PageIndex: AI Document Search Without Vector Complexity
Build smarter AI retrieval systems using reasoning, not just vector matching. Ideal for founders creating intelligent document parsing tools.
32,443 stars2,802 forksPythonUpdated 6/2/2026100% free ยท open source Creates semantic document retrieval systems that understand context and reasoning instead of just doing basic keyword or vector matching
- โขBuilding complex research or analysis AI assistants that require deep document comprehension
- โขCreating enterprise knowledge management tools with nuanced information extraction
- โขDeveloping AI systems that need to parse complex, multi-page technical documents
- 1Install PageIndex via pip: pip install pageindex
- 2Import library and create an index from your document collection
- 3Configure reasoning parameters to define how documents should be semantically analyzed
- 4Use query methods to retrieve contextually relevant information across your document set
Ready-to-paste prompt pageindex.create_semantic_index('technical_reports/', reasoning_depth=3)Topics
agentic-ai
agents
ai
ai-agents
context-engineering
information-retrieval
llm
rag
reasoning
retrieval
retrieval-augmented-generation
vector-database