This notebook serves as a guide for setting up and using Azure OpenAI with the LangChain framework in Python. It walks through obtaining necessary credentials, creating models, and practical examples of utilizing features like prompting, chaining, embeddings, and more. It is ideal for developers aiming to integrate Azure OpenAI capabilities with LangChain.
Python 3.7+ Jupyter Notebook or Jupyter Lab An Azure account with access to Azure OpenAI resources. Basic familiarity with Python and virtual environments.
Instructions on how to sign up for Azure and set up OpenAI resources. Information on storing credentials in a .env file for secure access.
Guidance on setting up environment variables for API keys, endpoints, and model details. Options for using dotenv to manage environment variables.
Examples of creating and configuring an AzureChatOpenAI model. Utilizing SystemMessage and HumanMessage for structured input.
How to create prompts with PromptTemplate and ChatPromptTemplate. Using FewShotPromptTemplate to provide examples for better model responses.
Using AzureOpenAIEmbeddings to transform text into vector representations. Performing vector-based search using cosine similarity. Organizing embeddings with pandas DataFrames.
Streaming chat responses for real-time interaction. Chaining LangChain components for seamless model integration using Runnable.
Defining custom tools for structured responses using Pydantic models. Generating JSON-based outputs for API integration. Examples of using images and audio data with models.
Retrieve images using HTTP requests and convert them into base64 format for compatibility with the API. Feed base64-encoded images to models along with instructions for analysis, such as object detection or description generation. Example: Converting an image URL into base64, sending it to the model, and interpreting the model's output.
Transcribing audio using the Whisper API and integrating transcriptions with LangChain models. Example: Converting audio files into text and using the transcribed text as input for AzureChatOpenAI. Integration with langchain_community for easy access to the Whisper transcription capabilities. Workflow for processing audio data, converting it into text, and using it for further analysis or conversation.
Translation tasks using AzureChatOpenAI. Building interactive chatbots with role-based responses. Advanced search queries with embeddings to find relevant content. Transcribing audio using Whisper API and integrating the text into chat models. Image analysis and interpretation using base64-encoded image data.
Monitoring Usage: Tips for tracking token usage and managing costs with get_openai_callback(). Rate Limit Management: Recommendations for handling rate limits with delays between API requests. Further Reading: Explore the LangChain Documentation (https://python.langchain.com/docs/introduction/) for more advanced features.
Azure OpenAI LangChain