(futures),
|
||||
desc="parallelQaAndEvaluate completing: ",
|
||||
):
|
||||
result = future.result()
|
||||
if result is not None:
|
||||
sample_idx, sample_id, prediction, metrics, traceLog = result
|
||||
sample = qaList[sample_idx]
|
||||
|
||||
sample["prediction"] = prediction
|
||||
sample["traceLog"] = traceLog
|
||||
sample["em"] = str(metrics["em"])
|
||||
sample["f1"] = str(metrics["f1"])
|
||||
|
||||
total_metrics["em"] += metrics["em"]
|
||||
total_metrics["f1"] += metrics["f1"]
|
||||
total_metrics["answer_similarity"] += metrics["answer_similarity"]
|
||||
total_metrics["processNum"] += 1
|
||||
|
||||
if sample_idx % 50 == 0:
|
||||
with open(resFilePath, "w") as f:
|
||||
json.dump(qaList, f)
|
||||
|
||||
with open(resFilePath, "w") as f:
|
||||
json.dump(qaList, f)
|
||||
|
||||
res_metrics = {}
|
||||
for item_key, item_value in total_metrics.items():
|
||||
if item_key != "processNum":
|
||||
res_metrics[item_key] = item_value / total_metrics["processNum"]
|
||||
else:
|
||||
res_metrics[item_key] = total_metrics["processNum"]
|
||||
CheckpointerManager.close()
|
||||
return res_metrics
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
demo = KagDemo()
|
||||
query = "什么知识图谱是什么？"
|
||||
answer,trace_log = demo.qa(query)
|
||||
print(f"Question: {query}")
|
||||
print(f"Answer: {answer}")
|
||||
print(f"TraceLog: {trace_log}")
|
||||
import_modules_from_path("./prompt")
|
||||
evalObj = EvaFor2wiki()
|
||||
|
||||
```
|
||||
* Run command:
|
||||
```sh
|
||||
python {project_dir}/solver/evaFor{project_name}.py
|
||||
start_time = time.time()
|
||||
filePath = "./data/2wiki_qa_sub.json"
|
||||
|
||||
evalObj.qa("When did Lothair Ii's mother die?")
|
||||
|
||||
qaFilePath = os.path.join(os.path.abspath(os.path.dirname(__file__)), filePath)
|
||||
resFilePath = os.path.join(
|
||||
os.path.abspath(os.path.dirname(__file__)), f"2wikitest_res_{start_time}.json"
|
||||
)
|
||||
total_metrics = evalObj.parallelQaAndEvaluate(
|
||||
qaFilePath, resFilePath, threadNum=20, upperLimit=10000
|
||||
)
|
||||
|
||||
total_metrics["cost"] = time.time() - start_time
|
||||
with open(f"./2wikitest_metrics_{start_time}.json", "w") as f:
|
||||
json.dump(total_metrics, f)
|
||||
print(total_metrics)
|
||||
```
|
||||
|
||||
### Step 4: Copy the answer generation prompt
|
||||
|
||||
## Restore project
|
||||
If you have a project locally but do **not** have in spgserver, **restore** cmd will recover your local project in the spgserver, for example:
|
||||
Copy the answer generation prompt from the built-in 2wiki project.
|
||||
|
||||
```sh
|
||||
knext project recover --host_addr http://{host_ip}:8887 --proj_path {proj_path}
|
||||
```bash
|
||||
cp ../2wiki/solver/prompt/resp_generator.py solver/prompt
|
||||
```
|
||||
|
||||
### Step 5: Execute the QA task
|
||||
|
||||
## Update project
|
||||
If you want to update your kag_config.cfg file, you can edit it and use update cmd, for example:
|
||||
```sh
|
||||
knext project update --proj_path {proj_path}
|
||||
```bash
|
||||
cd solver
|
||||
python qa.py
|
||||
```
|
||||
|
||||
After execution, the QA performance metrics will be printed.
|
||||
|
||||
## 5. Other built-in examples
|
||||
|
||||
You can enter the [kag/examples](.) directory to explore the built-in examples provided in the source code of KAG.
