Exploring LLAMA-3's Capabilities: RAG, Routing, and Function Calling

In this section, we will explore the capabilities of LLAMA-3 in performing query routing and function calling. We will use the Gro API to load the LLAMA-3 models and test their performance on various tasks.

First, we will create two different vector stores: one for document chunking and embeddings, and another for document summarization. This will allow us to test the model's ability to select the appropriate vector store based on the user's query.

Next, we will examine the model's performance on query routing. We will provide the model with queries that require either specific fact retrieval or document summarization, and observe how the model selects the appropriate vector store to generate the response.

Finally, we will explore the model's function calling capabilities. We will use the Gro API's implementation of tool usage to allow the model to call external functions, such as retrieving NBA game scores. We will observe how the model decides when to use the external function and how it integrates the function's output into the final response.

Throughout the section, we will compare the performance of the 38 billion and 70 billion versions of the LLAMA-3 model, highlighting the improvements in the larger model.

In this section, we will examine LLAMA-3's ability to perform query routing and function calling tasks. We will use the article "Synthetic Social Networking is Coming" from WGE as the source of data for our tests.

First, we set up the necessary components, including loading the LLAMA-3 model (both the 38 billion and 70 billion versions) using the Gro API, and creating two vector stores: one for document chunking and another for summarization.

We then test the model's query routing capabilities by asking questions that require retrieving specific facts or summarizing the entire document. The 70 billion version of LLAMA-3 demonstrates superior performance, providing more accurate and comprehensive responses compared to the 38 billion version.

Next, we explore LLAMA-3's function calling abilities, which are not officially supported but can be implemented using Gro's tool usage feature. We create a system message that instructs the model to use an external function to retrieve NBA game scores and include the team names and final scores in the response. Both the 38 billion and 70 billion versions of LLAMA-3 are able to successfully utilize the external function and provide the requested information.

Overall, the results show that LLAMA-3 is capable of performing well on a variety of RAG (Retrieval-Augmented Generation) tasks, with the larger 70 billion version exhibiting stronger performance across the board.

The 70 billion parameter LLAMA-3 model demonstrates significantly improved performance compared to the 38 billion parameter version. Key differences include:

• The 70 billion model provides much more detailed and accurate summaries of the information mentioned about companies like Meta and OpenAI. It is able to synthesize the key points rather than simply copying sentences from the source text.

• For more complex queries involving multiple parts, the 70 billion model does a better job at query routing - correctly identifying which of the available data sources (vector index or summary index) is most relevant to answering the question.

• When asked about topics unrelated to the available tool functions, the 70 billion model is able to recognize this and provide a thoughtful response, rather than attempting an inappropriate tool call like the 38 billion model.

Overall, the increased scale of the 70 billion parameter LLAMA-3 model results in substantially stronger language understanding and reasoning capabilities, allowing it to handle more nuanced and open-ended queries more effectively. The larger model size appears to be a key factor in enabling these performance improvements.

LLAMA-3 does not officially support function calling, but the Grok API provides an implementation for Tool Usage, which enables the language model to leverage external tools to answer complex queries.

The key steps involved in this process are:

1. Defining the Tool: The system message includes a detailed description of the available tool, including its input and output parameters. This allows the language model to determine which tool to use for a given query.

2. Tool Selection: When the user asks a question, the language model first checks if it needs to use an external tool. If so, it selects the appropriate tool based on the context of the query.

3. Tool Invocation: The language model makes a call to the selected tool, passing the necessary input parameters. The tool's response is then fed back into the language model.

4. Final Response Generation: The language model uses the information from the tool's response to generate the final answer for the user.

This approach allows LLAMA-3 to leverage external capabilities, such as performing complex calculations or retrieving specific data, to provide more accurate and comprehensive responses to user queries.

The example provided demonstrates how LLAMA-3 can be integrated with the Grok API to answer questions about NBA game scores. The model is able to select the appropriate tool, invoke the "get game score" function, and then use the retrieved information to generate a detailed response for the user.

Overall, this integration of LLAMA-3 with external tools expands the model's capabilities and enables it to handle a wider range of queries and tasks.

The Lama 3 model, both the 38 billion and 70 billion versions, have demonstrated impressive capabilities in various tasks such as query routing and function calling. The model's ability to accurately identify the relevant vector store or summary index based on the user's query showcases its strong understanding of context and relevance.

The 70 billion model, in particular, outperformed the smaller 38 billion version in more complex queries, providing more detailed and accurate responses. The implementation of the function calling feature, using the Gro API, further highlights the model's flexibility and its capacity to leverage external tools to enhance its capabilities.

Overall, the Lama 3 models have proven to be powerful language models that can handle a wide range of tasks effectively. The insights gained from this analysis can be valuable for developers and researchers working on similar projects, as they explore the potential of large language models and their practical applications.