A curated list of the best Search & Knowledge AI tools to explore, query, and retrieve knowledge from structured and unstructured data using modern AI technologies like RAG, embeddings, semantic search, and vector databases.
Search and Knowledge AI tools use artificial intelligence—especially Natural Language Processing (NLP) and vector embeddings—to help users find accurate, relevant information across vast datasets, documents, or knowledge bases. Instead of just keyword-matching like traditional search engines, these tools understand semantic meaning, context, and intent.
Understands the meaning of queries instead of just exact words.
For example, searching “How do I reset my account?” will match answers about “password recovery” or “account access.”
Combines LLMs (like GPT) with a search engine.
It first retrieves relevant documents and then generates a coherent answer using those documents.
Allows users to ask questions directly against files like PDFs, Word docs, or Notion pages and get context-aware answers.
Centralizes and organizes internal knowledge—help docs, product manuals, chats—into intelligent, queryable systems.
Converts text into numerical representations (vectors)
so semantically similar concepts can be identified and retrieved—even if they don’t share keywords.
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AI-powered support and search agents trained on your product and documentation. Instant answers, no tickets.
Build a fully integrated RAG-powered knowledge base search with embedding support and chat interfaces.
Interact with your documents privately using LLMs without sacrificing privacy.
Framework for building LLM apps with structured data using indexes, retrievers, and query engines.
Open-source framework for RAG pipelines — question answering, search over documents, and summarization.
Customize and fine-tune your own chat agents with a focus on search and retrieval.
Embedding-as-a-service with pluggable vector databases and search engines.
Chat with your documentation using vector search and embedding pipelines.
A lightweight semantic search engine that plugs into local or hosted embedding services.
Microsoft’s open-source RAG pipeline using AutoGen for search, summarization, and augmentation.
Build internal knowledge assistants from docs, wikis, and Notion pages.
Chain LLMs with tools like vector DBs and retrievers to build context-aware AI agents.
Self-hosted tool to chat with documentation using sentence-transformers and FAISS.
Preprocessing pipeline to clean and parse PDFs, HTMLs, and Office docs before feeding into search/RAG systems.
Try sentence-transformers and embedding visualizations live.
Fast local embedding generation using ONNX for offline search and retrieval.
Lightweight, developer-friendly vector DB for RAG workflows.
Generate structured knowledge bases from raw text using GPT-based extractors.
Powerful open-source vector DB with semantic filtering and hybrid search capabilities.
Manage datasets and embeddings at scale, purpose-built for AI pipelines.