|
||||
|
||||
* [musique](./musique/README.md) (Multi-hop Q&A)
|
||||
* [twowiki](./2wiki/README.md) (Multi-hop Q&A)
|
||||
* [hotpotqa](./hotpotqa/README.md) (Multi-hop Q&A)
|
||||
* [Risk Mining Knowledge Graph](./riskmining/README.md)
|
||||
* [Enterprise Supply Chain Knowledge Graph](./supplychain/README.md)
|
||||
* [Medical Knowledge Graph](./medicine/README.md)
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,518 @@
|
|||
# KAG 示例
|
||||
|
||||
[English](./README.md) |
|
||||
[简体中文](./README_cn.md)
|
||||
|
||||
## 1. 前置条件
|
||||
|
||||
参考文档 [快速开始](https://openspg.yuque.com/ndx6g9/0.6/quzq24g4esal7q17) 安装 KAG 及其依赖的 OpenSPG server，了解开发者模式 KAG 的使用流程。
|
||||
|
||||
## 2. 创建知识库
|
||||
|
||||
### 2.1 新建项目
|
||||
|
||||
#### Step 1：进入 examples 目录
|
||||
|
||||
```bash
|
||||
cd kag/examples
|
||||
```
|
||||
|
||||
#### Step 2：编辑项目配置
|
||||
|
||||
```bash
|
||||
vim ./example_config.yaml
|
||||
```
|
||||
|
||||
```yaml
|
||||
#------------project configuration start----------------#
|
||||
openie_llm: &openie_llm
|
||||
api_key: key
|
||||
base_url: https://api.deepseek.com
|
||||
model: deepseek-chat
|
||||
type: maas
|
||||
|
||||
chat_llm: &chat_llm
|
||||
api_key: key
|
||||
base_url: https://api.deepseek.com
|
||||
model: deepseek-chat
|
||||
type: maas
|
||||
|
||||
vectorize_model: &vectorize_model
|
||||
api_key: key
|
||||
base_url: https://api.siliconflow.cn/v1/
|
||||
model: BAAI/bge-m3
|
||||
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: TwoWikiTest
|
||||
#------------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
|
||||
similarity_threshold: 0.9
|
||||
splitter:
|
||||
type: length_splitter # kag.builder.component.splitter.length_splitter.LengthSplitter
|
||||
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: resp_simple # kag/examples/2wiki/solver/prompt/resp_generator.py
|
||||
reflector:
|
||||
type: default_reflector # kag.solver.implementation.default_reflector.DefaultReflector
|
||||
llm_client: *chat_llm
|
||||
|
||||
#------------kag-solver configuration end----------------#
|
||||
```
|
||||
|
||||
您需要更新其中的生成模型配置 ``openie_llm`` 和 ``chat_llm`` 和表示模型配置 ``vectorizer_model``。
|
||||
|
||||
您需要设置正确的 ``api_key``。如果使用的模型供应商和模型名与默认值不同，您还需要更新 ``base_url`` 和 ``model``。
|
||||
|
||||
#### Step 3：创建项目（与产品模式中的知识库一一对应）
|
||||
|
||||
```bash
|
||||
knext project create --config_path ./example_config.yaml
|
||||
```
|
||||
|
||||
#### Step 4：目录初始化
|
||||
|
||||
创建项目之后会在 ``kag/examples`` 目录下创建一个与 ``project`` 配置中 ``namespace`` 字段同名的目录（示例中为 ``TwoWikiTest``），并完成 KAG 项目代码框架初始化。
|
||||
|
||||
用户可以修改下述文件的一个或多个，完成业务自定义图谱构建 & 推理问答。
|
||||
|
||||
```text
|
||||
.
|
||||
├── builder
|
||||
│ ├── __init__.py
|
||||
│ ├── data
|
||||
│ │ └── __init__.py
|
||||
│ ├── indexer.py
|
||||
│ └── prompt
|
||||
│ └── __init__.py
|
||||
├── kag_config.yaml
|
||||
├── reasoner
|
||||
│ └── __init__.py
|
||||
├── schema
|
||||
│ ├── TwoWikiTest.schema
|
||||
│ └── __init__.py
|
||||
└── solver
|
||||
├── __init__.py
|
||||
├── data
|
||||
│ └── __init__.py
|
||||
└── prompt
|
||||
└── __init__.py
|
||||
```
|
||||
|
||||
### 2.2 更新项目（Optional）
|
||||
|
||||
如果有配置变更，可以参考本节内容，更新配置信息到服务端。
|
||||
|
||||
#### Step 1：进入项目目录
|
||||
|
||||
```bash
|
||||
cd kag/examples/TwoWikiTest
|
||||
```
|
||||
|
||||
#### Step 2：编辑项目配置
|
||||
|
||||
**注意**：由不同表示模型生成的 embedding 向量差异较大，``vectorizer_model`` 配置在项目创建后建议不再更新；如有更新 ``vectorizer_model`` 配置的需求，请创建一个新项目。
|
||||
|
||||
```bash
|
||||
vim ./kag_config.yaml
|
||||
```
|
||||
|
||||
#### Step 3：运行命令
|
||||
|
||||
配置修改后，需要使用 ``knext project update`` 命令将本地配置信息更新到 OpenSPG 服务端。
|
||||
|
||||
```bash
|
||||
knext project update --proj_path .
