LangChain is an open-source framework for constructing language-powered apps. You may use its features to create complex applications that use AI models like ChatGPT and integrate with external sources like Google Drive, Notion, and Wikipedia.

Harrison Chase founded LangChain as an open-source project in October 2022 while working at the machine learning firm Robust Intelligence. With hundreds of contributors on GitHub, trending debates on Twitter, robust activity on the project's Discord server, multiple YouTube lessons, and events in San Francisco and London, the project swiftly gained momentum. 

A week after announcing a $10 million early investment from Benchmark, the new startup raised more than $20 million in capital from venture firm Sequoia Capital at a valuation of at least $200 million. Text-based Large Language Models (LLMs), Chat Models, and Text Embedding models are all supported by LangChain. Chat Models use message-based input and output, whereas LLMs use text-based input and output.

Execution and Interfaces

LangChain has interfaces with systems such as Amazon, Google, and Microsoft Azure cloud storage as of March 2023; 

• API wrappers for news, movie information, and weather; and 
• Bash is used for summarization, syntax and semantics checking, and shell script execution. 
• Numerous web scraping subsystems and templates; 
• support for one-time learning prompt generation; 
• detecting and summarising "todo" items in code; 

Summarization, extraction, and generation of Google Drive documents, spreadsheets, and presentations; 

• Google Search and Microsoft Bing search engines; Language models from OpenAI, Anthropic, and Hugging Face; 
• Search and summary of iFixit repair instructions and wikis; 
• MapReduce is used for question answering, document combining, and question generating. 

N-gram overlap evaluation; For text extraction and manipulation in PDF files, use PyPDF. 

• Code generation, analysis, and debugging in Python and JavaScript; 
• Redis cache database storage; 
• Python RequestsWrapper and other API request methods; 
• SQL and NoSQL databases, including JSON support; Streamlit, with logging; 
• text mapping for k-nearest neighbours searches; 
• time zone conversion and calendar operations; 
• stack symbol tracing and recording in threaded and asynchronous subprocess runs, and the Wolfram Alpha website and SDK. 

It can read from more than 50 document kinds and data sources as of April 2023.

Applications

There are numerous applications for the modules above. Additionally, LangChain offers direction and support in this. Some of the frequent use cases that LangChain supports are listed below.

• Autonomous Agents: Autonomous agents are long-running, multi-step agents that work to achieve a goal. BabyAGI and AutoGPT are two examples.
• Personal Assistants: The primary use for LangChain is personal assistants. Personal assistants must be able to act, recall conversations, and understand your data.
• Chatbots: Language models are excellent at producing text, which makes them perfect for use in chatbot development.
• Understanding the Code: You should read this page if you want to know how to utilize LLMs to query source code from GitHub.
• API interaction: Allowing LLMs to engage with APIs is a very effective way to provide them with more recent information and allow them to take action.
• Extraction:  Extraction is about taking organized data out of the text.
• Summarization: Condensing longer documents into smaller, more concise informational units. A particular form of data-augmented generation.
• Evaluation: Generative models are notoriously difficult to assess using conventional criteria. Utilizing language models themselves to evaluate them is one novel approach. To help with this, LangChain offers several prompts and chains.

Conclusion

The key benefit of LangChain is that it allows you to easily give instructions to an LLM by constructing a series of commands. Each 'link' in this chain represents a command, and each order can either invoke an LLM or some other utility, enabling the development of intelligent agents with control over data processing based on user preferences.

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