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To pass a Pydantic BaseModel as tool input in LlamaIndex, you can use the
FunctionTool class from
llama_index.core.tools. Here's a step-by-step guide:
- Define your Pydantic BaseModel. For example, a
Song model:
from pydantic import BaseModel
class Song(BaseModel):
"""A song with name and artist"""
name: str
artist: str- Define a function that takes the fields of your BaseModel as arguments and returns an instance of your BaseModel. For example, a
generate_song function:
def generate_song(name: str, artist: str) -> Song:
"""Generates a song with provided name and artist."""
return Song(name=name, artist=artist)- Create a
FunctionTool from your function using the from_defaults method:
from llama_index.core.tools import FunctionTool
tool = FunctionTool.from_defaults(fn=generate_song)
- Use the
predict_and_call method of your LLM (either Upstage or OpenAI) to call the tool with your input:
from llama_index.llms.upstage import Upstage
llm = Upstage()
response = llm.predict_and_call([tool], "Generate a song")
In this example, "Generate a song" is the input to the LLM, which will be passed to the
generate_song function. The function will return a
Song object, which is an instance of the Pydantic BaseModel.
You can find more details in the
LlamaIndex documentation () and
GitHub examples ().
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