Create a native Agent with OpenVINO#
This Jupyter notebook can be launched after a local installation only.
LLM are limited to the knowledge on which they have been trained and the additional knowledge provided as context, as a result, if a useful piece of information is missing the provided knowledge, the model cannot “go around” and try to find it in other sources. This is the reason why we need to introduce the concept of Agents.
The core idea of agents is to use a language model to choose a sequence of actions to take. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Agents can be seen as applications powered by LLMs and integrated with a set of tools like search engines, databases, websites, and so on. Within an agent, the LLM is the reasoning engine that, based on the user input, is able to plan and execute a set of actions that are needed to fulfill the request.
agent#
This example will demonstrate how to create a native agent with OpenVINO.
Table of contents:
Installation Instructions#
This is a self-contained example that relies solely on its own code.
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.
Prerequisites#
import os
import requests
r = requests.get(
url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)
open("notebook_utils.py", "w").write(r.text)
r = requests.get(
url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/pip_helper.py",
)
open("pip_helper.py", "w").write(r.text)
os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
from pip_helper import pip_install
pip_install(
"-q",
"--extra-index-url",
"https://download.pytorch.org/whl/cpu",
"transformers>=4.43.1",
)
pip_install("-q", "git+https://github.com/huggingface/optimum-intel.git", "git+https://github.com/openvinotoolkit/nncf.git", "datasets", "accelerate")
pip_install("--pre", "-Uq", "openvino>=2024.4.0", "--extra-index-url", "https://storage.openvinotoolkit.org/simple/wheels/nightly")
Create LLM as agent#
Download LLM#
To run LLM locally, we have to download the model in the first step. It is possible to export your model to the OpenVINO IR format with the CLI, and load the model from local folder.
Large Language Models (LLMs) are a core component of agent. LlamaIndex
does not serve its own LLMs, but rather provides a standard interface
for interacting with many different LLMs. In this example, we can select
Qwen2.5 as LLM in agent pipeline.
qwen2.5-3b-instruct/qwen2.5-7b-instruct/qwen2.5-14b-instruct - Qwen2.5 is the latest series of Qwen large language models. Comparing with Qwen2, Qwen2.5 series brings significant improvements in coding, mathematics and general knowledge skills. Additionally, it brings long-context and multiple languages support including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. For more details, please refer to model_card, blog, GitHub, and Documentation.
import ipywidgets as widgets
llm_model_ids = ["Qwen/Qwen2.5-3B-Instruct", "Qwen/Qwen2.5-7B-Instruct", "Qwen/qwen2.5-14b-instruct"]
llm_model_id = widgets.Dropdown(
options=llm_model_ids,
value=llm_model_ids[0],
description="Model:",
disabled=False,
)
llm_model_id
Dropdown(description='Model:', options=('Qwen/Qwen2.5-3B-Instruct', 'Qwen/Qwen2.5-7B-Instruct', 'Qwen/qwen2.5-…
from pathlib import Path
llm_model_path = llm_model_id.value.split("/")[-1]
if not Path(llm_model_path).exists():
!optimum-cli export openvino --model {llm_model_id.value} --task text-generation-with-past --trust-remote-code --weight-format int4 --group-size 128 --ratio 1.0 --sym {llm_model_path}
Select inference device for LLM#
from notebook_utils import device_widget
llm_device = device_widget("CPU", exclude=["NPU"])
llm_device
[ERROR] 20:00:52.380 [NPUBackends] Cannot find backend for inference. Make sure the device is available.
Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
Instantiate LLM using Optimum Intel#
Optimum Intel can be used to load optimized models from the Hugging
Face Hub and
create pipelines to run an inference with OpenVINO Runtime using Hugging
Face APIs. The Optimum Inference models are API compatible with Hugging
Face Transformers models. This means we just need to replace
AutoModelForXxx class with the corresponding OVModelForXxx
class.
Below is an example of the RedPajama model
-from transformers import AutoModelForCausalLM
+from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer, pipeline
model_id = "togethercomputer/RedPajama-INCITE-Chat-3B-v1"
-model = AutoModelForCausalLM.from_pretrained(model_id)
+model = OVModelForCausalLM.from_pretrained(model_id, export=True)
Model class initialization starts with calling from_pretrained
method. When downloading and converting Transformers model, the
parameter export=True should be added (as we already converted model
before, we do not need to provide this parameter). We can save the
converted model for the next usage with the save_pretrained method.
Tokenizer class and pipelines API are compatible with Optimum models.
