Multiagent System Development with Open-Source LLM

 

Photo by Andrea Piacquadio at pexels

Multiagent systems (MAS) are distributed systems comprising multiple autonomous agents that interact and cooperate to achieve common goals. Integrating open-source Large Language Models (LLMs) into MAS development enables agents to leverage advanced natural language processing (NLP) capabilities, enhancing their decision-making and communication.

Key Components

Open-Source LLM: Utilize open-source LLMs like Bloom, LLaMA, or LaMDA to equip agents with advanced NLP capabilities.

Agent Framework: Choose a suitable agent framework (e.g., Python's Multiagent Framework, JADE, or PyAgent) to develop and manage agents.

Communication Protocol: Establish a communication protocol (e.g., FIPA-ACL, KQML) for agents to exchange information and coordinate actions.

Knowledge Representation: Define a knowledge representation scheme (e.g., ontologies, semantic networks) to facilitate agent understanding and sharing of information.

Development Steps

Agent Design: Design agents with specific roles, goals, and capabilities, integrating open-source LLMs for NLP tasks.

LLM Fine-Tuning: Fine-tune the open-source LLM for domain-specific tasks, such as text classification, sentiment analysis, or question-answering.

Agent Communication: Implement agent communication protocols, enabling agents to share knowledge and coordinate actions.

System Integration: Integrate agents with the open-source LLM, ensuring seamless interaction and knowledge sharing.

Testing and Evaluation: Test and evaluate the MAS, assessing agent performance, communication effectiveness, and overall system efficiency.

Applications

Smart Home Automation: Develop a MAS for smart home automation, where agents control devices, optimize energy consumption, and respond to user queries using open-source LLMs.

Healthcare Management: Create a MAS for healthcare management, where agents analyze patient data, provide personalized recommendations, and communicate with healthcare professionals using open-source LLMs.

Supply Chain Optimization: Design a MAS for supply chain optimization, where agents predict demand, manage inventory, and coordinate logistics using open-source LLMs.

Challenges and Future Directions

Scalability: Address scalability challenges when integrating open-source LLMs into MAS.

Explainability: Ensure explainability of agent decision-making processes, particularly when using complex LLMs.

Security: Develop secure communication protocols and data protection measures for MAS using open-source LLMs.

By integrating open-source LLMs into MAS development, we can create more sophisticated, human-like agents that effectively communicate, cooperate, and adapt in complex environments.

To develop #multiagent #application with more than one #opensource #foundation #model which one do you prefer to use for #search #internet #task?

Several open-source Large Language Models (LLMs) offer internet search capabilities. Here are a few options:

OpenAI's #GPT-4 with WebGPT: While not entirely open-source, OpenAI provides a limited open-source version of their GPT-4 model, which can be fine-tuned for internet search using the WebGPT framework.

#LaMDA: Developed by Google, LaMDA (Language Model for Dialogue Applications) is an open-source LLM that can be adapted for internet search. However, it requires significant computational resources and expertise.

#Bloom: An open-source LLM developed by BigScience, Bloom can be fine-tuned for internet search tasks. It's a multilingual model, making it suitable for searching in various languages.

#LLaMA: Developed by AI at Meta, LLaMA (Large Language Model Meta AI) is an open-source LLM that can be adapted for internet search. It's known for its efficiency and scalability.

Please note that these models require significant computational resources and expertise to fine-tune and deploy for internet search.

If you're looking for a more straightforward solution, you can explore open-source search engines like:

#Seachlite: A lightweight, open-source search engine that can index and search web pages.

#Apache #Solr: A popular open-source search platform that can be used for internet search.

Here is an example of a Python-based multi-agent application using an open-source LLM like GPT-J or GPT-Neo. The architecture involves multiple agents communicating with each other, each handling different tasks. We will use `langchain` for agent management and `transformers` for running the LLM.

