Index a dataset so text chunks become searchable. Splits content into RAG-ready chunks and generates embeddings for semantic retrieval.
MIT
510,539 tools. Updated 2026-09-04 07:53
"Semantic search, RAG, and memory systems" matching MCP tools:
- Reconstruct memory state at a past timestamp to run semantic search queries. Understand how agent context evolved over time.MIT
- Create a named document collection to group related evidence bundles for cross-document semantic search and RAG-based Q&A.MIT
- Generate vector embeddings from text for semantic search, RAG, clustering, or similarity tasks. Choose between query or document input type and adjust model quality and dimensionality.MIT
- Convert text into a numeric vector for semantic search, RAG, and similarity. Supports multiple languages.MIT
- List project memories for quick audits, inspections, or verifying a new memory was saved. Bypasses semantic search and uses no OpenAI quota.MIT
Matching MCP Servers
- AlicenseNot gradedqualityCmaintenancePersistent memory with semantic search for Claude and MCP-compatible clients, storing context that survives conversations and can be retrieved intelligently.1MIT
- FlicenseNot gradedqualityCmaintenanceChromaDB-powered semantic memory server that enables semantic search across past conversations, health snapshots, and cross-silo topics for the nanobot platform.
Matching MCP Connectors
Persistent long-term memory for AI agents: semantic search, knowledge graph, and task canvas.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
- Perform semantic search over a stored memory namespace to retrieve the most relevant entries. Use this to recall context for RAG pipelines.MIT
- Find the right integration tool for any task by searching available actions with semantic search. Describe what you want to do and get relevant tool suggestions.MIT
- Find document types semantically similar to a query via multilingual semantic search. Matches by meaning, not exact text, using RAG-based embeddings.MIT
- Search stored memories by semantic similarity. Filter results by tags, date range, tier, and memory type for precise retrieval.MIT
- Search the RAG corpus with semantic and keyword hybrid search to find relevant content marketing ideas.MIT
- Retrieve current connection status, latency, and version details for Memory Server, RAG Server, and Google services to verify service availability.MIT
- Verify RAG and Memory server availability, including collection and schema auto-creation status. Use detailed mode for protocol and latency details.MIT
- Generate 768-dimensional dense vector embeddings from text to enable RAG and semantic search. Accepts single strings or batches, converting them into vector representations via BAAI BGE-Base.MIT
- Search personal memory layers using vector similarity and keyword fusion to retrieve relevant episodic, semantic, or procedural information.AGPL 3.0
- Search stored memory by semantic meaning rather than keywords to retrieve relevant facts, documents, or past context for RAG and agent memory, ranked by similarity score.MIT
- Manage and search opencode session archives: list, inspect, archive, restore, rename, delete, full-text search, semantic RAG, and code-recall. Reads SQLite depot directly.MIT
- Check the local Yggdrasil memory engine's health by confirming running status, stored-memory count, and semantic search availability. Useful for diagnosing engine failures before retrying other operations.AGPL 3.0
- Persist and retrieve semantic memory across sessions. Supports storing key-value entries, appending fragments, editing sections, and semantic search.MIT
- Create a new vector store to organize documents for semantic search and RAG. Attach files, set expiration, and configure chunking strategy.MIT