Added a demonstartion notebook featuring the usage of Langchain with AutoGen

[Kirushikesh]

Why are these changes needed?

I was trying to use AutoGen powerful agentic framework with the other LLM's but i have seen autogen explicitly supports fewer LLM Providers by default and for the others you need to write a custom configuration, that too is difficult to design for different LLM's/LLM Provider's out there. Since the existing library like LangChain handles the tedious task of LLM compatibility, ig to have this notebook to show the how to use Langchain library with Autogen opens up the space for all the LLM supported by langchain used with Autogen. I have found this related notebook agentchat_langchain.ipynb which shows the langchain with autogen but i feel its not very simple and clear for the new users to understand.

Checks

• I've included any doc changes needed for https://microsoft.github.io/autogen/. See https://microsoft.github.io/autogen/docs/Contribute#documentation to build and test documentation locally.
• I've added tests (if relevant) corresponding to the changes introduced in this PR.
• I've made sure all auto checks have passed.

[Kirushikesh]

@microsoft-github-policy-service agree

[Hk669]

we would love to see the generated chat too, can you run this and push the changes, so it doesnt trouble developers using the notebook directly.

@Kirushikesh thanks for adding the notebook.

[marklysze]

Hey @Kirushikesh, thanks for creating this. Can I just clarify, it seems more that this is the use of LangChain's huggingface library as opposed to integration with LangChain?

Do you think a huggingface client class would be a similar outcome in this approach? It has been mentioned before on AutoGen's Discord and it may be worth creating one.

[Kirushikesh]

Hello @marklysze , for the first question no its not about langchain-huggingface instead its how to use Langchain with Autogen. Currently Langchain supports atmost all the LLM's out there from various the LLM Providers. We can load any LLM through Langchain which creates an abstraction and then use that LLM Class(BaseLanguageModel) with Autogen agentic capabilities. For demonstration i have selected HuggingFace in the notebook. We can literally use it with any LLM supported in Langchain, for ex: the below code explains how to use OpenAI LLM loaded through Langchain in Autogen, yes i know we can use the OpenAI model directly this is just for the demonstration of langchain.

import os
import json
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI

load_dotenv()

class CustomModelClientWithArguments(CustomModelClient):
    def __init__(self, config, **kwargs):
        print(f"CustomModelClientWithArguments config: {config}")

        self.model_name = config["model"]
        gen_config_params = config.get("params", {})

        self.model = ChatOpenAI(model=self.model_name, **gen_config_params)
        print(f"Loaded model {config['model']}")

os.environ["OAI_CONFIG_LIST"] = json.dumps([{
    "model": "gpt-4o-mini",
    "model_client_cls": "CustomModelClientWithArguments",
    "n": 1,
    "params": {
        "max_tokens": 100,
        "top_p": 1,
        "temperature": 0.1,
        "max_retries": 2,
    }
}])

config_list_custom = autogen.config_list_from_json(
    "OAI_CONFIG_LIST",
    filter_dict={"model_client_cls": ["CustomModelClientWithArguments"]},
)

assistant = AssistantAgent("assistant", llm_config={"config_list": config_list_custom})
assistant.register_model_client(
    model_client_cls=CustomModelClientWithArguments,
)

user_proxy.initiate_chat(assistant, message="Write python code to print Hello World!")

For the second question, i am not sure which huggingface client class you are mentioning about. Let me know if you have any further queries :)

[marklysze]

Thanks @Kirushikesh, would you be able to test some non-OpenAI models, such as Anthropic's and Meta's Llamas. The role and name fields aren't always compatible with AutoGen messages.

[Kirushikesh]

Hello @marklysze, unfortunately i don't have anthropic api-keys may be you can help me with this. For Meta Llama do you mean using the model through HuggingFace? If yes, the notebook already demonstrate how to load any model from huggingface, its just about changing microsoft/Phi-3.5-mini-instruct to meta-llama/Meta-Llama-3.1-8B-Instruct.

