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.
AI Platforms & Frameworks
Research
Information Processing
#semantic search
#AI agent workflows
#Open Source Library
#vector embedding
#data filtering
#recursive query refinement
#metadata tagging
#cost-effective search
#document summarization
#iterative information retrieval
#Scalable Search Solutions
#RAG integration
#Query Optimization
#nested information retrieval
#precision enhancement
#query pipeline design
#API call reduction
#large corpus handling
#efficiency in data searches
#AI-driven Insights
...
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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.
AI Platforms & Frameworks
Research
Information Processing
#semantic search
#AI agent workflows
#Open Source Library
#vector embedding
#data filtering
#recursive query refinement
#metadata tagging
#cost-effective search
#document summarization
#iterative information retrieval
#Scalable Search Solutions
#RAG integration
#Query Optimization
#nested information retrieval
#precision enhancement
#query pipeline design
#API call reduction
#large corpus handling
#efficiency in data searches
#AI-driven Insights
...
Featured
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
RecurSearch's Main Competitors and alternatives?
- LangChain
- Haystack
- LlamaIndex
- Weaviate
- Pinecone
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