Building AI Agents in Pure Python - Beginner Course

Key Concepts

• AI Agents/Systems: Programs designed to perform tasks autonomously, often by interacting with large language models (LLMs).
• LLM API: Application Programming Interface for large language models, allowing direct programmatic interaction.
• Python SDK: Software Development Kit for Python, enabling developers to use LLM APIs.
• System Prompt: Instructions given to an LLM to define its behavior and role.
• Structured Output: Obtaining LLM responses in a predefined, machine-readable format (e.g., JSON, Pydantic models).
• Pydantic: A Python library for data validation and settings management using Python type hints.
• Tools (Function Calling): Enabling LLMs to interact with external functions or APIs by providing their definitions.
• Memory: The ability of an AI system to retain and utilize past conversation history.
• Retrieval: Accessing and using external knowledge bases to inform LLM responses.
• Prompt Chaining: Decomposing a complex task into a sequence of LLM calls, where the output of one call feeds into the next.
• Routing: Directing a request to different LLM calls or logic paths based on its content or type.
• Parallelization: Executing multiple LLM API calls concurrently to reduce overall processing time.
• Guardrails: Implementing checks and safety mechanisms to prevent undesirable LLM behavior (e.g., prompt injection, harmful content).

Building Effective AI Systems with Pure Python

This video demonstrates how to build effective AI systems, referred to as "AI systems" rather than "AI agents," by directly interacting with Large Language Model (LLM) APIs using pure Python. The approach emphasizes understanding the underlying principles rather than relying solely on high-level tools and frameworks, which can sometimes obscure fundamental concepts. The content is based on an entropic blog post titled "Building Effective Agents."

Core Building Blocks for LLM Applications

The initial section focuses on the fundamental components required for building applications around LLMs.

1. Direct API Calls to LLMs

The most basic interaction involves making direct API calls to an LLM to receive a text-based response.

• Methodology: Utilize the OpenAI Python SDK to interact with models like GPT-4o.
• Process:
  1. Initialize an OpenAI client with an API key.
  2. Define a system_prompt to guide the LLM's behavior.
  3. Formulate a user_message (the query).
  4. Call client.chat.completions.create() with the model and messages.
  5. Process the returned text response.
• Example: A limerick about the Python programming language is generated by instructing the LLM with a system prompt and a user query. This can be the foundation for simple agents, like responding to emails.

2. Structured Output

To enable programmatic use of LLM responses, structured output is crucial. This allows the LLM to return data in a specific, predefined format.

• Concept: Instead of plain text, the LLM returns key-value pairs that can be directly parsed and used by an application.
• Methodology: Leverage the Pydantic library in Python to define data models and specify the desired output structure.
• Process:
  1. Define a Pydantic BaseModel class representing the desired output (e.g., CalendarEvent with fields like name, date, participants).
  2. When calling the OpenAI API, use response_format={"type": "json_object"} and specify the Pydantic model in the API call. The beta.chat.completions.parse method is used for this.
  3. The LLM will then attempt to return a JSON object that conforms to the specified Pydantic model.
• Example: An AI agent designed to manage calendar events. Given a user request like "Alice and Bob are going to ACI Fair on Friday," the LLM can extract the event name ("Science Fair"), date ("Friday"), and participants ("Alice", "Bob") into a structured CalendarEvent object. This structured data can then be used to interact with a calendar API.

3. Using Tools (Function Calling)

Tools allow LLMs to interact with external functionalities, such as APIs or custom functions.

• Concept: The LLM can decide to "call" a predefined tool based on the user's request and the tool's description. The LLM does not execute the tool itself but provides the necessary parameters for the developer to execute it.
• Methodology: Define tools using a specific JSON schema format that OpenAI understands.
• Process:
  1. Define the Tool Function: Create a Python function that performs the desired action (e.g., get_weather(latitude, longitude)).
  2. Define the Tool Schema: Represent the function and its parameters in a JSON format that includes name, description, and parameters (with types and requirements).
  3. Call the LLM with Tools: Pass the defined tools and tool_choice="auto" to the client.chat.completions.create() method.
  4. Handle Tool Calls: If the LLM decides to use a tool, the response will contain a tool_calls object with the function name and arguments.
  5. Execute the Tool: In your Python code, parse the arguments and call the corresponding Python function.
  6. Provide Tool Results to LLM: Append the result of the tool execution back to the conversation messages and call the LLM again to generate a final response based on the tool's output.
• Example: A weather assistant. The LLM is provided with a get_weather tool. When asked "What's the weather like in Paris today?", the LLM identifies it as a weather-related query, determines the latitude and longitude of Paris, and returns these parameters. The developer then uses these parameters to call the actual get_weather function, retrieves the weather data, and feeds it back to the LLM for a natural language response.

4. Retrieval

Retrieval enables AI systems to access and utilize information from external knowledge bases.

