Project_Prajit

Project_Prajit is a production-ready AI project that allows you to ask questions about your documents using the power of Large Language Models (LLMs), even in scenarios without an Internet connection. 100% private, no data leaves your execution environment at any point.

The project provides an API offering all the primitives required to build private, context-aware AI applications. It follows and extends the OpenAI API standard, and supports both normal and streaming responses.

The API is divided into two logical blocks:

High-level API, which abstracts all the complexity of a RAG (Retrieval Augmented Generation) pipeline implementation:

• Ingestion of documents: internally managing document parsing, splitting, metadata extraction, embedding generation and storage.
• Chat & Completions using context from ingested documents: abstracting the retrieval of context, the prompt engineering and the response generation.

Low-level API, which allows advanced users to implement their own complex pipelines:

• Embeddings generation: based on a piece of text.
• Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested documents.

Motivation behind Project_Prajit

Generative AI is a game changer for our society, but adoption in companies of all sizes and data-sensitive domains like healthcare or legal is limited by a clear concern: privacy. Not being able to ensure that your data is fully under your control when using third-party AI tools is a risk those industries cannot take.

Present and Future of Project_Prajit

Project_Prajit is now evolving towards becoming a gateway to generative AI models and primitives, including completions, document ingestion, RAG pipelines and other low-level building blocks. We want to make it easier for any developer to build AI applications and experiences, as well as provide a suitable extensive architecture for the community to keep contributing.

🧩 Architecture

Conceptually, Project_Prajit is an API that wraps a RAG pipeline and exposes its primitives.

• The API is built using FastAPI and follows OpenAI's API scheme.
• The RAG pipeline is based on LlamaIndex.

The design of Project_Prajit allows to easily extend and adapt both the API and the RAG implementation. Some key architectural decisions are:

• Dependency Injection, decoupling the different components and layers.
• Usage of LlamaIndex abstractions such as LLM, BaseEmbedding or VectorStore, making it immediate to change the actual implementations of those abstractions.
• Simplicity, adding as few layers and new abstractions as possible.
• Ready to use, providing a full implementation of the API and RAG pipeline.

Main building blocks:

• APIs are defined in private_gpt:server:<api>. Each package contains an <api>_router.py (FastAPI layer) and an <api>_service.py (the service implementation). Each Service uses LlamaIndex base abstractions instead of specific implementations, decoupling the actual implementation from its usage.
• Components are placed in private_gpt:components:<component>. Each Component is in charge of providing actual implementations to the base abstractions used in the Services - for example LLMComponent is in charge of providing an actual implementation of an LLM (for example LlamaCPP or OpenAI).

Key Components

Project_Prajit is built make use of:

• Qdrant, providing the default vector database
• Fern, providing Documentation and SDKs
• LlamaIndex, providing the base RAG framework and abstractions

This project has been strongly influenced and supported by other amazing projects like LangChain, GPT4All, LlamaCpp, Chroma and SentenceTransformers.