#LangChain

Discover NVIDIA's open-source solutions for refining generative AI integration, covering RAG pipelines, agentic workflows, and model fine-tuning. Explore efficient knowledge graph RAG, agentic workflows with Llama 3.1, and localized RAG deployment. Utilize detailed guides and community resources to improve LLM development.

GPTCache lowers LLM API expenses by up to 10 times and increases response speed by 100 times through semantic caching. It integrates seamlessly with LangChain and supports multiple languages, ensuring improved functionality and adaptability. This library addresses challenges posed by high API call costs and slow response times, enabling efficient application scaling. By using GPTCache, developers can benefit from reduced costs, faster performance, and a flexible development environment, providing an option for optimizing LLM-based applications.

LangCorn facilitates the deployment of LangChain models via FastAPI, ensuring high performance and scalability for language applications. It allows for easy setup of custom pipelines and includes built-in authentication and asynchronous processing for enhanced response times. With detailed RESTful API documentation and adaptable FastAPI server settings, it serves as a robust solution for language model deployment.

Explore an in-depth and practical guide to LangChain, showcasing key features and real-world applications. This course is designed for both beginners and professionals seeking to understand and utilize LangChain effectively. Stay informed with the latest methodologies and support Jia Ge's work by purchasing the accompanying book, GPT Illustrated, at a discounted rate.

Explore a carefully curated array of tools, projects, and resources built on the LangChain framework. This evolving ecosystem delivers solutions across domains like low-code platforms, services, agents, and templates. Learn through open-source projects and stay informed with regular newsletters. Engage with the community by contributing insights or initiating discussions as per guidelines. Perfect for developers aiming to optimize their LLM projects with LangChain.

Langchain-serve allows the deployment of LangChain applications on Jina AI Cloud for scalable and serverless solutions while maintaining ease of local development. It supports the creation of REST/Websocket APIs and integrates components like AutoGPT, Babyagi, pandas-ai on either cloud or personal infrastructure, securing data privacy. It offers enhanced deployment simplicity using one-command operations, FastAPI integrations, and provides Swagger UI for API documentation, suitable for straightforward AI app deployment with minimal infrastructure hassle.

Enhance document interaction with a tool that allows chatting with PDF and DOCX files using ChatGPT. Developed during a Bellingcat hackathon, the project involves integration with Weaviate and Cloudflare R2. The tool is installed with Node v18 using a T3 Stack architecture, including Next.js, Tailwind CSS, and tRPC, suitable for efficient document vectorization and indexing.

Renumics RAG offers a platform for visualizing retrieval-augmented generation using LangChain and Streamlit, suitable for both GPU and CPU setups. It supports configurations with OpenAI, Azure, and Hugging Face models, allowing the indexing and querying of customized data. The integrated web app facilitates interactive questioning, while Renumics Spotlight enhances visual exploration for detailed analysis of documents and queries, making it a robust tool for scalable data management in AI projects.

AIHub is a versatile client integrating multiple large model APIs, including support for OpenAI and Google Gemini. It allows for the creation of personalized AI assistants with custom plugins, supporting text and image dialogues, AI drawing, and knowledge base building via LangChain. Users can also generate reports with the AI calendar and utilize various AI mini-programs. The platform is multilingual and multi-thematic, designed for enhanced AI functionality across different systems. Access the latest release for a streamlined experience.

Discover an extensive collection of AI agent resources, featuring articles, videos, and innovative projects utilizing LLMs. This list includes insightful papers and advanced frameworks for building autonomous agents. Keep informed on the evolving landscape and find key initiatives like Auto-GPT and MetaGPT, along with tools from LangChain and AutoChain. Explore AI-driven solutions applicable to various platforms and sectors.

This repository integrates OpenAI APIs with open source models, providing capabilities in chat, audio, and image processing. It supports libraries like OpenAI and LangChain for applications from transcription to image creation. The repository, though deprecated, allows for custom frontend development and model management with configurable settings. Discover extensive model support and API features for AI project customization.

