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https://github.com/cutwell/langchain-zero-to-hero-agents
Get started with LangChain Agents, part of the zero-to-hero series
https://github.com/cutwell/langchain-zero-to-hero-agents
agents genai langchain-python langserve
Last synced: 29 days ago
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Get started with LangChain Agents, part of the zero-to-hero series
- Host: GitHub
- URL: https://github.com/cutwell/langchain-zero-to-hero-agents
- Owner: Cutwell
- License: mit
- Created: 2024-02-24T18:25:39.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-05-21T07:50:51.000Z (6 months ago)
- Last Synced: 2024-08-07T14:42:48.727Z (3 months ago)
- Topics: agents, genai, langchain-python, langserve
- Language: Python
- Homepage: https://www.youtube.com/watch?v=2uLl9Se4ISs
- Size: 188 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🦜👑 LangChain Zero-to-Hero / 🤖 _Agents_
Get started with LangChain Agents, part of the zero-to-hero series.
## Before you start
* This tutorial uses the terminal to install dependencies and run Python scripts.
* When you see the 🆕 emoji before a set of terminal commands, open a new terminal process.
* When you see the ♻️ emoji before a set of terminal commands, you can re-use the same terminal you used last time.
## Prerequisites
1. Download and install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer).
2. Setup a Poetry environment:
🆕
```sh
poetry init --no-interaction --python="^3.11" --dependency=langchain --dependency=langchain-openai --dependency=langchainhub --dependency="langserve[all]" --dependency=duckduckgo-search
poetry install
```
3. Get an OpenAI API key and save it as an environment variable (e.g.: with [DirEnv](https://direnv.net/)):
```sh
export OPENAI_API_KEY=...
```
## Getting Started
1. Let's build a simple agent script.
* Create a file `langchain_zero_to_hero_agents/src/main.py` and create a simple agent (code modified from the [LangChain Agent Cookbook](https://python.langchain.com/docs/expression_language/cookbook/agent))
```python
from langchain import hub
from langchain.agents import AgentExecutor, tool
from langchain.agents.output_parsers import XMLAgentOutputParser
from langchain_openai import ChatOpenAI
from langchain_community.tools import DuckDuckGoSearchResults
#######################
# LangChain Agent Code
#######################
model = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, streaming=True)
@tool
def search(query: str) -> str:
"""Search things about current events."""
search = DuckDuckGoSearchResults()
return search.run(query)
tool_list = [search]
prompt = hub.pull("hwchase17/xml-agent-convo")
def convert_intermediate_steps(intermediate_steps):
log = ""
for action, observation in intermediate_steps:
log += (
f"{action.tool}{action.tool_input}"
f"{observation}"
)
return log
def convert_tools(tools):
return "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: convert_intermediate_steps(
x["intermediate_steps"]
),
}
| prompt.partial(tools=convert_tools(tool_list))
| model.bind(stop=["", ""])
| XMLAgentOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=tool_list, verbose=True)
if __name__ == "__main__":
print(agent_executor.invoke({"input": "whats the weather in New york?"}))
```
2. Try running the script in the terminal to test it works:
♻️
```sh
poetry run python langchain_zero_to_hero_agents/src/main.py
```
3. To make this agent useful, we can setup a simple API with LangServe:
```python
from fastapi import FastAPI
from langchain.pydantic_v1 import BaseModel
from langserve import add_routes
from typing import Any
#######################
# LangChain Agent Code
#######################
# ...
######################
# LangServe API Code
######################
class Input(BaseModel):
input: str
class Output(BaseModel):
output: Any
app = FastAPI(
title="DuckDuckGo Agent",
version="1.0",
description="API for accessing a simple LangChain agent that can query the web with DuckDuckGo.",
)
add_routes(
app,
agent_executor.with_types(input_type=Input, output_type=Output).with_config(
{"run_name": "agent"}
),
path="/agent"
)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="localhost", port=8000)
```
4. Startup your LangServe API server:
♻️
```sh
poetry run python langchain_zero_to_hero_agents/src/main.py
```
5. Visit http://localhost:8000/agent/playground/ to access a simple UI for interacting with your agent.
6. Create a test script (`langchain_zero_to_hero_agents/tests/test.py`) to experiment with accessing your API via Python:
```python
import requests
response = requests.post(
"http://localhost:8000/agent/invoke",
json={'input': {"input": "what is the weather in new york"}}
)
print(response.json())
```
7. Run your test script and observe the structured JSON output:
🆕
```sh
poetry run python langchain_zero_to_hero_agents/tests/test.py
```
## License
MIT