Add files to a RAG system for document retrieval, supporting PDF, DOCX, TXT, MD, CSV, and JSON formats to enable semantic search and information access.
MIT
678,737 tools. Updated 2026-10-19 22:34
"Slack Bot with RAG Document Retrieval and Automated Response System" matching MCP tools:
- Create a searchable knowledge base from plain text, automatically chunked and indexed for RAG retrieval. Ideal for FAQs, product specs, and policies.MIT
- Create a named document collection to group related evidence bundles for cross-document semantic search and RAG-based Q&A.MIT
- Perform semantic search over a stored memory namespace to retrieve the most relevant entries. Use this to recall context for RAG pipelines.MIT
- Verify that an LLM response engaged with the provided source document using the Semantic Grounding Index score. Ideal for auditing RAG pipeline provenance.Apache 2.0
- Get RAG corpus statistics: document counts, hostnames, coverage. Use this to evaluate the scope and completeness of your content knowledge base.MIT
Matching MCP Servers
- AlicenseBqualityDmaintenanceEnables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.1Apache 2.0
- FlicenseNot gradedqualityCmaintenanceMCP server for a modular RAG system that enables natural language question answering over enterprise documents with intent-aware routing, adaptive retrieval, and citation-backed responses.-
Matching MCP Connectors
Draft a chargeback dispute-response evidence checklist and letter preview from order details.
System Dynamics
- Add knowledge entries to a RAG system to store insights, tips, and best practices. Categorize and tag them for efficient retrieval.MIT
- Index a document by ID and text for keyword search retrieval. Add documents to make them searchable across sessions.MIT
- Execute a complete RAG workflow: generate query embeddings, search document chunks, and return a direct answer grounded only in the retrieved context.MIT
- Configure a project's Slack workspace, default channel, and bot token secret reference to enable Slack sync.MIT
- Query your knowledge base with RAG-powered chat, multi-modal context, token optimization, and full document access to get comprehensive answers.MIT
- Prioritize a list of documents by relevance to a query, returning scored results for RAG retrieval. Drop-in Cohere-compatible response for existing pipelines.MIT
- Replace RAG retrieval tags on a crawled website source to control which documents the bot retrieves. Pass a new tag set or [] to clear all tags.MIT
- Update an ingested document's RAG retrieval tags by replacing its current tag set with the specified list; pass [] to clear all tags.MIT
- Scan document collections, training examples, or RAG retrieval sets to uncover semantic anomalies and poisoned clusters using local embeddings and Isolation Forest.MIT
- Query an AI Knowledge project with RAG to get LLM answers or retrieve document chunks as context.-
- Dispatch alerts to Slack, Teams, PagerDuty, email, or SMS with severity and context to ensure rapid response.Apache 2.0
- Discover available knowledge bases for Retrieval-Augmented Generation (RAG) workflows. Lists all document repositories to enable integration into AI applications.MIT
- Retrieve connected Slack workspace name, team ID, and bot user ID to verify Slack integration status.MIT
- Run faithfulness and retrieval quality evaluation over a batch of RAG pipeline cases, producing a summary report with mean metrics and worst-performing cases.MIT
- Update an app's AI bot settings: prompt, LLM, trigger, greeting, RAG behavior, identity, and public widget. Partial update leaves omitted fields unchanged.MIT