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LangChain
LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. 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. History LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London. In April 2023, LangChain had incorporated and the new startup raised over $20 million in funding at a valuation of at least $200 million from venture firm Sequoia Capital, a week after announcing a $10 million seed investment from Benchmark. In the third quarter ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Software Framework
In computer programming, a software framework is a software abstraction that provides generic functionality which developers can extend with custom code to create applications. It establishes a standard foundation for building and deploying software, offering reusable components and design patterns that handle common programming tasks within a larger software platform or environment. Unlike libraries where developers call functions as needed, frameworks implement inversion of control by dictating program structure and calling user code at specific points, while also providing default behaviors, structured extensibility mechanisms, and maintaining a fixed core that accepts extensions without direct modification. Frameworks also differ from regular applications that can be modified (like web browsers through extensions, video games through mods), in that frameworks are intentionally incomplete scaffolding meant to be extended through well-defined extension points and followin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Retrieval-augmented Generation
Retrieval-augmented generation (RAG) is a technique that enables large language model, large language models (LLMs) to retrieve and incorporate new information. With RAG, LLMs do not respond to user queries until they refer to a specified set of documents. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this helps LLM-based chatbot, chatbots access internal company data or generate responses based on authoritative sources. RAG improves large language models (LLMs) by incorporating information retrieval before generating responses. Unlike traditional LLMs that rely on static training data, RAG pulls relevant text from databases, uploaded documents, or web sources. According to ''Ars Technica'', "RAG is a way of improving LLM performance, in essence by blending the LLM process with a web search or other document look-up process ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