|
||||
```
|
||||
|
||||
## 3. 导入文档
|
||||
|
||||
### Step 1：进入项目目录
|
||||
|
||||
```bash
|
||||
cd kag/examples/TwoWikiTest
|
||||
```
|
||||
|
||||
### Step 2：获取语料数据
|
||||
|
||||
2wiki 数据集的测试语料数据为 ``kag/examples/2wiki/builder/data/2wiki_corpus.json``，有 6119 篇文档，和 1000 个问答对。为了迅速跑通整个流程，目录下还有一个 ``2wiki_corpus_sub.json`` 文件，只有 7 篇文档，我们以该小规模数据集为例进行试验。
|
||||
|
||||
将其复制到 ``TwoWikiTest`` 项目的同名目录下：
|
||||
|
||||
```bash
|
||||
cp ../2wiki/builder/data/2wiki_sub_corpus.json builder/data
|
||||
```
|
||||
|
||||
### Step 3：编辑 schema（Optional）
|
||||
|
||||
编辑 ``schema/TwoWikiTest.schema`` 文件，schema 文件格式参考 [声明式 schema](https://openspg.yuque.com/ndx6g9/0.6/fzhov4l2sst6bede) 相关章节。
|
||||
|
||||
### Step 4：提交 schema 到服务端
|
||||
|
||||
```bash
|
||||
knext schema commit
|
||||
```
|
||||
|
||||
### Step 5：执行构建任务
|
||||
|
||||
在 ``builder/indexer.py`` 文件中定义任务构建脚本：
|
||||
|
||||
```python
|
||||
import os
|
||||
import logging
|
||||
from kag.common.registry import import_modules_from_path
|
||||
|
||||
from kag.builder.runner import BuilderChainRunner
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def buildKB(file_path):
|
||||
from kag.common.conf import KAG_CONFIG
|
||||
|
||||
runner = BuilderChainRunner.from_config(
|
||||
KAG_CONFIG.all_config["kag_builder_pipeline"]
|
||||
)
|
||||
runner.invoke(file_path)
|
||||
|
||||
logger.info(f"\n\nbuildKB successfully for {file_path}\n\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import_modules_from_path(".")
|
||||
dir_path = os.path.dirname(__file__)
|
||||
# 将 file_path 设置为之前准备好的语料文件路径
|
||||
file_path = os.path.join(dir_path, "data/2wiki_sub_corpus.json")
|
||||
|
||||
buildKB(file_path)
|
||||
```
|
||||
|
||||
运行 ``indexer.py`` 脚本完成非结构化数据的图谱构建。
|
||||
|
||||
```bash
|
||||
cd builder
|
||||
python indexer.py
|
||||
```
|
||||
|
||||
构建脚本启动后，会在当前工作目录下生成任务的 checkpoint 目录，记录了构建链路的 checkpoint 和统计信息。
|
||||
|
||||
```text
|
||||
ckpt
|
||||
├── chain
|
||||
├── extractor
|
||||
├── kag_checkpoint_0_1.ckpt
|
||||
├── postprocessor
|
||||
├── reader
|
||||
└── splitter
|
||||
```
|
||||
|
||||
通过以下命令查看抽取任务统计信息，如每个文档抽取出多少点 / 边。
|
||||
|
||||
```bash
|
||||
less ckpt/kag_checkpoint_0_1.ckpt
|
||||
```
|
||||
|
||||
通过以下命令可以查看有多少文档数据被成功写入图数据库。
|
||||
|
||||
```bash
|
||||
wc -l ckpt/kag_checkpoint_0_1.ckpt
|
||||
```
|
||||
|
||||
KAG 框架基于 checkpoint 文件提供了断点续跑的功能。如果由于程序出错或其他外部原因（如 LLM 余额不足）导致任务中断，可以重新执行 indexer.py，KAG 会自动加载 checkpoint 文件并复用已有结果。
|
||||
|
||||
### Step 6：结果检查
|
||||
|
||||
当前，OpenSPG-KAG 在产品端已提供 [知识探查](https://openspg.yuque.com/ndx6g9/0.6/fw4ge5c18tyfl2yq) 能力，以及对应的 API 文档 [HTTP API Reference](https://openspg.yuque.com/ndx6g9/0.6/zde1yunbb8sncxtv)。
|
||||
|
||||
|
||||
|
||||
## 4. 推理问答
|
||||
|
||||
### Step 1：进入项目目录
|
||||
|
||||
```bash
|
||||
cd kag/examples/TwoWikiTest
|
||||
```
|
||||
|
||||
### Step 2：获取问答数据
|
||||
|
||||
与 ``2wiki_sub_corpus.json`` 数据集对应的问答数据为 ``kag/examples/2wiki/solver/data/2wiki_qa_sub.json``，有两个问答对。将其复制到 ``TwoWikiTest`` 项目的同名目录下。
