Using Python REPL LangChain to build a machine learning model | PythonREPL LangChain | LangChain

Описание: Using Python REPL LangChain to build a machine learning model | PythonREPL LangChain | LangChain
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LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:

Be data-aware: connect a language model to other sources of data

Be agentic: allow a language model to interact with its environment


There are several main modules that LangChain provides support for. For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides. These modules are, in increasing order of complexity:

Models: The various model types and model integrations LangChain supports.

Prompts: This includes prompt management, prompt optimization, and prompt serialization.

Memory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.

Indexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.

Chains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.

Agents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.

Callbacks: It can be difficult to track all that occurs inside a chain or agent - callbacks help add a level of observability and introspection.



ChatGPT is a large language model developed by OpenAI. It is trained on a diverse range of internet text and is able to generate human-like responses to a wide variety of prompts. It is based on the GPT (Generative Pre-training Transformer) architecture.

In this video, we introduce you to ChatGPT, a powerful language model developed by OpenAI. This model is trained on a diverse range of internet text and is able to generate human-like responses to a wide variety of prompts. With the help of ChatGPT, you can easily create your own AI-powered chatbot for customer service, e-commerce, or any other application that requires natural language understanding. The video demonstrates the capabilities of ChatGPT by showcasing examples of its usage and its ability to understand and respond to different prompts. We also show you how to integrate ChatGPT into your own applications. Don't forget to subscribe to our channel for more AI-related content and updates on the latest advancements in this field.

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