Create information extraction task

POST

/302/langextract/task

LangExtract, an open-source project from Google: https://github.com/google/langextract

LangExtract is a Python library developed by Google, focused on "extracting structured information from unstructured text using large language models (LLMs)." Its core features include:
It can extract structured data such as entities, attributes, and relationships from text (e.g., medical reports, literary works) based on your custom rules and examples.
It can precisely locate results within the original text, making it easy to implement highlighting, visualization, and traceability.
Especially suitable for long texts, such as parsing an entire novel or lengthy medical report at once, using chunked parallel processing and multiple iterations to improve recall.
Built-in interactive HTML visualization allows you to vividly display extraction results on web pages.

Pricing: Calculated based on model invocation costs

Request

Header Params

Body Params application/json

Examples

Example 1示例 2

{
    "model_id": "gemini-2.5-flash",
    "text_or_documents": "Lady Juliet gazed longingly at the stars, her heart aching for Romeo",
    "prompt": "\n\t\t\tExtract characters, emotions, and relationships in order of appearance.\n\t\t    Use exact text for extractions. Do not paraphrase or overlap entities.\n\t\t    Provide meaningful attributes for each entity to add context.\n\t\t\t",
    "examples": [
        {
            "extractions": [
                {
                    "attributes": {
                        "emotional_state": "wonder"
                    },
                    "extraction_class": "character",
                    "extraction_text": "ROMEO"
                },
                {
                    "attributes": {
                        "feeling": "gentle awe"
                    },
                    "extraction_class": "emotion",
                    "extraction_text": "But soft!"
                },
                {
                    "attributes": {
                        "type": "metaphor"
                    },
                    "extraction_class": "relationship",
                    "extraction_text": "Juliet is the sun"
                }
            ],
            "text": "\n\t\t\t\t\tROMEO. But soft! What light through yonder window breaks? It is the east, and Juliet is the sun.\n\t\t\t\t\t"
        }
    ]
}

Request Code Samples

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Python

HTTP

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cURL

Example 1示例 2

curl --location --request POST 'https://api.302.ai/302/langextract/task' \
--header 'Authorization: Bearer sk-jls4AaVBGoe1GwZD64qZA1qyKTN1MPHa4NmvH1cT68z7K1Zz' \
--header 'Content-Type: application/json' \
--data-raw '{
    "model_id": "gemini-2.5-flash",
    "text_or_documents": "Lady Juliet gazed longingly at the stars, her heart aching for Romeo",
    "prompt": "\n\t\t\tExtract characters, emotions, and relationships in order of appearance.\n\t\t    Use exact text for extractions. Do not paraphrase or overlap entities.\n\t\t    Provide meaningful attributes for each entity to add context.\n\t\t\t",
    "examples": [
        {
            "extractions": [
                {
                    "attributes": {
                        "emotional_state": "wonder"
                    },
                    "extraction_class": "character",
                    "extraction_text": "ROMEO"
                },
                {
                    "attributes": {
                        "feeling": "gentle awe"
                    },
                    "extraction_class": "emotion",
                    "extraction_text": "But soft!"
                },
                {
                    "attributes": {
                        "type": "metaphor"
                    },
                    "extraction_class": "relationship",
                    "extraction_text": "Juliet is the sun"
                }
            ],
            "text": "\n\t\t\t\t\tROMEO. But soft! What light through yonder window breaks? It is the east, and Juliet is the sun.\n\t\t\t\t\t"
        }
    ]
}'

Responses

🟢200成功

application/json

成功响应

Body

Example

"string"

🟠422参数错误

Modified at 2025-08-19 06:09:20

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