pdfGPT

Uses a Deep Averaging Network Encoder for embeddings, enabling precise information retrieval as highlighted in the README, reducing hallucinations compared to naive GPT outputs.

Responses include page number citations in square brackets, adding credibility and aiding quick reference to the source material, a key feature mentioned in the solution description.

Avoids third-party APIs like Langchain and vector databases, simplifying dependencies and deployment, which aligns with the philosophy of prioritizing simplicity over complexity.

Supports GPT-3.5 Turbo, GPT-4, and text-DaVinci-003, with plans for open-source models like Falcon and Llama, offering flexibility in model choice as per the version updates.

Provides containerized setup via Docker Compose for easy self-hosting, making it accessible for deployment without extensive configuration, as noted in the Docker section.