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import argparse |
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import torch |
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import json |
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from config import config |
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from typing import List, Dict |
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from logger import logger |
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from transformers import AutoTokenizer |
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import functions |
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from prompter import PromptManager |
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from validator import validate_function_call_schema |
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from langchain_community.chat_models import ChatOllama |
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from langchain_community.llms import Ollama |
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from langchain.prompts import PromptTemplate |
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from langchain_core.output_parsers import StrOutputParser |
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from utils import ( |
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get_chat_template, |
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validate_and_extract_tool_calls |
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) |
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class ModelInference: |
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def __init__(self, chat_template: str): |
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self.prompter = PromptManager() |
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self.model = Ollama(model=config.ollama_model, temperature=0.0, format='json') |
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template = PromptTemplate(template="""<|im_start|>system\nYou are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:\n<tool_call>\n{"arguments": <args-dict>, "name": <function-name>}\n</tool_call><|im_end|>\n""", input_variables=["question"]) |
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chain = template | self.model | StrOutputParser() |
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self.tokenizer = AutoTokenizer.from_pretrained(config.hf_model, trust_remote_code=True) |
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self.tokenizer.pad_token = self.tokenizer.eos_token |
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self.tokenizer.padding_side = "left" |
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if self.tokenizer.chat_template is None: |
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print("No chat template defined, getting chat_template...") |
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self.tokenizer.chat_template = get_chat_template(chat_template) |
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logger.info(f"Model loaded: {self.model}") |
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def process_completion_and_validate(self, completion, chat_template): |
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if completion: |
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validation, tool_calls, error_message = validate_and_extract_tool_calls(completion) |
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if validation: |
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logger.info(f"parsed tool calls:\n{json.dumps(tool_calls, indent=2)}") |
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return tool_calls, completion, error_message |
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else: |
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tool_calls = None |
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return tool_calls, completion, error_message |
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else: |
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logger.warning("Assistant message is None") |
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raise ValueError("Assistant message is None") |
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def execute_function_call(self, tool_call): |
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function_name = tool_call.get("name") |
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function_to_call = getattr(functions, function_name, None) |
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function_args = tool_call.get("arguments", {}) |
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logger.info(f"Invoking function call {function_name} ...") |
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function_response = function_to_call(*function_args.values()) |
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results_dict = f'{{"name": "{function_name}", "content": {function_response}}}' |
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return results_dict |
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def run_inference(self, prompt: List[Dict[str, str]]): |
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inputs = self.tokenizer.apply_chat_template( |
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prompt, |
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add_generation_prompt=True, |
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tokenize=False, |
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) |
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inputs = inputs.replace("<|begin_of_text|>", "") |
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completion = self.model.invoke(inputs, format='json') |
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return completion.content |
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def generate_function_call(self, query, chat_template, num_fewshot, max_depth=5): |
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try: |
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depth = 0 |
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user_message = f"{query}\nThis is the first turn and you don't have <tool_results> to analyze yet" |
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chat = [{"role": "user", "content": user_message}] |
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tools = functions.get_openai_tools() |
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prompt = self.prompter.generate_prompt(chat, tools, num_fewshot) |
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completion = self.run_inference(prompt) |
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def recursive_loop(prompt, completion, depth): |
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nonlocal max_depth |
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tool_calls, assistant_message, error_message = self.process_completion_and_validate(completion, chat_template) |
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prompt.append({"role": "assistant", "content": assistant_message}) |
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tool_message = f"Agent iteration {depth} to assist with user query: {query}\n" |
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logger.info(f"Found tool calls: {tool_calls}") |
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if tool_calls: |
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logger.info(f"Assistant Message:\n{assistant_message}") |
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for tool_call in tool_calls: |
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validation, message = validate_function_call_schema(tool_call, tools) |
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if validation: |
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try: |
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function_response = self.execute_function_call(tool_call) |
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tool_message += f"<tool_response>\n{function_response}\n</tool_response>\n" |
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logger.info(f"Here's the response from the function call: {tool_call.get('name')}\n{function_response}") |
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except Exception as e: |
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logger.info(f"Could not execute function: {e}") |
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tool_message += f"<tool_response>\nThere was an error when executing the function: {tool_call.get('name')}\nHere's the error traceback: {e}\nPlease call this function again with correct arguments within XML tags <tool_call></tool_call>\n</tool_response>\n" |
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else: |
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logger.info(message) |
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tool_message += f"<tool_response>\nThere was an error validating function call against function signature: {tool_call.get('name')}\nHere's the error traceback: {message}\nPlease call this function again with correct arguments within XML tags <tool_call></tool_call>\n</tool_response>\n" |
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prompt.append({"role": "tool", "content": tool_message}) |
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depth += 1 |
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if depth >= max_depth: |
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print(f"Maximum recursion depth reached ({max_depth}). Stopping recursion.") |
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completion = self.run_inference(prompt) |
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return completion |
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completion = self.run_inference(prompt) |
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return recursive_loop(prompt, completion, depth) |
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elif error_message: |
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logger.info(f"Assistant Message:\n{assistant_message}") |
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tool_message += f"<tool_response>\nThere was an error parsing function calls\n Here's the error stack trace: {error_message}\nPlease call the function again with correct syntax<tool_response>" |
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prompt.append({"role": "tool", "content": tool_message}) |
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depth += 1 |
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if depth >= max_depth: |
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print(f"Maximum recursion depth reached ({max_depth}). Stopping recursion.") |
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return completion |
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completion = self.run_inference(prompt) |
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return recursive_loop(prompt, completion, depth) |
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else: |
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logger.info(f"Assistant Message:\n{assistant_message}") |
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return assistant_message |
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return recursive_loop(prompt, completion, depth) |
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except Exception as e: |
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logger.error(f"Exception occurred: {e}") |
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raise e |
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