Just to demonstrate the use of Non-OpenAI model with Langchain, i have used Google Gemini Model loading through langchain here which also works.

import os
import json
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI

load_dotenv()

class CustomModelClientWithGemini(CustomModelClient):
    def __init__(self, config, **kwargs):
        print(f"CustomModelClientWithGemini config: {config}")

        self.model_name = config["model"]
        gen_config_params = config.get("params", {})

        self.model = ChatGoogleGenerativeAI(model=self.model_name, **gen_config_params)
        print(f"Loaded model {config['model']}")


os.environ["OAI_CONFIG_LIST"] = json.dumps([{
    "model": "gemini-1.5-flash",
    "model_client_cls": "CustomModelClientWithGemini",
    "n": 1,
}])

config_list_custom = autogen.config_list_from_json(
    "OAI_CONFIG_LIST",
    filter_dict={"model_client_cls": ["CustomModelClientWithGemini"]},
)

assistant = AssistantAgent("assistant", llm_config={"config_list": config_list_custom})
assistant.register_model_client(
    model_client_cls=CustomModelClientWithGemini,
)

user_proxy.initiate_chat(assistant, message="Write python code to print Hello World!")

Output:

[autogen.oai.client: 09-03 02:34:47] {484} INFO - Detected custom model client in config: CustomModelClientWithGemini, model client can not be used until register_model_client is called.
CustomModelClientWithGemini config: {'model': 'gemini-1.5-flash', 'model_client_cls': 'CustomModelClientWithGemini', 'n': 1}
Loaded model gemini-1.5-flash
�[33muser_proxy�[0m (to assistant):

Write python code to print Hello World!

