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Enterprise RAG Platform

Enterprise knowledge retrieval that grounds LLM answers in indexed company documents using embeddings, a vector database and embedding-based semantic search.

Overview

A retrieval-augmented generation platform for enterprise document and knowledge search. Documents are ingested, embedded and indexed so an LLM-powered assistant can answer questions from company knowledge instead of relying on model memory.

Problem

Enterprise knowledge is scattered across documents and systems, and keyword search returns file lists rather than answers. General-purpose LLMs do not know internal content and cannot be trusted to guess it.

Solution

Ingest documents, split them into chunks, embed each chunk, and store the vectors in a vector database. At query time, run embedding-based semantic search (vector similarity, such as cosine similarity) and optionally hybrid retrieval, assemble the retrieved context, and have the LLM generate an answer grounded in that context.

Architecture

Enterprise RAG Platform architecture, top to bottom
  1. 01

    Ingestion

    • Document loaders
    • Chunking
    • Embedding model
  2. 02

    Storage

    • Vector database
    • Document metadata
  3. 03

    Retrieval

    • Semantic search
    • Hybrid retrieval
    • Context construction
  4. 04

    Generation

    • Prompt assembly
    • LLM
    • Grounded answer
  5. 05

    Delivery

    • REST API
    • Assistant interface
  • Clean separation between ingestion, retrieval and generation so each stage can be evaluated and replaced independently.
  • Embedding model and vector store are configuration choices (OpenAI text-embedding-3-small, Sentence Transformers, ChromaDB, Qdrant).
  • Retrieval quality and answer groundedness are treated as first-class evaluation targets.

Key features

  • Semantic document search over an embedded corpus
  • LLM-powered assistant with contextual retrieval
  • Chunking and embedding pipeline for document ingestion
  • Swappable embedding models and vector stores

Engineering challenges

  • Choosing chunk size and overlap so retrieved context is complete but focused.
  • Balancing semantic and keyword matching for enterprise terminology and identifiers.
  • Measuring retrieval quality and groundedness rather than judging answers by eye.

Results

No measured outcomes are published for this project. I only list results that have been verified.