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pgvector, a guide for DBA – Part3 AI agent and workflows
- The next phase of AI evolution involves implementing AI workflows through agentic frameworks for large to medium-sized organizations to address issues and limitations faced by AI users.
- An agentic framework enables organizations to provide operational data through Retrieve Augmented Generative (RAG) Search, which, combined with business logic and an iterative refinement process, enhances internal productivity significantly.
- Creating AI agents involves using frameworks like LangChain or OpenAI APIs, tailored to an organization's unique requirements and workflow.
- The article delves into the workflow of creating pgvector embeddings for documents and utilizing them in the RAG Search process.
- Steps include document ingestion and chunking, storing in pgvector, user query/embedding, vector similarity search, constructing LLM input, and LLM response generation for precise answers.
- For building AI agents, prompt engineering to control external information sources can provide more specific and relevant responses from local databases.
- The code example in the article demonstrates how to generate answers based on user queries and context, leveraging embeddings and PostgreSQL searches.
- Implementing data privacy solutions, such as filtering data sent to APIs, anonymization, and tight access control to vector databases, is crucial to prevent leaks of sensitive business data.
- Future articles in the series will cover recommended actions for complying with data privacy best practices, exploring topics like RAG with Differential Privacy.
- Overall, the article highlights the advancements in AI workflows and the potential for AI agents to boost productivity and address organizational needs effectively.
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