Spaces:
Sleeping
Sleeping
tmzh
commited on
Commit
•
95bf5e4
1
Parent(s):
a0193f9
working chat agent
Browse files- agent.py +82 -51
- functions.py +3 -1
- requirements.txt +1 -4
- templates/query.html +12 -0
- tool_use.ipynb +589 -0
- tools.py +4 -74
agent.py
CHANGED
@@ -1,70 +1,101 @@
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import re
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import json
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import functools
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import functions
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import torch
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from tools import tools
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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BitsAndBytesConfig
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)
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#
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all_functions = [func for func in dir(functions) if callable(
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getattr(functions, func)) and not func.startswith("__")]
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# Create names_to_function dict containing partials for all functions in functions.py
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names_to_functions = {func: functools.partial(
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getattr(functions, func)) for func in all_functions}
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# specify how to quantize the model
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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)
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def
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else:
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return None
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def
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]
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import functools
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import json
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import os
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import logging
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from groq import Groq
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import functions
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from tools import tools
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# Set up logging
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logging.basicConfig(level=logging.DEBUG)
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client = Groq(api_key=os.environ["GROQ_API_KEY"])
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MODEL = "llama3-groq-70b-8192-tool-use-preview"
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all_functions = [func for func in dir(functions) if callable(
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getattr(functions, func)) and not func.startswith("__")]
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names_to_functions = {func: functools.partial(
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getattr(functions, func)) for func in all_functions}
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def create_message(prompt):
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logging.debug(f"Creating message with prompt: {prompt}")
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return [
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{
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"role": "system",
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"content": "You are a movie search assistant bot who uses TMDB to help users find movies. Think step by step and identify the sequence of function calls that will help to answer.",
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},
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{
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"role": "user",
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"content": prompt,
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},
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]
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def get_response(client, model, messages, tool_choice="auto"):
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logging.debug(
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f"Getting response with model: {model}, messages: {messages}, tool_choice: {tool_choice}")
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return client.chat.completions.create(
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model=model,
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messages=messages,
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tools=tools,
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tool_choice=tool_choice,
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temperature=0,
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max_tokens=4096,
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)
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def generate_reasoning_chain(messages):
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logging.debug(f"Generating reasoning chain with messages: {messages}")
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cot_response = get_response(client, MODEL, messages, tool_choice="none")
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if cot_response.choices[0].finish_reason == "stop":
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messages.append({
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'role': cot_response.choices[0].message.role,
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'content': cot_response.choices[0].message.content})
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return messages
