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Build a Local RAG Using Ollama, PostgreSQL and BladePipe
- Enterprise-grade RAG emphasizes integration, data control, and alignment with business systems, aiming to boost automation and intelligence.
- BladePipe supports building local RAG services with Ollama for enterprises concerned about data security and compliance needs.
- Key traits of enterprise-grade RAG systems include a fully private stack, diverse data sources, incremental data syncing, and integrated tool calling.
- BladePipe's RagApi simplifies building RAG services with vector search and LLM-based Q&A capabilities, supporting various models and platforms.
- Advantages of using RagApi include two DataJobs setup, zero-code deployment, adjustable parameters, multi-model support, and an OpenAI-compatible API.
- The article guides users on preparing, deploying, and building a secure RAG service using Ollama, PostgreSQL for vector storage, and BladePipe RagApi.
- Detailed instructions are provided for running Ollama, setting up PGVector with PostgreSQL, and deploying BladePipe on Docker in an enterprise environment.
- Steps for adding data sources, creating DataJobs for vectorizing documents and building RagApi services, along with testing procedures, are outlined.
- Users are walked through the process of configuring BladePipe, data processing, and performing tests to ensure the functionality of the RagApi service.
- By combining BladePipe and Ollama, enterprises can achieve a fully private RAG service deployment that prioritizes data privacy and control.
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