--------------------------------------------------------------------------------
�[33massistant�[0m (to user_proxy):

```python
# filename: hello.py
print("Hello World!")

Let me know if you have any additional questions.

[gagb]

For some reason I am not able to request a review from @marklysze -- double checking what happened there.

[marklysze]

For some reason I am not able to request a review from @marklysze -- double checking what happened there.

Thanks @gagb, it may be that my permissions have changed? It looks like I can still add a review. I'll try to do a review shortly.

[gagb]

Thats weird-- double checking asap! We are having slightly trouble accessing repo management – will add you asap.

[marklysze]

Thanks @Kirushikesh for the Gemini code sample, that worked for me. Unfortunately the notebook code isn't working for me. It's not getting past this block of code in the init of the CustomModelClient.

        pipeline = HuggingFacePipeline.from_model_id(
            model_id=self.model_name,
            task="text-generation",
            pipeline_kwargs=gen_config_params,
            device=self.device
        )

It is pulling down the tensors but just stops at that point, without an exception. I'm not sure why. I'm running in AutoGen's dev container (docker). Though I ran the Gemini code above and it worked okay. Perhaps it's when pulling a model down.

Is there another model you have had success with (that pulls down to run locally)?

[Kirushikesh]

@marklysze i am not sure what's the issue, its literally loading the model from huggingface. I have tried with mistralai/Mistral-7B-Instruct-v0.1 model as well it worked. Btw i am running it in my local machine not in docker setup. Can you just test whether its possible for you to download a model from huggingface.

[gagb]

Thats weird-- double checking asap! We are having slightly trouble accessing repo management – will add you asap.

@marklysze -- your role had auto-expired, so sorry about this! I believe it was restored can you please check? @jackgerrits told me that you need to accept some invite?

[jackgerrits]

Thats weird-- double checking asap! We are having slightly trouble accessing repo management – will add you asap.

@marklysze -- your role had auto-expired, so sorry about this! I believe it was restored can you please check? @jackgerrits told me that you need to accept some invite?

Sorry about the auto-expire! As soon as you accept I can double check the role too

[marklysze]

Thats weird-- double checking asap! We are having slightly trouble accessing repo management – will add you asap.

@marklysze -- your role had auto-expired, so sorry about this! I believe it was restored can you please check? @jackgerrits told me that you need to accept some invite?

Sorry about the auto-expire! As soon as you accept I can double check the role too

Thanks @gagb and @jackgerrits, got it and I've accepted, if you can check the role that would be appreciated :)

[marklysze]

@marklysze i am not sure what's the issue, its literally loading the model from huggingface. I have tried with mistralai/Mistral-7B-Instruct-v0.1 model as well it worked. Btw i am running it in my local machine not in docker setup. Can you just test whether its possible for you to download a model from huggingface.

Thanks @Kirushikesh, I'll give Mistral a go... It's downloading the models, yep (e.g. Llama 3.1 8B downloaded almost 20GB)

[jackgerrits]

Thats weird-- double checking asap! We are having slightly trouble accessing repo management – will add you asap.

@marklysze -- your role had auto-expired, so sorry about this! I believe it was restored can you please check? @jackgerrits told me that you need to accept some invite?

Sorry about the auto-expire! As soon as you accept I can double check the role too

Thanks @gagb and @jackgerrits, got it and I've accepted, if you can check the role that would be appreciated :)

All sorted now!

[marklysze]

Thanks @gagb and @jackgerrits, got it and I've accepted, if you can check the role that would be appreciated :)

All sorted now!

Looks good, thanks!

[marklysze]

Sorry @Kirushikesh, I had limited success in getting it running in Docker or my Ubuntu environment. I tried Llama 3.1 8B and Mistral 7B v0.3.

Would you be able to test using a couple of my test python scripts under https://github.com/marklysze/AutoGenClientTesting?
test_calc.py
test_chess.py

[Kirushikesh]

@marklysze , sorry i am not sure if there is some issue in my code. I can resolve if you point any, So you want me to use these scripts test_calc.py, test_chess.py to test my current notebook right?. Also these scripts requires tool-calling capabilities which is not possible with llama or mistral models in huggingface ig if i am right.

Can you please tell me whats the error you are facing when running in the docker/ur ubuntu environment?

[marklysze]

@marklysze , sorry i am not sure if there is some issue in my code. I can resolve if you point any, So you want me to use these scripts test_calc.py, test_chess.py to test my current notebook right?. Also these scripts requires tool-calling capabilities which is not possible with llama or mistral models in huggingface ig if i am right.

Can you please tell me whats the error you are facing when running in the docker/ur ubuntu environment?

As I can't test it (it's literally just stopping, no exceptions), I can't check that the custommodelclass works in various scenarios. It seems that tool calling is available, though I don't have any experience using the huggingface API.

So, it would be good for you/someone to test various AutoGen workflows and see how they go.

[Hk669]

As I can't test it (it's literally just stopping, no exceptions), I can't check that the custommodelclass works in various scenarios. It seems that tool calling is available, though I don't have any experience using the huggingface API.

So, it would be good for you/someone to test various AutoGen workflows and see how they go.

me neither have experience with the HuggingFace API. @Kirushikesh let us know your thoughts on exploring the tool call.

[Hk669]

