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KAG Examples

1. Precondition

Please refer to Quick Start to install KAG and its dependency OpenSPG server, and learn about using KAG in developer mode.

2. Create a knowledge base

2.1 Create the project

Step 1: Enter the examples directory

cd kag/examples

Step 2: Edit project configuration

vim ./example_config.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----------------#

Update the generative model configurations openie_llm and chat_llm and the representational model configuration vectorize_model in the configuration file.

You need to fill in correct api_keys. If your model providers and model names are different from the default values, you also need to update base_url and model.

Step 3: Create the project (i.e. knowledge base in product mode)

knext project create --config_path ./example_config.yaml

Step 4: Initial contents of the directory

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.

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.

.
├── 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 Update the project (Optional)

If there are configuration changes, you can refer to this section to update the configuration information to the server.

Step 1: Enter the project directory

cd kag/examples/TwoWikiTest

Step 2: Edit project configuration

Note: The embedding vectors generated by different representation models can vary significantly. It is recommended not to update the vectorize_model configuration after the project is created. If you need to update the vectorize_model configuration, please create a new project.

vim ./kag_config.yaml

Step 3: Run the update command

After editing the project configuration, use the knext project update command to update the local configuration information to the OpenSPG server.

knext project update --proj_path .

3. Import documents

Step 1: Enter the project directory

cd kag/examples/TwoWikiTest

Step 2: Retrieve corpus data

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 3 documents. We will use this smaller dataset as an example for the experiment.

Copy it to the directory with the same name as the TwoWikiTest project:

cp ../2wiki/builder/data/2wiki_sub_corpus.json builder/data

Step 3: Edit the schema (Optional)

Edit the schema file schema/TwoWikiTest.schema. For an introduction of OpenSPG schema, please refer to Declarative Schema.

Step 4: Commit the schema to OpenSPG server

knext schema commit

Step 5: Execute the build task

Define the build task in the file builder/indexer.py:

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__)
    # Set file_path to the path of the corpus file prepared earlier
    file_path = os.path.join(dir_path, "data/2wiki_sub_corpus.json")

    buildKB(file_path)

Run the indexer.py script to complete the knowledge graph construction for unstructured data.

cd builder
python indexer.py

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.

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:

less ckpt/kag_checkpoint_0_1.ckpt

To see how many document entries were successfully written to the graph database, use the following command:

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 capability in product mode, along with the corresponding API documentation HTTP API Reference.

4. Reasoning-based question answering

Step 1: Enter the project directory

cd kag/examples/TwoWikiTest

Step 2: Edit the QA script

vim ./solver/qa.py

Paste the following content into qa.py.

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

if __name__ == "__main__":
    import_modules_from_path("./prompt")
    evalObj = EvaFor2wiki()

    evalObj.qa("Which Stanford University professor works on Alzheimer's?")

Step 3: Execute the QA task

cd solver
python qa.py

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.

12 KiB Raw Blame History

KAG Examples

1. Precondition

2. Create a knowledge base

2.1 Create the project

Step 1: Enter the examples directory

Step 2: Edit project configuration

Step 3: Create the project (i.e. knowledge base in product mode)

Step 4: Initial contents of the directory

2.2 Update the project (Optional)

Step 1: Enter the project directory

Step 2: Edit project configuration

Step 3: Run the update command

3. Import documents

Step 1: Enter the project directory

Step 2: Retrieve corpus data

Step 3: Edit the schema (Optional)

Step 4: Commit the schema to OpenSPG server

Step 5: Execute the build task

Step 6: Inspect the constructed knowledge graph

4. Reasoning-based question answering

Step 1: Enter the project directory

Step 2: Edit the QA script

Step 3: Execute the QA task

5. Other built-in examples

12 KiB

Raw Blame History