Difference between revisions of "LangChain"
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* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] | ||
* [[ChatGPT#Integration| ChatGPT Integration]][https://twitter.com/hwchase17?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor Twitter] | * [[ChatGPT#Integration| ChatGPT Integration]][https://twitter.com/hwchase17?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor Twitter] | ||
| − | * [[Generative AI]] | + | * [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]] |
* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]] | * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]] | ||
* [[Prompt Engineering (PE)]] ...[[Prompt Engineering (PE)#PromptBase|PromptBase]] ... [[Prompt Injection Attack]] | * [[Prompt Engineering (PE)]] ...[[Prompt Engineering (PE)#PromptBase|PromptBase]] ... [[Prompt Injection Attack]] | ||
Revision as of 18:26, 12 July 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Assistants ... Personal Companions ... Agents ... Negotiation ... LangChain
- Excel ... Documents ... Database ... Graph ... LlamaIndex
- Immersive Reality ... Metaverse ... Digital Twin ... Internet of Things (IoT) ... Transhumanism
- LangChain | GitHub
- Zapier
- Python ... Generative AI with Python ... Javascript ... Generative AI with Javascript
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Development ... Notebooks ... AI Pair Programming ... Codeless, Generators, Drag n' Drop ... AIOps/MLOps ... AIaaS/MLaaS
- Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Metaverse ... Quantum ... Game Theory
- ChatGPT IntegrationTwitter
- Generative AI ... Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Prompt Engineering (PE) ...PromptBase ... Prompt Injection Attack
- Auto-GPT
- 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, Auto-GPT 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
Documents
- Excel ... Documents ... Database ... Graph ... LlamaIndex
- 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
Tabular Data
- 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
Weights & Biases (W&B)
W&B Sweeps and LangChain integration is a feature that allows you to fine-tune LLMs with your own data using W&B Sweeps and LangChain visualization and debugging. W&B Sweeps is a hyperparameter optimization tool that helps you find the best combination of hyperparameters for your model. W&B Sweeps and LangChain integration can:
- Create a LangChain model, chain, or agent that uses an LLM as a backend.
- Import WandbTracer from wandb.integration.langchain and use it to continuously log calls to your LangChain object.
- Use W&B dashboard to visualize and debug your LangChain object, such as viewing the prompts, responses, metrics, and errors.
- Use W&B Sweeps to optimize the hyperparameters of your LangChain object, such as the prompt template, the context length, the temperature, and the top-k.
Weights & Biases Logging/LLMops is a feature of the Weights & Biases platform, which is a developer-first MLOps platform that provides enterprise-grade, end-to-end MLOps workflow to accelerate ML activities. Weights & Biases Logging/LLMops enables you to optimize LLM operations and prompt engineering with W&B.
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