zeph-memory

Semantic memory with SQLite and Qdrant for Zeph agent.

Overview

Provides durable conversation storage via SQLite and semantic retrieval through Qdrant vector search. The SemanticMemory orchestrator combines both backends, enabling the agent to recall relevant context from past conversations using embedding similarity.

Includes a document ingestion subsystem for loading, chunking, and storing user documents (text, Markdown, PDF) into Qdrant for RAG workflows.

Key modules

Module	Description
`sqlite`	SQLite storage for conversations and messages
`sqlite::history`	Input history persistence for CLI channel
`qdrant`	Qdrant client for vector upsert and search
`qdrant_ops`	`QdrantOps` — high-level Qdrant operations
`semantic`	`SemanticMemory` — orchestrates SQLite + Qdrant
`document`	Document loading, splitting, and ingestion pipeline
`document::loader`	`TextLoader` (.txt/.md), `PdfLoader` (feature-gated: `pdf`)
`document::splitter`	`TextSplitter` with configurable chunking
`document::pipeline`	`IngestionPipeline` — load, split, embed, store via Qdrant
`vector_store`	`VectorStore` trait and `VectorPoint` types
`embedding_store`	`EmbeddingStore` — high-level embedding CRUD
`embeddable`	`Embeddable` trait and `EmbeddingRegistry<T>` — generic Qdrant sync/search for any embeddable type
`types`	`ConversationId`, `MessageId`, shared types
`error`	`MemoryError` — unified error type

Re-exports: MemoryError, QdrantOps, ConversationId, MessageId, Document, DocumentLoader, TextLoader, TextSplitter, IngestionPipeline, Chunk, SplitterConfig, DocumentError, DocumentMetadata, PdfLoader (behind pdf feature), Embeddable, EmbeddingRegistry

Features

Feature	Description
`pdf`	PDF document loading via `pdf-extract`
`mock`	In-memory `VectorStore` implementation for testing

Installation

cargo add zeph-memory

# With PDF support
cargo add zeph-memory --features pdf

License