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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n"
]
}
],
"source": [
"print(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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",
" from .autonotebook import tqdm as notebook_tqdm\n",
"DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): huggingface.co:443\n",
"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",
"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",
"DEBUG:bitsandbytes.cextension:Loading bitsandbytes native library from: /home/tamizh/miniconda3/envs/movies-app/lib/python3.11/site-packages/bitsandbytes/libbitsandbytes_cuda121.so\n",
"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",
"Loading checkpoint shards: 100%|██████████| 4/4 [00:07<00:00, 1.81s/it]\n",
"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"
]
}
],
"source": [
"import re\n",
"import json\n",
"\n",
"from functions import *\n",
"from transformers import pipeline\n",
"from tools import tools\n",
"\n",
"import functions\n",
"import torch\n",
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" BitsAndBytesConfig\n",
")\n",
"\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"\n",
"quantization_config = BitsAndBytesConfig(\n",
" load_in_8bit=True,\n",
" load_in_4bit=False,\n",
" bnb_4bit_quant_type=\"nf4\",\n",
" bnb_4bit_compute_dtype=torch.bfloat16\n",
")\n",
"\n",
"model_id = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(model_id, \n",
" device_map=\"auto\", \n",
" quantization_config=quantization_config)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def generate_reasoning_chain(query):\n",
" user_message = f\"\"\"\n",
" Given the user query: \"{query}\"\n",
" Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.\n",
" \"\"\"\n",
"\n",
" messages = [\n",
" {\"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",
" {\"role\": \"user\", \"content\": user_message},\n",
" ]\n",
"\n",
" tokenized_chat = tokenizer.apply_chat_template(\n",
" messages, tools=tools, add_generation_prompt=False, tokenize=True, return_tensors=\"pt\")\n",
"\n",
"\n",
" outputs = model.generate(tokenized_chat, max_new_tokens=128)\n",
" # return tokenizer.batch_decode(outputs[:, tokenized_chat.shape[1]:])[0]\n",
" return tokenizer.batch_decode(outputs)[0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{{- bos_token }}\n",
"{%- if custom_tools is defined %}\n",
" {%- set tools = custom_tools %}\n",
"{%- endif %}\n",
"{%- if not tools_in_user_message is defined %}\n",
" {%- set tools_in_user_message = true %}\n",
"{%- endif %}\n",
"{%- if not date_string is defined %}\n",
" {%- set date_string = \"26 Jul 2024\" %}\n",
"{%- endif %}\n",
"{%- if not tools is defined %}\n",
" {%- set tools = none %}\n",
"{%- endif %}\n",
"\n",
"{#- This block extracts the system message, so we can slot it into the right place. #}\n",
"{%- if messages[0]['role'] == 'system' %}\n",
" {%- set system_message = messages[0]['content']|trim %}\n",
" {%- set messages = messages[1:] %}\n",
"{%- else %}\n",
" {%- set system_message = \"\" %}\n",
"{%- endif %}\n",
"\n",
"{#- System message + builtin tools #}\n",
"{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n",
"{%- if builtin_tools is defined or tools is not none %}\n",
" {{- \"Environment: ipython\\n\" }}\n",
"{%- endif %}\n",
"{%- if builtin_tools is defined %}\n",
" {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n",
"{%- endif %}\n",
"{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n",
"{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n",
"{%- if tools is not none and not tools_in_user_message %}\n",
" {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n",
" {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n",
" {{- \"Do not use variables.\\n\\n\" }}\n",
" {%- for t in tools %}\n",
" {{- t | tojson(indent=4) }}\n",
" {{- \"\\n\\n\" }}\n",
" {%- endfor %}\n",
"{%- endif %}\n",
"{{- system_message }}\n",
"{{- \"<|eot_id|>\" }}\n",
"\n",
"{#- Custom tools are passed in a user message with some extra guidance #}\n",
"{%- if tools_in_user_message and not tools is none %}\n",
" {#- Extract the first user message so we can plug it in here #}\n",
" {%- if messages | length != 0 %}\n",
" {%- set first_user_message = messages[0]['content']|trim %}\n",
" {%- set messages = messages[1:] %}\n",
" {%- else %}\n",
" {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n",
"{%- endif %}\n",
" {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n",
" {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n",
" {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n",
" {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n",
" {{- \"Do not use variables.\\n\\n\" }}\n",
" {%- for t in tools %}\n",
" {{- t | tojson(indent=4) }}\n",
" {{- \"\\n\\n\" }}\n",
" {%- endfor %}\n",
" {{- first_user_message + \"<|eot_id|>\"}}\n",
"{%- endif %}\n",
"\n",
"{%- for message in messages %}\n",
" {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n",
" {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n",
" {%- elif 'tool_calls' in message %}\n",
" {%- if not message.tool_calls|length == 1 %}\n",
" {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n",
" {%- endif %}\n",
" {%- set tool_call = message.tool_calls[0].function %}\n",
" {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n",
" {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n",
