
any-parser
Accurate, private and configurable document retrieval LLM
Stars: 129

AnyParser provides an API to accurately extract unstructured data (e.g., PDFs, images, charts) into a structured format. Users can set up their API key, run synchronous and asynchronous extractions, and perform batch extraction. The tool is useful for extracting text, numbers, and symbols from various sources like PDFs and images. It offers flexibility in processing data and provides immediate results for synchronous extraction while allowing users to fetch results later for asynchronous and batch extraction. AnyParser is designed to simplify data extraction tasks and enhance data processing efficiency.
README:
AnyParser provides an API to accurately extract unstructured data (e.g., PDFs, images, charts) into a structured format.
To get started, generate your API key from the Sandbox Account Page. Each account comes with 100 free pages.
⚠️ Note: The free API is limited to 10 pages/call.
For more information or to inquire about larger usage plans, feel free to contact us at [email protected].
To set up your API key (CAMBIO_API_KEY
), follow these steps:
- Create a
.env
file in the root directory of your project. - Add the following line to the
.env
file:
CAMBIO_API_KEY=0cam************************
First, create and activate a new Conda environment, then install AnyParser:
conda create -n any-parse python=3.10 -y
conda activate any-parse
pip3 install any-parser
Use your API key to create an instance of AnyParser. Make sure you’ve set up your .env file to store your API key securely:
import os
from dotenv import load_dotenv
from any_parser import AnyParser
# Load environment variables
load_dotenv(override=True)
# Get the API key from the environment
example_apikey = os.getenv("CAMBIO_API_KEY")
# Create an AnyParser instance
ap = AnyParser(api_key=example_apikey)
To extract data synchronously and receive immediate results:
# Extract content from the file and get the markdown output along with processing time
markdown, total_time = ap.parse(file_path="./data/test.pdf")
For asynchronous extraction, send the file for processing and fetch results later:
# Send the file to begin asynchronous extraction
file_id = ap.async_parse(file_path="./data/test.pdf")
# Fetch the extracted content using the file ID
markdown = ap.async_fetch(file_id=file_id)
For batch extraction, send the file to begin processing and fetch results later:
# Send the file to begin batch extraction
response = ap.batches.create(file_path="./data/test.pdf")
request_id = response.requestId
# Fetch the extracted content using the request ID
markdown = ap.batches.retrieve(request_id)
Batch API for folder input:
# Send the folder to begin batch extraction
WORKING_FOLDER = "./sample_data"
# This will generate a jsonl with filename and requestID
response = ap.batches.create(WORKING_FOLDER)
Each response in the JSONL file contains:
- The filename
- A unique request ID
- Additional processing metadata You can later use these request IDs to retrieve the extracted content for each file:
# Fetch the extracted content using the request ID from the jsonl file
markdown = ap.batches.retrieve(request_id)
For more details about code implementation of batch API, refer to examples/parse_batch_upload.py and examples/parse_batch_fetch.py
⚠️ Note: Batch extraction is currently in beta testing. Processing time may take up to 12 hours to complete.
⚠️ Important: API keys generated from cambioml.com do not automatically have batch processing permissions. Please contact [email protected] to request batch processing access for your API key.
Check out these examples to see how you can utilize AnyParser to extract text, numbers, and symbols in fewer than 10 lines of code!
Are you an AI engineer looking to accurately extract both the text and layout (e.g., table of contents or Markdown headers hierarchy) from a PDF? Check out this 3-minute notebook demo.
Are you a financial analyst needing to accurately extract numbers from a table within an image? Explore this 3-minute notebook example.
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