133 lines
4.1 KiB
Markdown
133 lines
4.1 KiB
Markdown
---
|
|
layout: default
|
|
title: "Agent"
|
|
parent: "Design Pattern"
|
|
nav_order: 6
|
|
---
|
|
|
|
# Agent
|
|
|
|
Agent is a powerful design pattern in which nodes can take dynamic actions based on the context.
|
|
|
|
The core of building **high-performance** and **reliable** agents boils down to:
|
|
|
|
1. **Input Context:** Provide *relevant, minimal context.* For example, rather than including an entire chat history, retrieve the most relevant via [RAG](./rag.md). Even with larger context windows, LLMs still fall victim to ["lost in the middle"](https://arxiv.org/abs/2307.03172), overlooking mid-prompt content.
|
|
|
|
2. **Action Space:** Provide *a well-structured and unambiguous* set of actions—avoiding overlap like separate `read_databases` or `read_csvs`. Instead, import CSVs into the database and then use one parameterized (e.g., table name) or programmable action (e.g., via SQL) to query data.
|
|
|
|
<div align="center">
|
|
<img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/agent.png?raw=true" width="350"/>
|
|
</div>
|
|
|
|
Agent Implementation Steps:
|
|
|
|
1. **Context and Action:** Implement nodes that supply context and perform actions.
|
|
2. **Branching:** Use branching to connect each action node to an agent node, allowing the agent to direct the [flow](../core_abstraction/flow.md) between action nodes—and potentially loop back as needed.
|
|
3. **Agent Node:** Provide a prompt—for example:
|
|
|
|
```python
|
|
f"""
|
|
Here is the context: {context}
|
|
|
|
Here are the actions:
|
|
1. Name: search
|
|
Description: Use web search to get results
|
|
Parameters:
|
|
query: str of what to search
|
|
2. Name: answer
|
|
Description: Conclude based on the results
|
|
Parameters:
|
|
result: str of what to answer
|
|
|
|
Now decide your action by returning:
|
|
```yaml
|
|
thinking: |
|
|
Based on the context, ...
|
|
action: search or answer
|
|
parameters:
|
|
...
|
|
```"""
|
|
```
|
|
|
|
### Example: Search Agent
|
|
|
|
This agent:
|
|
1. Decides whether to search or answer
|
|
2. If searches, loops back to decide if more search needed
|
|
3. Answers when enough context gathered
|
|
|
|
```python
|
|
class DecideAction(Node):
|
|
def prep(self, shared):
|
|
context = shared.get("context", "No previous search")
|
|
query = shared["query"]
|
|
return query, context
|
|
|
|
def exec(self, inputs):
|
|
query, context = inputs
|
|
prompt = f"""
|
|
Given input: {query}
|
|
Previous search results: {context}
|
|
Should I: 1) Search web for more info 2) Answer with current knowledge
|
|
Output in yaml:
|
|
```yaml
|
|
action: search/answer
|
|
reason: why this action
|
|
search_term: search phrase if action is search
|
|
```"""
|
|
resp = call_llm(prompt)
|
|
yaml_str = resp.split("```yaml")[1].split("```")[0].strip()
|
|
result = yaml.safe_load(yaml_str)
|
|
|
|
assert isinstance(result, dict)
|
|
assert "action" in result
|
|
assert "reason" in result
|
|
assert result["action"] in ["search", "answer"]
|
|
if result["action"] == "search":
|
|
assert "search_term" in result
|
|
|
|
return result
|
|
|
|
def post(self, shared, prep_res, exec_res):
|
|
if exec_res["action"] == "search":
|
|
shared["search_term"] = exec_res["search_term"]
|
|
return exec_res["action"]
|
|
|
|
class SearchWeb(Node):
|
|
def prep(self, shared):
|
|
return shared["search_term"]
|
|
|
|
def exec(self, search_term):
|
|
return search_web(search_term)
|
|
|
|
def post(self, shared, prep_res, exec_res):
|
|
prev_searches = shared.get("context", [])
|
|
shared["context"] = prev_searches + [
|
|
{"term": shared["search_term"], "result": exec_res}
|
|
]
|
|
return "decide"
|
|
|
|
class DirectAnswer(Node):
|
|
def prep(self, shared):
|
|
return shared["query"], shared.get("context", "")
|
|
|
|
def exec(self, inputs):
|
|
query, context = inputs
|
|
return call_llm(f"Context: {context}\nAnswer: {query}")
|
|
|
|
def post(self, shared, prep_res, exec_res):
|
|
print(f"Answer: {exec_res}")
|
|
shared["answer"] = exec_res
|
|
|
|
# Connect nodes
|
|
decide = DecideAction()
|
|
search = SearchWeb()
|
|
answer = DirectAnswer()
|
|
|
|
decide - "search" >> search
|
|
decide - "answer" >> answer
|
|
search - "decide" >> decide # Loop back
|
|
|
|
flow = Flow(start=decide)
|
|
flow.run({"query": "Who won the Nobel Prize in Physics 2024?"})
|
|
``` |