tools.py

from abc import ABC, abstractmethod
from typing import Dict, Union, get_origin, get_args
from pydantic import BaseModel, Field
from types import UnionType
import os
import logging

from src.vectorstore import VectorStore
# from langchain.tools import tool


class ToolBase(BaseModel, ABC):
    @abstractmethod
    def invoke(cls, input: Dict):
        pass

    @classmethod
    def to_openai_tool(cls):
        """
        Extracts function metadata from a Pydantic class, including function name, parameters, and descriptions.
        Formats it into a structure similar to OpenAI's function metadata.
        """
        function_metadata = {
            "type": "function",
            "function": {
                "name": cls.__name__,  # Function name is same as the class name, in lowercase
                "description": cls.__doc__.strip(),
                "parameters": {
                    "type": "object",
                    "properties": {},
                    "required": [],
                },
            },
        }

        # Iterate over the fields to add them to the parameters
        for field_name, field_info in cls.model_fields.items():
            # Field properties
            field_type = "string"  # Default to string, will adjust if it's a different type
            annotation = field_info.annotation.__args__[0] if getattr(field_info.annotation, "__origin__", None) is Union else field_info.annotation
            
            has_none = False
            if get_origin(annotation) is UnionType:  # Check if it's a Union type
                args = get_args(annotation)
                if type(None) in args:
                    has_none = True
                args = [arg for arg in args if type(None) != arg]
                if len(args) > 1:
                    raise TypeError("It can be union of only a valid type (str, int, bool, etc) and None")
                elif len(args) == 0:
                    raise TypeError("There must be a valid type (str, int, bool, etc) not only None")
                else:
                    annotation = args[0]
            
            if annotation == int:
                field_type = "integer"
            elif annotation == bool:
                field_type = "boolean"
            
            # Add the field's description and type to the properties
            function_metadata["function"]["parameters"]["properties"][field_name] = {
                "type": field_type,
                "description": field_info.description,
            }

            # Determine if the field is required (not Optional or None)
            if field_info.is_required():
                function_metadata["function"]["parameters"]["required"].append(field_name)
                has_none = True

            # If there's an enum (like for `unit`), add it to the properties
            if hasattr(field_info, 'default') and field_info.default is not None and isinstance(field_info.default, list):
                function_metadata["function"]["parameters"]["properties"][field_name]["enum"] = field_info.default
                if not has_none:
                    function_metadata["function"]["parameters"]["required"].append(field_name)

        return function_metadata

tools: Dict[str, ToolBase] = {}
oitools = []


vector_store = VectorStore(
    # embeddings_model="BAAI/bge-m3", 
    embeddings_model=os.environ.get("EMBEDDINGS_MODEL"),
    vs_local_path=os.environ.get("VS_LOCAL_PATH"), 
    vs_hf_path=os.environ.get("VS_HF_PATH"))


def tool_register(cls: BaseModel):
    oaitool = cls.to_openai_tool()
    
    oitools.append(oaitool)
    tools[oaitool["function"]["name"]] = cls


@tool_register
class get_documents(ToolBase):
    """Retrieves general information from Wikipedia based on the user's query.    """
    
    logging.info("@tool_register: get_documents()")  
    
    query: str = Field(description="Search query to retrieve relevant documents.")
    logging.info(f"query: {query}")

    @classmethod
    def invoke(cls, input: Dict) -> str:

        logging.info(f"get_documents.invoke() input: {input}")
        # Check if the input is a dictionary

        query = input.get("query", None)
        if not query:
            return "Missing required argument: query."
        
        # return "We are currently working on it. You can't use this tool right now—please try again later. Thank you for your patience!"
        return vector_store.get_context(query)