forked from phoenix-oss/llama-stack-mirror
23a99a4b22
# What does this PR do? This PR modifies some of the docs to help them map to (1) the mental model of software engineers building AI models starting with RAG and then moving to Agents and (2) aligning the navbar somewhat closer to the diagram on the home page. ## Test Plan N/A Tested locally. # Documentation Take a look at the screen shot for below and after. ## Before  ## After  --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
92 lines
2.5 KiB
Markdown
92 lines
2.5 KiB
Markdown
# Agents
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An Agent in Llama Stack is a powerful abstraction that allows you to build complex AI applications.
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The Llama Stack agent framework is built on a modular architecture that allows for flexible and powerful AI
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applications. This document explains the key components and how they work together.
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## Core Concepts
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### 1. Agent Configuration
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Agents are configured using the `AgentConfig` class, which includes:
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- **Model**: The underlying LLM to power the agent
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- **Instructions**: System prompt that defines the agent's behavior
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- **Tools**: Capabilities the agent can use to interact with external systems
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- **Safety Shields**: Guardrails to ensure responsible AI behavior
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```python
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from llama_stack_client import Agent
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# Create the agent
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agent = Agent(
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llama_stack_client,
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model="meta-llama/Llama-3-70b-chat",
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instructions="You are a helpful assistant that can use tools to answer questions.",
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tools=["builtin::code_interpreter", "builtin::rag/knowledge_search"],
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)
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```
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### 2. Sessions
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Agents maintain state through sessions, which represent a conversation thread:
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```python
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# Create a session
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session_id = agent.create_session(session_name="My conversation")
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```
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### 3. Turns
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Each interaction with an agent is called a "turn" and consists of:
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- **Input Messages**: What the user sends to the agent
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- **Steps**: The agent's internal processing (inference, tool execution, etc.)
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- **Output Message**: The agent's response
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```python
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from llama_stack_client import AgentEventLogger
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# Create a turn with streaming response
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turn_response = agent.create_turn(
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session_id=session_id,
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messages=[{"role": "user", "content": "Tell me about Llama models"}],
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)
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for log in AgentEventLogger().log(turn_response):
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log.print()
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```
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### Non-Streaming
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```python
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from rich.pretty import pprint
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# Non-streaming API
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response = agent.create_turn(
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session_id=session_id,
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messages=[{"role": "user", "content": "Tell me about Llama models"}],
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stream=False,
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)
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print("Inputs:")
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pprint(response.input_messages)
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print("Output:")
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pprint(response.output_message.content)
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print("Steps:")
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pprint(response.steps)
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```
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### 4. Steps
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Each turn consists of multiple steps that represent the agent's thought process:
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- **Inference Steps**: The agent generating text responses
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- **Tool Execution Steps**: The agent using tools to gather information
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- **Shield Call Steps**: Safety checks being performed
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## Agent Execution Loop
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Refer to the [Agent Execution Loop](agent_execution_loop) for more details on what happens within an agent turn.
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