Building a Local RAG System with Go, PostgreSQL, and Ollama Without Cloud APIs
Ollama
The article explores creating a fully local RAG system using Go, PostgreSQL with pgvector, and Ollama, avoiding heavy frameworks like LangChain and cloud APIs. It covers the architecture, indexing, semantic search, and generation, highlighting the benefits of local processing for privacy and control.
The article describes building a local RAG (Retrieval-Augmented Generation) system without cloud APIs, using Go, PostgreSQL with the pgvector extension, and Ollama. The system consists of two main components: an ingest CLI for offline indexing of documents and a REST API for handling user queries in real time. Documents are split into chunks, embedded using the nomic-embed-text model, and stored in PostgreSQL with pgvector for semantic search. At query time, the system generates an embedding for the query, finds similar chunks using approximate nearest neighbor search (HNSW), and passes the relevant context to the llama3.2 model via Ollama for answer generation. This approach ensures full data privacy and autonomy, avoiding external API calls. The article emphasizes the importance of using the same embedding model for both indexing and queries to maintain vector space consistency.
- Abbreviations
- RAG = Retrieval-Augmented Generation — генерация с дополнением извлечением
- API = Application Programming Interface — интерфейс программирования приложений
- LLM = Large Language Model — большая языковая модель
- CLI = Command-Line Interface — интерфейс командной строки
- ANN = Approximate Nearest Neighbor — приближенный поиск ближайшего соседа
- HNSW = Hierarchical Navigable Small World — иерархический навигационный малый мир
- REST = Representational State Transfer — передача состояния представления
Source: Habr — хаб NLP —
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