dosubot[bot]
commented
6 months ago Hey @tdr1991, great to see you back! How's everything going on your end?
Based on the information provided, it seems like you're trying to use the "gemma:2b" model with the is_chat_model flag set to True. However, from the context provided, it doesn't appear that the "gemma:2b" model specifically requires the "api/generate" to build the rag pipeline when "is_chat_model" is set to True. The model used for response generation is specified with the llm field in the RagCLI class, which defaults to OpenAI(model="gpt-3.5-turbo", streaming=True). There is no direct reference to "gemma:2b" or a condition that alters behavior based on the "is_chat_model" flag within the provided context. You can refer to the RagCLI class for more details.
The function is_chat_model in the LlamaIndex codebase is designed to determine if a given language model (represented by an instance of BaseLLM) is a chat model. It does this by accessing the is_chat_model attribute from the model's metadata (llm.metadata.is_chat_model) and returning its value. The return value is a boolean (True or False), indicating whether the model is a chat model or not. You can refer to the utils.py for more details.
Unfortunately, I couldn't find any specific reference to the "gemma:2b" model in the LlamaIndex codebase. It would be helpful if you could provide more context or information about this model.
The "is_chat_model" flag in the metadata property of the Gemini class is hardcoded to True, indicating that the model is configured or recognized as a chat model. This flag does not dynamically change behavior based on its value being set to True or False within the given code context. Instead, it serves as a static piece of information indicating the model's capability or intended use as a chat model. This could be used elsewhere in the LlamaIndex codebase or by external integrations to determine how to interact with the model (e.g., preferring chat-like interactions over other types of content generation). However, without additional context on how this flag is used outside of the provided code snippet, it's clear that within this specific class definition, the flag's value does not directly alter the model's behavior or methods. You can refer to the base.py for more details.
The is_chat_model flag, when set to True, indicates that the Gemini model is configured to handle chat-based interactions. This is evident from the metadata property within the Gemini class. This setting signifies that the model is capable of generating responses in a chat or conversational context, as opposed to generating standalone text completions. The presence of dedicated methods for chat interactions, such as chat and stream_chat, further supports this capability. These methods are designed to handle sequences of chat messages, process them, and generate appropriate responses, leveraging the underlying chat model capabilities of the Gemini API. You can refer to the gemini.py for more details.
I hope this information helps! If you have any more questions or need further clarification, feel free to ask.
Sources
- llama-index-legacy/llama_index/legacy/command_line/rag.py
- llama-index-core/llama_index/core/prompts/utils.py
- llama-index-integrations/llms/llama-index-llms-gemini/llama_index/llms/gemini/base.py
- llama-index-legacy/llama_index/legacy/llms/gemini.py
logan-markewich
Question Validation
Question
hi everyone: i have use ollama load gemma:2b,if set is_chat_model=True, not work
if set set is_chat_model=False, it will work
and i test this pipeline by langchain, it work normally
so for gemma:2b, it should request "api/generate" to build rag pipeline ?