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Tag: LLMs

  • Exploring LangSmith Observability in Detail

    LangSmith Observability in Detail LangSmith provides comprehensive observability for your LLM applications, offering detailed insights into the execution flow, performance, and outputs of your chains, agents, and tools. It helps you understand what’s happening inside your LLM application, making it easier to debug, evaluate, and improve its reliability and quality. 1. Tracing: End-to-End Visibility Detailed Read more

  • Exploring LangChain, LangGraph, and LangSmith

    Exploring LangChain, LangGraph, and LangSmith The LangChain ecosystem provides a comprehensive suite of tools for building, deploying, and managing applications powered by Large Language Models (LLMs). It consists of three key components: LangChain, LangGraph, and LangSmith. LangChain: The Building Blocks LangChain is an open-source framework designed to simplify the development of LLM-powered applications. It provides Read more

  • Understanding Agentic Retrieval-Augmented Generation (RAG)

    Understanding Agentic RAG Agentic Retrieval-Augmented Generation (RAG) goes beyond standard RAG by incorporating more sophisticated agent-like behaviors to enhance the generation process. Think of it as a proactive and strategic assistant for information retrieval and content generation. Key Differences from Standard RAG Decision-Making in Retrieval: Agentic RAG decides *when* and *how* to retrieve information, unlike Read more

  • Test Cases for Training LLMs

    Test Cases for Training LLMs When training Large Language Models (LLMs), particularly for tasks like **extracting information from tax documents**, writing effective test cases is crucial for ensuring your model learns as intended and can accurately perform the desired function. These test cases differ significantly from traditional software testing due to the probabilistic and generative Read more

  • Implementing Locally running Mistral Chatbot with RAG

    Locally running Mistral Chatbot with RAG Let’s implement a local running chatbot with Mistral LLM using RAG to retrieve documents from a locally running Vector DB that also contains FAQs. Here’s a breakdown of the steps and the Python code to achieve this: Phase 1: Setting Up the Local Environment Install Dependencies: pip install transformers Read more

  • Top 10 LLMs on Hugging Face for Chatbot & RAG Use (Early May 2025)

    Top 10 LLMs on Hugging Face for Chatbot & RAG This list is based on a combination of factors including general popularity, instruction-following capabilities, context window size, and community interest relevant to chatbot and Retrieval-Augmented Generation (RAG) applications. 1. mistralai/Mixtral-8x7B-Instruct-v0.1 Use Cases: Excellent for instruction following, complex reasoning in chatbots, and can handle long contexts Read more

  • Top 10 LLMs on Hugging Face & Use Cases: Part 2

    Another Top 10 LLMs on Hugging Face & Use Cases Another Top 10 Popular LLMs on Hugging Face & Their Use Cases Here’s another selection of popular and interesting Large Language Models available on Hugging Face, showcasing the diversity of the open-source LLM landscape as of early May 2025. 1. google/gemma-7b-it Use Cases: Instruction tuning, Read more

  • Top 10 LLMs on Hugging Face & Use Cases

    Top 10 LLMs on Hugging Face & Use Cases Top 10 Popular LLMs on Hugging Face & Their Use Cases Please note that “top” can be subjective and based on various factors like downloads, recent interest, and performance on specific benchmarks. This list reflects a mix of widely used and influential models as of early Read more

  • Pretrained Models for Document Extraction

    Pretrained Models for Document Extraction Cloud-Based Pretrained Models Google Cloud Document AI: Offers pretrained models for various document types (invoices, receipts, IDs, etc.) for key-value pair, table extraction, and classification. AWS Textract: Provides pretrained models for OCR, key-value pair extraction, and table extraction from documents and images. Azure Form Recognizer (now Document Intelligence): Offers pretrained Read more

  • Tensor Reshaping with PyTorch and CUDA

    Tensor Reshaping with PyTorch and CUDA Tensor Reshaping involves changing the shape of a tensor without altering its underlying data. This operation is frequently used to prepare tensors for different operations in neural networks and other numerical computations. While the reshaping operation itself is typically not computationally intensive, performing it on a GPU using CUDA Read more