Explore an insightful Korean tutorial offering free e-books, YouTube guides, and blog resources aimed at enhancing LangChain usage. Learn practical applications like local LLM hosting, task automation, and AI model implementation. Enjoy frequent updates and diverse tutorials specifically designed for Korean users, providing comprehensive insights into LangChain's functionalities. Suitable for developers and enthusiasts looking to expand their expertise in LangChain's capabilities.

This innovative project seamlessly integrates GPT, Pinecone, and LangChain to deliver a versatile chatbot platform. It allows users to create diverse chat topics, manage numerous files with embedded content, and operate multiple chat windows efficiently within a browser. The system supports various file formats, such as .pdf, .docx, and .txt, transforming them into embeddings stored within Pinecone namespaces. Automatic storage and retrieval of chat histories are ensured via local storage. Designed for both development and production environments, it offers extensive customization options to meet unique needs. Originally derived from a GPT-4 and LangChain repository, this iteration introduces substantial updates and enhancements, focusing on streamlining chatbot customization and management.

Discover a terminal-focused AI environment for building modular AI agents and creating document indexes, improving question-answering capabilities. This platform supports Python package integration, secure Docker execution, and offers tools for custom workflows. With features like a dedicated prompt console, dynamic terminal interface, and remote access options, it enhances AI solutions within the LangChain ecosystem. Ideal for developers aiming to automate tasks with AI in secure, adaptable environments.

LangServe provides seamless deployment of runnables as REST APIs, integrated with FastAPI and Pydantic for precise data validation. It offers efficient endpoints for invoking, batching, and streaming with high concurrency. The platform includes auto-inferred schemas, detailed API documentation, and tracing to LangSmith. With JavaScript client support and a comprehensive SDK, LangServe facilitates robust API management and interactive testing, featuring various deployment examples to optimize development workflows.

Ragas offers a toolkit designed for evaluating and optimizing Large Language Model applications through objective metrics and automated test data generation. It integrates smoothly with LLM frameworks like LangChain to facilitate efficient evaluation workflows and feedback loops, ensuring continuous improvement in application performance. Ragas helps transition from subjective to data-driven assessments for more reliable LLM evaluations.

docGPT facilitates seamless communication with various document formats including PDF, DOCX, CSV, and TXT, enhancing workflow without expensive API keys or subscriptions using 'gpt4free'. Deploy it easily on platforms like Streamlit for flexible access. Leveraging LangChain's capabilities, docGPT empowers users to address complex queries, including those beyond 2020. Suitable for diverse use cases, it provides guidance on deploying personalized models such as OpenAI's gpt-3.5-turbo.

This detailed guide explores AI application development with open source tools, focusing on large language models, LangChain basics, and practical implementations. It covers additional topics such as LlamaIndex, HuggingGPT, and LLMOps, alongside the latest updates in LLM application evaluation, RAG series, and major domestic API interpretations. An ideal resource for those interested in enhancing their AI knowledge and engaging with a collaborative learning community.

Interact with ArXiv's scientific papers through ArXiv ChatGuru, powered by LangChain and Redis. This educational tool aids understanding of Retrieval Augmented Generation (RAG) systems by explaining context windows, vector distances, and document retrieval. Use it to segment and index papers using Redis, enhancing accessibility. Designed for learning rather than production, it allows thorough exploration of scientific content.

The course provides a comprehensive guide for developers interested in utilizing the LangChain framework to enhance language model applications. Topics covered include interacting with LLMs, crafting prompts, utilizing memory capabilities, and constructing operational chains to improve reasoning and data processing. Hosted by LangChain creator Harrison Chase and Andrew Ng, the course facilitates rapid development of dynamic applications, optimizing proprietary data use within just an hour.

This project integrates various Large Language Models (LLMs) within Google Colab, providing an environment for AI capability enhancement and experimentation. It includes repositories such as AutoGPT, MiniGPT-4, and LangChain, allowing efficient deployment of advanced models. Users can explore zero-shot anomaly detection, Chinese language models, and multimodal capabilities. The project offers guidance on utilizing these models in various applications, supported by diverse tools and datasets, facilitating easy integration and fostering innovation in AI development.

