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RAG & Knowledge Systems

Enterprise Rag Framework

Enterprise Rag Framework is a Python framework for building retrieval-augmented generation applications that search organizational documents and provide relevant context to language models. It combines BM25 and embedding-based retrieval and describes document processing, answer evaluation, source tracking, and monitoring.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Enterprise Rag Framework — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when building a Python application that needs to retrieve context from an organizational knowledge base before generating answers, particularly when hybrid search, source tracking, and evaluation are relevant.

Who it is for

  • Developers building question-answering applications over organizational documents.
  • Teams that need to inspect retrieved sources and evaluate retrieval or generated answers.
  • Engineers configuring a RAG system with selectable retrieval, vector-store, and model components.

Documented uses

  • Process and index a directory of documents for question answering.
  • Retrieve relevant passages using sparse and dense search, with optional reranking.
  • Generate answers with source information and confidence scores.
  • Measure retrieval and answer quality and monitor usage or performance.
Before you choose

Requirements & limits

The README documents installation through pip or from source and identifies Python 3.10 and 3.11.

  • The framework’s example uses configurable vector-store, retrieval, and generation settings; the README names FAISS, Pinecone, and Weaviate as vector-store options.
  • The documented retrieval choices include hybrid, dense, and sparse search, with optional reranking.
  • Document processing is described for common text and office formats, with OCR integration for images and scanned documents.
  • The README describes Python API, REST API, and Docker and Kubernetes deployment options, but deployment details vary by configuration.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Index documents and query them with the Python API

Process a directory of documents, add them to a configured RAG system, save the index, and request an answer with sources.

Before you start

  • Python 3.10 or 3.11, as identified by the repository README.
  • The package installed with pip or installed from a local repository clone.
  • A document directory and configuration for a vector store, retrieval method, and generation model.

Steps

  1. Install the package with the command below, or clone the repository and install it in development mode with the command below.

    Documented command
    pip install enterprise-rag-framework
    Documented command
    pip install -e .

  2. Initialize RAGSystem with vector-store, retrieval, and generation configuration; the example uses FAISS, hybrid retrieval, and an OpenAI model.

  3. Create a DocumentProcessor, process the document directory, and pass the resulting documents to rag_system.add_documents(docs).

  4. Call rag_system.save_index(), then use rag_system.query(...) to retrieve an answer and inspect its sources and metrics.

Expected outcome

A saved document index and a query response whose example includes an answer, source snippets, confidence scores, and metrics.

Guide limitations

  • The README provides example configuration but does not specify the setup required for each possible vector store or model.
  • The documented example does not establish that every listed file format, OCR path, or provider works in a particular environment.
Documentation 1 ↗
Source-based practical guide

Start the documented REST API

Start the framework’s API server as the documented next step toward making query requests.

Before you start

  • The framework installed in a Python environment.
  • A configured framework application and its required dependencies.

Steps

  1. Run the command below to start the API server.

    Documented command
    python -m enterprise_rag_framework.deployment.api

  2. Use the README’s REST API example as the reference for sending a query request to the local server.

Expected outcome

The documented API server start command and an example query-request stage.

Guide limitations

  • The supplied API example is incomplete, so it does not document a complete request payload or full client workflow.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Enterprise Rag FrameworkProcess and index a directory of documents for question answering.ServerThe README documents installation through pip or from source and identifies Python 3.10 and 3.11.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
TaimoorKhan10/Enterprise-RAG-Framework · Official homepage title · retained in its source language
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