|
||||
|
||||
```bash
|
||||
cp ../2wiki/solver/data/2wiki_qa_sub.json solver/data
|
||||
```
|
||||
|
||||
### Step 3：编写问答脚本
|
||||
|
||||
```bash
|
||||
vim ./solver/qa.py
|
||||
```
|
||||
|
||||
将以下内容粘贴到 ``qa.py`` 中（也可直接复制内置的 2wiki 项目的问答脚本）。
|
||||
|
||||
```python
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
from kag.common.benchmarks.evaluate import Evaluate
|
||||
from kag.solver.logic.solver_pipeline import SolverPipeline
|
||||
from kag.common.conf import KAG_CONFIG
|
||||
from kag.common.registry import import_modules_from_path
|
||||
|
||||
from kag.common.checkpointer import CheckpointerManager
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class EvaFor2wiki:
|
||||
"""
|
||||
init for kag client
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
"""
|
||||
qa from knowledge base,
|
||||
"""
|
||||
|
||||
def qa(self, query):
|
||||
resp = SolverPipeline.from_config(KAG_CONFIG.all_config["kag_solver_pipeline"])
|
||||
answer, traceLog = resp.run(query)
|
||||
|
||||
logger.info(f"\n\nso the answer for '{query}' is: {answer}\n\n")
|
||||
return answer, traceLog
|
||||
|
||||
"""
|
||||
parallel qa from knowledge base
|
||||
and getBenchmarks(em, f1, answer_similarity)
|
||||
"""
|
||||
|
||||
def parallelQaAndEvaluate(
|
||||
self, qaFilePath, resFilePath, threadNum=1, upperLimit=10
|
||||
):
|
||||
ckpt = CheckpointerManager.get_checkpointer(
|
||||
{"type": "zodb", "ckpt_dir": "ckpt"}
|
||||
)
|
||||
|
||||
def process_sample(data):
|
||||
try:
|
||||
sample_idx, sample = data
|
||||
sample_id = sample["_id"]
|
||||
question = sample["question"]
|
||||
gold = sample["answer"]
|
||||
if question in ckpt:
|
||||
print(f"found existing answer to question: {question}")
|
||||
prediction, traceLog = ckpt.read_from_ckpt(question)
|
||||
else:
|
||||
prediction, traceLog = self.qa(question)
|
||||
ckpt.write_to_ckpt(question, (prediction, traceLog))
|
||||
|
||||
evalObj = Evaluate()
|
||||
metrics = evalObj.getBenchMark([prediction], [gold])
|
||||
return sample_idx, sample_id, prediction, metrics, traceLog
|
||||
except Exception as e:
|
||||
import traceback
|
||||
|
||||
logger.warning(
|
||||
f"process sample failed with error:{traceback.print_exc()}\nfor: {data}"
|
||||
)
|
||||
return None
|
||||
|
||||
qaList = json.load(open(qaFilePath, "r"))
|
||||
total_metrics = {
|
||||
"em": 0.0,
|
||||
"f1": 0.0,
|
||||
"answer_similarity": 0.0,
|
||||
"processNum": 0,
|
||||
}
|
||||
with ThreadPoolExecutor(max_workers=threadNum) as executor:
|
||||
futures = [
|
||||
executor.submit(process_sample, (sample_idx, sample))
|
||||
for sample_idx, sample in enumerate(qaList[:upperLimit])
|
||||
]
|
||||
for future in tqdm(
|
||||
as_completed(futures),
|
||||
total=len(futures),
|
||||
desc="parallelQaAndEvaluate completing: ",
|
||||
):
|
||||
result = future.result()
|
||||
if result is not None:
|
||||
sample_idx, sample_id, prediction, metrics, traceLog = result