" {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n",
" {%- for arg_name, arg_val in tool_call.arguments | items %}\n",
" {{- arg_name + '=\"' + arg_val + '\"' }}\n",
" {%- if not loop.last %}\n",
" {{- \", \" }}\n",
" {%- endif %}\n",
" {%- endfor %}\n",
" {{- \")\" }}\n",
" {%- else %}\n",
" {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n",
" {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n",
" {{- '\"parameters\": ' }}\n",
" {{- tool_call.arguments | tojson }}\n",
" {{- \"}\" }}\n",
" {%- endif %}\n",
" {%- if builtin_tools is defined %}\n",
" {#- This means we're in ipython mode #}\n",
" {{- \"<|eom_id|>\" }}\n",
" {%- else %}\n",
" {{- \"<|eot_id|>\" }}\n",
" {%- endif %}\n",
" {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n",
" {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n",
" {%- if message.content is mapping or message.content is iterable %}\n",
" {{- message.content | tojson }}\n",
" {%- else %}\n",
" {{- message.content }}\n",
" {%- endif %}\n",
" {{- \"<|eot_id|>\" }}\n",
" {%- endif %}\n",
"{%- endfor %}\n",
"{%- if add_generation_prompt %}\n",
" {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n",
"{%- endif %}\n",
"\n"
]
}
],
"source": [
"print(tokenizer.chat_template)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'type': 'function',\n",
" 'function': {'name': 'search_person',\n",
" 'description': 'Search for people in the entertainment industry.',\n",
" 'parameters': {'type': 'object',\n",
" 'properties': {'query': {'type': 'string',\n",
" 'description': 'The search query for the person'},\n",
" 'include_adult': {'type': 'boolean',\n",
" 'description': 'Include adult (pornography) content in the results',\n",
" 'default': False},\n",
" 'language': {'type': 'string',\n",
" 'description': 'Language for the search results',\n",
" 'default': 'en-US'},\n",
" 'page': {'type': 'integer',\n",
" 'description': 'Page number of results',\n",
" 'default': 1}},\n",
" 'required': ['query']}}},\n",
" {'type': 'function',\n",
" 'function': {'name': 'get_person_details',\n",
" 'description': 'Get detailed information about a specific person.',\n",
" 'parameters': {'type': 'object',\n",
" 'properties': {'person_id': {'type': 'integer',\n",
" 'description': 'The ID of the person to get details for'},\n",
" 'language': {'type': 'string',\n",
" 'description': 'Language for the person details',\n",
" 'default': 'en-US'},\n",
" 'append_to_response': {'type': 'string',\n",
" 'description': \"Comma-separated list of additional details to append to the response (e.g., 'images,credits')\"}},\n",
" 'required': ['person_id']}}}]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tools = [\n",
" {'type': 'function', 'function': {'name': 'search_person'}},\n",
" {'type': 'function', 'function': {'name': 'get_person_details'}} \n",
"]\n",
"\n",
"messages = [\n",
" {\"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",
" {\"role\": \"user\", \"content\": \"\"\"Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.\n",
" Reasoning chain:\n",
" \"\"\"},\n",
" {\"role\": \"assistant\", \"content\": \"Model response\"},\n",
" ]\n",
"\n",
"\n",
"expected_rendered_text = \"\"\"\n",
"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
"\n",
"Environment: ipython\n",
"Cutting Knowledge Date: December 2023\n",
"Today Date: 26 Jul 2024\n",
"\n",
"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",
"<|start_header_id|>user<|end_header_id|>\n",
"Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.\n",
"<|eot_id|>\n",
"<|start_header_id|>assistant<|end_header_id|>model_response<|eot_id|>\n",
"<|start_header_id|>user<|end_header_id|>\n",
"Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.\n",
"\n",
"Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.Do not use variables.\n",
"\n",
"{\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"discover_movie\"}\n",
"}\n",
"\n",
"{\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_person_details\"}\n",
"}\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
"\n",
"Environment: ipython\n",
"Cutting Knowledge Date: December 2023\n",
"Today Date: 26 Jul 2024\n",
"\n",
"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",
"\n",
"Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.\n",
"\n",
"Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.Do not use variables.\n",
"\n",
"{\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"discover_movie\",\n",
" \"description\": \"Find movies using over 30 filters and sort options\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"region\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"ISO 3166-1 code to filter release dates\"\n",
" },\n",
" \"sort_by\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Sort the results\"\n",
" },\n",
" \"release_date.gte\": {\n",
" \"type\": \"string\",\n",
" \"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",
" },\n",
" \"release_date.lte\": {\n",
" \"type\": \"string\",\n",
" \"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",
" },\n",
" \"with_release_type\": {\n",
" \"type\": \"integer\",\n",
" \"description\": \"Specify a comma (AND) or pipe (OR) separated value to filter release types\"\n",
" },\n",
" \"year\": {\n",
" \"type\": \"integer\",\n",
" \"description\": \"Filter the results to only include movies that have a release year that equals the specified value\"\n",
" },\n",