@Kirushikesh also run the command pre-commit run --all-files to fix this code formatting. thanks

[Kirushikesh]

Hello @marklysze, sorry for the delay in addressing the issue.

First, i have updated the notebook by the following changes:

1. Changed the model from Phi-3.5 to Mistral7Bv0.2
2. Added HuggingFace Login code snippet since Mistral is a gated model it requires huggingface login.

(I remember you mentioned that you have tried Llama3.1 and Mistral and the notebook terminated, both are gated models may be you have not logged in caused the issue)

Testing the notebook on AutoGenClientTesting programs:

1. Even though, tool calling is possible in Huggingface thanks for your referrence, but integrating it with the Autogen framework is still an open issue.

2. But i have tried the same code with other non function calling programs[ test_code_gen.py & test_reflection.py].

Can you please let me know if i need to perform any other analysis.

[Kirushikesh]

The Code to test the CODE GENERATION AND EXECUTION (test_code_gen.py)

from pathlib import Path

from autogen import AssistantAgent, UserProxyAgent
from autogen.coding import LocalCommandLineCodeExecutor

# Setting up the code executor
workdir = Path("coding")
workdir.mkdir(exist_ok=True)
code_executor = LocalCommandLineCodeExecutor(work_dir=workdir)

# Setting up the agents

# The UserProxyAgent will execute the code that the AssistantAgent provides
user_proxy_agent = UserProxyAgent(
    name="User",
    code_execution_config={"executor": code_executor},
    is_termination_msg=lambda msg: "FINISH" in msg.get("content"),
)

system_message = """You are a helpful AI assistant who writes code and the user executes it.
Solve tasks using your coding and language skills.
In the following cases, suggest python code (in a python coding block) for the user to execute.
Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.
When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.
IMPORTANT: Wait for the user to execute your code and then you can reply with the word "FINISH". DO NOT OUTPUT "FINISH" after your code block."""

# The AssistantAgent, using Huggingface model, will take the coding request and return code
assistant_agent = AssistantAgent(
    name="Together Assistant",
    system_message=system_message,
    llm_config={"config_list": config_list_custom},
)

assistant_agent.register_model_client(model_client_cls=CustomModelClient)

# Start the chat, with the UserProxyAgent asking the AssistantAgent the message
chat_result = user_proxy_agent.initiate_chat(
    assistant_agent,
    message="Provide code to count the number of prime numbers from 1 to 10000.",
)

The Output:

[autogen.oai.client: 09-14 08:38:33] {484} INFO - Detected custom model client in config: CustomModelClient, model client can not be used until register_model_client is called.
CustomModelClient config: {'model': 'mistralai/Mistral-7B-Instruct-v0.2', 'model_client_cls': 'CustomModelClient', 'device': 0, 'n': 1, 'params': {'max_new_tokens': 500, 'top_k': 50, 'temperature': 0.1, 'do_sample': True, 'return_full_text': False}}
2024-09-14 08:38:36.694675: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Loaded model mistralai/Mistral-7B-Instruct-v0.2 to 0

------------------------------------------------------------------------------
Writer (to User):

 Title: Navida: The Unsung Hero in the World of Quantum Computing

Navida, a pioneering company in the quantum computing industry, is making strides in making this advanced technology more accessible and affordable for researchers and businesses. Navida's quantum computing system, the Q-bit One, is a 1-qubit quantum processor, which means it is a two-level quantum system that can represent both 0 and 1 at the same time, thanks to the principle of superposition. This compact, desktop-sized device uses a unique approach to quantum computing called ion-trap quantum computing, where electric fields are employed to trap and manipulate ions, making the system more stable and reliable than other methods.

Navida's mission is not to compete with industry giants in terms of qubit count but to provide an entry-level quantum computing system for exploration and innovation. The Q-bit One can be used for various applications, including optimization problems, machine learning, and quantum chemistry.

Navida's team of experienced physicists and engineers is dedicated to pushing the boundaries of what's possible with quantum computing. They are constantly improving the Q-bit One's performance and functionality while exploring new applications for their technology.

Despite being a newcomer to the quantum computing scene, Navida has already gained attention from researchers and businesses worldwide. Their affordable and accessible quantum computing system is opening up new possibilities for innovation and discovery. However, it is essential to be aware of the regulatory landscape in this field. For instance, the National Institute of Standards and Technology (NIST) is developing quantum-resistant cryptography standards to protect against quantum computers' ability to break traditional encryption algorithms. Navida, like other quantum computing companies, will need to comply with these regulations to ensure the security and integrity of their systems.

In conclusion, Navida is an exciting and innovative company that is making quantum computing more accessible and affordable for researchers and businesses. Their unique approach to ion-trap quantum computing and commitment to pushing the boundaries of what's possible make them a company to watch in the rapidly evolving world of quantum technology. Stay tuned for more updates on Navida and their groundbreaking work in the world of quantum computing while ensuring technical accuracy and regulatory compliance.

--------------------------------------------------------------------------------

User (to Together Assistant):

Provide code to count the number of prime numbers from 1 to 10000.

--------------------------------------------------------------------------------
Together Assistant (to User):

 Here's the Python code for finding prime numbers up to 10000:

```python
def is_prime(n):
    """
    Helper function to check if a number is prime
    """
    if n <= 1:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

primes = []
for num in range(1, 10001):
    if is_prime(num):
        primes.append(num)

print(len(primes))
\```

This code defines a helper function `is_prime` to check if a number is prime. The main loop iterates through numbers from 1 to 10000 and checks if each number is prime using the helper function. If a prime number is found, it is added to a list called `primes`. Finally, the length of the list `primes` is printed, which represents the number of prime numbers found.

--------------------------------------------------------------------------------

\>\>\>\>\>\>\>\> NO HUMAN INPUT RECEIVED.

\>\>\>\>\>\>\>\> USING AUTO REPLY...

\>\>\>\>\>\>\>\> EXECUTING CODE BLOCK (inferred language is python)...
User (to Together Assistant):

exitcode: 0 (execution succeeded)
Code output: 1229


--------------------------------------------------------------------------------
Together Assistant (to User):

 FINISH

The code executed successfully and printed the number of prime numbers found between 1 and 10000, which is 1229.

--------------------------------------------------------------------------------

\>\>\>\>\>\>\>\> NO HUMAN INPUT RECEIVED.

[Kirushikesh]

The Code to test the LLM Reflection (test_reflection.py)

writer = AssistantAgent(
    name="Writer",
    llm_config={"config_list": config_list_custom},
    system_message="""
    You are a professional writer, known for your insightful and engaging articles.
    You transform complex concepts into compelling narratives.
    You should imporve the quality of the content based on the feedback from the user.
    """,
)

user_proxy = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
    is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
    code_execution_config={
        "last_n_messages": 1,
        "work_dir": "tasks",
        "use_docker": False,
    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
)

critic = AssistantAgent(
    name="Critic",
    llm_config={"config_list": config_list_custom},
    system_message="""
    You are a critic, known for your thoroughness and commitment to standards.
    Your task is to scrutinize content for any harmful elements or regulatory violations, ensuring
    all materials align with required guidelines.
    For code
    """,
)

def reflection_message(recipient, messages, sender, config):
    print("Reflecting...", "yellow")
    return f"Reflect and provide critique on the following writing. \n\n {recipient.chat_messages_for_summary(sender)[-1]['content']}"

writer.register_model_client(model_client_cls=CustomModelClient)
critic.register_model_client(model_client_cls=CustomModelClient)

task = """Write a concise but engaging blogpost about Navida."""

user_proxy.register_nested_chats(
    [{"recipient": critic, "message": reflection_message, "summary_method": "last_msg", "max_turns": 1}],
    trigger=writer,
)

res = user_proxy.initiate_chat(recipient=writer, message=task, max_turns=2, summary_method="last_msg")

The Output:

[autogen.oai.client: 09-14 08:36:33] {484} INFO - Detected custom model client in config: CustomModelClient, model client can not be used until register_model_client is called.
[autogen.oai.client: 09-14 08:36:33] {484} INFO - Detected custom model client in config: CustomModelClient, model client can not be used until register_model_client is called.
CustomModelClient config: {'model': 'mistralai/Mistral-7B-Instruct-v0.2', 'model_client_cls': 'CustomModelClient', 'device': 0, 'n': 1, 'params': {'max_new_tokens': 500, 'top_k': 50, 'temperature': 0.1, 'do_sample': True, 'return_full_text': False}}

Loading checkpoint shards: 100%
 3/3 [00:09<00:00,  3.01s/it]
Loaded model mistralai/Mistral-7B-Instruct-v0.2 to 0
CustomModelClient config: {'model': 'mistralai/Mistral-7B-Instruct-v0.2', 'model_client_cls': 'CustomModelClient', 'device': 0, 'n': 1, 'params': {'max_new_tokens': 500, 'top_k': 50, 'temperature': 0.1, 'do_sample': True, 'return_full_text': False}}
Loading checkpoint shards: 100%
 3/3 [00:09<00:00,  3.03s/it]
Loaded model mistralai/Mistral-7B-Instruct-v0.2 to 0
User (to Writer):

Write a concise but engaging blogpost about Navida.

--------------------------------------------------------------------------------
Writer (to User):

 Title: Navida: The Unsung Hero in the World of Quantum Computing

Navida, a relatively new player in the quantum computing industry, is making waves with its innovative approach to building a quantum computer that is accessible to researchers and businesses alike. While IBM, Google, and other tech giants have been leading the charge in quantum research, Navida is focusing on making this cutting-edge technology more accessible and affordable.

Navida's quantum computing system, called the Q-bit One, is a 1-qubit quantum processor. It may not sound impressive compared to the 53-qubit quantum computer IBM recently unveiled, but Navida's goal is not to compete with the big players in terms of qubit count. Instead, they aim to provide an entry-level quantum computing system that researchers and businesses can use to explore the potential of quantum computing without the need for massive investments.

The Q-bit One is a compact, desktop-sized device that can be used for various applications, including optimization problems, machine learning, and even quantum chemistry. Navida's system uses a unique approach to quantum computing, which they call "ion-trap quantum computing." This technology uses electric fields to trap and manipulate ions, making the system more stable and reliable than other quantum computing methods.

