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Documentation Index

Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt

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Conversational search is still in early development and conversational agents can hallucinate. LLMs may occasionally produce inaccurate or misleading answers even when the retrieved source documents are correct. Monitor responses closely in production, follow the hallucination reduction guide, and configure guardrails to minimize this risk.
Conversational search is an AI-powered feature built on top of Meilisearch’s search engine. It works as a built-in Retrieval Augmented Generation (RAG) system: when a user asks a question, Meilisearch retrieves relevant documents from its indexes, then uses an LLM to generate a response grounded in those results. With proper configuration, such as system prompt engineering and guardrails, you can ensure that responses are based on your indexed data rather than the LLM’s general knowledge. This is similar to how Perplexity works: every answer comes with source documents so users can verify the information. Meilisearch brings the same pattern to your own data.

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Use cases

Conversational search supports three main use cases, all powered by the same /chats API route:

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Multi-turn chat

Build a full conversational interface where users ask follow-up questions and the agent maintains context across the conversation. This is ideal for knowledge bases, customer support, and documentation search. Example: A user asks “What models do you support?”, then follows up with “Which one is the fastest?” without restating the context.

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One-shot answer summarization

Generate a single, concise answer to a user’s question without maintaining conversation history. This is useful when you want to display a summarized answer alongside traditional search results. Example: A user searches “How do I reset my password?” and gets a direct answer synthesized from your help articles, displayed above the regular search results.

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RAG pipelines

Integrate Meilisearch as the retrieval layer in a broader RAG architecture. Meilisearch handles query understanding and hybrid retrieval, while your application controls the generation step. Example: A product recommendation engine that retrieves matching products via Meilisearch, then uses a custom prompt to generate personalized suggestions.

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How it works

  1. Query understanding: Meilisearch automatically transforms the user’s natural language question into optimized search parameters
  2. Hybrid retrieval: combines keyword and semantic search for better relevancy
  3. Answer generation: your chosen LLM generates a response using only the retrieved documents as context
  4. Source attribution: every response can include references to the source documents used to generate the answer

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Implementation strategies

In the majority of cases, you should use the /chats route to build conversational search. This API consolidates the entire RAG pipeline into a single endpoint, handling retrieval, context management, and generation. Follow the getting started guide to set up conversational search, then build a chat interface or generate summarized answers. Consult the chat completions API reference for the full list of supported parameters.

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Model Context Protocol (MCP)

An alternative method is using a Model Context Protocol (MCP) server. MCPs are designed for broader uses that go beyond answering questions, but can be useful in contexts where having up-to-date data is more important than comprehensive answers. Follow the dedicated MCP guide if you want to implement it in your application.