asepharyana
164c1860da
- ENV: AI_LLM_BASE_URL/API_KEY/MODEL, AI_EMBEDDING_MODEL (gemini-embedding-001), QDRANT_URL
- ai_agent module: chunking materi, embed_text + chat_completion (9router, SSE parse), Qdrant repo (dimentorin_materi collection, 3072d cosine)
- routes: POST /ai/chat (RAG answer + sources), POST /ai/materials/{id}/index, POST /ai/reindex
- e2e verified: reindex 3 chunks; chat 'ownership' -> materi Rust paling relevan 0.88; chat 'endpoint axum' -> materi Axum 0.82; jawaban gronding konteks
26 lines
693 B
Rust
26 lines
693 B
Rust
use async_trait::async_trait;
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use uuid::Uuid;
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use super::rag_document::RagDocument;
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use imphnen_utils::AppError;
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#[async_trait]
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pub trait RagRepository: Send + Sync {
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/// Upsert a document chunk into the vector store.
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async fn upsert_document(
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&self,
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point_id: u64,
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doc: &RagDocument,
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embedding: Vec<f32>,
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) -> Result<(), AppError>;
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/// Search the vector store for the closest chunks to `embedding`.
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async fn search(
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&self,
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embedding: Vec<f32>,
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limit: u64,
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material_id: Option<Uuid>,
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) -> Result<Vec<(RagDocument, f32)>, AppError>;
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/// Remove all chunks for a material (re-index support).
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async fn delete_material(&self, material_id: Uuid) -> Result<(), AppError>;
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}
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