" \"with_cast\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"A comma separated list of person ID's to filter the results with\"\n",
" },\n",
" \"with_crew\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"A comma separated list of person ID's to filter the results with\"\n",
" },\n",
" \"with_people\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"A comma separated list of person ID's to filter the results with\"\n",
" },\n",
" \"with_companies\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"A comma separated list of production company ID's to filter the results with\"\n",
" },\n",
" \"with_genres\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"A comma separated list of genre ID's to filter the results with\"\n",
" },\n",
" \"without_genres\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"A comma separated list of genre ID's to exclude from the results\"\n",
" },\n",
" \"with_keywords\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"A comma separated list of keyword ID's to filter the results with\"\n",
" },\n",
" \"without_keywords\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"A comma separated list of keyword ID's to exclude from the results\"\n",
" }\n",
" },\n",
" \"required\": []\n",
" }\n",
" }\n",
"}\n",
"\n",
"{\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_movie_details\",\n",
" \"description\": \"Get the top level details of a movie by ID\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"movie_id\": {\n",
" \"type\": \"integer\",\n",
" \"description\": \"The ID of the movie to get details for\"\n",
" },\n",
" \"append_to_response\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Comma-separated list of sub requests to append to the response\"\n",
" }\n",
" },\n",
" \"required\": [\n",
" \"movie_id\"\n",
" ]\n",
" }\n",
" }\n",
"}\n",
"\n",
"{\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"search_person\",\n",
" \"description\": \"Search for people in the entertainment industry.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"query\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The search query for the person\"\n",
" },\n",
" \"include_adult\": {\n",
" \"type\": \"boolean\",\n",
" \"description\": \"Include adult (pornography) content in the results\",\n",
" \"default\": false\n",
" },\n",
" \"language\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Language for the search results\",\n",
" \"default\": \"en-US\"\n",
" },\n",
" \"page\": {\n",
" \"type\": \"integer\",\n",
" \"description\": \"Page number of results\",\n",
" \"default\": 1\n",
" }\n",
" },\n",
" \"required\": [\n",
" \"query\"\n",
" ]\n",
" }\n",
" }\n",
"}\n",
"\n",
"{\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_person_details\",\n",
" \"description\": \"Get detailed information about a specific person.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"person_id\": {\n",
" \"type\": \"integer\",\n",
" \"description\": \"The ID of the person to get details for\"\n",
" },\n",
" \"language\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Language for the person details\",\n",
" \"default\": \"en-US\"\n",
" },\n",
" \"append_to_response\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Comma-separated list of additional details to append to the response (e.g., 'images,credits')\"\n",
" }\n",
" },\n",
" \"required\": [\n",
" \"person_id\"\n",
" ]\n",
" }\n",
" }\n",
"}\n",
"\n",
"{\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_movie_genres\",\n",
" \"description\": \"Get the list of official genres for movies.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"language\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Language for the genre names\",\n",
" \"default\": \"en-US\"\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"Given the user query: \"What are the genres of the movie 'The Dark Knight'?\"\n",
" Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.<|eot_id|>\n"
]
}
],
"source": [
"import textwrap\n",
"\n",
"\n",
"query = \"What are the genres of the movie 'The Dark Knight'?\"\n",
"\n",
"user_message = f\"\"\"\n",
" Given the user query: \"{query}\"\n",
" Generate a multi-step reasoning chain to answer the query. Include steps for using available tools if necessary.\n",
" \"\"\"\n",
"messages = [\n",
" {\"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",
" {\"role\": \"user\", \"content\": user_message},\n",
" ]\n",
"\n",
"chat = tokenizer.apply_chat_template(\n",
" messages, tools=tools, \n",
" add_generation_prompt=False, \n",
" tools_in_user_message=True,\n",
" tokenize=False, \n",
" return_tensors=\"pt\")\n",
"print(chat)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = generate_reasoning_chain(\"What are the genres of the movie 'The Dark Knight'?\")\n",
"print(response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"<|begin_of_text|>\n",
"<|start_header_id|>system<|end_header_id|> {{ system_prompt }}<|eot_id|>\n",
"<|start_header_id|>user<|end_header_id|> {{ user_message_1 }}<|eot_id|>\n",
"<|start_header_id|>assistant<|end_header_id|> <|python_tag|>{{ model_tool_call_1 }}<|eom_id|>\n",
"<|start_header_id|>ipython<|end_header_id|> {{ tool_response }}<|eot_id|>\n",
"<|start_header_id|>assistant<|end_header_id|> {{ model_response_based_on_tool_response }}<|eot_id|>\n",
"\"\"\"\n",
"print(response)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "movies-app",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|