You can find more details about OpenVINO LLM inference using HuggingFace Optimum API in LLM inference guide.
from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer, AutoConfig, TextStreamer
from transformers.generation import (
StoppingCriteriaList,
StoppingCriteria,
)
import openvino.properties as props
import openvino.properties.hint as hints
import openvino.properties.streams as streams
import json
import json5
import torch
tokenizer = AutoTokenizer.from_pretrained(llm_model_path, trust_remote_code=True)
ov_config = {hints.performance_mode(): hints.PerformanceMode.LATENCY, streams.num(): "1", props.cache_dir(): ""}
llm = OVModelForCausalLM.from_pretrained(
llm_model_path,
device=llm_device.value,
ov_config=ov_config,
config=AutoConfig.from_pretrained(llm_model_path, trust_remote_code=True),
trust_remote_code=True,
)
llm.generation_config.top_k = 1
llm.generation_config.max_length = 2000
Create text generation method#
In this example, we would like to stream the output text though
TextStreamer, and stop text generation before Observation
received from tool calling..
class StopSequenceCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever a sequence of tokens is encountered.
Args:
stop_sequences (`str` or `List[str]`):
The sequence (or list of sequences) on which to stop execution.
tokenizer:
The tokenizer used to decode the model outputs.
"""
def __init__(self, stop_sequences, tokenizer):
if isinstance(stop_sequences, str):
stop_sequences = [stop_sequences]
self.stop_sequences = stop_sequences
self.tokenizer = tokenizer
def __call__(self, input_ids, scores, **kwargs) -> bool:
decoded_output = self.tokenizer.decode(input_ids.tolist()[0])
return any(decoded_output.endswith(stop_sequence) for stop_sequence in self.stop_sequences)
def text_completion(prompt: str, stop_words) -> str:
im_end = "<|im_end|>"
if im_end not in stop_words:
stop_words = stop_words + [im_end]
streamer = TextStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
stopping_criteria = StoppingCriteriaList([StopSequenceCriteria(stop_words, tokenizer)])
input_ids = torch.tensor([tokenizer.encode(prompt)])
generate_kwargs = dict(
input_ids=input_ids,
streamer=streamer,
stopping_criteria=stopping_criteria,
)
output = llm.generate(**generate_kwargs)
output = output.tolist()[0]
output = tokenizer.decode(output, errors="ignore")
assert output.startswith(prompt)
output = output[len(prompt) :].replace("<|endoftext|>", "").replace(im_end, "")
for stop_str in stop_words:
idx = output.find(stop_str)
if idx != -1:
output = output[: idx + len(stop_str)]
return output
Create prompt template#
A prompt for a language model is a set of instructions or input provided by a user to guide the model’s response, helping it understand the context and generate relevant and coherent language-based output, such as answering questions, completing sentences, or engaging in a conversation.
Different agents have different prompting styles for reasoning. In this example, we will use ReAct agent with its typical prompt template. For a full list of built-in agents see agent types.
react#
A ReAct prompt consists of few-shot task-solving trajectories, with human-written text reasoning traces and actions, as well as environment observations in response to actions. ReAct prompting is intuitive and flexible to design, and achieves state-of-the-art few-shot performances across a variety of tasks, from question answering to online shopping!
In an prompt template for agent, query is user’s query and other
parameter should be a sequence of messages that contains the
descriptions and parameters of agent tool.
TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters}"""
PROMPT_REACT = """Answer the following questions as best you can. You have access to the following APIs:
{tools_text}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tools_name_text}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: {query}"""
Meanwhile we have to create function for consolidate the tools information and conversation history into the prompt template.
def build_input_text(chat_history, list_of_tool_info) -> str:
tools_text = []
for tool_info in list_of_tool_info:
tool = TOOL_DESC.format(
name_for_model=tool_info["name_for_model"],
name_for_human=tool_info["name_for_human"],
description_for_model=tool_info["description_for_model"],
parameters=json.dumps(tool_info["parameters"], ensure_ascii=False),
)
if tool_info.get("args_format", "json") == "json":
tool += " Format the arguments as a JSON object."
elif tool_info["args_format"] == "code":
tool += " Enclose the code within triple backticks (`) at the beginning and end of the code."
else:
raise NotImplementedError
tools_text.append(tool)
tools_text = "\n\n".join(tools_text)
tools_name_text = ", ".join([tool_info["name_for_model"] for tool_info in list_of_tool_info])
messages = [{"role": "system", "content": "You are a helpful assistant."}]
for i, (query, response) in enumerate(chat_history):
if list_of_tool_info:
if (len(chat_history) == 1) or (i == len(chat_history) - 2):
query = PROMPT_REACT.format(
tools_text=tools_text,
tools_name_text=tools_name_text,
query=query,
)
if query:
messages.append({"role": "user", "content": query})
if response:
messages.append({"role": "assistant", "content": response})
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False, return_tensors="pt")
return prompt
Create parser#
A Parser is used to convert raw output of LLM to the input arguments of tools.
