#Langchain

The application efficiently transforms various formats of unstructured data into a structured Neo4j knowledge graph. It utilizes Large Language Models such as OpenAI and Gemini to extract nodes, relationships, and properties via the Langchain framework. Files can be uploaded from local devices, Google Cloud Storage (GCS), or Amazon S3, with the flexibility to select a preferred LLM model. Key features include supporting custom schemas, interactive graph visualization in Bloom, and conducting data interactions using conversational queries within Neo4j. Ensure a Neo4j Database V5.15+ with APOC support. Available for deployment via Docker locally or Google Cloud Platform.

This resource provides structured insights on Large Language Models (LLMs) and Generative AI (GenAI) for development, security, and operations fields. Covering topics like OpenAI integration, debugging, prompt templates, and security strategies, it uses the Langchain framework to offer practical examples and exercises. Developed during a GenAI hackathon and enhanced through community collaboration, this repository supports continual learning and practical application across technical domains.

Facilitates effective semantic code search and interactive chatting with the codebase through command-line tools. Seamlessly syncs the vector database with code updates for rapid local operations without data leaks. Utilizes cutting-edge technologies such as Langchain, Treesitter, and Faiss to enable comprehensive embeddings and LLMs for engaging code interactions. Offers complete local operations using resources like Sentence-transformers and llama.cpp, with optional support for services like OpenAI and Azure OpenAI. Suitable for developers aiming to optimize their workflow, enhance system security, and employ machine learning to comprehend and efficiently manage code.

Utilize the MPT-30B model to securely chat with documents offline, optimized for systems with 32GB RAM. Through Langchain, users can seamlessly interact with various document formats by setting up Python 3.10 and installing necessary libraries. Following a one-time model setup, enjoy entirely offline operations ensuring data privacy, making it perfect for secure environments needing effective document interaction.

Cognita simplifies the organization of Rapid Application Generation (RAG) systems by using Langchain and LlamaIndex to build a modular and scalable code structure suitable for production. This framework supports structured, API-driven development and offers both local testing and production-ready environments with a user-friendly, no-code interface. Key features like incremental indexing, alongside new tools such as AudioParser and VideoParser, highlight Cognita’s dedication to continuous improvement. Recent updates include a Metadata Store powered by Prisma and Postgres, and enhanced model gateway management, emphasizing seamless scalability and smooth transition from prototype to production.

Discover the advanced use of Retrieval-Augmented Generation (RAG) with the Langchain framework for Python, designed to enhance the understanding capabilities of Large Language Models (LLMs). This resource provides insights into essential components such as the Multi Query Retriever and discusses advanced techniques including Agentic RAG variants, which improve contextual knowledge and generate more accurate responses. Explore methods for query transformation, routing to data sources, and indexing within VectorDBs to refine retrieval processes. Understand various RAG systems, ranging from basic models to complex self-reflective and corrective agentic processes, aimed at achieving adaptability and precision in language generation.

This tutorial is an extensive resource for Typescript and Javascript developers looking to leverage Langchain to build custom language model applications. It covers installation, usage examples, and integrates with tools such as OpenAI, Pinecone, and SerpApi. Developers can effectively create conversational AI and personal assistant bots that process natural language and utilize external data and APIs by following the structured guidance.

Explore the Langchain project, examining its features, tool integrations, and common issues. Discover community perspectives on Langchain's limitations and debugging challenges, alongside comparisons to general large model problems. The article offers potential solutions using LLMs, planning, and retrieval. It also provides insights into optimizing prompts and embeddings in specific domains, aiding in the effective use of Langchain for complex tasks.

The Chrome-GPT project uses Langchain and Selenium to develop a dynamic web interaction AutoGPT agent capable of scrolling, clicking, and filling forms. It supports Google search and various agent types, including Zero-shot and BabyAGI. Noted limitations include slower response times and occasional web crawling issues. It operates with Python and requires OpenAI API keys. Discover the capabilities of automated Chrome browsing with this innovative tool.

PyGPT is a versatile, open-source desktop AI assistant connecting with OpenAI's GPT models. It facilitates tasks such as chat, image generation, and system automation. Enhanced with Langchain and Llama-index, it supports multiple LLMs for web search and file management. With plugin and speech functionalities, it's accessible for diverse users, supporting Linux, Windows, and Mac platforms with just an OpenAI API key.

Chat-With-Excel offers a new way to engage with tabular data by removing the necessity for formula memorization or advanced programming skills. This tool supports direct training of machine learning models using natural language, thus streamlining data analysis. The code is immediately available, with Replit and Streamlit versions in development. Setup is simplified through a step-by-step guide in Google Colab, requiring only OpenAI key configuration. Updates and tutorials are accessible via Twitter and YouTube, with a demo link for firsthand experience of this novel data tool.

Boxcars is a Ruby gem facilitating AI composability for system development. It incorporates concepts like LLMs, SQL, and Rails Active Record, inspired by Langchain but optimized for Ruby users. With support for engines such as OpenAI and Anthropic's Claude API, it ensures smooth integration for complex tasks. Boxcars is modular, extendable, and suitable for performing searches, mathematical computations, and more. This makes it a strong choice for developers aiming to leverage AI within Ruby projects, complemented by comprehensive documentation and continuous updates.

Uncover a wide range of open-source GitHub repositories centered on ChatGPT. This curated list provides insights into numerous projects such as prompts, CLIs, and NLP tools. With regular updates, it serves as a valuable resource for developers and researchers interested in improving ChatGPT solutions. A search tool is also available on Hugging Face Spaces for easier navigation. Contributions are encouraged, adhering to clear guidelines to maintain quality and uniformity. Stay informed with the latest developments within the ChatGPT ecosystem.

Examine a broad array of open-source LLM agents encompassing frameworks, applications, and platforms aimed at improving AI interactions. Utilize tools like Langchain and Haystack for efficient natural language application development. Access resources and community help via Llama Index and explore applications like VisualGPT for image-enhanced conversations. Keep updated with new research papers and expansive talks, enhancing AI technology advancement. Ideal for developers and enthusiasts wishing to harness LLM potentials in their work.

The Python application facilitates interaction with PDFs via natural language inquiries, utilizing an LLM for accurate content-based responses. It employs OpenAI embeddings and integrates Langchain for smooth functionality. Featuring a GUI built on Streamlit, the tool is intuitive for extracting and querying PDF information. It serves as an instructional resource, supported by a detailed Youtube tutorial, offering a step-by-step guide for learners and developers to implement this AI-powered document solution.

The financial agent, built on Langchain and FastAPI, provides robust financial data such as current and historical prices and latest news through the Polygon API. It supports calculations like owner earnings, return on equity, and discounted cash flow valuation. Deployment options include a secure Docker container or a Python-based local setup with Poetry. Designed for providing informational and entertaining financial insights, access requires OpenAI and Polygon API keys, offering valuable support for investment analysis.