dewy 0.1.0

Knowledge base service.

Dewy - The Knowledgebase for AI

Opinionated knowledge extraction and semantic retrieval for Gen AI applications.
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About The Project

Dewy helps you build AI agents and RAG applications by managing the extraction of knowledge from your documents and implementing semantic search over the extracted content. Load your documents and Dewy takes care of parsing, chunking, summarizing, and indexing for retrieval Dewy builds on the lessons of putting real Gen AI applications into production so you can focus on getting 💩 done, rather than comparing vector databases and building data extraction infrastructure.

Below is the typical architecture of an AI agent performing RAG. Dewy handles all of the parts shown in brown, letting you focus on your application -- the parts in green.

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Getting Started

To get a local copy up and running follow these steps.

1. (Optional) Start a pgvector instance to persist your data

   docker run -d \
     -p 5432:5432 \
     -e POSTGRES_DB=dewydb \
     -e POSTGRES_USER=dewydbuser \
     -e POSTGRES_PASSWORD=dewydbpwd \
     -e POSTGRES_HOST_AUTH_METHOD=trust \
     ankane/pgvector
   

2. Install Dewy

   pip install dewy
   

3. Fire up Dewy

   // Configure your OpenAI key (optional - local models will be used if not provided)
   export OPENAI_API_KEY=...
   
   // Configure your pgvector endpoint (this assumes you installed with Docker as shown previously)
   export DB=postgresql://dewydbuser:dewydbpwd@localhost/dewydb
   
   // Run the docker container
   dewy
   
   // Go to the management console to start creating resources!
   open http://localhost/admin
   

4. Install the API client library

   npm install dewy-ts
   

5. Add documents

   import { Dewy } from 'dewy_ts';
   const dewy = new Dewy({endpoint: “localhost:3000”})
   
   await dewy.addDocument({url: “https://arxiv.org/abs/2005.11401”})
   

6. Retrieve document chunks for LLM prompting

   import { Dewy } from 'dewy_ts';
   const dewy = new Dewy({endpoint: “localhost:3000”})
   
   const context = await dewy.retrieveChunks({query: "tell me about RAG", n: 10});
   
   const prompt = [
     {
       role: 'system',
       content: `You are a helpful assistant.
       You will take into account any CONTEXT BLOCK that is provided in a conversation.
       START CONTEXT BLOCK
       ${context.results.map((c: any) => c.chunk.text).join("\n")}
       END OF CONTEXT BLOCK
       `,
     },
   ]
   
   const response = await openai.chat.completions.create({
     model: 'gpt-3.5-turbo',
     stream: true,
     messages: [...prompt, [{role: 'user': content: 'Tell me about RAG'}]]
   })
   

Swagger docs at http://localhost:8000/docs. Notebook example_notebook.ipynb uses the REST API from Python.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Development Installation

1. Clone the repo

   git clone https://github.com/DewyKB/dewy.git
   

2. Install Python packages

   poetry install
   

3. Configure Dewy. Dewy will read env vars from an .env file if provided. You can also set these directly in the environment, for example when configuring an instance running in docker / kubernetes.

   cat > .env << EOF
   ENVIRONMENT=LOCAL
   DB=postgresql://...
   OPENAI_API_KEY=...
   EOF
   

4. Build the frontend

   cd frontend && npm install && npm run build
   

5. Run the Dewy service

   poetry run dewy
   

Practices

Some skeleton code based on best practices from https://github.com/zhanymkanov/fastapi-best-practices.

The following commands run tests and apply linting. If you're in a poetry shell, you can omit the poetry run:

• Running tests: poetry run pytest
• Linting (and formatting): poetry run ruff check --fix
• Formatting: poetry run ruff format
• Type Checking: poetry run mypy app

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License

Distributed under the Apache 2 License. See LICENSE.txt for more information.

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