Difference between revisions of "LangChain"

From
Jump to: navigation, search
m (Pinecone)
m
Line 27: Line 27:
 
** [https://www.linkedin.com/in/harrison-chase-961287118/ Harrison Chase | LinkedIn]  ... [https://github.com/hwchase17 GitHub]  ...  
 
** [https://www.linkedin.com/in/harrison-chase-961287118/ Harrison Chase | LinkedIn]  ... [https://github.com/hwchase17 GitHub]  ...  
 
* [[Zapier]]
 
* [[Zapier]]
* [[Case Studies]]
 
** [[Writing / Publishing]]
 
 
* [[Python]]  ... [[Generative AI with Python]]  ... [[Javascript]]  ... [[Generative AI with Javascript]]  
 
* [[Python]]  ... [[Generative AI with Python]]  ... [[Javascript]]  ... [[Generative AI with Javascript]]  
* [[Development]] ... [[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]] ... [[Visualization]] ... [[Diagrams for Business Analysis]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+
* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Loop]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Bayes]] ... [[Network Pattern]]
 +
* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 
* [[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]

Revision as of 07:40, 5 July 2023

YouTube ... Quora ...Google search ...Google News ...Bing News


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.


Getting Started

Data Independent - tutorial videos

reference videos throughout page



Documents


Long documents

Memory

Emails

Tabular Data

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.


More