Tools on LangChain

from langchain.agents import AgentExecutor
from langchain_cohere.react_multi_hop.agent import create_cohere_react_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain_cohere.chat_models import ChatCohere

from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.pydantic_v1 import BaseModel, Field

os.environ["TAVILY_API_KEY"] = #<your-api-key>

internet_search = TavilySearchResults()
internet_search.name = "internet_search"
internet_search.description = "Returns a list of relevant document snippets for a textual query retrieved from the internet."

class TavilySearchInput(BaseModel):
    query: str = Field(description="Query to search the internet with")
internet_search.args_schema = TavilySearchInput

# LLM
llm = ChatCohere(model="command-r-plus", temperature=0)

# Preamble
preamble = """
You are an expert who answers the user's question with the most relevant datasource. You are equipped with an internet search tool and a special vectorstore of information about how to write good essays.
"""

# Prompt template
prompt = ChatPromptTemplate.from_template("{input}")

# Create the ReAct agent
agent = create_cohere_react_agent(
    llm=llm,
    tools=[internet_search],
    prompt=prompt,
)

agent_executor = AgentExecutor(agent=agent, tools=[internet_search], verbose=True)

response = agent_executor.invoke({
    "input": "I want to write an essay. Any tips?",
    "preamble": preamble,
})

print(response['output'])

### Router
from typing import Literal

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_cohere import ChatCohere

# Data model
class web_search(BaseModel):
    """
    The internet. Use web_search for questions that are related to anything else than agents, prompt engineering, and adversarial attacks.
    """
    query: str = Field(description="The query to use when searching the internet.")

class vectorstore(BaseModel):
    """
    A vectorstore containing documents related to agents, prompt engineering, and adversarial attacks. Use the vectorstore for questions on these topics.
    """
    query: str = Field(description="The query to use when searching the vectorstore.")

# Preamble
preamble = """You are an expert at routing a user question to a vectorstore or web search.
The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.
Use the vectorstore for questions on these topics. Otherwise, use web-search."""

# LLM with tool use and preamble
llm = ChatCohere()
structured_llm_router = llm.bind_tools(tools=[web_search, vectorstore], preamble=preamble)

# Prompt
route_prompt = ChatPromptTemplate.from_messages(
    [
        ("human", "{question}"),
    ]
)

question_router = route_prompt | structured_llm_router
response = question_router.invoke({"question": "Who will the Bears draft first in the NFL draft?"})
print(response.response_metadata['tool_calls'])
response = question_router.invoke({"question": "What are the types of agent memory?"})
print(response.response_metadata['tool_calls'])
response = question_router.invoke({"question": "Hi how are you?"})
print('tool_calls' in response.response_metadata)