https://github.com/jlenon7/openai_rag

🤖 Implementation of RAG techniques to answer questions about PEC 42/2023.
https://github.com/jlenon7/openai_rag

Last synced: 25 days ago
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🤖 Implementation of RAG techniques to answer questions about PEC 42/2023.

• Host: GitHub
• URL: https://github.com/jlenon7/openai_rag
• Owner: jlenon7
• License: mit
• Created: 2024-11-26T20:14:10.000Z (26 days ago)
• Default Branch: main
• Last Pushed: 2024-11-26T20:14:55.000Z (26 days ago)
• Last Synced: 2024-11-27T06:47:44.664Z (26 days ago)
• Language: Python
• Size: 320 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# OpenAI RAG 🤖

> Implementation of RAG techniques to answer questions about PEC 42/2023.

## Tools

The project uses the following tools to work:

- [LangChain](https://python.langchain.com/docs/introduction/)

- [Chroma as Vector DB](https://www.trychroma.com/)

- [GPT 3.5 Turbo as LLM](https://platform.openai.com/docs/models#gpt-3-5-turbo)

- [ADA 002 for Text Embedding](https://platform.openai.com/docs/models#embeddings)

## Running

To run the project first create a new Python environment and activate it. I'm using [Anaconda](https://www.anaconda.com/) for setting the python version that pipenv should use to set up the environment. The command bellow will automatically setup the environment with conda and pipenv:

```shell

make env

```

Now install all the project dependencies:

```shell

make install-all

```

Insert the file chunks in Chroma database by running:

```shell

make insert

```

Ask something by running the following:

```shell

make ask

```

You can change your question inside `bin/ask.py`.