Azure Cosmos DB in AI-Driven Applications on the GPT Store
GPT Description
GPT Prompt Starters
- `EXPLAIN EACH COMMAND FOR ACTION` ➤ DIRECTION - `0-10` **!temp [value]:** ➤ **Adjust AI Creativity & Diversity:** This command allows you to modulate the creativity and diversity of the AI's previous response. Setting the value to 0 minimizes randomness for straightforward, concise answers, while 10 maximizes creativity for a wide range of possibilities. The AI will then repeat its last response with the newly adjusted temperature setting, offering a different perspective or formulation of the answer. - `!code` ➤ **Execute Python Code Demonstrations:** By using this command, you can instruct the AI to execute and display Python code snippets within the 'terminal' environment, presented as a '.txt' code block. This showcases the functionality and behavior of the code, providing a practical demonstration of programming concepts or solutions to coding problems. - `!web [query]` ➤ **Enhance Conversations with Web Searches:** This command enables the AI to perform web searches based on a specific query, enriching the conversation with additional information or context. It leverages GPT-4's comprehensive understanding to find and integrate relevant details from the web, ensuring the response is both informative and up-to-date. - `/c` **Chain of Thought** ➤ **Logical & Structured Reasoning:** Utilize this command to guide the AI in employing a clear, stepwise logic and prescribed syntax to progress through a conversation or problem-solving process. Minimal user input is required, as the AI develops its response using bold formatting, bullet points, and code snippets where appropriate to enhance readability and user engagement. - `/d` **Summarize with CoDT** ➤ **Concise Conversation Summaries:** This command instructs the AI to condense the conversation into coherent 80-word summaries for each exchange, applying clear language, syntax, and formatting techniques such as bolding, bullet points, and code snippets. The summary aims to enhance readability and engagement, concluding with a logical follow-up question to continue the dialogue effectively. - `/e` **Enhance with RAG & Style** ➤ **Browser-Sourced Information Integration:** Use this command to integrate information sourced from the web into the AI's responses. It ensures that the content is clear, accurate, and well-formatted, incorporating bolding, bullet points, and code snippets to maintain readability and coherence. This enhances the response with additional, relevant information, providing a richer and more informative answer. - `/s` **Save, Zip, Download** ➤ **Efficient File Management:** This command facilitates the bundling of all files located in `/mnt/data/` into a single `.zip` archive, streamlining the download and transfer process. It adheres to clear syntax and styling standards to ensure readability. Upon execution, the AI generates a secure and accessible link, allowing for easy retrieval and management of saved files.
- Could you summarize each section from the knowledgebase.json file related to leveraging vector databases, Azure Cosmos DB, and their integration with AI-driven applications, including details on RAG, prompt engineering, and the application examples provided?
- You are an expert on creating Azure OpenAI integration code for a given input. You will create code for the different agents required for a complex task. (find many examples of Azure OpenAI integration scripts here: <https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts>) Capabilities: - You can generate Azure OpenAI integration code based on given inputs. - You have access to tools such as a browser for real-time information retrieval, Python for executing code, and a file browser for managing uploaded files. - You can interpret and manipulate text-based data, create detailed responses, and provide instructions or code snippets relevant to Azure OpenAI and related technologies. ```plaintext llm_config = {"config_list": config_list_gpt4, "cache_seed": 42} user_proxy = aoai.UserProxyAgent( name="User_proxy", system_message="A human admin.", code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"}, human_input_mode="TERMINATE" ) coder = aoai.AssistantAgent( name="Coder", llm_config=llm_config, ) pm = aoai.AssistantAgent( name="Product_manager", system_message="Creative in software product ideas.", llm_config=llm_config, ) groupchat = aoai.GroupChat(agents=[user_proxy, coder, pm], messages=[], max_round=12) manager = aoai.GroupChatManager(groupchat=groupchat, llm_config=llm_config) ``` Explore the repository to get a detailed view of its structure, contents, various components like backend, data, frontend, scripts, and more from [microsoft/sample-app-aoai-chatGPT on GitHub](https://github.com/microsoft/sample-app-aoai-chatGPT) . Then display the structure of the repository similar to executing a `tree` command and exporting it to a file (like `tree > tree.md`), showing the output based on the repository's contents. Show every file in ''/main/' and '/main/scripts/' in a '.txt' code block. PAUSE, and take a deep breath ---> THE USE PYTHON TOOL AND WRITE {{CONVERSATION HISTORY}} TO TXT CODE BLOCK THEN SAVE {CONVERSATION HISTORY} AS A '.txt' FILE TO '/mnt/data/' MAKE SURE TO INCLUDE THE ENTIRE CONVERSATION INCLUDING ALL TEXT IN CODE BLOCKS.
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