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Add descriptions & open in studio button
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@hinthornw
hinthornw committed Sep 17, 2024
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191 changes: 3 additions & 188 deletions README.md
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[![CI](https://github.com/langchain-ai/react-agent/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/langchain-ai/react-agent/actions/workflows/unit-tests.yml)
[![Integration Tests](https://github.com/langchain-ai/react-agent/actions/workflows/integration-tests.yml/badge.svg)](https://github.com/langchain-ai/react-agent/actions/workflows/integration-tests.yml)
[![Open in - LangGraph Studio](https://img.shields.io/badge/Open_in-LangGraph_Studio-00324d.svg?logo=data:image/svg%2bxml;base64,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)](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/langchain-ai/react-agent)

This template showcases a [ReAct agent](https://arxiv.org/abs/2210.03629) implemented using [LangGraph](https://github.com/langchain-ai/langgraph), designed for [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio). ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools.

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-->

<details>
<summary>Setup for `model_name` and `scraper_tool_model_name`</summary>
<summary>Setup for `model_name`</summary>
The `llm` configuration defaults are shown below:

```yaml
model_name: anthropic/claude-3-5-sonnet-20240620
scraper_tool_model_name: accounts/fireworks/models/firefunction-v2
```
Follow the instructions below to get set up, or pick one of the additional options.
Expand Down Expand Up @@ -125,192 +125,7 @@ Configuration auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
"model_name": {
"type": "string",
"default": "anthropic/claude-3-5-sonnet-20240620",
"environment": [
{
"value": "anthropic/claude-1.2",
"variables": "ANTHROPIC_API_KEY"
},
{
"value": "anthropic/claude-2.0",
"variables": "ANTHROPIC_API_KEY"
},
{
"value": "anthropic/claude-2.1",
"variables": "ANTHROPIC_API_KEY"
},
{
"value": "anthropic/claude-3-5-sonnet-20240620",
"variables": "ANTHROPIC_API_KEY"
},
{
"value": "anthropic/claude-3-haiku-20240307",
"variables": "ANTHROPIC_API_KEY"
},
{
"value": "anthropic/claude-3-opus-20240229",
"variables": "ANTHROPIC_API_KEY"
},
{
"value": "anthropic/claude-3-sonnet-20240229",
"variables": "ANTHROPIC_API_KEY"
},
{
"value": "anthropic/claude-instant-1.2",
"variables": "ANTHROPIC_API_KEY"
},
{
"value": "fireworks/gemma2-9b-it",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/llama-v3-70b-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/llama-v3-70b-instruct-hf",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/llama-v3-8b-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/llama-v3-8b-instruct-hf",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/llama-v3p1-405b-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/llama-v3p1-405b-instruct-long",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/llama-v3p1-70b-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/llama-v3p1-8b-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/mixtral-8x22b-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/mixtral-8x7b-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/mixtral-8x7b-instruct-hf",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/mythomax-l2-13b",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/phi-3-vision-128k-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/phi-3p5-vision-instruct",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/starcoder-16b",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "fireworks/yi-large",
"variables": "FIREWORKS_API_KEY"
},
{
"value": "openai/gpt-3.5-turbo",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-3.5-turbo-0125",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-3.5-turbo-0301",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-3.5-turbo-0613",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-3.5-turbo-1106",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-3.5-turbo-16k",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-3.5-turbo-16k-0613",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-0125-preview",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-0314",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-0613",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-1106-preview",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-32k",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-32k-0314",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-32k-0613",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-turbo",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-turbo-preview",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4-vision-preview",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4o",
"variables": "OPENAI_API_KEY"
},
{
"value": "openai/gpt-4o-mini",
"variables": "OPENAI_API_KEY"
}
]
},
"scraper_tool_model_name": {
"type": "string",
"default": "accounts/fireworks/models/firefunction-v2",
"description": "The name of the language model to use for the agent's main interactions. Should be in the form: provider/model-name.",
"environment": [
{
"value": "anthropic/claude-1.2",
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43 changes: 21 additions & 22 deletions src/react_agent/configuration.py
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class Configuration:
"""The configuration for the agent."""

