1. Chroma DB (Database): Chroma DB is a database that stores structured information about concepts, entities, and relationships. It's designed to efficiently handle large volumes of data while allowing for flexible querying and retrieval. In Todob, Chroma DB serves as the backbone for storing and managing the knowledge base.

2. In-Context Rag (Response Attribution Graph): In-Context Rag is a technique used to contextualize responses generated by language models. It helps ensure that the generated responses are coherent, relevant, and consistent with the conversation context. In Todob, In-Context Rag is employed to filter and refine the output from LLAMA2, ensuring that the responses are appropriate and accurate.

3. LLAMA2 (Large Language Model Access Model): LLAMA2 is a large language model, similar to GPT (Generative Pre-trained Transformer) models like GPT-3.5. It's trained on a vast amount of text data and is capable of generating human-like responses to a wide range of prompts. In Todob, LLAMA2 is used for generating responses to user queries and providing information based on the contents of Chroma DB.

4. Function Calling: Function calling involves invoking specific functions or processes within the Todob framework to perform various tasks, such as data retrieval, analysis, or response generation. These functions are designed to interact with Chroma DB, LLAMA2, and other components of Todob to execute specific actions in response to user requests.

By combining these elements, Todob creates a powerful system for processing user queries, accessing structured knowledge from Chroma DB, generating coherent responses using LLAMA2, and ensuring relevance and contextuality through In-Context Rag. This integrated approach enables Todob to provide accurate, informative, and contextually appropriate responses to a wide range of user inquiries.