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RAG & Knowledge Systems

Hybrid Search Rag

Hybrid Search Rag is a Python package for building retrieval-augmented generation workflows, with documented hybrid retrieval methods, Milvus ingestion, MLflow tracking, Ragas evaluation and asyncio support.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Hybrid Search Rag — 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 developing a Python RAG application and you need configurable retrieval stages alongside documented tracking or evaluation tools.

Who it is for

  • Developers building knowledge-based applications with language models.
  • Teams comparing retrieval configurations or monitoring and evaluating RAG pipelines.

Documented uses

  • Combine document retrieval and language-model generation in a configurable workflow.
  • Ingest vector data into Milvus and retrieve with dense, sparse or other documented search strategies.
  • Track pipeline experiments and traces, evaluate answer quality, and monitor configured API costs.
  • Use asynchronous execution in RAG workflows.
Before you choose

Requirements & limits

  • The repository documents a wheel-based Poetry build and pip installation sequence.
  • The README identifies Python 3.11+ for its asynchronous support.
  • Milvus is named for vector ingestion and optimized retrieval; MLflow and Ragas are named for tracking and evaluation.
  • Configuration is described through .env.example, and the repository points to examples/* and module-specific READMEs for usage.

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

Build and install the package wheel

Build a wheel from the repository and install it with pip.

Before you start

  • Git
  • Python package tooling including pip
  • Poetry

Steps

  1. Clone https://github.com/kolhesamiksha/Hybrid-Search-RAG.

  2. Change into the cloned Hybrid-Search-RAG directory.

  3. Install Poetry with pip.

  4. Run Poetry to build the package wheel.

  5. Install the generated hybrid_rag-0.1.0-py3-none-any.whl file with pip.

Expected outcome

The README's documented sequence builds and installs the hybrid_rag 0.1.0 wheel.

Guide limitations

  • This sequence covers packaging and installation, not running a RAG workflow.
  • The README points to examples/* and module-level documentation for use instructions.
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
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
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.
Evidence and transparency

Sources & review

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