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else:
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return None
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def gather_movie_data(messages, iteration=0, max_iterations=2):
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logging.debug(
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f"Gathering movie data with messages: {messages}, iteration: {iteration}")
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response = get_response(client, MODEL, messages, tool_choice="required")
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if response.choices[0].finish_reason == "tool_calls":
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tool_calls = response.choices[0].message.tool_calls
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updated_messages = messages.copy()
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for tool_call in tool_calls:
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tool_output = execute_tool(tool_call)
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logging.debug(
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f"Tool call: {tool_call.function.name}, output: {tool_output}")
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if tool_call.function.name == "discover_movie":
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return tool_output["results"] # A list of movies
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else:
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updated_messages.append(
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{
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"tool_call_id": tool_call.id,
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"role": "tool",
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"name": tool_call.function.name,
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"content": str(tool_output),
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}
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)
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if iteration < max_iterations:
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return gather_movie_data(updated_messages, iteration + 1)
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def execute_tool(tool_call):
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logging.debug(f"Executing tool: {tool_call.function.name}")
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function_to_call = names_to_functions[tool_call.function.name]
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function_args = json.loads(tool_call.function.arguments)
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return function_to_call(**function_args)
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def chatbot(user_prompt):
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messages = create_message(user_prompt)
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cot = generate_reasoning_chain(messages)
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movie_list = gather_movie_data(cot)
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return movie_list
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if __name__ == "__main__":
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print(json.dumps(chatbot("List comedy movies with tom cruise in it"), indent=2))
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functions.py
CHANGED
@@ -1,3 +1,4 @@
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import requests
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import os
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import logging
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}
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response = requests.get(endpoint, headers=headers, params=params)
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# Log the response
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logging.debug(
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return response.json()
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import json
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import requests
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import os
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import logging
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}
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response = requests.get(endpoint, headers=headers, params=params)
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# Log the response
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logging.debug(
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f"Response from {endpoint}:\n{json.dumps(response.json(), indent=4)}")
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return response.json()
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requirements.txt
CHANGED
@@ -1,6 +1,3 @@
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Flask
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requests
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torch
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bitsandbytes
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accelerate
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Flask
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requests
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groq==0.9.0
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templates/query.html
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{% extends 'base.html' %}
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{% block content %}
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<h1>Query</h1>
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<form action="/query" method="post">
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<input type="text" name="query" placeholder="Enter your query...">
