Is your feature request related to a problem? Please describe.
When searching archived emails, results are limited to exact keyword matches. This makes it difficult to find emails by concept or intent searching "budget approval" won't surface "sign-off on Q3 spend" even though they're semantically equivalent. For e-Discovery and compliance use cases, this is a significant gap since relevant emails often use varied or indirect terminology.
Describe the solution you'd like
Enable semantic (vector) search and RAG (Retrieval-Augmented Generation) over archived emails by leveraging Meilisearch's built-in embedding and hybrid search support, which is already available since Meilisearch v1.6.
Since OpenArchiver already uses Meilisearch as its search backend, no additional vector database or search infrastructure is required. Meilisearch can be configured with an embedder (e.g. Ollama, OpenAI, or HuggingFace) to automatically generate and store vector embeddings at index time, and its native hybrid search combines full-text and semantic ranking in a single query.
At a high level, this would allow users to:
- Find emails by meaning, not just keywords
- Ask natural language questions against their email archive and get cited, grounded answers
Describe alternatives you've considered
- External vector database (Qdrant, Chroma, pgvector) adds infrastructure complexity and duplicates data already in Meilisearch. Unnecessary given Meilisearch's native capabilities.
- Meilisearch's built-in
/chat endpoint viable for a basic implementation, though a custom RAG layer would allow better control over scoping, citation tracking, and conversation history.
Additional context
Reference: How to build a RAG system (with Meilisearch)
Is your feature request related to a problem? Please describe.
When searching archived emails, results are limited to exact keyword matches. This makes it difficult to find emails by concept or intent searching "budget approval" won't surface "sign-off on Q3 spend" even though they're semantically equivalent. For e-Discovery and compliance use cases, this is a significant gap since relevant emails often use varied or indirect terminology.
Describe the solution you'd like
Enable semantic (vector) search and RAG (Retrieval-Augmented Generation) over archived emails by leveraging Meilisearch's built-in embedding and hybrid search support, which is already available since Meilisearch v1.6.
Since OpenArchiver already uses Meilisearch as its search backend, no additional vector database or search infrastructure is required. Meilisearch can be configured with an embedder (e.g. Ollama, OpenAI, or HuggingFace) to automatically generate and store vector embeddings at index time, and its native hybrid search combines full-text and semantic ranking in a single query.
At a high level, this would allow users to:
Describe alternatives you've considered
/chatendpoint viable for a basic implementation, though a custom RAG layer would allow better control over scoping, citation tracking, and conversation history.Additional context
Reference: How to build a RAG system (with Meilisearch)