Library facilitating the integration of different LLM providers in LangChain (e.g. `ollama`, `Google Gemini`, etc).
Project description
LangChain Utility
Description
LangChain is a framework for developing applications powered by language models. It enables application that:
- Are context aware; i.e. connect a language model to sources context
- Reason; i.e. rely on a language model to reason
This library offers integration with the following Generative AI servers:
This library extends LangChain by providing facilities to define configurations, templates for execution of LLMs.
Documentation
To get started refer to the documentation.
Development
Requirements
- Git
- llm-guard >= 0.3
- langchain >= 0.2
- traceloop-sdk >= 0.13
- Python = 3.11
- Poetry >= 1.7.0
- streamlit >= 1.32
Environment Variables
Name | Description | Default |
---|---|---|
LOG_DIR |
Location of the logging files | logs/ |
LOG_LEVEL |
Logging level to be applied during execution | INFO |
AUDIO_TMP_FOLDER |
Location of temporary audio files when transcribing videos | tmp/audio/ |
PROMPT_CONFIG_FOLDER |
Location of the Prompt configuration for execution | prompt_configs/ |
How to prepare the environment
- Install dependencies
poetry install
NOTE To update dependencies, it may be needed to run the following command prior to installing the packages:poetry lock
- Test unit test coverage for the project
Note Report is only generated if all unit test have completed successfully.poetry run coverage run -m pytest && poetry run coverage report -m
About RQle.AI
RQle.AI believes in the transformative potential of Generative AI. More specifically, it focuses on showcasing real-world applications of how Generative AI can empower individuals and organizations worldwide in addressing customers' "job to be done" problems and create value for them.
Disclaimer
This library and its use of Large Language Models (LLMs) are subject to the following disclaimers:
- LLMs are still under development and may generate inaccurate, incomplete, or biased output;
- LLMs can inherit and reflect biases present in their training data;
- Developers of this library are not liable for any damages or losses arising from its use;
- You are responsible for using the library and LLMs in an ethical and responsible manner.
By using this library, you acknowledge and agree to these disclaimers and limitations.
RQle.AI - 2024
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