Develop a robust question answering system employing LangChain and large language models such as OpenAI's GPT3. The project includes a Python backend powered by FastAPI and a React frontend to transform PDFs into searchable text fragments, utilizing sentence embeddings for swift and economical processing. With capabilities for integrating vector databases, it adapts to multiple document formats. Planned improvements feature streaming responses, caching, enhanced UI, and support for diverse document types, rendering it a flexible framework for advanced question-answering applications.

Explore how LangChain enables large language model applications through a detailed series. Covering topics from chatbots and text summarization to code understanding, each section provides clear insights with Python notebooks. Discover LangChain models, embeddings, prompts, indexes, memory, chains, agents, and callbacks, by either forking the repository or using Google Colab. Ideal for developers seeking to leverage open-source tools in machine learning projects.

PAutoBot provides a private, secure task assistant using offline language models running on CPUs, ensuring user data remains within their environment. Utilizing a straightforward coding structure with Next.js and Python, PAutoBot facilitates model interaction and document queries without the need for internet access. Incorporating advanced technologies such as LangChain and GPT4All, it accommodates multiple document formats like PDF, Word, and HTML, offering flexibility for various applications. The interface supports easy installation and operation, applicable for both local and network deployment.

Discover how to build a website chatbot with LangChain and Supabase using Next.js and TypeScript. This guide offers detailed steps for setting up a database with Supabase, scraping data, and converting it into vectors with OpenAI's embeddings. Learn customization techniques for chatbot integration, perfect for developers aiming to improve website user interaction with a robust AI chatbot.

This guide provides an in-depth introduction to LangChain, an open-source library designed for developing applications with language models. It outlines key functionalities including integration with external data sources and model interaction capabilities. Essential concepts such as document loaders, text splitters, vector stores, and chains are explained, supplemented with practical examples like conducting Q&A sessions, performing Google searches, and summarizing extended texts using OpenAI models. The guide also covers building a local knowledge-based Q&A bot, facilitating enhanced applications utilizing the OpenAI API. This resource is suitable for developers looking to fully leverage language models in their applications.

This AI standard library supports seamless integration with top AI SDKs, such as LangChain, LlamaIndex, and OpenAI. It allows developers to efficiently utilize AI tools with TypeScript for diverse tasks, eliminating the need for extra glue code. The library includes specific functions for AI clients like WeatherClient, Perigon, and Serper, facilitating smooth cross-platform integration and optimizing AI application development.

Kor provides a system for extracting structured data from text using Language Model Models (LLMs). Users can define extraction schemas and provide examples to enhance results. Kor works with various LLMs, specializing in prompt-based and parsing methods, and is compatible with the LangChain framework for seamless integration. It supports pydantic versions 1 and 2, ensuring robust schema validation. Kor is suitable for integrating with AI assistants and enabling natural language interaction with APIs, although it has limitations with large prompts and text inputs.

Learn how to deploy open-source LLMs such as Llama 2 on CPUs for effective document Q&A in a privacy compliant manner. Utilize tools including C Transformers, GGML, and LangChain to efficiently manage resources, minimizing reliance on expensive GPU usage. The project provides detailed guidance on local CPU inference from setup to query execution, offering a solution that respects data privacy and avoids third-party dependencies.

Explore LangChain, a versatile open-source framework for AI app development using large language models like ChatGPT. This course introduces LangChain in four modules blending theory and practice. Participants should have basic Python and JavaScript skills and the course will guide in setting up tools like the OpenAI API Key, making it a perfect opportunity to advance machine learning capabilities within an engaged community. Stay informed with regular updates by subscribing to associated YouTube channels and joining the community Discord server.