def parse_latest_tool_call(text):
tool_name, tool_args = "", ""
i = text.rfind("\nAction:")
j = text.rfind("\nAction Input:")
k = text.rfind("\nObservation:")
if 0 <= i < j: # If the text has `Action` and `Action input`,
if k < j: # but does not contain `Observation`,
# then it is likely that `Observation` is ommited by the LLM,
# because the output text may have discarded the stop word.
text = text.rstrip() + "\nObservation:" # Add it back.
k = text.rfind("\nObservation:")
tool_name = text[i + len("\nAction:") : j].strip()
tool_args = text[j + len("\nAction Input:") : k].strip()
text = text[:k]
return tool_name, tool_args, text
Create tools calling#
In this examples, we will create 2 customized tools for
image generation and weather qurey. A detailed description of
these tools should be defined in json format, which will be used as part
of prompt.
tools = [
{
"name_for_human": "get weather",
"name_for_model": "get_weather",
"description_for_model": 'Get the current weather in a given city name."',
"parameters": [
{
"name": "city_name",
"description": "City name",
"required": True,
"schema": {"type": "string"},
}
],
},
{
"name_for_human": "image generation",
"name_for_model": "image_gen",
"description_for_model": "AI painting (image generation) service, input text description, and return the image URL drawn based on text information.",
"parameters": [
{
"name": "prompt",
"description": "describe the image",
"required": True,
"schema": {"type": "string"},
}
],
},
]
Then we should implement these tools with inputs and outputs, and execute them according to the output of LLM.
def call_tool(tool_name: str, tool_args: str) -> str:
if tool_name == "get_weather":
city_name = json5.loads(tool_args)["city_name"]
key_selection = {
"current_condition": [
"temp_C",
"FeelsLikeC",
"humidity",
"weatherDesc",
"observation_time",
],
}
resp = requests.get(f"https://wttr.in/{city_name}?format=j1")
resp.raise_for_status()
resp = resp.json()
ret = {k: {_v: resp[k][0][_v] for _v in v} for k, v in key_selection.items()}
return str(ret)
elif tool_name == "image_gen":
import urllib.parse
tool_args = tool_args.replace("(", "").replace(")", "")
prompt = json5.loads(tool_args)["prompt"]
prompt = urllib.parse.quote(prompt)
return json.dumps(
{"image_url": f"https://image.pollinations.ai/prompt/{prompt}"},
ensure_ascii=False,
)
else:
raise NotImplementedError
def llm_with_tool(prompt: str, history, list_of_tool_info=()):
chat_history = [(x["user"], x["bot"]) for x in history] + [(prompt, "")]
planning_prompt = build_input_text(chat_history, list_of_tool_info)
text = ""
while True:
output = text_completion(planning_prompt + text, stop_words=["Observation:", "Observation:\n"])
action, action_input, output = parse_latest_tool_call(output)
if action:
observation = call_tool(action, action_input)
output += f"\nObservation: = {observation}\nThought:"
observation = f"{observation}\nThought:"
print(observation)
text += output
else:
text += output
break
new_history = []
new_history.extend(history)
new_history.append({"user": prompt, "bot": text})
return text, new_history
Run agent#
history = []
query = "get the weather in London, and create a picture of Big Ben based on the weather information"
response, history = llm_with_tool(prompt=query, history=history, list_of_tool_info=tools)
Thought: First, I need to use the get_weather API to get the current weather in London.
Action: get_weather
Action Input: {"city_name": "London"}
Observation:
{'current_condition': {'temp_C': '11', 'FeelsLikeC': '10', 'humidity': '94', 'weatherDesc': [{'value': 'Overcast'}], 'observation_time': '12:23 AM'}}
Thought:
Now that I have the weather information, I will use the image_gen API to generate an image of Big Ben based on the weather conditions.
Action: image_gen
Action Input: {"prompt": "Big Ben under overcast sky with temperature 11°C and humidity 94%"}
Observation:
{"image_url": "https://image.pollinations.ai/prompt/Big%20Ben%20under%20overcast%20sky%20with%20temperature%2011%C2%B0C%20and%20humidity%2094%25"}
Thought:
The image has been generated successfully.
Final Answer: The current weather in London is overcast with a temperature of 11°C and humidity of 94%. Based on this information, here is the image of Big Ben under an overcast sky: 