Navida's team of experienced physicists and engineers is dedicated to pushing the boundaries of what's possible with quantum computing. They are constantly working on improving the Q-bit One's performance and functionality, and they are also exploring new applications for their technology.

Despite being a newcomer to the quantum computing scene, Navida has already gained the attention of researchers and businesses around the world. Their affordable and accessible quantum computing system is opening up new possibilities for innovation and discovery, and it's only a matter of time before Navida becomes a household name in the world of quantum computing.

In conclusion, Navida is an exciting and innovative company that is making quantum computing more accessible and affordable for researchers and businesses. Their unique approach to ion-trap quantum computing and their commitment to pushing the boundaries of what's possible make them a company to watch in the rapidly evolving world of quantum technology. Stay tuned for more updates on Navida and their groundbreaking work in the world of quantum computing.

--------------------------------------------------------------------------------
Reflecting... yellow

********************************************************************************
Starting a new chat....

********************************************************************************
User (to Critic):

Reflect and provide critique on the following writing. 

  Title: Navida: The Unsung Hero in the World of Quantum Computing

Navida, a relatively new player in the quantum computing industry, is making waves with its innovative approach to building a quantum computer that is accessible to researchers and businesses alike. While IBM, Google, and other tech giants have been leading the charge in quantum research, Navida is focusing on making this cutting-edge technology more accessible and affordable.

Navida's quantum computing system, called the Q-bit One, is a 1-qubit quantum processor. It may not sound impressive compared to the 53-qubit quantum computer IBM recently unveiled, but Navida's goal is not to compete with the big players in terms of qubit count. Instead, they aim to provide an entry-level quantum computing system that researchers and businesses can use to explore the potential of quantum computing without the need for massive investments.

The Q-bit One is a compact, desktop-sized device that can be used for various applications, including optimization problems, machine learning, and even quantum chemistry. Navida's system uses a unique approach to quantum computing, which they call "ion-trap quantum computing." This technology uses electric fields to trap and manipulate ions, making the system more stable and reliable than other quantum computing methods.

Navida's team of experienced physicists and engineers is dedicated to pushing the boundaries of what's possible with quantum computing. They are constantly working on improving the Q-bit One's performance and functionality, and they are also exploring new applications for their technology.

Despite being a newcomer to the quantum computing scene, Navida has already gained the attention of researchers and businesses around the world. Their affordable and accessible quantum computing system is opening up new possibilities for innovation and discovery, and it's only a matter of time before Navida becomes a household name in the world of quantum computing.

In conclusion, Navida is an exciting and innovative company that is making quantum computing more accessible and affordable for researchers and businesses. Their unique approach to ion-trap quantum computing and their commitment to pushing the boundaries of what's possible make them a company to watch in the rapidly evolving world of quantum technology. Stay tuned for more updates on Navida and their groundbreaking work in the world of quantum computing.

--------------------------------------------------------------------------------
Critic (to User):

 Title: Navida: The Unsung Hero in the World of Quantum Computing

The article provides a good introduction to Navida and its innovative approach to making quantum computing more accessible and affordable. However, there are a few areas that could use improvement in terms of technical accuracy and regulatory compliance.

1. Technical Accuracy:

a. The article mentions that Navida's quantum computing system, called the Q-bit One, is a "1-qubit quantum processor." While it is true that the Q-bit One is a 1-qubit system, it is essential to clarify that a qubit is a two-level quantum system, not a single bit. A single bit can only represent a 0 or 1, whereas a qubit can represent both 0 and 1 at the same time, a property known as superposition.

b. The article states that Navida's system uses a unique approach to quantum computing, which they call "ion-trap quantum computing." It is important to note that ion-trap quantum computing is just one of several approaches to building a quantum computer. Other approaches include superconducting quantum computing, topological quantum computing, and trapped-ion quantum computing, among others.

2. Regulatory Compliance:

The article does not mention any regulatory compliance issues related to quantum computing. However, it is essential to be aware of the regulatory landscape in this field. For instance, the National Institute of Standards and Technology (NIST) is developing quantum-resistant cryptography standards to protect against quantum computers' ability to break traditional encryption algorithms. It would be beneficial to include a brief mention of regulatory compliance in the article.