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{% elif result is defined %}
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<p>No results found.</p>
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{% endif %}
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{% endblock %}
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{% extends 'base.html' %}
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{% block content %}
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<h1>Query</h1>
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<form action="/query" method="post">
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<input type="text" name="query" placeholder="Enter your query...">
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{% elif result is defined %}
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<p>No results found.</p>
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{% endif %}
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<div id="overlay" onclick="closePanel()"></div> <!-- Overlay for closing panel -->
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<div id="side-panel">
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<img id="movie-poster" src="" alt="${movie.title} Poster" style="max-width: 150px;">
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<div id="movie-details">
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<!-- Movie details will be injected here -->
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</div>
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<span id="close-button" onclick="closePanel()">×</span>
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</div>
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{% endblock %}
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tool_use.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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9 |
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"hello\n"
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+
]
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}
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],
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"source": [
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"print(\"hello\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/tamizh/miniconda3/envs/movies-app/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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30 |
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): huggingface.co:443\n",
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"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /meta-llama/Meta-Llama-3.1-8B-Instruct/resolve/main/tokenizer_config.json HTTP/11\" 200 0\n",
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+
"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /meta-llama/Meta-Llama-3.1-8B-Instruct/resolve/main/config.json HTTP/11\" 200 0\n",
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+
"DEBUG:bitsandbytes.cextension:Loading bitsandbytes native library from: /home/tamizh/miniconda3/envs/movies-app/lib/python3.11/site-packages/bitsandbytes/libbitsandbytes_cuda121.so\n",
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35 |
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"INFO:accelerate.utils.modeling:We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).\n",
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"Loading checkpoint shards: 100%|██████████| 4/4 [00:07<00:00, 1.81s/it]\n",
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+
"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /meta-llama/Meta-Llama-3.1-8B-Instruct/resolve/main/generation_config.json HTTP/11\" 200 0\n"
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]
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}
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],
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"source": [
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42 |
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"import re\n",
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43 |
+
"import json\n",
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44 |
+
"\n",
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45 |
+
"from functions import *\n",
|
46 |
+
"from transformers import pipeline\n",
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47 |
+
"from tools import tools\n",
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48 |
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"\n",
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49 |
+
"import functions\n",
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50 |
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"import torch\n",
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51 |
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"from transformers import (\n",
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52 |
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" AutoModelForCausalLM,\n",
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53 |
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" AutoTokenizer,\n",
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54 |
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" BitsAndBytesConfig\n",
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55 |
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")\n",
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56 |
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"\n",
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57 |
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"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
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58 |
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"\n",
