A Github Gist containing a Python script for text classification using the TxTail API
The llmsherpa project provides APIs to accelerate Large Language Model (LLM) projects. It includes features like LayoutPDFReader for PDF text parsing, smart chunking for vector search and Retrieval Augmented Generation, and table analysis. It is open-sourced under Apache 2.0 license.
This article explains Retrieval Augmented Generation (RAG), a method to reduce the risk of hallucinations in Large Language Models (LLMs) by limiting the context in which they generate answers. RAG is demonstrated using txtai, an open-source embeddings database for semantic search, LLM orchestration, and language model workflows.
Reader helps convert any URL into content suitable for LLMs, including automatic image captioning and web search.
The API is split into two functions: 'Read' and 'Search'. Read converts any URL into content suitable for LLMs and returns the LLM-friendly data. Search allows users to input a search query and receives the top five results in a simplified format.
The API is split into two functions: 'Read' and 'Search'. Read converts any URL into content suitable for LLMs and returns the LLM-friendly data. Search allows users to input a search query and receives the top five results in a simplified format.
DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted string manipulation tricks into truly modular generalized operations that learn to adapt to your task.
