Interpretable and Debuggable Knowledge: Graphs offer a human-navigable view of knowledge that can be queried, visualized, and updated.
可解释和可调试的知识：图提供了一种人类可导航的知识视图，可以查询、可视化和更新。
Fast, Low-cost, and Efficient: Designed to run at scale without heavy resource or cost requirements.
快速、低成本和高效：旨在大规模运行，而无需大量的资源或成本要求。
Dynamic Data: Automatically generate and refine graphs to best fit your domain and ontology needs.
动态数据：自动生成和改进图，以最适合您的领域和本体需求。（ontology needs.）
Incremental Updates: Supports real-time updates as your data evolves.
增量更新：支持数据演变时的实时更新。
Intelligent Exploration: Leverages PageRank-based graph exploration for enhanced accuracy and dependability.
智能探索：利用基于 PageRank 的图探索来提高准确性和可靠性。
Asynchronous & Typed: Fully asynchronous, with complete type support for robust and predictable workflows.
异步和类型化：完全异步，具有完整的类型支持，可实现健壮且可预测的工作流程。

# clone this repo first
cd fast_graphrag
poetry install

pip install fast-graphrag

export OPENAI_API_KEY="sk-..."

curl https://raw.githubusercontent.com/circlemind-ai/fast-graphrag/refs/heads/main/mock_data.txt > ./book.txt

export CONCURRENT_TASK_LIMIT=8

from fast_graphrag import GraphRAG

DOMAIN = "Analyze this story and identify the characters. Focus on how they interact with each other, the locations they explore, and their relationships."

EXAMPLE_QUERIES = [
    "What is the significance of Christmas Eve in A Christmas Carol?",
    "How does the setting of Victorian London contribute to the story's themes?",
    "Describe the chain of events that leads to Scrooge's transformation.",
    "How does Dickens use the different spirits (Past, Present, and Future) to guide Scrooge?",
    "Why does Dickens choose to divide the story into \"staves\" rather than chapters?"
]

ENTITY_TYPES = ["Character", "Animal", "Place", "Object", "Activity", "Event"]

grag = GraphRAG(
    working_dir="./book_example",
    domain=DOMAIN,
    example_queries="\n".join(EXAMPLE_QUERIES),
    entity_types=ENTITY_TYPES
)

with open("./book.txt") as f:
    grag.insert(f.read())

print(grag.query("Who is Scrooge?").response)

custom_llm.py: a brief example on how to configure fast-graphrag to run with different OpenAI API compatible language models and embedders;
custom_llm.py：一个简短的示例，说明如何配置 fast-graphrag 以使用不同的 OpenAI API 兼容的语言模型和嵌入器运行；（language models and embedders;）
checkpointing.ipynb: a tutorial on how to use checkpoints to avoid irreversible data corruption;
checkpointing.ipynb：一个关于如何使用检查点以避免不可逆转的数据损坏的教程；（checkpoints）
query_parameters.ipynb: a tutorial on how to use the different query parameters. In particular, it shows how to include references to the used information in the provided answer (using the with_references=True parameter).
query_parameters.ipynb：一个关于如何使用不同的查询参数的教程。 特别是，它展示了如何将对所用信息的引用包含在提供的答案中（使用 with_references=True 参数）。