Install Required Packages

```bash

pip install langchain transformers

```

Application Structure

1. Agent1: Handles text summarization.

2. Agent2: Handles sentiment analysis.

3. Agent3: Handles question answering.

Code Implementation

```python

from langchain import LLMChain, OpenAI

from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

# Load Open-Source LLM (e.g., GPT-Neo)

model_name = "EleutherAI/gpt-neo-1.3B"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(model_name)

# Agent 1: Text Summarization

def summarize_text(text):

    summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)

    summary = summarizer(text, max_length=50, min_length=25, do_sample=False)

    return summary[0]['summary_text']

# Agent 2: Sentiment Analysis

def analyze_sentiment(text):

    sentiment_analyzer = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

    result = sentiment_analyzer(text)

    return result[0]['label']

# Agent 3: Question Answering

def answer_question(context, question):

    qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)

    result = qa_pipeline(question=question, context=context)

    return result['answer']

# Main Multi-Agent Orchestrator

def multiagent_system(input_text, question):

    # Step 1: Summarize the input text using Agent 1

    summary = summarize_text(input_text)

    

    # Step 2: Perform sentiment analysis on the summary using Agent 2

    sentiment = analyze_sentiment(summary)

    

    # Step 3: Answer a question based on the original input text using Agent 3

    answer = answer_question(input_text, question)

    

    # Final output

    return {

        "summary": summary,

        "sentiment": sentiment,

        "answer": answer

    }

# Example Usage

if __name__ == "__main__":

    text = """

    Artificial intelligence is transforming industries across the world. From healthcare to finance, AI systems are improving efficiencies and enabling new innovations.

    """

    question = "How is AI impacting industries?"

    result = multiagent_system(text, question)

    print("Summary:", result["summary"])

    print("Sentiment:", result["sentiment"])

    print("Answer:", result["answer"])

```

Extending the System

- You can add more agents for different tasks like translation, text generation, etc.

- Modify or extend the orchestrator logic to suit your application’s needs.

Just to let you know, these search engines might not offer the same level of language understanding and contextual search capabilities as LLMs. So you can use #knn or similar #machinelearning #model to find out similarities from the cache using #redis

And last, don't forget to patronise Wikipedia, the Free Encyclopedia foundation and #donate for their #information.

Preparing a Dataset for Fine-Tuning Foundation Model

 

I am trying to preparing a Dataset for Fine-Tuning on Pathology Lab Data.

1. Dataset Collection

   - Sources: Gather data from pathology lab reports, medical journals, and any other relevant medical documents.

   - Format: Ensure that the data is in a readable format like CSV, JSON, or text files.

2. Data Preprocessing

   - Cleaning: Remove any irrelevant data, correct typos, and handle missing values.

   - Formatting: Convert the data into a format suitable for fine-tuning, usually pairs of input and output texts.

   - Example Format:

     - Input: "Patient exhibits symptoms of hyperglycemia."

     - Output: "Hyperglycemia"

3. Tokenization

   - Tokenize the text using the tokenizer that corresponds to the model you intend to fine-tune.

Example Code for Dataset Preparation

Using Pandas and Transformers for Preprocessing

1. Install Required Libraries:

   ```sh

   pip install pandas transformers datasets

   ```

2. Load and Clean the Data:

   ```python

   import pandas as pd

   # Load your dataset

   df = pd.read_csv("pathology_lab_data.csv")

   # Example: Remove rows with missing values

   df.dropna(inplace=True)

   # Select relevant columns (e.g., 'report' and 'diagnosis')

   df = df[['report', 'diagnosis']]

   ```

3. Tokenize the Data:

   ```python

   from transformers import AutoTokenizer

   model_name = "pretrained_model_name"

   tokenizer = AutoTokenizer.from_pretrained(model_name)

   def tokenize_function(examples):

       return tokenizer(examples['report'], padding="max_length", truncation=True)

   tokenized_dataset = df.apply(lambda x: tokenize_function(x), axis=1)

   ```

4. Convert Data to HuggingFace Dataset Format:

   ```python

   from datasets import Dataset

   dataset = Dataset.from_pandas(df)

   tokenized_dataset = dataset.map(tokenize_function, batched=True)

   ```

5. Save the Tokenized Dataset:

   ```python

   tokenized_dataset.save_to_disk("path_to_save_tokenized_dataset")

   ```

Example Pathology Lab Data Preparation Script

Here is a complete script to prepare pathology lab data for fine-tuning:

```python

import pandas as pd

from transformers import AutoTokenizer

from datasets import Dataset

# Load your dataset

df = pd.read_csv("pathology_lab_data.csv")

# Clean the dataset (remove rows with missing values)

df.dropna(inplace=True)

# Select relevant columns (e.g., 'report' and 'diagnosis')

df = df[['report', 'diagnosis']]

# Initialize the tokenizer

model_name = "pretrained_model_name"

tokenizer = AutoTokenizer.from_pretrained(model_name)

# Tokenize the data

def tokenize_function(examples):

    return tokenizer(examples['report'], padding="max_length", truncation=True)

dataset = Dataset.from_pandas(df)

tokenized_dataset = dataset.map(tokenize_function, batched=True)

# Save the tokenized dataset

tokenized_dataset.save_to_disk("path_to_save_tokenized_dataset")

```

Notes

- Handling Imbalanced Data: If your dataset is imbalanced (e.g., more reports for certain diagnoses), consider techniques like oversampling, undersampling, or weighted loss functions during fine-tuning.

- Data Augmentation: You may also use data augmentation techniques to artificially increase the size of your dataset.

By following these steps, you'll have a clean, tokenized dataset ready for fine-tuning a model on pathology lab data.

You can read my other article about data preparation.