RecurSearch

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RecurSearch offers chainable, recursive searches over document stores using vector embeddings and metadata filters. It refines queries at each step, summarizes intermediate results, and progressively narrows down to the most relevant information for AI agents and RAG workflows.
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May 04 2025
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RecurSearch

RecurSearch

0
0
RecurSearch
RecurSearch offers chainable, recursive searches over document stores using vector embeddings and metadata filters. It refines queries at each step, summarizes intermediate results, and progressively narrows down to the most relevant information for AI agents and RAG workflows.
Added on:
Social & Email:
Platform:
May 04 2025
--
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What is RecurSearch?

RecurSearch is an open-source Python library designed to improve Retrieval-Augmented Generation (RAG) and AI agent workflows by enabling recursive semantic search. Users define a search pipeline that embeds queries and documents into vector spaces, then iteratively refines queries based on prior results, applies metadata or keyword filters, and summarizes or aggregates findings. This step-by-step refinement yields higher precision, reduces API calls, and helps agents surface deeply nested or context-specific information from large corpora.

Who will use RecurSearch?

  • AI developers building RAG chatbots
  • Data scientists working on semantic search
  • Research engineers exploring knowledge discovery
  • Software teams integrating document QA

How to use the RecurSearch?

  • Step1: pip install recursearch
  • Step2: Import RecursiveSearcher from recursearch
  • Step3: Initialize your vector store (FAISS/Pinecone/Weaviate)
  • Step4: Configure RecursiveSearcher with embedding model and filters
  • Step5: Call .search_recursive(query, depth, options)
  • Step6: Process and integrate returned summaries or documents into your pipeline

Platform

  • mac
  • windows
  • linux

RecurSearch's Core Features & Benefits

The Core Features

  • Chainable recursive semantic search
  • Vector embedding integration (FAISS, Pinecone, Weaviate)
  • Metadata and keyword filtering
  • Intermediate result summarization
  • Configurable recursion depth and branching

The Benefits

  • Higher precision through iterative refinement
  • Reduced API and compute costs
  • Easy integration into existing RAG/agent workflows
  • Scalable across large document collections
  • Open-source and extensible

RecurSearch's Main Use Cases & Applications

  • Building Retrieval-Augmented Generation chatbots
  • Legal document deep-dive and clause extraction
  • Scientific literature knowledge discovery
  • Multi-stage customer support knowledge retrieval
  • Corporate intranet search with context refinement

FAQs of RecurSearch

RecurSearch Company Information

RecurSearch Reviews

5/5
Do You Recommend RecurSearch? Leave a Comment Below!

RecurSearch's Main Competitors and alternatives?

  • LangChain
  • Haystack
  • LlamaIndex
  • Weaviate
  • Pinecone

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