Learn how to utilize GPT-4 and LangChain to build advanced chatbots capable of handling multiple large PDF files efficiently. This project uses Pinecone, Typescript, and Next.js to guide the creation of scalable AI applications. It includes comprehensive instructions on repository cloning, package installation, setup, and transforming PDFs into embeddings for effective data retrieval. Additionally, it offers troubleshooting tips to ensure proper integration of key components like Pinecone vectorstore and the OpenAI API. Suitable for developers aiming to leverage AI for large-scale document management, this repository provides a detailed approach to modern chatbot development.

AgentKit, built on LangChain, offers a complete solution for developing scalable, chat-based Agent apps. It simplifies full stack development through modularity in FastAPI/Nextjs and features like data streaming and a reliable routing system. Designed for efficiency, it supports rapid prototyping and stable production deployments, emphasizing configurability and user feedback for tailored applications.

This repository offers a range of examples to quickly build and deploy large language model applications using Python libraries LangChain and Ray. It includes cases like open-source search engines, scalable embedding generation, and retrieval-based QA systems, all designed to integrate efficiently into your projects. The site also provides community links, documentation, and resources for comprehensive support in LLM development.

Explore a seamless method to create automated workflows using AI. With simple drag-and-drop tools, complex tasks can be managed without programming. Integrate large language models for intelligent operations, and learn about installation, configuration, and API optimization through online tutorials. Experience customizable automation solutions tailored to various tasks.

Learn how to effectively integrate LangChain with Next.js for creating efficient chat applications that are easy to modify and deploy. This guide assists developers in setting up applications, utilizing customizable API routes, and maximizing the potential of LangChain's robust backend. It is crafted for developers interested in developing dynamic, interactive chat solutions using the Next.js framework, with access to extensive learning resources and community support.

This open-source project offers an OpenAI-compatible endpoint for text embeddings, supporting models such as BAAI/bge-large-en and intfloat/e5-large-v2. It allows flexible input handling and supports deployment on local and cloud platforms like AWS and Modal, with GPU optimization for enhanced performance. Ideal for developers, it provides robust text embeddings suitable for various applications.

Discover Panel's versatile chat components, integrating technologies like LangChain and OpenAI. This project offers hands-on examples for implementing AI-enhanced interactions in applications. Gain insights into Panel's capabilities through a straightforward setup and execution, available on GitHub.

Learn about using Elasticsearch as a vector database for advanced search capabilities with AI/ML. Access resources such as Python notebooks and sample apps to explore use cases like retrieval augmented generation and question answering. Stay informed on Elastic's latest features such as Elastic Learned Sparse Encoder and reciprocal rank fusion, and see how to integrate Elasticsearch with OpenAI and Hugging Face. Utilize Elasticsearch to support LLM-based applications by leveraging a strong search infrastructure. Visit Elasticsearch Labs on GitHub for up-to-date articles and guides.

Explore complete tools, prompts, frameworks, and a rich knowledge hub essential for building Large Language Model (LLM) agents. The project converts user inputs into interactive Streamlit apps, leveraging GPT-3.5-turbo for automatic LangChain code. Supporting various LLM models, DemoGPT continuously adopts new tech developments. Its workflow includes planning, task creation, code snippet generation, and final app assembly. Future updates will focus on integrating with external APIs and streamlining workflows for effective LLM development.

Lumos is a Chrome extension powered by local LLMs, designed to summarize discussions, articles, and answer questions from documents using local server support. It features content parsing, multimodal capabilities, and customizable settings to streamline web interactions. Set up a local Ollama server to leverage Lumos's advanced tools and shortcuts for enhanced browsing.

The JARVIS-ChatGPT is a voice-based AI assistant that integrates OpenAI Whisper, IBM Watson, and OpenAI ChatGPT technologies for real-time conversational support. Primarily suitable for professionals and tech enthusiasts interested in research tasks, it features a 'Research Mode' for accessing databases, downloading papers, and managing information. The system operates through authorized microphones and uses synthetic voices including the J.A.R.V.I.S voice for an engaging experience. Requires an OpenAI account and API keys; installation options support various functionalities.

