Difference between revisions of "LangChain"
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* [[Case Studies]] | * [[Case Studies]] | ||
** [[Writing/Publishing]] | ** [[Writing/Publishing]] | ||
| − | * [[ | + | * [[Assistants]] ... [[Agents]] ... [[Negotiation]] ... [[Hugging_Face#HuggingGPT|HuggingGPT]] ... [[LangChain]] |
** [[ChatGPT#Integration| ChatGPT Integration]] | ** [[ChatGPT#Integration| ChatGPT Integration]] | ||
* [[Python]] ... [[Generative AI with Python]] ... [[Javascript]] ... [[Generative AI with Javascript]] ... [[Game Development with Generative AI]] | * [[Python]] ... [[Generative AI with Python]] ... [[Javascript]] ... [[Generative AI with Javascript]] ... [[Game Development with Generative AI]] | ||
Revision as of 16:32, 20 April 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- LangChain | GitHub
- Harrison Chase | LinkedIn ... GitHub ... Twitter
- Case Studies
- Assistants ... Agents ... Negotiation ... HuggingGPT ... LangChain
- Python ... Generative AI with Python ... Javascript ... Generative AI with Javascript ... Game Development with Generative AI
- Development ...AI Pair Programming Tools ... Analytics ... Visualization ... Diagrams for Business Analysis
- Generative AI ... Conversational AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's Bing ... You ...Google's Bard ... Baidu's Ernie
- Natural Language Processing (NLP) ...Generation ...LLM ...Tools & Services
- Prompt Engineering (PE)
- LangChain Chat
- Building a GPT-3 Enabled Document Assistant with LangChain | Peter Foy - MLQ.ai
- Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications | Yohei Nakajima
- AgentGPT template | Vercel ... Assemble, configure, and deploy autonomous AI Agents in your browser, using LangChain, OpenAI, AutoGPT and T3 Stack
LangChain is a Python framework built around Large Language Model (LLM) that can be used for chatbots, Generative Question-Answering (GQA), summarization, and more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs. LLMs are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge.
Contents
Getting Started
Data Independent - tutorial videos
reference videos throughout page
My Documents
- LangChain QA over docs | Colab
- Vectorstores | LangChain
- A step-by-step beginners program on how to build a ChatGPT chatbot for your data
- GPT-4 & LangChain - Create a ChatGPT Chatbot for Your PDF Files
Long documents
Memory
Emails
Zapier
Tabular Data
- Microsoft Excel with ChatGPT | Microsoft
- Querying Tabular Data | Harrison Chase - LangChain
- SQLite example | Harrison Chase - LangChain
- CSV files | Harrison Chase - LangChain
- Support (optional) direct return on SQLDatabaseChain to prevent passing data to LLM #821 | Zach Schillaci & Harrison Chase - LangChain
- Amazon Relational Database Services (RDS) | Zapier
Javascript
Pinecone
Supabase
Water
Visual ChatGPT
Summarization
Hugging Face
LLama
GPT-Index
Comparing Large Language Models (LLM)
Gradio
Filtering LLM
Taxes
More