|
||||
sample = qaList[sample_idx]
|
||||
|
||||
sample["prediction"] = prediction
|
||||
sample["traceLog"] = traceLog
|
||||
sample["em"] = str(metrics["em"])
|
||||
sample["f1"] = str(metrics["f1"])
|
||||
|
||||
total_metrics["em"] += metrics["em"]
|
||||
total_metrics["f1"] += metrics["f1"]
|
||||
total_metrics["answer_similarity"] += metrics["answer_similarity"]
|
||||
total_metrics["processNum"] += 1
|
||||
|
||||
if sample_idx % 50 == 0:
|
||||
with open(resFilePath, "w") as f:
|
||||
json.dump(qaList, f)
|
||||
|
||||
with open(resFilePath, "w") as f:
|
||||
json.dump(qaList, f)
|
||||
|
||||
res_metrics = {}
|
||||
for item_key, item_value in total_metrics.items():
|
||||
if item_key != "processNum":
|
||||
res_metrics[item_key] = item_value / total_metrics["processNum"]
|
||||
else:
|
||||
res_metrics[item_key] = total_metrics["processNum"]
|
||||
CheckpointerManager.close()
|
||||
return res_metrics
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import_modules_from_path("./prompt")
|
||||
evalObj = EvaFor2wiki()
|
||||
|
||||
start_time = time.time()
|
||||
filePath = "./data/2wiki_qa_sub.json"
|
||||
|
||||
evalObj.qa("When did Lothair Ii's mother die?")
|
||||
|
||||
qaFilePath = os.path.join(os.path.abspath(os.path.dirname(__file__)), filePath)
|
||||
resFilePath = os.path.join(
|
||||
os.path.abspath(os.path.dirname(__file__)), f"2wikitest_res_{start_time}.json"
|
||||
)
|
||||
total_metrics = evalObj.parallelQaAndEvaluate(
|
||||
qaFilePath, resFilePath, threadNum=20, upperLimit=10000
|
||||
)
|
||||
|
||||
total_metrics["cost"] = time.time() - start_time
|
||||
with open(f"./2wikitest_metrics_{start_time}.json", "w") as f:
|
||||
json.dump(total_metrics, f)
|
||||
print(total_metrics)
|
||||
```
|
||||
|
||||
### Step 4：复制答案生成 prompt
|
||||
|
||||
复制 2wiki 项目的答案生成 prompt。
|
||||
|
||||
```bash
|
||||
cp ../2wiki/solver/prompt/resp_generator.py solver/prompt
|
||||
```
|
||||
|
||||
### Step 5：运行命令
|
||||
|
||||
```bash
|
||||
cd solver
|
||||
python qa.py
|
||||
```
|
||||
|
||||
执行结束后，会打印 QA 的效果指标。
|
||||
|
||||
## 5. 其他内置案例
|
||||
|
||||
可进入 [kag/examples](.) 目录体验源码中自带的案例。
|
||||
|
||||
* [musique](./musique/README_cn.md)（多跳问答）
|
||||
* [twowiki](./2wiki/README_cn.md)（多跳问答）
|
||||
* [hotpotqa](./hotpotqa/README_cn.md)（多跳问答）
|
||||
* [黑产挖掘](./riskmining/README_cn.md)
|
||||
* [企业供应链](./supplychain/README_cn.md)
|
||||
* [医疗图谱](./medicine/README_cn.md)
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 7.5 KiB |
|
|
@ -0,0 +1,14 @@
|
|||
# Copyright 2023 OpenSPG Authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
|
||||
# in compliance with the License. You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software distributed under the License
|
||||
# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
|
||||
# or implied.
|
||||
|
||||
"""
|
||||
Place the files to be used for building the index in this directory.
|
||||
"""
|
||||
Loading…
Reference in New Issue