odtp-llm-parser

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Note

This repository makes use of submodules. Therefore, when cloning it you need to include them.

git clone --recurse-submodules https://github.com/odtp-org/odtp-llm-parser

The component is a Python script that extracts structured information from a text input using the OpenAI API based on a provided JSON schema. It accepts command-line arguments for the OpenAI API key, the JSON schema file path, the text input (either from a file or direct input), and an optional output file path. The script processes the text, calls the OpenAI API to extract information according to the schema, and writes the extracted data to a JSON file.

Table of Contents

• Tools Information
• How to add this component to your ODTP instance
• Data sheet
  • Parameters
  • Secrets
  • Input Files
  • Output Files
• Tutorial
  • How to run this component as docker
  • Development Mode
  • Running with GPU
  • Running in API Mode
• Credits and References

Tools Information

Tool	Semantic Versioning	Commit	Documentation
openai	v1.54.4	---	API Reference

How to add this component to your ODTP instance

In order to add this component to your ODTP CLI, you can use. If you want to use the component directly, please refer to the docker section.

odtp new odtp-component-entry \
--name odtp-llm-parser \
--component-version v0.0.1 \
--repository https://github.com/sdsc-ordes/odtp-llm-parser 

Data sheet

Parameters

No Parameters

Secrets

| Secret Name | Description | Type | Required | Default Value | Constraints | Notes | | | OPENAI_KEY | OpenAI API key used for authentication | string | yes | None | Must be a valid OpenAI API key | Required for accessing OpenAI API |

Input Files

Input Files

File/Folder	Description	File Type	Required	Format	Notes
schema.json	JSON schema file that defines the structure for information extraction	.json	Yes	JSON	Must be a valid JSON schema
input.txt	Text file containing the content to extract information from	.txt	Yes	Text	Plain text file

Output Files

File/Folder	Description	File Type	Contents	Usage
output.json	Extracted structured information	.json	JSON data following schema	Contains extracted data

Tutorial

How to run this component as docker

Build the dockerfile.

docker build -t odtp-llm-parser .

Run the following command. Mount the correct volumes for input/output/logs folders.

docker run -it --rm \
-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \
-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \
-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \
--env-file .env odtp-llm-parser

Development Mode

To run the component in development mode, mount the app folder inside the container:

docker run -it --rm \
-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \
-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \
-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \
-v {PATH_TO_YOUR_APP_FOLDER}:/odtp/app \
--env-file .env odtp-llm-parser

Running with GPU

To run the component with GPU support, use the following command:

docker run -it --rm \
--gpus all \
-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \
-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \
-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \
--env-file .env odtp-llm-parser

Running in API Mode

To run the component in API mode and expose a port, use the following command:

docker run -it --rm \
-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \
-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \
-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \
-p 7860:7860 \
--env-file .env \
--entrypoint python3 \
odtp-llm-parser \
/odtp/odtp-app/gradio_api.py

Credits and references

SDSC

This component has been created using the odtp-component-template v0.5.0.

docker run -it --rm -p 7860:7860 --env-file .env --entrypoint python3 odtp-llm-parser /odtp/odtp-app/gradio_app.py