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https://github.com/simonpierreboucher/llm_flow_notebook

This repository provides a modular framework for interacting with multiple language model APIs (OpenAI, Anthropic, and Mistral). It enables text generation, embedding retrieval, semantic search, and multi-step prompt flows, allowing users to leverage various models in a structured workflow.
https://github.com/simonpierreboucher/llm_flow_notebook

anthropic large-language-models llm mistral openai workflow

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This repository provides a modular framework for interacting with multiple language model APIs (OpenAI, Anthropic, and Mistral). It enables text generation, embedding retrieval, semantic search, and multi-step prompt flows, allowing users to leverage various models in a structured workflow.

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# LLM Flow Notebook

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[![Python Version](https://img.shields.io/badge/python-3.7%2B-blue.svg)](https://www.python.org/downloads/)

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This repository provides a modular framework for interacting with multiple language model APIs (OpenAI, Anthropic, and Mistral). It enables text generation, embedding retrieval, semantic search, and multi-step prompt flows, allowing users to leverage various models in a structured workflow.



## Repository Structure



- **[LLM_FLOW_1.ipynb](https://github.com/simonpierreboucher/llm_flow_notebook/blob/main/LLM_FLOW_1.ipynb)**: Introduces the modular framework, including setup for different language model APIs and initial examples of text generation and embedding retrieval.

- **[LLM_FLOW_2.ipynb](https://github.com/simonpierreboucher/llm_flow_notebook/blob/main/LLM_FLOW_2.ipynb)**: Expands on the framework to include multi-step prompt flows and complex workflows, demonstrating semantic search, text processing, and advanced interactions.



## Overview



The notebooks implement a flexible and dynamic workflow for:

- **Text Generation**: Generate responses and creative content with various LLMs.

- **Embedding Retrieval**: Retrieve embeddings for documents and perform similarity searches.

- **Semantic Search**: Use embeddings and cosine similarity to find relevant information.

- **Multi-Step Prompt Flows**: Design custom workflows that chain multiple prompts and processes together.



This setup enables efficient text generation and data processing with multiple models in a single, cohesive workflow.



## Getting Started



### Prerequisites



To run these notebooks, you will need:

- **Python 3.8+**

- **Jupyter Notebook**

- API keys for each language model service (OpenAI, Anthropic, Mistral)

- Required dependencies as listed in `requirements.txt`



### Installation



1. Clone the repository:



   ```bash

   git clone https://github.com/simonpierreboucher/llm_flow_notebook.git

   cd llm_flow_notebook

   ```



2. Install the dependencies:



   ```bash

   pip install -r requirements.txt

   ```



### Running the Notebooks



1. **Start Jupyter Notebook**: Open Jupyter by navigating to the repository folder and running:

   ```bash

   jupyter notebook

   ```

2. **Select a Notebook**: Open either `LLM_FLOW_1.ipynb` or `LLM_FLOW_2.ipynb` to explore the modular framework and interact with the LLM APIs.

3. **Follow Instructions**: Each notebook includes code and setup instructions for different API interactions and use cases.



## Use Cases



- **Modular Text Generation**: Use different LLMs interchangeably for text creation tasks.

- **Cross-Model Embedding and Semantic Search**: Retrieve embeddings from various models and apply semantic search techniques.

- **Custom Prompt Workflows**: Design and implement complex multi-step workflows that chain prompts across different models and APIs.



## Contributing



We welcome contributions! Feel free to submit issues or pull requests to add features, fix bugs, or improve the framework.



## License



This repository is licensed under the MIT License.