JSONLoader 集成

使用 LangChain Python 集成 JSONLoader 文档加载器。

This guide provides a quick overview for getting started with the JSONLoader document loader. For detailed documentation of all JSONLoader features and configurations head to the API reference.

• TODO: Add any other relevant links, like information about underlying API, etc.

概述

集成详情

Class	Package	Local	Serializable	JS support
JSONLoader	langchain-community	✅	❌	✅

加载器特性

Source	Document Lazy Loading	Native Async Support
JSONLoader	✅	❌

设置

To access JSON document loader you’ll need to install the langchain-community integration package as well as the jq Python 包。

凭证

No credentials are required to use the JSONLoader class.

要启用模型调用的自动追踪，请设置你的 LangSmith API 密钥：

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

安装

Install langchain-community and jq:

pip install -qU langchain-community jq

初始化

现在我们可以实例化模型对象并加载文档：

• TODO: Update model instantiation with relevant params.

from langchain_community.document_loaders import JSONLoader

loader = JSONLoader(
    file_path="./example_data/facebook_chat.json",
    jq_schema=".messages[].content",
    text_content=False,
)

加载

docs = loader.load()
docs[0]

Document(metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}, page_content='Bye!')

print(docs[0].metadata)

{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}

惰性加载

pages = []
for doc in loader.lazy_load():
    pages.append(doc)
    if len(pages) >= 10:
        # do some paged operation, e.g.
        # index.upsert(pages)

        pages = []

Read from JSON lines file

If you want to load documents from a JSON Lines file, you pass json_lines=True and specify jq_schema to extract page_content from a single JSON object.

loader = JSONLoader(
    file_path="./example_data/facebook_chat_messages.jsonl",
    jq_schema=".content",
    text_content=False,
    json_lines=True,
)

docs = loader.load()
print(docs[0])

page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}

Read specific content keys

Another option is to set jq_schema='.' and provide a content_key in order to only load specific content:

loader = JSONLoader(
    file_path="./example_data/facebook_chat_messages.jsonl",
    jq_schema=".",
    content_key="sender_name",
    json_lines=True,
)

docs = loader.load()
print(docs[0])

page_content='User 2' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}

JSON file with jq schema content_key

To load documents from a JSON file using the content_key within the jq schema, set is_content_key_jq_parsable=True. Ensure that content_key is compatible and can be parsed using the jq schema.

loader = JSONLoader(
    file_path="./example_data/facebook_chat.json",
    jq_schema=".messages[]",
    content_key=".content",
    is_content_key_jq_parsable=True,
)

docs = loader.load()
print(docs[0])

page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}

Extracting metadata

Generally, we want to include metadata available in the JSON file into the documents that we create from the content.

以下demonstrates how metadata can be extracted using the JSONLoader.

There are some key changes to be noted. In the previous example where we didn’t collect the metadata, we managed to directly specify in the schema where the value for the page_content can be extracted from.

In this example, we have to tell the loader to iterate over the records in the messages field. The jq_schema then has to be .messages[]

This allows us to pass the records (dict) into the metadata_func that has to be implemented. The metadata_func is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final Document object.

Additionally, we now have to explicitly specify in the loader, via the content_key argument, the key from the record where the value for the page_content needs to be extracted from.

# Define the metadata extraction function.
def metadata_func(record: dict, metadata: dict) -> dict:
    metadata["sender_name"] = record.get("sender_name")
    metadata["timestamp_ms"] = record.get("timestamp_ms")

    return metadata


loader = JSONLoader(
    file_path="./example_data/facebook_chat.json",
    jq_schema=".messages[]",
    content_key="content",
    metadata_func=metadata_func,
)

docs = loader.load()
print(docs[0].metadata)

{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}

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API 参考

For detailed documentation of all JSONLoader features and configurations head to the API reference

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