https://www.langchain.com/
LangChain helps developers build, observe, and deploy context-aware, reasoning applications with large language models (LLMs). LangSmith provides engineering rigor and visibility for LLM development, testing, and monitoring.

https://python.langchain.com/v0.2/docs/introduction/
Introduction. LangChain is a framework for developing applications powered by large language models (LLMs). LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks and components. Hit the ground running using third-party integrations and Templates.

https://github.com/langchain-ai/langchain
LangChain is a Python library for building and deploying context-aware reasoning applications powered by large language models (LLMs). It offers modular components, off-the-shelf chains, LangChain Expression Language, and integrations with various providers and platforms.

https://python.langchain.com/v0.1/docs/get_started/quickstart/
LangChain enables connecting external data and computation to large language models (LLMs). Learn how to use LangChain, LangSmith and LangServe to build, trace and serve LLM applications with various models and components.

https://www.langchain.com/langchain
Yes, LangChain 0.1 and later are production-ready. We've streamlined the package, which has fewer dependencies for better compatibility with the rest of your code base. We're also committed to no breaking changes on any minor version of LangChain after 0.1, so you can upgrade your patch versions (e.g., 0.2.x) on any minor version without impact.

https://en.wikipedia.org/wiki/LangChain
LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.

https://python.langchain.com/v0.1/docs/modules/chains/
There are two types of off-the-shelf chains that LangChain supports: Chains that are built with LCEL. In this case, LangChain offers a higher-level constructor method. However, all that is being done under the hood is constructing a chain with LCEL. [Legacy] Chains constructed by subclassing from a legacy Chain class.

https://js.langchain.com/v0.1/docs/get_started/
Get started with LangChain. 📄️ Introduction. LangChain is a framework for developing applications powered by language models. It enables applications that: 📄️ Installation. Supported Environments. 📄️ Quickstart. In this quickstart we'll show you how to:

https://www.langchain.com/langsmith
LangSmith is a unified platform for developing, testing, deploying, and monitoring LLM applications, whether you use LangChain or not. It provides full visibility, collaboration, evaluation, and observability features for LLM-powered applications.

https://blog.langchain.dev/
LangChain is a platform for building and deploying large-scale language applications. Read the latest release notes, tutorials, and success stories on LangChain and its components, such as LangSmith, LangGraph, and Dosu.

https://aws.amazon.com/what-is/langchain/
LangChain is a Python framework that simplifies AI development by connecting large language models (LLMs) with external data sources and custom prompts. Learn how LangChain works, its benefits, and its core components, and how AWS can help you build generative AI applications with it.

https://www.ibm.com/topics/langchain
LangChain is a Python and Javascript library that simplifies the development of applications using large language models (LLMs). Learn how LangChain integrates LLMs with external data sources and software workflows, and explore its features and use cases.

https://js.langchain.com/v0.2/docs/introduction/
Introduction. LangChain is a framework for developing applications powered by large language models (LLMs).. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks and components.Hit the ground running using third-party integrations.; Productionization: Use LangSmith to inspect, monitor and evaluate

https://blog.langchain.dev/langchain-v0-1-0/
LangChain is a library that simplifies the process of creating and using large language models (LLMs) for various tasks. Learn about its new features, architecture, integrations, and observability tools in this blog post.

https://nanonets.com/blog/langchain/
LangChain is a toolkit for creating context-aware and reasoning applications with language models. Learn how to install, use, and deploy LangChain modules, templates, and components with examples and tutorials.

https://python.langchain.com/v0.2/docs/tutorials/sql_qa/
Architecture. At a high-level, the steps of these systems are: Convert question to DSL query: Model converts user input to a SQL query. Execute SQL query: Execute the query. Answer the question: Model responds to user input using the query results. Note that querying data in CSVs can follow a similar approach.

https://www.youtube.com/@LangChain
Learn more about building LLM applications with LangChain

https://docs.smith.langchain.com/
Get started with LangSmith. LangSmith is a platform for building production-grade LLM applications. It allows you to closely monitor and evaluate your application, so you can ship quickly and with confidence. Use of LangChain is not necessary - LangSmith works on its own! 1. Install LangSmith. Python. TypeScript. pip install -U langsmith.

https://arxiv.org/abs/2406.18122
Building on this, we further design a novel method of indirect jailbreak attack, termed Poisoned-LangChain (PLC), which leverages a poisoned external knowledge base to interact with large language models, thereby causing the large models to generate malicious non-compliant dialogues.We tested this method on six different large language models

https://stackoverflow.com/questions/78680123/why-is-my-json-object-not-inserting-correctly-in-chromadb-using-langchain-and-py
import json import uuid from langchain_chroma import Chroma from langchain.docstore.document import Document from langchain_text_splitters import RecursiveCharacterTextSplitter, RecursiveJsonSplitter from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings from langchain_openai import OpenAIEmbeddings import

https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/create-your-first-visual-agent-using-aoai-and-ai-search-search/ba-p/4175291
from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun from langchain_core.tools import BaseTool from util import prompt_search class ImageSearchResults(BaseTool): """Tool that queries the Fashion Image Search API and gets back json.""" name: str = "image_search_results_json" description: str = ( "A wrapper

https://allainews.com/item/learn-langchain-in-7-easy-steps-full-interactive-beginner-tutorial-2024-06-25/
In this tutorial, I will teach you LangChain as efficiently as possible by breaking down the framework into seven key components you need to understand to start developing more advanced LLM applications. Link to written tutorial with code and interactive

https://python.langchain.com/v0.2/docs/tutorials/
LangSmith. LangSmith allows you to closely trace, monitor and evaluate your LLM application. It seamlessly integrates with LangChain, and you can use it to inspect and debug individual steps of your chains as you build. LangSmith documentation is hosted on a separate site. You can peruse LangSmith tutorials here.

https://allainews.com/item/part-2-mastering-prompts-and-language-models-with-langchain-2024-06-26/
In the previous part of our LangChain tutorial series, we introduced the core components of the library. Now, let's dive deeper into two essential aspects of building LangChain applications: prompts and language models (LLMs). You'll learn how to create effective prompts, integrate various LLMs, and customize them for your specific use cases.

https://allainews.com/item/week-of-624-langchain-release-notes-2024-06-28/
LangChain blog.langchain.dev We've launched LangGraph Cloud in closed beta, self-improving evaluators in LangSmith, and more. We also have a new customer story, use cases for tool calling with Fireworks and llama.cpp, and a 3-part agent evaluation video series.

https://www.linkedin.com/in/jessicaou
Experience: LangChain · Education: University of California, Berkeley, Haas School of Business · Location: San Francisco Bay Area · 500+ connections on LinkedIn. View Jess Ou's profile on