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59 |
+
"quantization_config = BitsAndBytesConfig(\n",
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60 |
+
" load_in_8bit=True,\n",
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61 |
+
" load_in_4bit=False,\n",
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62 |
+
" bnb_4bit_quant_type=\"nf4\",\n",
|
63 |
+
" bnb_4bit_compute_dtype=torch.bfloat16\n",
|
64 |
+
")\n",
|
65 |
+
"\n",
|
66 |
+
"model_id = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n",
|
67 |
+
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
|
68 |
+
"model = AutoModelForCausalLM.from_pretrained(model_id, \n",
|
69 |
+
" device_map=\"auto\", \n",
|
70 |
+
" quantization_config=quantization_config)"
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71 |
+
]
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72 |
+
},
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73 |
+
{
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74 |
+
"cell_type": "code",
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75 |
+
"execution_count": 5,
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+
"metadata": {},
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77 |
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"outputs": [],
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"source": [
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79 |
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"def generate_reasoning_chain(query):\n",
|
80 |
+
" user_message = f\"\"\"\n",
|
81 |
+
" Given the user query: \"{query}\"\n",
|
82 |
+
" Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.\n",
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83 |
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" \"\"\"\n",
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84 |
+
"\n",
|
85 |
+
" messages = [\n",
|
86 |
+
" {\"role\": \"system\", \"content\": \"You are a movie search assistant bot who uses TMDB to help users find movies. Think step by step and identify the sequence of function calls that will help to answer.\"},\n",
|
87 |
+
" {\"role\": \"user\", \"content\": user_message},\n",
|
88 |
+
" ]\n",
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89 |
+
"\n",
|
90 |
+
" tokenized_chat = tokenizer.apply_chat_template(\n",
|
91 |
+
" messages, tools=tools, add_generation_prompt=False, tokenize=True, return_tensors=\"pt\")\n",
|
92 |
+
"\n",
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93 |
+
"\n",
|
94 |
+
" outputs = model.generate(tokenized_chat, max_new_tokens=128)\n",
|
95 |
+
" # return tokenizer.batch_decode(outputs[:, tokenized_chat.shape[1]:])[0]\n",
|
96 |
+
" return tokenizer.batch_decode(outputs)[0]"
|
97 |
+
]
|
98 |
+
},
|
99 |
+
{
|
100 |
+
"cell_type": "code",
|
101 |
+
"execution_count": 3,
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102 |
+
"metadata": {},
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103 |
+
"outputs": [
|
104 |
+
{
|
105 |
+
"name": "stdout",
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106 |
+
"output_type": "stream",
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107 |
+
"text": [
|
108 |
+
"{{- bos_token }}\n",
|
109 |
+
"{%- if custom_tools is defined %}\n",
|
110 |
+
" {%- set tools = custom_tools %}\n",
|
111 |
+
"{%- endif %}\n",
|
112 |
+
"{%- if not tools_in_user_message is defined %}\n",
|
113 |
+
" {%- set tools_in_user_message = true %}\n",
|
114 |
+
"{%- endif %}\n",
|
115 |
+
"{%- if not date_string is defined %}\n",
|
116 |
+
" {%- set date_string = \"26 Jul 2024\" %}\n",
|
117 |
+
"{%- endif %}\n",
|
118 |
+
"{%- if not tools is defined %}\n",
|
119 |
+
" {%- set tools = none %}\n",
|
120 |
+
"{%- endif %}\n",
|
121 |
+
"\n",
|
122 |
+
"{#- This block extracts the system message, so we can slot it into the right place. #}\n",
|
123 |
+
"{%- if messages[0]['role'] == 'system' %}\n",
|
124 |
+
" {%- set system_message = messages[0]['content']|trim %}\n",
|
125 |
+
" {%- set messages = messages[1:] %}\n",
|
126 |
+
"{%- else %}\n",
|
127 |
+
" {%- set system_message = \"\" %}\n",
|
128 |
+
"{%- endif %}\n",
|
129 |
+
"\n",
|
130 |
+
"{#- System message + builtin tools #}\n",
|
131 |
+
"{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n",
|
132 |
+
"{%- if builtin_tools is defined or tools is not none %}\n",
|
133 |
+
" {{- \"Environment: ipython\\n\" }}\n",
|
134 |
+
"{%- endif %}\n",
|
135 |
+
"{%- if builtin_tools is defined %}\n",
|
136 |
+
" {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n",
|
137 |
+
"{%- endif %}\n",
|
138 |
+
"{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n",
|
139 |
+
"{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n",
|
140 |
+
"{%- if tools is not none and not tools_in_user_message %}\n",
|
141 |
+
" {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n",
|
142 |
+
" {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n",
|
143 |
+
" {{- \"Do not use variables.\\n\\n\" }}\n",
|
144 |
+
" {%- for t in tools %}\n",
|
145 |
+
" {{- t | tojson(indent=4) }}\n",
|
146 |
+
" {{- \"\\n\\n\" }}\n",
|
147 |
+
" {%- endfor %}\n",
|
148 |
+
"{%- endif %}\n",
|
149 |
+
"{{- system_message }}\n",
|
150 |
+
"{{- \"<|eot_id|>\" }}\n",
|
151 |
+
"\n",
|
152 |
+
"{#- Custom tools are passed in a user message with some extra guidance #}\n",
|
153 |
+
"{%- if tools_in_user_message and not tools is none %}\n",
|
154 |
+
" {#- Extract the first user message so we can plug it in here #}\n",
|
155 |
+
" {%- if messages | length != 0 %}\n",
|
156 |
+
" {%- set first_user_message = messages[0]['content']|trim %}\n",
|
157 |
+
" {%- set messages = messages[1:] %}\n",
|
158 |
+
" {%- else %}\n",
|
159 |
+
" {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n",
|
160 |
+
"{%- endif %}\n",
|
161 |
+
" {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n",
|
162 |
+
" {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n",
