Welcome to the final lesson of this course! In this lesson, we will integrate context retrieval with a chat model using Go. This builds on the skills you've developed in previous lessons, where you learned about document embeddings and similarity search. Today, we'll focus on using templates to format messages with extra context, enabling you to ask questions and receive answers based on the retrieved document content. This lesson will bring together all the skills you've learned so far, culminating in a comprehensive understanding of document processing and retrieval with Go.

Let's quickly recap what we've learned in previous lessons about preparing documents and creating a vector store. We'll load and prepare our document, "Alice in Wonderland," and generate embeddings to create a vector store. This process is essential for effective context retrieval.

In this code, we load a document, split it into chunks, generate embeddings, and create a vector store, setting the stage for efficient context retrieval in our question-answering tasks.

Note: The RecursiveCharacter splitter uses character-based measurements for ChunkSize and ChunkOverlap. If you use a token-based splitter like instead, these parameters would represent tokens rather than characters—so 500 tokens is not equivalent to 500 characters and would typically represent significantly more text.

Now that we have our vector store, we can integrate context retrieval with a chat model. First, we'll define a query and perform a similarity search to retrieve relevant documents based on the query. This will allow us to combine the retrieved document content to form a context for our question.

In this example, we define a query and retrieve the top three most relevant document chunks using the SimilaritySearch method. The content of these chunks is combined to form a context that will be used in the next step.

To effectively communicate with the chat model, we need to format our messages using templates. In Go, we can use the text/template package to create these templates. It allows us to define a structure for our message, ensuring that the chat model receives all the necessary information to provide an accurate response.

In this code, we create a prompt template that includes placeholders for the context and the question. The text/template package helps us define this structure by allowing us to specify template variables using {{.FieldName}} syntax.

Now, let's print the formatted template to see how the context and question are structured in the message.

This will output something like:

By printing the formatted template, we can verify that the context and question are correctly inserted into the template. This ensures that the chat model receives a well-structured message, allowing it to generate a relevant and accurate response.

With our prompt ready, we can now move on to interacting with the chat model. We'll use the OpenAI LLM client from LangChain to get a response. Note that we create a new LLM instance configured for chat completion rather than embeddings.

In this section, we initialize a separate OpenAI LLM client configured for chat completion with the gpt-3.5-turbo model. We then use llms.GenerateFromSinglePrompt to send our formatted prompt to the model. The model processes the input and generates an answer based on the context and question provided. By printing both the question and the AI's answer, we can see how effectively the integration of context retrieval and templates enhances the interaction.

The output will look something like this:

This demonstrates how the chat model uses the context retrieved from the vector store to provide a relevant and accurate response to the question.

You've successfully completed this lesson, where you learned how to integrate context retrieval with a chat model using Go and LangChain. We explored the use of templates to format messages with extra context, allowing you to ask questions and receive answers based on the retrieved document content. This lesson consolidated all the skills you've acquired so far, equipping you with a solid understanding of document processing and retrieval with Go.

Key takeaways:

• Templates help structure prompts with context and questions for chat models
• Go's text/template package provides powerful templating capabilities
• Retrieved context from similarity search enhances the quality of AI responses
• Separate LLM instances are used for embeddings and chat completion
• The complete RAG (Retrieval-Augmented Generation) pipeline combines all these components

As you continue your learning journey, consider experimenting with different queries and document types to deepen your understanding. Try adjusting the number of retrieved documents (k parameter) to see how it affects response quality. Stay tuned for the practice exercises, where you'll get hands-on experience implementing these concepts yourself!