diff --git a/kag/examples/README.md b/kag/examples/README.md
index 3e629a64..d5582999 100644
--- a/kag/examples/README.md
+++ b/kag/examples/README.md
@@ -1,172 +1,518 @@
-# Example
-## Create project
-Create your new knext project from knext cli tool.
-* Edit your config file like example.cfg
-    ```python
-    [project]
-    project_name = KagDemo
-    description = A knext demo project showcasing KAG features.
-    namespace = KagDemo
-    project_dir = kagdemo
-    host_addr = http://localhost:8887
+# KAG Examples
 
-    [vectorizer]
-    type = openai
-    model = bge-m3
-    api_key = EMPTY
-    base_url = http://127.0.0.1:11434/v1
-    vector_dimensions = 1024
+[English](./README.md) |
+[简体中文](./README_cn.md)
 
-    [llm]
-    type = ollama
-    base_url = http://localhost:11434/api/generate
-    model = llama3.1
+## 1. Precondition
 
-    [prompt]
-    biz_scene = default
-    language = zh
+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.
 
-    [indexer]
-    with_semantic = False
-    similarity_threshold = 0.8
+## 2. Create a knowledge base
 
-    [retriever]
-    with_semantic = False
-    pagerank_threshold = 0.9
-    match_threshold = 0.8
-    top_k = 10
+### 2.1 Create the project
 
-    [log]
-    level = INFO
+#### Step 1: Enter the examples directory
 
-    ```
-* Run command:
-  ```sh
-  kag project create --config_path example.cfg
-  ```
-* Put your source data in {project_dir}/builder/data (in the example kagdemo, we put a pdf file in the data directory.)
-* Edit and your schema file.(**Optional**)
-  * Edit the schema in {project_dir}/schema/{project_name}.schema
-  * Run command in work directory {project_dir}:
-    ```sh
-    knext schema commit
-    ```
-* **Define your BuilderChain**:
-  * In the {project_dir}/builder/indexer.py, define your BuilderChain by extend the BuilderChainABC class and override the build method. For example:
-    ```python
-        import os
+```bash
+cd kag/examples
+```
 
-        from kag.builder.component.reader import DocxReader, PDFReader
-        from kag.builder.component.splitter import LengthSplitter, OutlineSplitter
-        from knext.builder.builder_chain_abc import BuilderChainABC
-        from kag.builder.component.extractor import KAGExtractor
-        from kag.builder.component.writer import KGWriter
-        from kag.solver.logic.solver_pipeline import SolverPipeline
-        import logging
-        from kag.common.env import init_kag_config
+#### Step 2: Edit project configuration
+
+```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----------------#
+```
+
+Update the generative model configurations ``openie_llm`` and ``chat_llm`` and the representive model configuration ``vectorizer_model`` in the configuration file.
+
+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``.
+
+#### Step 3: Create the project (i.e. knowledge base in product mode)
+
+```bash
+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.
+
+```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 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
+
+```bash
+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 ``vectorizer_model`` configuration after the project is created. If you need to update the ``vectorizer_model`` configuration, please create a new project.
+
+```bash
+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.
+
+```bash
+knext project update --proj_path .
+```
+
+## 3. Import documents
+
+### Step 1: Enter the project directory
+
+```bash
+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 7 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:
+
+```bash
+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](https://openspg.yuque.com/ndx6g9/cwh47i/fiq6zum3qtzr7cne).
+
+### Step 4: Commit the schema to OpenSPG server
+
+```bash
+knext schema commit
+```
+
+### Step 5: Execute the build task
+
+Define the build task in the file ``builder/indexer.py``:
+
+```python
+import os
+import logging
+from kag.common.registry import import_modules_from_path
+
+from kag.builder.runner import BuilderChainRunner
+
+logger = logging.getLogger(__name__)
 
 
-        logger = logging.getLogger(__name__)
+def buildKB(file_path):
+    from kag.common.conf import KAG_CONFIG
 
-        file_path = os.path.dirname(__file__)
+    runner = BuilderChainRunner.from_config(
+        KAG_CONFIG.all_config["kag_builder_pipeline"]
+    )
+    runner.invoke(file_path)
 
-        suffix_mapping = {
-            "docx": DocxReader,
-            "pdf": PDFReader
+    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.
+
+```bash
+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.
+
+```text
+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
+less ckpt/kag_checkpoint_0_1.ckpt
+```
+
+To see how many document entries were successfully written to the graph database, use the following command:
+
+```bash
+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).