|
163 |
+
" {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n",
|
164 |
+
" {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n",
|
165 |
+
" {{- \"Do not use variables.\\n\\n\" }}\n",
|
166 |
+
" {%- for t in tools %}\n",
|
167 |
+
" {{- t | tojson(indent=4) }}\n",
|
168 |
+
" {{- \"\\n\\n\" }}\n",
|
169 |
+
" {%- endfor %}\n",
|
170 |
+
" {{- first_user_message + \"<|eot_id|>\"}}\n",
|
171 |
+
"{%- endif %}\n",
|
172 |
+
"\n",
|
173 |
+
"{%- for message in messages %}\n",
|
174 |
+
" {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n",
|
175 |
+
" {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n",
|
176 |
+
" {%- elif 'tool_calls' in message %}\n",
|
177 |
+
" {%- if not message.tool_calls|length == 1 %}\n",
|
178 |
+
" {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n",
|
179 |
+
" {%- endif %}\n",
|
180 |
+
" {%- set tool_call = message.tool_calls[0].function %}\n",
|
181 |
+
" {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n",
|
182 |
+
" {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n",
|
183 |
+
" {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n",
|
184 |
+
" {%- for arg_name, arg_val in tool_call.arguments | items %}\n",
|
185 |
+
" {{- arg_name + '=\"' + arg_val + '\"' }}\n",
|
186 |
+
" {%- if not loop.last %}\n",
|
187 |
+
" {{- \", \" }}\n",
|
188 |
+
" {%- endif %}\n",
|
189 |
+
" {%- endfor %}\n",
|
190 |
+
" {{- \")\" }}\n",
|
191 |
+
" {%- else %}\n",
|
192 |
+
" {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n",
|
193 |
+
" {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n",
|
194 |
+
" {{- '\"parameters\": ' }}\n",
|
195 |
+
" {{- tool_call.arguments | tojson }}\n",
|
196 |
+
" {{- \"}\" }}\n",
|
197 |
+
" {%- endif %}\n",
|
198 |
+
" {%- if builtin_tools is defined %}\n",
|
199 |
+
" {#- This means we're in ipython mode #}\n",
|
200 |
+
" {{- \"<|eom_id|>\" }}\n",
|
201 |
+
" {%- else %}\n",
|
202 |
+
" {{- \"<|eot_id|>\" }}\n",
|
203 |
+
" {%- endif %}\n",
|
204 |
+
" {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n",
|
205 |
+
" {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n",
|
206 |
+
" {%- if message.content is mapping or message.content is iterable %}\n",
|
207 |
+
" {{- message.content | tojson }}\n",
|
208 |
+
" {%- else %}\n",
|
209 |
+
" {{- message.content }}\n",
|
210 |
+
" {%- endif %}\n",
|
211 |
+
" {{- \"<|eot_id|>\" }}\n",
|
212 |
+
" {%- endif %}\n",
|
213 |
+
"{%- endfor %}\n",
|
214 |
+
"{%- if add_generation_prompt %}\n",
|
215 |
+
" {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n",
|
216 |
+
"{%- endif %}\n",
|
217 |
+
"\n"
|
218 |
+
]
|
219 |
+
}
|
220 |
+
],
|
221 |
+
"source": [
|
222 |
+
"print(tokenizer.chat_template)"
|
223 |
+
]
|
224 |
+
},
|
225 |
+
{
|
226 |
+
"cell_type": "code",
|
227 |
+
"execution_count": 4,
|
228 |
+
"metadata": {},
|
229 |
+
"outputs": [
|
230 |
+
{
|
231 |
+
"data": {
|
232 |
+
"text/plain": [
|
233 |
+
"[{'type': 'function',\n",
|
234 |
+
" 'function': {'name': 'search_person',\n",
|
235 |
+
" 'description': 'Search for people in the entertainment industry.',\n",
|
236 |
+
" 'parameters': {'type': 'object',\n",
|
237 |
+
" 'properties': {'query': {'type': 'string',\n",
|
238 |
+
" 'description': 'The search query for the person'},\n",
|
239 |
+
" 'include_adult': {'type': 'boolean',\n",
|
240 |
+
" 'description': 'Include adult (pornography) content in the results',\n",
|
241 |
+
" 'default': False},\n",
|
242 |
+
" 'language': {'type': 'string',\n",
|
243 |
+
" 'description': 'Language for the search results',\n",
|
244 |
+
" 'default': 'en-US'},\n",
|
245 |
+
" 'page': {'type': 'integer',\n",
|
246 |
+
" 'description': 'Page number of results',\n",
|
247 |
+
" 'default': 1}},\n",
|
248 |
+
" 'required': ['query']}}},\n",
|
249 |
+
" {'type': 'function',\n",
|
250 |
+
" 'function': {'name': 'get_person_details',\n",
|
251 |
+
" 'description': 'Get detailed information about a specific person.',\n",
|
252 |
+
" 'parameters': {'type': 'object',\n",
|
253 |
+
" 'properties': {'person_id': {'type': 'integer',\n",
|
254 |
+
" 'description': 'The ID of the person to get details for'},\n",
|
255 |
+
" 'language': {'type': 'string',\n",
|
256 |
+
" 'description': 'Language for the person details',\n",
|
257 |
+
" 'default': 'en-US'},\n",
|
258 |
+
" 'append_to_response': {'type': 'string',\n",
|
259 |
+
" 'description': \"Comma-separated list of additional details to append to the response (e.g., 'images,credits')\"}},\n",
|
260 |
+
" 'required': ['person_id']}}}]"
|
261 |
+
]
|
262 |
+
},
|
263 |
+
"execution_count": 4,
|
264 |
+
"metadata": {},
|
265 |
+
"output_type": "execute_result"
|
266 |
+
}
|
267 |
+
],
|
268 |
+
"source": [
|
269 |
+
"tools = [\n",
|
270 |
+
" {'type': 'function', 'function': {'name': 'search_person'}},\n",
|
271 |
+
" {'type': 'function', 'function': {'name': 'get_person_details'}} \n",
|
272 |
+
"]\n",
|
273 |
+
"\n",
|
274 |
+
"messages = [\n",
|
275 |
+
" {\"role\": \"system\", \"content\": \"You are a movie search assistant bot who uses TMDB to help users find movies. Think step by step and identify the sequence of function calls that will help to answer.\"},\n",
|
276 |
+
" {\"role\": \"user\", \"content\": \"\"\"Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.\n",
|
277 |
+
" Reasoning chain:\n",
|
278 |
+
" \"\"\"},\n",
|
279 |
+
" {\"role\": \"assistant\", \"content\": \"Model response\"},\n",
|
280 |
+
" ]\n",
|
281 |
+
"\n",
|
282 |
+
"\n",
|
283 |
+
"expected_rendered_text = \"\"\"\n",
|
284 |
+
"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
|
285 |
+
"\n",
|
286 |
+
"Environment: ipython\n",
|
287 |
+
"Cutting Knowledge Date: December 2023\n",
|
288 |
+
"Today Date: 26 Jul 2024\n",
|
289 |
+
"\n",
|
290 |
+
"You are a movie search assistant bot who uses TMDB to help users find movies. Think step by step and identify the sequence of function calls that will help to answer.<|eot_id|>\n",
