Chat Langchain GPT-AI-powered interactive assistant

Example

Using the ChatModel class to interact with multiple LLM providers like OpenAI and Cohere seamlessly.

Scenario

A developer wants to create an application that can switch between different language models based on user needs or cost considerations. By using Langchain's standardized interface, they can integrate multiple models without changing their application code.

Example

Using BufferMemory or Momento-backed chat memory to maintain chat history across sessions.

Scenario

Building a customer service chatbot that needs to remember previous interactions to provide contextually relevant responses in future conversations. The developer can use BufferMemory for local persistence or Momento for a distributed, serverless solution.

Example

Combining retrieval mechanisms with LLMs to enhance response accuracy by providing contextually relevant information.

Scenario

Creating a document summarization tool where the application retrieves relevant document sections using vector stores and then generates summaries using an LLM. This ensures that the generated summaries are accurate and relevant to the retrieved content.