Many companies are looking at Retrieval-Augmented Generation to minimize hallucinations. How effectively does Generative AI (ChatGPT, Gemini) integrate with vector databases like Pinecone or Milvus? I am trying to build a private knowledge base and want to know if these models can handle complex, unstructured PDF data without losing context.
3 answers
Integration is getting much smoother with frameworks like LangChain or LlamaIndex. Using Generative AI (ChatGPT, Gemini) as the reasoning engine on top of a vector store works wonders for internal HR or technical support bots. The key is in the "chunking" strategy for your PDFs. If the chunks are too small, the LLM loses context; if too large, you get irrelevant noise. We’ve had success using Gemini 1.5 Pro’s large context window for long-form documents, which significantly reduces the need for aggressive preprocessing.
Are you finding that the latency of these API calls is a deal-breaker for real-time customer-facing applications?
We switched to a hybrid search approach combining keyword and semantic search. It made the AI's answers much more grounded in our specific terminology.
Hybrid search is the gold standard right now. It ensures that specific technical terms aren't "smoothed over" by the semantic embedding process.
Latency is definitely a concern, Jason. For real-time needs, we usually implement a streaming response or use smaller, fine-tuned local models for the initial query intent before hitting the heavy LLMs.