🕵 QAX - Q&A over indeX

qax is a small proof-of-concept implementation for Retrieval Question & Answer (Q&A) aimed to store embeddings for code repositories or any folder full of text files. This code is a simple adaption of the langchain examples, embedded in docker containers

With qax, you can create embeddings for folder structures containing text files, such as code repositories, leverage OpenAI's text-embedding-ada-002 model and store them in a pgVector container. After the index has been built, the app uses langchain´s RetrievelQA chain with gpt-3.5-turbo (or gpt-4 if available) model for performing Q&A on the indexed data.

(see langchain QA Docs)

1. Installation and Setup

Prerequisites

• OpenAI API Key
• Docker

Build the application container

Build a lokal docker container to run the app:

docker build -t qax .

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2. Prepare the data source

To get started, clone any repository and navigate to its root directory:

git clone https://github.com/your_username/example.git && cd example

Set up Environment Variables

Create two .env files in the root directory of the repository with the following contents:

.env:

OPENAI_API_KEY=your_openai_api_key_here

# Database Connection
PGVECTOR_HOST=IP_OF_HOST
PGVECTOR_PORT=5432
PGVECTOR_COLLECTION=qax

db.env:

POSTGRES_USER=victor
POSTGRES_PASSWORD=vector
POSTGRES_DB=vectordb
PGDATA=/.vectordb

Start the pgVector Container

🐧 Linux

docker run -d \
--name pgvector \
--env-file=db.env \
-p 5432:5432 \
-v $PWD/.vectordb:/var/lib/postgresql \
ankane/pgvector

⌛ Windows

docker run -d `
--name pgvector `
--env-file=db.env `
-p 5432:5432 `
-v ${PWD}/.vectordb:/var/lib/postgresql `
ankane/pgvector

Enable pgVector Extension

docker cp ./load-ext.sh pgvector:/load-ext.sh && \
docker exec pgvector /load-ext.sh

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3. Parse files and create embeddings

Run the following command to create embeddings for the files in the repository:

docker run --rm -it \
--env-file=.env \
--env-file=db.env \
-v ${PWD}:/repository \
qax --index

This will, by default, create a .vectordb folder in your repository to store the index. To change the name, adjust PGDATA variable in db.env

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4. Query the Index 💬

To ask questions about the indexed data, use the command below:

docker run --rm -it \
--env-file=.env \
--env-file=db.env \
-v ${PWD}:/repository \
qax [QUERY]

Example 1:

docker run --rm -it \
--env-file=.env \
--env-file=db.env \
-v ${PWD}:/repository \
qax "Which libraries are needed to build the app?"

> Entering new RetrievalQA chain...

> Finished chain.
 The libraries needed to build the app are langchain, openai, tiktoken, pathspec, python-dotenv, psycopg2-binary, and pgvector.
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Sources:
  - /repository/requirements.txt
  - /repository/Dockerfile
  - /repository/load-ext.sh
  - /repository/README.md

Example 2:

docker run --rm -it \
--env-file=.env \
--env-file=db.env \
-v ${PWD}:/repository \
qax "Create inline documentation for the main function"

> Entering new RetrievalQA chain...

> Finished chain.


    """The main function of the document indexing and similarity search program.

    Args:
        index (bool): Whether to perform document indexing.
    """
    embeddings = OpenAIEmbeddings()
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Sources:
  - /repository/app.py
  - /repository/requirements.txt
  - /repository/README.md
  - /repository/app.py

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License

This project is licensed under the MIT License.

Contribution

We welcome contributions from the community! Please follow our contribution guidelines for more details.

Contact

For any inquiries or support, feel free to join our community on Discord.