|
291 |
+
"<|start_header_id|>user<|end_header_id|>\n",
|
292 |
+
"Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.\n",
|
293 |
+
"<|eot_id|>\n",
|
294 |
+
"<|start_header_id|>assistant<|end_header_id|>model_response<|eot_id|>\n",
|
295 |
+
"<|start_header_id|>user<|end_header_id|>\n",
|
296 |
+
"Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.\n",
|
297 |
+
"\n",
|
298 |
+
"Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.Do not use variables.\n",
|
299 |
+
"\n",
|
300 |
+
"{\n",
|
301 |
+
" \"type\": \"function\",\n",
|
302 |
+
" \"function\": {\n",
|
303 |
+
" \"name\": \"discover_movie\"}\n",
|
304 |
+
"}\n",
|
305 |
+
"\n",
|
306 |
+
"{\n",
|
307 |
+
" \"type\": \"function\",\n",
|
308 |
+
" \"function\": {\n",
|
309 |
+
" \"name\": \"get_person_details\"}\n",
|
310 |
+
"}\n",
|
311 |
+
"\"\"\"\n"
|
312 |
+
]
|
313 |
+
},
|
314 |
+
{
|
315 |
+
"cell_type": "code",
|
316 |
+
"execution_count": 4,
|
317 |
+
"metadata": {},
|
318 |
+
"outputs": [
|
319 |
+
{
|
320 |
+
"name": "stdout",
|
321 |
+
"output_type": "stream",
|
322 |
+
"text": [
|
323 |
+
"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
|
324 |
+
"\n",
|
325 |
+
"Environment: ipython\n",
|
326 |
+
"Cutting Knowledge Date: December 2023\n",
|
327 |
+
"Today Date: 26 Jul 2024\n",
|
328 |
+
"\n",
|
329 |
+
"You are a movie search assistant bot who uses TMDB to help users find movies. Think step by step and identify the sequence of function calls that will help to answer.<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
|
330 |
+
"\n",
|
331 |
+
"Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.\n",
|
332 |
+
"\n",
|
333 |
+
"Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.Do not use variables.\n",
|
334 |
+
"\n",
|
335 |
+
"{\n",
|
336 |
+
" \"type\": \"function\",\n",
|
337 |
+
" \"function\": {\n",
|
338 |
+
" \"name\": \"discover_movie\",\n",
|
339 |
+
" \"description\": \"Find movies using over 30 filters and sort options\",\n",
|
340 |
+
" \"parameters\": {\n",
|
341 |
+
" \"type\": \"object\",\n",
|
342 |
+
" \"properties\": {\n",
|
343 |
+
" \"region\": {\n",
|
344 |
+
" \"type\": \"string\",\n",
|
345 |
+
" \"description\": \"ISO 3166-1 code to filter release dates\"\n",
|
346 |
+
" },\n",
|
347 |
+
" \"sort_by\": {\n",
|
348 |
+
" \"type\": \"string\",\n",
|
349 |
+
" \"description\": \"Sort the results\"\n",
|
350 |
+
" },\n",
|
351 |
+
" \"release_date.gte\": {\n",
|
352 |
+
" \"type\": \"string\",\n",
|
353 |
+
" \"description\": \"Filter and only include movies that have a release date (looking at all release dates) that is greater or equal to the specified value\"\n",
|
354 |
+
" },\n",
|
355 |
+
" \"release_date.lte\": {\n",
|
356 |
+
" \"type\": \"string\",\n",
|
357 |
+
" \"description\": \"Filter and only include movies that have a release date (looking at all release dates) that is less than or equal to the specified value\"\n",
|
358 |
+
" },\n",
|
359 |
+
" \"with_release_type\": {\n",
|
360 |
+
" \"type\": \"integer\",\n",
|
361 |
+
" \"description\": \"Specify a comma (AND) or pipe (OR) separated value to filter release types\"\n",
|
362 |
+
" },\n",
|
363 |
+
" \"year\": {\n",
|
364 |
+
" \"type\": \"integer\",\n",
|
365 |
+
" \"description\": \"Filter the results to only include movies that have a release year that equals the specified value\"\n",
|
366 |
+
" },\n",
|
367 |
+
" \"with_cast\": {\n",
|
368 |
+
" \"type\": \"string\",\n",
|
369 |
+
" \"description\": \"A comma separated list of person ID's to filter the results with\"\n",
|
370 |
+
" },\n",
|
371 |
+
" \"with_crew\": {\n",
|
372 |
+
" \"type\": \"string\",\n",
|
373 |
+
" \"description\": \"A comma separated list of person ID's to filter the results with\"\n",
|
374 |
+
" },\n",
|
375 |
+
" \"with_people\": {\n",
|
376 |
+
" \"type\": \"string\",\n",
|
377 |
+
" \"description\": \"A comma separated list of person ID's to filter the results with\"\n",
|
378 |
+
" },\n",
|
379 |
+
" \"with_companies\": {\n",
|
380 |
+
" \"type\": \"string\",\n",
|
381 |
+
" \"description\": \"A comma separated list of production company ID's to filter the results with\"\n",
|
382 |
+
" },\n",
|
383 |
+
" \"with_genres\": {\n",
|
384 |
+
" \"type\": \"string\",\n",
|
385 |
+
" \"description\": \"A comma separated list of genre ID's to filter the results with\"\n",
|
386 |
+
" },\n",
|
387 |
+
" \"without_genres\": {\n",
|
388 |
+
" \"type\": \"string\",\n",
|
389 |
+
" \"description\": \"A comma separated list of genre ID's to exclude from the results\"\n",
|
390 |
+
" },\n",
|
391 |
+
" \"with_keywords\": {\n",
|
392 |
+
" \"type\": \"string\",\n",
|
393 |
+
" \"description\": \"A comma separated list of keyword ID's to filter the results with\"\n",
|
394 |
+
" },\n",
|
395 |
+
" \"without_keywords\": {\n",
|
396 |
+
" \"type\": \"string\",\n",
|
397 |
+
" \"description\": \"A comma separated list of keyword ID's to exclude from the results\"\n",
|
398 |
+
" }\n",
|
399 |
+
" },\n",
|
400 |
+
" \"required\": []\n",
|
401 |
+
" }\n",
|
402 |
+
" }\n",
|
403 |
+
"}\n",
|
404 |
+
"\n",
|
405 |
+
"{\n",
|
406 |
+
" \"type\": \"function\",\n",
|
407 |
+
" \"function\": {\n",
|
408 |
+
" \"name\": \"get_movie_details\",\n",
|
409 |
+
" \"description\": \"Get the top level details of a movie by ID\",\n",
|
410 |
+
" \"parameters\": {\n",
|
411 |
+
" \"type\": \"object\",\n",
|
412 |
+
" \"properties\": {\n",
|
413 |
+
" \"movie_id\": {\n",
|
414 |
+
" \"type\": \"integer\",\n",
|
415 |
+
" \"description\": \"The ID of the movie to get details for\"\n",
|
416 |
+
" },\n",
|
417 |
+
" \"append_to_response\": {\n",
|
418 |
+
" \"type\": \"string\",\n",
|
419 |
+
" \"description\": \"Comma-separated list of sub requests to append to the response\"\n",
|
420 |
+
" }\n",
|
421 |
+
" },\n",
|
422 |
+
" \"required\": [\n",
|
423 |
+
" \"movie_id\"\n",
|
424 |
+
" ]\n",
|
425 |
+
" }\n",
|
426 |
+
" }\n",
|
427 |
+
"}\n",
|
428 |
+
"\n",
|
