Chat History
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Comparing OpenAI Chatbot & Gemini Chatbot
Gemini has added the Files API, which can allow multi-modal input, such as documents and images. The process of building an app with the Gemini API is very similar to OpenAI. There are many overlapping features between the two. When building the app for this lesson, you’ll utilize system conversation history in a similar way as you did with OpenAI. You’ll also use system instructions to customize the model. OpenAI has a similar parallel function calling feature. Lastly, you can format your responses from Gemini in JSON and provide a schema to make it more advanced.
Deciding Between Gemini and ChatGPT Based on Results
If you’re already working heavily within the Google Cloud ecosystem and need an information retrieval-focused chatbot, Gemini is a good choice. If you prioritize creative generation, conversational abilities, customization, and broad platform integration, ChatGPT could be a better fit. Both APIs are rapidly evolving, so it’s crucial to stay updated with the latest developments. Consider factors like project requirements, model capabilities, cost, and the development ecosystem when making your decision.
Chat History
Chat history is an important aspect of a request because it maintains conversation context. First, you’ll compare the OpenAI module app with the one you’re building in this module.
Defining Conversation Context
Conversation history, in the context of AI text generation, refers to the record of previous interactions within a dialogue. This record serves as the conversational context, providing the AI model with crucial information about the ongoing discussion. The context derived from previous interactions includes current topics, user preferences, and established relationships.
Using the Chat History Object
The history is included as part of the response. It can be a single item or a list of items. You can pass history into start_chat to start a conversation that needs to retain the history. You won’t need any extra parameters when using start_chat. You can also provide a conversation history when you call start_chat:
chat_session = model.start_chat(
history = [
{
"role": "user",
"parts": [
"I have two dogs and three cats.",
],
},
{
"role": "model",
"parts": [
"That's a full house! 🐶🐶🐱🐱🐱\n\nDo you have any fun stories about them or
are you looking for tips on how to manage a multi-pet household? Tell me more
about your furry family! 😊 \n",
],
},
{
"role": "user",
"parts": [
"That sounds like a wonderful menagerie!",
],
},
{
"role": "model",
"parts": [
"You're right, \"menagerie\" is a perfect word for it!
It sounds like you appreciate a lively, animal-loving home. 😊 \n\n
Do you have any pets yourself? \n",
],
},
]
)
Understanding Roles
In Gemini, there are only two roles to consider: the model and the user. Unlike the OpenAI app, Gemini doesn’t support System. One way to simulate this feature is to pre-populate the chat history to give the model context information. There’s another feature you’ll explore later called system instructions.
response = chat_session.send_message("How many paws are in my house?")
That's a fun math problem! Let's solve it:
* **Dogs:** 2 dogs * 4 paws/dog = 8 paws
* **Cats:** 3 cats * 4 paws/cat = 12 paws
**Total: 8 paws + 12 paws = 20 paws**
You have a grand total of 20 paws running around your house! 🐾