• Concept: Similar to tools, retrieval involves defining a function that can search a knowledge base. The LLM can then invoke this function to find relevant information.
• Methodology: A search_kb function is defined, which takes a query and returns relevant data from a knowledge base (e.g., a JSON file).
• Process:
  1. Define a search_kb function that queries a knowledge base.
  2. Define a tool schema for this search_kb function.
  3. Provide this tool to the LLM.
  4. When a user asks a question that requires information from the knowledge base (e.g., "What's the return policy?"), the LLM will call the search_kb tool with the query.
  5. The developer executes the search_kb function, retrieves the information, and feeds it back to the LLM.
  6. The LLM then uses this retrieved information to formulate a response, potentially including the source.
• Example: An e-commerce assistant. When a user asks about the return policy, the LLM invokes the search_kb tool. The tool retrieves the return policy details from a knowledge base, and the LLM then presents this information to the user, along with the source. If the user asks a question outside the scope of the knowledge base (e.g., "What's the weather in Tokyo?"), and no weather tool is provided, the LLM will simply state it cannot provide that information.

Workflow Patterns for Robust AI Systems

Once the core building blocks are understood, various patterns can be combined to create more sophisticated AI systems.

1. Prompt Chaining

Prompt chaining breaks down a complex task into a sequence of smaller, manageable steps, each handled by a separate LLM call.

• Concept: The output of one LLM call serves as the input for the next, creating a pipeline of operations. This enhances reliability and debuggability.
• Methodology:
  1. Decompose the Task: Identify the sequential steps required to solve the problem.
  2. Define Data Models: For each step, define Pydantic models to structure the input and output.
  3. Create LLM Calls: Implement separate LLM calls for each step, using specific system prompts tailored to that step's objective.
  4. Implement Gates/Checks: Introduce conditional logic (if statements) between LLM calls to validate outputs or control the flow.
• Example: A calendar agent.
  • Step 1 (Event Extraction): An LLM call to determine if the user's request is a calendar event and extract initial information, including a confidence score. A gate checks if the confidence is above a threshold (e.g., 0.7).
  • Step 2 (Parse Event Details): If the gate passes, another LLM call extracts specific details like name, date, duration, and participants.
  • Step 3 (Generate Confirmation): A final LLM call generates a natural language confirmation message and potentially a calendar link.
• Key Argument: Breaking down complex problems into sequential LLM calls allows for better control, debugging, and modularity. The system can gracefully handle irrelevant inputs by failing at the gate check.

2. Routing

Routing directs a request to different LLM calls or logic paths based on its type or intent.

• Concept: Similar to prompt chaining, but instead of a strict sequence, routing involves choosing one of several possible paths.
• Methodology:
  1. Identify Request Types: Determine the different categories of user requests the system needs to handle (e.g., create event, modify event, unsupported request).
  2. Define Data Models: Create Pydantic models for each request type.
  3. Implement Routing Logic: Use an initial LLM call to classify the request type. Based on this classification, route the request to the appropriate subsequent LLM calls or logic.
• Example: An enhanced calendar agent that can both schedule new events and modify existing ones.
  • An initial LLM call determines if the request is to "create" or "modify" an event.
  • If "create," it proceeds with the event extraction and detail parsing for new events.
  • If "modify," it extracts details for modification, including the specific changes required.
  • If neither, it identifies the request as unsupported.
• Key Argument: Routing allows for more flexible and adaptable AI systems that can handle diverse user intents by intelligently directing the workflow. This can be combined with tools, where a routed path might involve calling a specific tool.

3. Parallelization

Parallelization involves executing multiple LLM API calls concurrently when they do not depend on each other.

• Concept: This pattern significantly speeds up processing time by performing independent tasks simultaneously, rather than sequentially.
• Methodology:
  1. Identify Independent Tasks: Determine which LLM calls can be executed in parallel because their outcomes do not influence each other.
  2. Use Asynchronous Programming: Employ Python's async and await keywords with an asynchronous LLM client (e.g., async_openai).
  3. Aggregate Results: Once all parallel calls are complete, aggregate their results.
• Example: Implementing guardrails for LLM responses.
  • A request can be simultaneously checked for being a valid calendar event and for any security concerns or prompt injection attempts.
  • These two checks are independent and can be performed in parallel.
  • If either check fails, the system can flag the request or provide a warning.
• Key Argument: Parallelization is crucial for improving the responsiveness of customer-facing applications where latency is a concern. It's particularly useful for implementing multiple, independent validation checks.

Conclusion and Next Steps

The video emphasizes that building effective AI systems primarily requires understanding core LLM interaction patterns and leveraging pure Python. The fundamental building blocks (direct API calls, structured output, tools, retrieval) and workflow patterns (prompt chaining, routing, parallelization) are sufficient for creating robust AI solutions. The key to success lies in starting with the problem, breaking it down logically, and strategically applying AI where it adds the most value.

The presenter also briefly mentions the next step of deploying these Python-based AI systems into production applications, hinting at their "Gen Launchpad" product for this purpose. Additionally, a video on essential Python libraries for AI engineers is recommended for further learning.