Algis DumbrisandClaude Opus 4.6
5dcb9e6149
Add HNSW-based vector search with configurable embedding providers (OpenAI, Ollama) and automatic FTS5 fallback when no provider is configured. Background pipeline embeds messages asynchronously on ingest, stores vectors in a pure-Go HNSW index, and retries on failure with exponential backoff. The search_messages MCP tool now supports search_mode (auto/semantic/fulltext) and returns ranked results with similarity scores. All existing tests continue to pass, CGO_ENABLED=0 cross-compilation verified. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
17 lines
620 B
Go
17 lines
620 B
Go
// Package embedding provides embedding provider implementations for semantic search.
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package embedding
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import "context"
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// EmbeddingProvider generates vector embeddings from text.
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type EmbeddingProvider interface {
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// Embed generates an embedding vector for a single text.
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Embed(ctx context.Context, text string) ([]float32, error)
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// EmbedBatch generates embedding vectors for multiple texts.
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EmbedBatch(ctx context.Context, texts []string) ([][]float32, error)
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// Dimensions returns the embedding dimensionality.
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Dimensions() int
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// Name returns the provider name (e.g. "openai", "ollama").
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Name() string
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}
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