Pondhouse Data Blog
Artikel auf EnglischChat with your Confluence: A Step-by-Step Guide using Airbyte, Langchain and PGVector
Build a Confluence chatbot in Python: load pages with PyAirbyte and LangChain's AirbyteLoader, store embeddings in PGVector, answer with GPT-4 Turbo.
Artikel auf EnglischAdvanced RAG: Improving Retrieval-Augmented Generation with Hypothetical Document Embeddings (HyDE)
HyDE has an LLM write a fake answer document and searches with its embedding instead of the query's. How it works, plus a LlamaIndex example.
Artikel auf EnglischGenAI: Technological Masterpiece or Ecological Disaster?
GenAI's energy use is growing fast. The IEA and de Vries numbers, plus the techniques that cut it: TPUs, MoE, distillation, MoD, layer pruning and BitNet.
Artikel auf EnglischHow to boost your database performance with OpenAIs new v3 embeddings
OpenAI's v3 embeddings can be shortened thanks to Matryoshka training. Adaptive retrieval uses that for faster vector search: 580 QPS at 99% accuracy.
Artikel auf EnglischIntroduction to Retrieval Augmented Generators (RAG): Enhancing Virtual Assistants with Domain-Specific Knowledge
How RAG works step by step: ingestion, chunking, embeddings, vector search and the LLM answer, and why it is easier to update than a fine-tuned model.
Artikel auf EnglischAdvanced RAG: Increase RAG Quality with ColBERT Reranker and llamaindex
Add a ColBERT reranker to a LlamaIndex RAG pipeline: why semantic search ranks the wrong chunks first, and the Python code to rerank them before the LLM call.
Artikel auf EnglischAdvanced RAG: Recursive Retrieval with llamaindex
Recursive retrieval in LlamaIndex links PDF tables to pandas query engines, so RAG can answer questions from table data. Step-by-step Python example.
Artikel auf EnglischIntegrating enterprise knowledge with LLMs
Fine-tuning, training your own model or RAG: how each way of adding company knowledge to an LLM works, its pros and cons, and why RAG usually wins.
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