Overall, the article provides a good overview of Navida and its innovative approach to quantum computing. However, it is crucial to ensure technical accuracy and regulatory compliance to maintain credibility and provide accurate information to readers.

--------------------------------------------------------------------------------
User (to Writer):

 Title: Navida: The Unsung Hero in the World of Quantum Computing

The article provides a good introduction to Navida and its innovative approach to making quantum computing more accessible and affordable. However, there are a few areas that could use improvement in terms of technical accuracy and regulatory compliance.

1. Technical Accuracy:

a. The article mentions that Navida's quantum computing system, called the Q-bit One, is a "1-qubit quantum processor." While it is true that the Q-bit One is a 1-qubit system, it is essential to clarify that a qubit is a two-level quantum system, not a single bit. A single bit can only represent a 0 or 1, whereas a qubit can represent both 0 and 1 at the same time, a property known as superposition.

b. The article states that Navida's system uses a unique approach to quantum computing, which they call "ion-trap quantum computing." It is important to note that ion-trap quantum computing is just one of several approaches to building a quantum computer. Other approaches include superconducting quantum computing, topological quantum computing, and trapped-ion quantum computing, among others.

2. Regulatory Compliance:

The article does not mention any regulatory compliance issues related to quantum computing. However, it is essential to be aware of the regulatory landscape in this field. For instance, the National Institute of Standards and Technology (NIST) is developing quantum-resistant cryptography standards to protect against quantum computers' ability to break traditional encryption algorithms. It would be beneficial to include a brief mention of regulatory compliance in the article.

Overall, the article provides a good overview of Navida and its innovative approach to quantum computing. However, it is crucial to ensure technical accuracy and regulatory compliance to maintain credibility and provide accurate information to readers.

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Writer (to User):

 Title: Navida: The Unsung Hero in the World of Quantum Computing

Navida, a pioneering company in the quantum computing industry, is making strides in making this advanced technology more accessible and affordable for researchers and businesses. Navida's quantum computing system, the Q-bit One, is a 1-qubit quantum processor, which means it is a two-level quantum system that can represent both 0 and 1 at the same time, thanks to the principle of superposition. This compact, desktop-sized device uses a unique approach to quantum computing called ion-trap quantum computing, where electric fields are employed to trap and manipulate ions, making the system more stable and reliable than other methods.

Navida's mission is not to compete with industry giants in terms of qubit count but to provide an entry-level quantum computing system for exploration and innovation. The Q-bit One can be used for various applications, including optimization problems, machine learning, and quantum chemistry.

Navida's team of experienced physicists and engineers is dedicated to pushing the boundaries of what's possible with quantum computing. They are constantly improving the Q-bit One's performance and functionality while exploring new applications for their technology.

Despite being a newcomer to the quantum computing scene, Navida has already gained attention from researchers and businesses worldwide. Their affordable and accessible quantum computing system is opening up new possibilities for innovation and discovery. However, it is essential to be aware of the regulatory landscape in this field. For instance, the National Institute of Standards and Technology (NIST) is developing quantum-resistant cryptography standards to protect against quantum computers' ability to break traditional encryption algorithms. Navida, like other quantum computing companies, will need to comply with these regulations to ensure the security and integrity of their systems.

In conclusion, Navida is an exciting and innovative company that is making quantum computing more accessible and affordable for researchers and businesses. Their unique approach to ion-trap quantum computing and commitment to pushing the boundaries of what's possible make them a company to watch in the rapidly evolving world of quantum technology. Stay tuned for more updates on Navida and their groundbreaking work in the world of quantum computing while ensuring technical accuracy and regulatory compliance.

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[ekzhu]

Thanks for the PR.

[Hk669]

@ekzhu please merge the PR. i dont have the access.

Thanks @Kirushikesh for the PR.

[rysweet]

This PR is against AutoGen 0.2. AutoGen 0.2 has been moved to the 0.2 branch. Please rebase your PR on the 0.2 branch or update it to work with the new AutoGen 0.4 that is now in main.

[Kirushikesh]

@rysweet i have rebased the PR to the autogen 0.2 branch. Please review.