Multi-file API processing

This sample code utilizes the legacy Unstructured Partition Endpoint.

Introduction

This guide demonstrates how to process multiple files using the Unstructured API and S3 Connector and implement context-aware chunking. The process involves installing dependencies, configuring settings, and utilizing Python scripts to manage and chunk data effectively.

Prerequisites

Ensure you have Unstructured API key and access to an S3 bucket containing the target files.

Step-by-Step Process

Step 1: Install Unstructured and S3 Dependency

Install the unstructured package with S3 support.

pip install "unstructured-ingest[s3]"

Step 2: Import Libraries

Import necessary libraries from the unstructured package for chunking and S3 processing.

from unstructured_ingest.interfaces import (
    FsspecConfig,
    PartitionConfig,
    ProcessorConfig,
    ReadConfig,
)
from unstructured_ingest.runner import S3Runner

from unstructured.chunking.title import chunk_by_title
from unstructured.staging.base import dict_to_elements

Step 3: Configuration

Set up the API key and S3 URL for accessing the data.

UNSTRUCTURED_API_KEY = os.getenv('UNSTRUCTURED_API_KEY')
S3_URL = "s3://rh-financial-reports/world-development-bank-2023/"

Step 4: Python Runner

Configure and run the S3Runner for processing the data.

runner = S3Runner(
     processor_config=ProcessorConfig(
         verbose=True,
         output_dir=os.getenv("LOCAL_FILE_OUTPUT_DIR"),
         num_processes=8,
     ),
     read_config=ReadConfig(),
     partition_config=PartitionConfig(
         partition_endpoint=os.getenv("UNSTRUCTURED_API_URL"),
         partition_by_api=True,
         api_key=os.getenv("UNSTRUCTURED_API_KEY"),
         strategy="hi_res",
     ),
     fsspec_config=FsspecConfig(
         remote_url=S3_URL,
     ),
 )

runner.run(anonymous=True)

Step 5: Combine JSON Files from Multi-files Ingestion

Combine JSON files into a single dataset for further processing.

combined_json_data = read_and_combine_json("Connector-Output/world-development-bank-2023")

Step 6: Convert into Unstructured Elements for Chunking

Convert the combined JSON data into Unstructured Elements and apply chunking by title.

elements = dict_to_elements(combined_json_data)
chunks = chunk_by_title(elements)

Conclusion

Following these steps allows for efficient processing of multiple files using the Unstructured S3 Connector. The context-aware chunking helps in organizing and analyzing the data effectively.

Concepts

Cookbooks

Support

Unstructured Foundation (Early Access)

Multi-file API processing

Introduction

Prerequisites

Step-by-Step Process

Step 1: Install Unstructured and S3 Dependency

Step 2: Import Libraries

Step 3: Configuration

Step 4: Python Runner

Step 5: Combine JSON Files from Multi-files Ingestion

Step 6: Convert into Unstructured Elements for Chunking

Conclusion

​Introduction

​Prerequisites

​Step-by-Step Process

​Step 1: Install Unstructured and S3 Dependency

​Step 2: Import Libraries

​Step 3: Configuration

​Step 4: Python Runner

​Step 5: Combine JSON Files from Multi-files Ingestion

​Step 6: Convert into Unstructured Elements for Chunking

​Conclusion

Introduction

Prerequisites

Step-by-Step Process

Step 1: Install Unstructured and S3 Dependency

Step 2: Import Libraries

Step 3: Configuration

Step 4: Python Runner

Step 5: Combine JSON Files from Multi-files Ingestion

Step 6: Convert into Unstructured Elements for Chunking

Conclusion