system_prompt: str = field(default=prompts.SYSTEM_PROMPT)
"""The system prompt to use for the agent's interactions.
This prompt sets the context and behavior for the agent.
"""

model_name: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = (
"anthropic/claude-3-5-sonnet-20240620"
system_prompt: str = field(
default=prompts.SYSTEM_PROMPT,
metadata={
"description": "The system prompt to use for the agent's interactions. "
"This prompt sets the context and behavior for the agent."
},
)

model_name: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
default="anthropic/claude-3-5-sonnet-20240620",
metadata={
"description": "The name of the language model to use for the agent's main interactions. "
"Should be in the form: provider/model-name."
},
)

max_search_results: int = field(
default=10,
metadata={
"description": "The maximum number of search results to return for each search query."
},
)
"""The name of the language model to use for the agent's main interactions.
Should be in the form: provider/model-name.
"""

scraper_tool_model_name: Annotated[
str, {"__template_metadata__": {"kind": "llm"}}
] = "accounts/fireworks/models/firefunction-v2"
"""The name of the language model to use for the web scraping tool.
This model is specifically used for summarizing and extracting information from web pages.
"""
max_search_results: int = 10
"""The maximum number of search results to return for each search query."""

@classmethod
def from_runnable_config(
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2 changes: 1 addition & 1 deletion src/react_agent/graph.py
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Expand Up @@ -125,4 +125,4 @@ def route_model_output(state: State) -> Literal["__end__", "tools"]:
interrupt_before=[], # Add node names here to update state before they're called
interrupt_after=[], # Add node names here to update state after they're called
)
graph.name = "ReAct Agent" # This customizes the name in LangSmith
graph.name = "ReAct Agent" # This customizes the name in LangSmith
44 changes: 2 additions & 42 deletions src/react_agent/tools.py
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"""This module provides example tools for web scraping and search functionality.
It includes:
- A web scraper that uses an LLM to summarize content based on instructions
- A basic Tavily search function
It includes a basic Tavily search function (as an example)
These tools are intended as free examples to get started. For production use,
consider implementing more robust and specialized tools tailored to your needs.
"""

from datetime import datetime, timezone
from typing import Any, Callable, List, Optional, cast

import httpx
from langchain.chat_models import init_chat_model
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.runnables import RunnableConfig
from langchain_core.tools import InjectedToolArg
from typing_extensions import Annotated

from react_agent.configuration import Configuration
from react_agent.utils import get_message_text


# note that arguments typed as "RunnableConfig" in tools will be excluded from the schema generated
# for the model.
# They are treated as "injected arguments"
async def scrape_webpage(url: str, instructions: str, *, config: RunnableConfig) -> str:
"""Scrape the given webpage and return a summary of text based on the instructions.
Args:
url: The URL of the webpage to scrape.
instructions: The instructions to give to the scraper. An LLM will be used to respond using the
instructions and the scraped text.
"""
async with httpx.AsyncClient() as client:
response = await client.get(url)
web_text = response.text

configuration = Configuration.from_runnable_config(config)
model = init_chat_model(configuration.model_name)
response_msg = await model.ainvoke(
[
(
"system",
"You are a helpful web scraper AI assistant. You are working in extractive Q&A mode, meaning you refrain from making overly abstractive responses."
"Respond to the user's instructions."
" Based on the provided webpage. If you are unable to answer the question, let the user know. Do not guess."
" Provide citations and direct quotes when possible."
f" \n\n<webpage_text>\n{web_text}\n</webpage_text>"
f"\n\nSystem time: {datetime.now(tz=timezone.utc)}",
),
("user", instructions),
]
)
return get_message_text(response_msg)


async def search(
Expand All @@ -71,4 +31,4 @@ async def search(
return cast(list[dict[str, Any]], result)


TOOLS: List[Callable[..., Any]] = [scrape_webpage, search]
TOOLS: List[Callable[..., Any]] = [search]
2 changes: 1 addition & 1 deletion tests/unit_tests/test_configuration.py
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@@ -1,5 +1,5 @@
from react_agent.configuration import Configuration


def test_configuration_empty():
def test_configuration_empty() -> None:
Configuration.from_runnable_config({})

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