mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
5f38770161
Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Nuno Campos <nuno@boringbits.io>
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
from typing import List, Tuple
|
|
|
|
from langchain.agents import AgentExecutor
|
|
from langchain.agents.format_scratchpad import format_to_openai_function_messages
|
|
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
|
|
from langchain.chat_models import ChatOpenAI
|
|
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
|
|
from langchain.pydantic_v1 import BaseModel, Field
|
|
from langchain.schema.messages import AIMessage, HumanMessage
|
|
from langchain.tools.render import format_tool_to_openai_function
|
|
from langchain.tools.tavily_search import TavilySearchResults
|
|
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
|
|
|
|
# Create the tool
|
|
search = TavilySearchAPIWrapper()
|
|
description = """"A search engine optimized for comprehensive, accurate, \
|
|
and trusted results. Useful for when you need to answer questions \
|
|
about current events or about recent information. \
|
|
Input should be a search query. \
|
|
If the user is asking about something that you don't know about, \
|
|
you should probably use this tool to see if that can provide any information."""
|
|
tavily_tool = TavilySearchResults(api_wrapper=search, description=description)
|
|
|
|
tools = [tavily_tool]
|
|
|
|
llm = ChatOpenAI(temperature=0)
|
|
assistant_system_message = """You are a helpful assistant. \
|
|
Use tools (only if necessary) to best answer the users questions."""
|
|
prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
("system", assistant_system_message),
|
|
MessagesPlaceholder(variable_name="chat_history"),
|
|
("user", "{input}"),
|
|
MessagesPlaceholder(variable_name="agent_scratchpad"),
|
|
]
|
|
)
|
|
|
|
llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
|
|
|
|
|
|
def _format_chat_history(chat_history: List[Tuple[str, str]]):
|
|
buffer = []
|
|
for human, ai in chat_history:
|
|
buffer.append(HumanMessage(content=human))
|
|
buffer.append(AIMessage(content=ai))
|
|
return buffer
|
|
|
|
|
|
agent = (
|
|
{
|
|
"input": lambda x: x["input"],
|
|
"chat_history": lambda x: _format_chat_history(x["chat_history"]),
|
|
"agent_scratchpad": lambda x: format_to_openai_function_messages(
|
|
x["intermediate_steps"]
|
|
),
|
|
}
|
|
| prompt
|
|
| llm_with_tools
|
|
| OpenAIFunctionsAgentOutputParser()
|
|
)
|
|
|
|
|
|
class AgentInput(BaseModel):
|
|
input: str
|
|
chat_history: List[Tuple[str, str]] = Field(
|
|
..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
|
|
)
|
|
|
|
|
|
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
|
|
input_type=AgentInput
|
|
)
|