This project streamlines AI operations by offering a versatile tool ecosystem tailored for LLM agents. Leveraging the capabilities of Upsonic, it enables the creation of custom environments and automates document generation. Features include executing code, utilizing search engines, and managing calendars, all aiding in the efficient task performance of AI. Compatible with frameworks such as crewAI, LangChain, and AutoGen, it fosters integration across multiple technological platforms, providing an open-source tool resource beneficial for developers interested in sophisticated AI applications.

Discover the capabilities of AI via Python with interactive projects and tutorials centered on ChatGPT/GPT-4 and LangChain. Develop applicable solutions, such as training models like Llama 2 and deploying AI systems through LangChain. The guide provides detailed walkthroughs for importing data, leveraging AI models, building smart chatbots, and handling complex operations with AI agents. Improve expertise with instructional videos and articles explaining practical implementations and benefits of these technologies, and begin crafting AI-driven solutions suited for various tasks.

Offers a unified API for open-source large language models based on OpenAI's standards, featuring real-time streaming responses, text embedding, and support for tools such as langchain and vLLM. Allows easy substitution of ChatGPT with open models through simple environment changes, supporting various applications. Compatible with custom-trained LoRA models and optimized for rapid processing with vLLM's acceleration. Integrates with popular models like MiniCPM-Llama3 and GLM-4V for seamless project compatibility.

This tool uses LangChain and the OpenAI API to deliver detailed binary file analysis, helping CTF enthusiasts identify vulnerabilities efficiently. By simplifying binary comprehension, users can focus on problem-solving. Key features include user-friendly, precise, and automated analysis through AI and machine learning. Installation options via PyPI and local setups offer deployment flexibility, complemented by an intuitive command-line interface for smooth interaction. An essential tool for advancing in binary analysis and vulnerability identification.

A comprehensive course on Large Language Models (LLMs) leveraging OpenAI and Hugging Face libraries for practical applications such as chatbot design, code generation, and model optimization techniques like PEFT and LoRA. The course is divided into segments covering foundational techniques, project implementation, and enterprise integration without overstating capabilities. Features insights from Medium articles and interactive Colab/Kaggle notebooks aimed at both tech enthusiasts and professionals.

Explore a diverse range of applications utilizing LangChain for large language model capabilities. This collection includes examples like chatbots, document summarization, and generative Q&A, presented through interactive Streamlit apps. Understand AI technologies better through projects demonstrating LLM observability and search queries with APIs such as OpenAI, Chroma, and Pinecone. Perfect for developers and AI researchers looking for practical insights into cutting-edge AI tools.

Discover a customizable chatbot using OpenAI GPT-3.5, designed to respond to queries from a specific knowledge base. Follow steps to clone the repository, install dependencies, and incorporate documents such as texts, CSVs, or PDFs. Utilizing Llama Index and LangChain ensures efficient data processing. Open-source under the MIT license, this project invites community inputs to enhance its features. Although the project development is complete, users might explore and adapt the code, integrating with alternatives like PrivateGPT. Explore this GPT-3.5 chatbot to create responses based closely on your data.

Explore how ChatGPT and GPT models transform writing, research, and information processing in 'Generative AI with LangChain'. This guide offers practical use cases and detailed insights into leveraging the LangChain framework for building strong LLM applications in areas like customer support and software development. It covers key topics like fine-tuning, prompt engineering, and deployment strategies, along with understanding transformer models, data analysis automation with Python, and chatbot creation. The book underscores maintaining privacy with open-source LLMs. Ideal for developers interested in utilizing generative AI, it helps build innovative solutions, with the repository continually updated in sync with LangChain's progress.

LangChain offers a versatile framework for developing applications with large language models (LLMs), simplifying the application lifecycle with open-source components and seamless integrations. It facilitates the creation of reasoning applications using LangGraph for stateful agents and provides LangChain Expression Language (LCEL) for clear workflow articulation. LangSmith aids in app debugging, while LangGraph Cloud supports deployment, ensuring efficient transition from prototype to production. Comprehensive tutorials and documentation further support developers in leveraging LangChain's full potential.