AI Multi Agents: PyAutogen

• https://pypi.org/project/langgraph/
• https://pypi.org/project/langchain-openai/

LangChain is a framework that focuses on chaining together multiple language models to generate more accurate and informative responses. By leveraging the strengths of individual models, LangChain creates a robust system capable of tackling complex linguistic tasks. The video highlights LangChain’s ability to handle long-range dependencies, making it an attractive solution for applications that require detailed explanations or storytelling.

LangGraph, on the other hand, takes a more graph-based approach to language understanding. By representing language as a graph, LangGraph can capture relationships between entities, concepts, and relationships, enabling more nuanced and context-aware interactions. The video showcases LangGraph’s strength in handling ambiguous or unclear input, making it an ideal choice for applications that require high accuracy and precision. Key Differences and Applications

LangChain excels in tasks that require creativity and coherence, such as writing stories or generating dialogues. LangGraph, with its graph-based approach, is better suited for tasks that require precision and accuracy, such as question answering or entity disambiguation.

Takeaways

LangChain and LangGraph are two distinct frameworks with unique strengths and weaknesses.
LangChain is ideal for applications that require creativity, coherence, and long-range dependencies.
LangGraph is better suited for applications that require precision, accuracy, and nuanced language understanding.
Understanding the differences between these frameworks is crucial for harnessing the power of AI in various industries.

Research Agent

• https://github.com/assafelovic/gpt-researcher
  • https://gptr.dev/

LLM based autonomous agent that conducts local and web research on any topic and generates a comprehensive report with citations - Apache v2

Yes, PyAutoGen can be used to get information about websites and databases, although the methods and complexity vary. Here’s how:

1. Websites:

• Web Scraping:
  • AutoGen agents can use Python libraries like requests and Beautiful Soup (or Scrapy) to scrape information from websites.
  • Agents can be instructed to:
    • Download HTML content.
    • Parse the HTML to extract specific data.
    • Follow links to navigate through the website.
  • Example:
    • An agent could be asked to get the current price of a product from an e-commerce website.
    • Another agent could be used to summarize the content of a news article.
• Web APIs:
  • If the website provides a public API, agents can use requests to interact with the API and retrieve structured data (JSON, XML).
  • This is generally more reliable and efficient than web scraping.
• Browser Automation:
  • For websites that rely heavily on JavaScript or require user interaction, agents can use libraries like Selenium or Playwright to automate browser actions.
  • This allows agents to:
    • Load web pages.
    • Interact with elements (buttons, forms).
    • Extract data from dynamic content.

2. Databases:

• SQL Queries:
  • AutoGen agents can use Python libraries like sqlite3, psycopg2 (for PostgreSQL), mysql-connector-python (for MySQL), or SQLAlchemy to connect to databases and execute SQL queries.
  • Agents can be instructed to:
    • Connect to a database.
    • Formulate SQL queries based on user requests.
    • Retrieve and process data from the database.
  • Example:
    • An agent could be asked to retrieve a list of customers from a database.
    • Another agent could be used to calculate aggregate statistics (e.g., average sales).
• Database APIs:
  • Some databases provide REST APIs or other interfaces that agents can use to interact with the database.
  • This can be useful for cloud-based databases or when direct SQL access is not available.
• Langchain integration:
  • AutoGen can work in conjunction with langchain, and therefore use any of the database integrations that langchain provides. This can simplify the database interactions.

Key Considerations:

• Security:
  • When interacting with websites or databases, it’s crucial to handle credentials and sensitive data securely.
  • Avoid hardcoding API keys or database passwords in your code. Use environment variables or secure configuration files.
• Rate Limiting:
  • Websites and APIs often have rate limits to prevent abuse.
  • Implement mechanisms to handle rate limits and avoid overloading servers.
• Data Parsing:
  • Parsing HTML or database results can be complex, especially when dealing with unstructured or inconsistent data.
  • Use robust parsing techniques and error handling.
• Ethical Considerations:
  • Respect website terms of service and database usage policies.
  • Avoid scraping or accessing data that you are not authorized to access.

AutoGen’s Role:

• AutoGen provides the framework for orchestrating the agents that perform these tasks.
• It enables agents to collaborate, communicate, and coordinate their efforts to achieve complex goals involving websites and databases.
• AutoGen can be used to create agents that can decide which tool, or function to use, in order to get the required information from the website or database.

In summary, AutoGen can be a powerful tool for building applications that retrieve and process information from websites and databases, but it’s important to consider the technical and ethical implications of these activities.

Pydantic is a data validation and settings management library in Python.

It’s widely used for validating data and ensuring that inputs conform to the expected types and formats.

https://github.com/widgetti/solara

A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps

Build a Python WebApp in 3 minutes with Solara (Streamlit Alternative)