This summary of the video was created by an AI. It might contain some inaccuracies.
00:00:00 – 00:17:58
The video explores using Lama 3 for query routing and function calling, focusing on installing packages, converting document chunks, and utilizing Vector stores. It discusses creating Vector stores for specific facts and document summaries, emphasizing effective query routing based on user queries. The model's enhanced capabilities through the gron API, such as improved responses and function calling for complex queries, are highlighted. The importance of selecting the right model based on query types, demonstrated by comparing the 8 billion and 70 billion models, is emphasized for accurate and relevant responses without the need for function calls. The video also showcases the process of setting up the Gro client for Python, using API keys, and obtaining NBA game scores through the model. The speaker recommends exploring different models for varying query needs, showcasing the advancements in OpenAI Chat GPT and Meta's chatbots.
00:00:00
In this part of the video, the focus is on testing Lama 3’s ability in query routing and function calling. Function calling is not officially supported, so the gro implementation is used. A source of data is needed, and a synthetic social networking article from WGE is used. The key actions include installing required packages, loading web page content using Beautiful Soup, and loading the Lama model through the Gro API. Two Vector stores are created to convert document chunks and facilitate query routing.
00:03:00
In this segment of the video, two query routing options are discussed for switching between them, with helpful login code to track activities. The process of querying is explained as creating a Vector store, query engine, and passing queries for model responses. Differences between OpenAI and Meta AI tools are highlighted, with models generating concise responses. New features added to OpenAI Chat GPT include voice and image capabilities, accurately identified by the model. Effective query routing is explained as selecting the appropriate Vector store based on the query, tailored to various use cases like education or company operations.
00:06:00
In this part of the video, the speaker discusses the importance of having the ability to select between different Vector stores based on user queries and authorization levels. They mention creating two tools: a Vector store for searching specific facts and a summary tool for summarizing entire documents. A query engine with a selector is used to determine which tool to pick based on the question asked. The speaker demonstrates using these tools for different queries related to specific facts and summarization, showcasing the effectiveness of the system.
00:09:00
In this segment of the video, the speaker discusses the change to the 70 billion model and the improved responses for queries using the gron API. The model summarizes information about Meta’s 28 personality-driven chatbots and updates to Chat GPT with more human-like features. The model excels in query routing for complex queries and has the capability for function calling through an external tool implementation by Croc. Function calling is used when the model needs to use an external tool to answer a query, and the tool’s response is integrated into the LM’s final response. This function calling feature is also implemented in the LMA 3 and MixLMoe models by Grock.
00:12:00
In this part of the video, the speaker demonstrates installing the Gro client for Python, setting up API keys, and using a model to get NBA game scores based on different team scenarios. The main loop involves system messages, calling functions to extract data and provide responses including team names and scores. Detailed tool descriptions help in tool selection, and multiple tools can be used. The output is the team name the model returns. The process involves making calls to the model, checking tool usage, and feeding responses back into the model.
00:15:00
In this segment of the video, the speaker discusses the process of using a model to generate responses to user queries. They explain how the model makes function calls based on the query prompt, providing an example of a sports-related query about the Warriors game. The speaker compares the responses generated by a smaller 8 billion model and a larger 70 billion model, highlighting the differences in their capabilities. The larger model delivers a more detailed and relevant response without the need for a function call. The importance of choosing the appropriate model based on the query type is emphasized, with a recommendation to explore the capabilities of these models offered by the speaker’s company.