ReAct
This walkthrough showcases using an agent to implement the ReAct logic.
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai import OpenAI
API Reference:
Initialize tools
Let’s load some tools to use.
tools = [TavilySearchResults(max_results=1)]
Create Agent
# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/react")
# Choose the LLM to use
llm = OpenAI()
# Construct the ReAct agent
agent = create_react_agent(llm, tools, prompt)
Run Agent
# Create an agent executor by passing in the agent and tools
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is LangChain?"})
> Entering new AgentExecutor chain...
I should research LangChain to learn more about it.
Action: tavily_search_results_json
Action Input: "LangChain"[{'url': 'https://www.ibm.com/topics/langchain', 'content': 'LangChain is essentially a library of abstractions for Python and Javascript, representing common steps and concepts LangChain is an open source orchestration framework for the development of applications using large language models other LangChain features, like the eponymous chains. LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector storesLangChain is a tool for building applications using large language models (LLMs) like chatbots and virtual agents. It simplifies the process of programming and integration with external data sources and software workflows. It supports Python and Javascript languages and supports various LLM providers, including OpenAI, Google, and IBM.'}] I should read the summary and look at the different features and integrations of LangChain.
Action: tavily_search_results_json
Action Input: "LangChain features and integrations"[{'url': 'https://www.ibm.com/topics/langchain', 'content': "LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector stores LangChain is an open source orchestration framework for the development of applications using large language models other LangChain features, like the eponymous chains. LangChain is essentially a library of abstractions for Python and Javascript, representing common steps and conceptsLaunched by Harrison Chase in October 2022, LangChain enjoyed a meteoric rise to prominence: as of June 2023, it was the single fastest-growing open source project on Github. 1 Coinciding with the momentous launch of OpenAI's ChatGPT the following month, LangChain has played a significant role in making generative AI more accessible to enthusias..."}] I should take note of the launch date and popularity of LangChain.
Action: tavily_search_results_json
Action Input: "LangChain launch date and popularity"[{'url': 'https://www.ibm.com/topics/langchain', 'content': "LangChain is an open source orchestration framework for the development of applications using large language models other LangChain features, like the eponymous chains. LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector stores LangChain is essentially a library of abstractions for Python and Javascript, representing common steps and conceptsLaunched by Harrison Chase in October 2022, LangChain enjoyed a meteoric rise to prominence: as of June 2023, it was the single fastest-growing open source project on Github. 1 Coinciding with the momentous launch of OpenAI's ChatGPT the following month, LangChain has played a significant role in making generative AI more accessible to enthusias..."}] I now know the final answer.
Final Answer: LangChain is an open source orchestration framework for building applications using large language models (LLMs) like chatbots and virtual agents. It was launched by Harrison Chase in October 2022 and has gained popularity as the fastest-growing open source project on Github in June 2023.
> Finished chain.
{'input': 'what is LangChain?',
'output': 'LangChain is an open source orchestration framework for building applications using large language models (LLMs) like chatbots and virtual agents. It was launched by Harrison Chase in October 2022 and has gained popularity as the fastest-growing open source project on Github in June 2023.'}
Using with chat history
When using with chat history, we will need a prompt that takes that into account
# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/react-chat")
# Construct the ReAct agent
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
from langchain_core.messages import AIMessage, HumanMessage
agent_executor.invoke(
{
"input": "what's my name? Only use a tool if needed, otherwise respond with Final Answer",
# Notice that chat_history is a string, since this prompt is aimed at LLMs, not chat models
"chat_history": "Human: Hi! My name is Bob\nAI: Hello Bob! Nice to meet you",
}
)
API Reference:
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? No
Final Answer: Your name is Bob.
> Finished chain.
{'input': "what's my name? Only use a tool if needed, otherwise respond with Final Answer",
'chat_history': 'Human: Hi! My name is Bob\nAI: Hello Bob! Nice to meet you',
'output': 'Your name is Bob.'}