+
+![KAG Knowledge Inspection Diagram](./images/kag-knowledge-inspection-diag.png)
+
+## 4. Reasoning-based question answering
+
+### Step 1: Enter the project directory
+
+```bash
+cd kag/examples/TwoWikiTest
+```
+
+### 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
+cp ../2wiki/solver/data/2wiki_qa_sub.json solver/data
+```
+
+### Step 3: Edit the QA script
+
+```bash
+vim ./solver/qa.py
+```
+
+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 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]
 
-        class KagDemoBuildChain(BuilderChainABC):
+                    sample["prediction"] = prediction
+                    sample["traceLog"] = traceLog
+                    sample["em"] = str(metrics["em"])
+                    sample["f1"] = str(metrics["f1"])
 
-            def build(self, **kwargs):
-                file_path = kwargs.get("file_path","a.docx")
-                suffix = file_path.split(".")[-1]
-                reader = suffix_mapping[suffix]()
-                if reader is None:
-                    raise NotImplementedError
-                length_splitter = LengthSplitter(split_length=8000)
-                outline_splitter = OutlineSplitter()
-                project_id = int(os.getenv("KAG_PROJECT_ID"))
-                extractor = KAGExtractor(project_id=project_id)
-                writer = KGWriter()
+                    total_metrics["em"] += metrics["em"]
+                    total_metrics["f1"] += metrics["f1"]
+                    total_metrics["answer_similarity"] += metrics["answer_similarity"]
+                    total_metrics["processNum"] += 1
 
-                chain = reader >> length_splitter >> outline_splitter >> extractor >> writer
-                return chain
+                    if sample_idx % 50 == 0:
+                        with open(resFilePath, "w") as f:
+                            json.dump(qaList, f)
 
-        def buildKG(test_file,**kwargs):
-            chain = KagDemoBuildChain(file_path = test_file)
-            chain.invoke(test_file, max_workers=10)
+        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__":
-            init_kag_config(os.path.join(file_path,"../../../tests/builder/component/test_config.cfg"))
-            test_docx = os.path.join(file_path,"../../../tests/builder/data/test_docx.docx")
-            test_pdf = os.path.join(file_path,"../../../tests/builder/data/KnowledgeGraphTutorialSub.pdf")
-            buildKG(test_pdf)
-    ```
-  * Build Knowledge Graph by run:
-    ```sh
-    python {project_dir}/builder/indexer.py
-    ```
+if __name__ == "__main__":
+    import_modules_from_path("./prompt")
+    evalObj = EvaFor2wiki()
 
-* Question Answer(QA):
-  * Define SolverPipeline in {project_dir}/solver/evalFor{project_name}.py, for example:
-    ```python
-    import logging
-    import os
+    start_time = time.time()
+    filePath = "./data/2wiki_qa_sub.json"
 
-    from kag.common.env import init_kag_config
-    from kag.solver.logic.solver_pipeline import SolverPipeline
+    evalObj.qa("When did Lothair Ii's mother die?")
 
-    logger = logging.getLogger(__name__)
+    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
+    )
 
-
-    class KagDemo:
-
-        """
-        init for kag client
-        """
-
-        def __init__(self):
-            pass
-
-        def qa(self, query):
-            resp = SolverPipeline()
-            answer, trace_log = resp.run(query)
-
-            return answer,trace_log
-
-        """
-            parallel qa from knowledge base
-            and getBenchmarks(em, f1, answer_similarity)
-
-        """
-
-
-    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}")
-
-    ```
-  * Run command:
-    ```sh
-    python {project_dir}/solver/evaFor{project_name}.py
-    ```
-
-
-## 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:
-
- ```sh
-knext project recover --host_addr http://{host_ip}:8887 --proj_path {proj_path}
+    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
 
-## 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}
+Copy the answer generation prompt from the built-in 2wiki project.
+
+```bash
+cp ../2wiki/solver/prompt/resp_generator.py solver/prompt
 ```
+
+### Step 5: Execute the QA task
+
+```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)
+
diff --git a/kag/examples/README_cn.md b/kag/examples/README_cn.md
new file mode 100644
index 00000000..81b8987d
--- /dev/null
+++ b/kag/examples/README_cn.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)。
+
+![KAG Knowledge Inspection Diagram](./images/kag-knowledge-inspection-diag.png)
+
+## 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)
+
diff --git a/kag/examples/images/kag-knowledge-inspection-diag.png b/kag/examples/images/kag-knowledge-inspection-diag.png
new file mode 100644
index 00000000..5b2d99e4
Binary files /dev/null and b/kag/examples/images/kag-knowledge-inspection-diag.png differ
diff --git a/kag/templates/project/solver/data/__init__.py b/kag/templates/project/solver/data/__init__.py
new file mode 100644
index 00000000..59bacd4d
--- /dev/null
+++ b/kag/templates/project/solver/data/__init__.py
@@ -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.
+"""