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Conversational search in one API call

Skip the complex RAG pipeline. The `/chat` endpoint transforms your data into AI-powered conversations without the engineering complexity.

Query transformer
Vector Database
Reranker
Response Generator

Trusted by leading industry innovators

Why teams are building RAG with Meilisearch

Transform your data into conversational experiences without the RAG engineering complexity. One API call handles query optimization, retrieval, and response generation—all powered by Meilisearch's superior hybrid search.

  • RAG simplified to one endpoint illustration

    RAG simplified to one endpoint

    No more multi-step retrieval pipelines. Meilisearch's /chat endpoint handles the entire RAG workflow—from query understanding to answer generation—in a single call, eliminating the need for separate vector DBs, rerankers, and complex orchestration layers.

  • Direct answers, not just links illustration

    Direct answers, not just links

    Get human-like responses directly from your content. The /chat endpoint leverages Meilisearch's hybrid search to deliver truly relevant answers—not guesses—with context from your documents, responding to users in natural conversation rather than just returning document links.

  • Production-ready in minutes, not months illustration

    Production-ready in minutes, not months

    Implement complete conversational AI with an OpenAI-compatible API. Get fine-grained control over answer quality, conversation context, and content grounding—while scaling confidently across millions of documents without the usual RAG infrastructure complexity.

⚡ 90% less code

Implement conversational AI with one API call instead of complex pipelines

🔄 Fewer LLM calls

Optimized workflow reduces API costs with fewer model invocations

🛡️ Ground truth anchoring

Responses are factually grounded in your content, minimizing hallucinations

      Natural Language Understanding illustration

      Natural Language Understanding

      Automatically transforms natural questions into optimal search parameters, maintaining conversation context for follow-up questions

      Hybrid Retrieval Engine illustration

      Hybrid Retrieval Engine

      Combines keyword and semantic search in one query for superior relevance without a separate reranking step

      Response Generation illustration

      Response Generation

      Returns human-like answers based on your content, maintaining factual accuracy without hallucinations

      Simplified Integration illustration

      Simplified Integration

      OpenAI-compatible interface allows developers to integrate in minutes using familiar patterns and existing code

      Context Management illustration

      Context Management

      Maintains conversation history for contextual responses, enabling natural follow-up questions and topic refinement

      Complete RAG Pipeline illustration

      Complete RAG Pipeline

      Handles the entire RAG workflow in one endpoint—from query understanding to document retrieval to answer generation

Why our customers choose Meilisearch

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Agora customer spotlight

Scaling to billions: How Agora elevates e-commerce search with Meilisearch AI

Read Agora's success story →
  • Lightning-fast search-as-you-type results
  • Hybrid semantic & keyword search
  • Customizable schema and indexing flexibility
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CarbonGraph customer spotlight

CarbonGraph consolidated its search by migrating to Meilisearch from Pinecone

  • Automated embeddings
  • Search consolidation
  • Straightforward setup
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HitPay customer spotlight

HitPay streamlines customers’ search capabilities

Read HitPay's success story →
  • Easy to integrate
  • Developer experience
  • Higher out-of-the-box relevancy
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Qogita customer spotlight

Qogita simplifies B2B trade with Meilisearch Cloud

Read Qogita's success story →
  • Cost-effectiveness
  • Developer experience
  • Agile product delivery

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Build intelligent RAG systems and AI agents

Skip the vector DB. Meilisearch gives you the AI foundation you need—easy to maintain & without the black box.