429 |
+
"{\n",
|
430 |
+
" \"type\": \"function\",\n",
|
431 |
+
" \"function\": {\n",
|
432 |
+
" \"name\": \"search_person\",\n",
|
433 |
+
" \"description\": \"Search for people in the entertainment industry.\",\n",
|
434 |
+
" \"parameters\": {\n",
|
435 |
+
" \"type\": \"object\",\n",
|
436 |
+
" \"properties\": {\n",
|
437 |
+
" \"query\": {\n",
|
438 |
+
" \"type\": \"string\",\n",
|
439 |
+
" \"description\": \"The search query for the person\"\n",
|
440 |
+
" },\n",
|
441 |
+
" \"include_adult\": {\n",
|
442 |
+
" \"type\": \"boolean\",\n",
|
443 |
+
" \"description\": \"Include adult (pornography) content in the results\",\n",
|
444 |
+
" \"default\": false\n",
|
445 |
+
" },\n",
|
446 |
+
" \"language\": {\n",
|
447 |
+
" \"type\": \"string\",\n",
|
448 |
+
" \"description\": \"Language for the search results\",\n",
|
449 |
+
" \"default\": \"en-US\"\n",
|
450 |
+
" },\n",
|
451 |
+
" \"page\": {\n",
|
452 |
+
" \"type\": \"integer\",\n",
|
453 |
+
" \"description\": \"Page number of results\",\n",
|
454 |
+
" \"default\": 1\n",
|
455 |
+
" }\n",
|
456 |
+
" },\n",
|
457 |
+
" \"required\": [\n",
|
458 |
+
" \"query\"\n",
|
459 |
+
" ]\n",
|
460 |
+
" }\n",
|
461 |
+
" }\n",
|
462 |
+
"}\n",
|
463 |
+
"\n",
|
464 |
+
"{\n",
|
465 |
+
" \"type\": \"function\",\n",
|
466 |
+
" \"function\": {\n",
|
467 |
+
" \"name\": \"get_person_details\",\n",
|
468 |
+
" \"description\": \"Get detailed information about a specific person.\",\n",
|
469 |
+
" \"parameters\": {\n",
|
470 |
+
" \"type\": \"object\",\n",
|
471 |
+
" \"properties\": {\n",
|
472 |
+
" \"person_id\": {\n",
|
473 |
+
" \"type\": \"integer\",\n",
|
474 |
+
" \"description\": \"The ID of the person to get details for\"\n",
|
475 |
+
" },\n",
|
476 |
+
" \"language\": {\n",
|
477 |
+
" \"type\": \"string\",\n",
|
478 |
+
" \"description\": \"Language for the person details\",\n",
|
479 |
+
" \"default\": \"en-US\"\n",
|
480 |
+
" },\n",
|
481 |
+
" \"append_to_response\": {\n",
|
482 |
+
" \"type\": \"string\",\n",
|
483 |
+
" \"description\": \"Comma-separated list of additional details to append to the response (e.g., 'images,credits')\"\n",
|
484 |
+
" }\n",
|
485 |
+
" },\n",
|
486 |
+
" \"required\": [\n",
|
487 |
+
" \"person_id\"\n",
|
488 |
+
" ]\n",
|
489 |
+
" }\n",
|
490 |
+
" }\n",
|
491 |
+
"}\n",
|
492 |
+
"\n",
|
493 |
+
"{\n",
|
494 |
+
" \"type\": \"function\",\n",
|
495 |
+
" \"function\": {\n",
|
496 |
+
" \"name\": \"get_movie_genres\",\n",
|
497 |
+
" \"description\": \"Get the list of official genres for movies.\",\n",
|
498 |
+
" \"parameters\": {\n",
|
499 |
+
" \"type\": \"object\",\n",
|
500 |
+
" \"properties\": {\n",
|
501 |
+
" \"language\": {\n",
|
502 |
+
" \"type\": \"string\",\n",
|
503 |
+
" \"description\": \"Language for the genre names\",\n",
|
504 |
+
" \"default\": \"en-US\"\n",
|
505 |
+
" }\n",
|
506 |
+
" }\n",
|
507 |
+
" }\n",
|
508 |
+
" }\n",
|
509 |
+
"}\n",
|
510 |
+
"\n",
|
511 |
+
"Given the user query: \"What are the genres of the movie 'The Dark Knight'?\"\n",
|
512 |
+
" Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.<|eot_id|>\n"
|
513 |
+
]
|
514 |
+
}
|
515 |
+
],
|
516 |
+
"source": [
|
517 |
+
"import textwrap\n",
|
518 |
+
"\n",
|
519 |
+
"\n",
|
520 |
+
"query = \"What are the genres of the movie 'The Dark Knight'?\"\n",
|
521 |
+
"\n",
|
522 |
+
"user_message = f\"\"\"\n",
|
523 |
+
" Given the user query: \"{query}\"\n",
|
524 |
+
" Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.\n",
|
525 |
+
" \"\"\"\n",
|
526 |
+
"messages = [\n",
|
527 |
+
" {\"role\": \"system\", \"content\": \"You are a movie search assistant bot who uses TMDB to help users find movies. Think step by step and identify the sequence of function calls that will help to answer.\"},\n",
|
528 |
+
" {\"role\": \"user\", \"content\": user_message},\n",
|
529 |
+
" ]\n",
|
530 |
+
"\n",
|
531 |
+
"chat = tokenizer.apply_chat_template(\n",
|
532 |
+
" messages, tools=tools, \n",
|
533 |
+
" add_generation_prompt=False, \n",
|
534 |
+
" tools_in_user_message=True,\n",
|
535 |
+
" tokenize=False, \n",
|
536 |
+
" return_tensors=\"pt\")\n",
|
537 |
+
"print(chat)\n"
|
538 |
+
]
|
539 |
+
},
|
540 |
+
{
|
541 |
+
"cell_type": "code",
|
542 |
+
"execution_count": null,
|
543 |
+
"metadata": {},
|
544 |
+
"outputs": [],
|
545 |
+
"source": [
|
546 |
+
"response = generate_reasoning_chain(\"What are the genres of the movie 'The Dark Knight'?\")\n",
|
547 |
+
"print(response)"
|
548 |
+
]
|
549 |
+
},
|
550 |
+
{
|
551 |
+
"cell_type": "code",
|
552 |
+
"execution_count": null,
|
553 |
+
"metadata": {},
|
554 |
+
"outputs": [],
|
555 |
+
"source": [
|
556 |
+
"\"\"\"\n",
|
557 |
+
"<|begin_of_text|>\n",
|
558 |
+
"<|start_header_id|>system<|end_header_id|> {{ system_prompt }}<|eot_id|>\n",
|
559 |
+
"<|start_header_id|>user<|end_header_id|> {{ user_message_1 }}<|eot_id|>\n",
|
560 |
+
"<|start_header_id|>assistant<|end_header_id|> <|python_tag|>{{ model_tool_call_1 }}<|eom_id|>\n",
|
561 |
+
"<|start_header_id|>ipython<|end_header_id|> {{ tool_response }}<|eot_id|>\n",
|
562 |
+
"<|start_header_id|>assistant<|end_header_id|> {{ model_response_based_on_tool_response }}<|eot_id|>\n",
|
563 |
+
"\"\"\"\n",
|
564 |
+
"print(response)"
|
565 |
+
]
|
566 |
+
}
|
567 |
+
],
|
568 |
+
"metadata": {
|
569 |
+
"kernelspec": {
|
570 |
+
"display_name": "movies-app",
|
571 |
+
"language": "python",
|
572 |
+
"name": "python3"
|
573 |
+
},
|
574 |
+
"language_info": {
|
575 |
+
"codemirror_mode": {
|
576 |
+
"name": "ipython",
|
577 |
+
"version": 3
|
578 |
+
},
|
579 |
+
"file_extension": ".py",
|
580 |
+
"mimetype": "text/x-python",
|
581 |
+
"name": "python",
|
582 |
+
"nbconvert_exporter": "python",
|
583 |
+
"pygments_lexer": "ipython3",
|
584 |
+
"version": "3.11.9"
|
585 |
+
}
|
586 |
+
},
|
587 |
+
"nbformat": 4,
|
588 |
+
"nbformat_minor": 2
|
589 |
+
}
|
tools.py
CHANGED
@@ -7,14 +7,6 @@ tools = [
|
|
7 |
