framework-api

Build a REST API for PDF vector search by deploying a FastAPI-based service that handles PDF ingestion, indexing, and retrieval. It provides endpoint routing and an OpenAI-compatible interface to integrate RAG workflows directly into your applications.

Yes, the API provides an OpenAI-compatible interface for chat and embeddings. This allows you to connect existing applications to your PDF vector processing pipeline and execute RAG-style QA without modifying standard API call structures.

Index PDFs for semantic search in a FastAPI application by utilizing a dedicated REST API service that processes and vectors documents. This approach provides health checks and endpoint routing to ensure scalable document retrieval and governance.

Yes, the API explicitly supports endpoint routing and health checks. These features ensure your PDF ingestion, vector indexing, and RAG workflows remain robust and easily monitored across frontend apps and internal automation pipelines.

Query a knowledge graph from indexed PDFs by sending requests to the exposed REST API endpoints. The service handles vector processing and graph-based QA workflows, enabling you to retrieve structured intelligence directly from your unstructured document repository.

Avoid using a REST API for PDF vector processing if your application requires real-time, in-memory processing without network overhead, or if you need to embed vector search logic directly within a monolithic application without external endpoint calls.