"parameters": {
|
8 |
"type": "object",
|
9 |
"properties": {
|
10 |
-
"region": {
|
11 |
-
"type": "string",
|
12 |
-
"description": "ISO 3166-1 code to filter release dates",
|
13 |
-
},
|
14 |
-
"sort_by": {
|
15 |
-
"type": "string",
|
16 |
-
"description": "Sort the results",
|
17 |
-
},
|
18 |
"release_date.gte": {
|
19 |
"type": "string",
|
20 |
"description": "Filter and only include movies that have a release date (looking at all release dates) that is greater or equal to the specified value",
|
@@ -33,35 +25,11 @@ tools = [
|
|
33 |
},
|
34 |
"with_cast": {
|
35 |
"type": "string",
|
36 |
-
"description": "A comma separated list of person ID's to filter the results with",
|
37 |
-
},
|
38 |
-
"with_crew": {
|
39 |
-
"type": "string",
|
40 |
-
"description": "A comma separated list of person ID's to filter the results with",
|
41 |
-
},
|
42 |
-
"with_people": {
|
43 |
-
"type": "string",
|
44 |
-
"description": "A comma separated list of person ID's to filter the results with",
|
45 |
-
},
|
46 |
-
"with_companies": {
|
47 |
-
"type": "string",
|
48 |
-
"description": "A comma separated list of production company ID's to filter the results with",
|
49 |
},
|
50 |
"with_genres": {
|
51 |
"type": "string",
|
52 |
-
"description": "A comma separated list of genre ID's to filter the results with",
|
53 |
-
},
|
54 |
-
"without_genres": {
|
55 |
-
"type": "string",
|
56 |
-
"description": "A comma separated list of genre ID's to exclude from the results",
|
57 |
-
},
|
58 |
-
"with_keywords": {
|
59 |
-
"type": "string",
|
60 |
-
"description": "A comma separated list of keyword ID's to filter the results with",
|
61 |
-
},
|
62 |
-
"without_keywords": {
|
63 |
-
"type": "string",
|
64 |
-
"description": "A comma separated list of keyword ID's to exclude from the results",
|
65 |
},
|
66 |
},
|
67 |
"required": [],
|
@@ -78,11 +46,7 @@ tools = [
|
|
78 |
"properties": {
|
79 |
"movie_id": {
|
80 |
"type": "integer",
|
81 |
-
"description": "The ID of the movie to get details for",
|
82 |
-
},
|
83 |
-
"append_to_response": {
|
84 |
-
"type": "string",
|
85 |
-
"description": "Comma-separated list of sub requests to append to the response",
|
86 |
},
|
87 |
},
|
88 |
"required": ["movie_id"],
|
@@ -101,21 +65,6 @@ tools = [
|
|
101 |
"type": "string",
|
102 |
"description": "The search query for the person"
|
103 |
},
|
104 |
-
"include_adult": {
|
105 |
-
"type": "boolean",
|
106 |
-
"description": "Include adult (pornography) content in the results",
|
107 |
-
"default": False
|
108 |
-
},
|
109 |
-
"language": {
|
110 |
-
"type": "string",
|
111 |
-
"description": "Language for the search results",
|
112 |
-
"default": "en-US"
|
113 |
-
},
|
114 |
-
"page": {
|
115 |
-
"type": "integer",
|
116 |
-
"description": "Page number of results",
|
117 |
-
"default": 1
|
118 |
-
}
|
119 |
},
|
120 |
"required": ["query"]
|
121 |
}
|
@@ -131,17 +80,8 @@ tools = [
|
|
131 |
"properties": {
|
132 |
"person_id": {
|
133 |
"type": "integer",
|
134 |
-
"description": "The ID of the person to get details for"
|
135 |
},
|
136 |
-
"language": {
|
137 |
-
"type": "string",
|
138 |
-
"description": "Language for the person details",
|
139 |
-
"default": "en-US"
|
140 |
-
},
|
141 |
-
"append_to_response": {
|
142 |
-
"type": "string",
|
143 |
-
"description": "Comma-separated list of additional details to append to the response (e.g., 'images,credits')"
|
144 |
-
}
|
145 |
},
|
146 |
"required": ["person_id"]
|
147 |
}
|
@@ -152,16 +92,6 @@ tools = [
|
|
152 |
"function": {
|
153 |
"name": "get_movie_genres",
|
154 |
"description": "Get the list of official genres for movies.",
|
155 |
-
"parameters": {
|
156 |
-
"type": "object",
|
157 |
-
"properties": {
|
158 |
-
"language": {
|
159 |
-
"type": "string",
|
160 |
-
"description": "Language for the genre names",
|
161 |
-
"default": "en-US"
|
162 |
-
}
|
163 |
-
}
|
164 |
-
}
|
165 |
}
|
166 |
}
|
167 |
]
|
|
|
7 |
"parameters": {
|
8 |
"type": "object",
|
9 |
"properties": {
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
10 |
"release_date.gte": {
|
11 |
"type": "string",
|
12 |
"description": "Filter and only include movies that have a release date (looking at all release dates) that is greater or equal to the specified value",
|
|
|
25 |
},
|
26 |
"with_cast": {
|
27 |
"type": "string",
|
28 |
+
"description": "A comma separated list of person ID's to filter the results with. Use seach_person function to find the ID of a person.",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
29 |
},
|
30 |
"with_genres": {
|
31 |
"type": "string",
|
32 |
+
"description": "A comma separated list of genre ID's to filter the results with. Use get_movie_genres function to find the ID of a genre.",
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|
33 |
},
|
34 |
},
|
35 |
"required": [],
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|
46 |
"properties": {
|
47 |
"movie_id": {
|
48 |
"type": "integer",
|
49 |
+
"description": "The ID of the movie to get details for. Use discover_movie to find the ID of a movie.",
|
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|
50 |
},
|
51 |
},
|
52 |
"required": ["movie_id"],
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|
65 |
"type": "string",
|
66 |
"description": "The search query for the person"
|
67 |
},
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|
68 |
},
|
69 |
"required": ["query"]
|
70 |
}
|
|
|
80 |
"properties": {
|
81 |
"person_id": {
|
82 |
"type": "integer",
|
83 |
+
"description": "The ID of the person to get details for. Use search_person to find the ID of a person."
|
84 |
},
|
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|
85 |
},
|
86 |
"required": ["person_id"]
|
87 |
}
|
|
|
92 |
"function": {
|
93 |
"name": "get_movie_genres",
|
94 |
"description": "Get the list of official genres for movies.",
|
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|
95 |
}
|
96 |
}
|
97 |
]
|