mirror of
https://github.com/hwchase17/langchain
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3e0cd11f51
Co-authored-by: Tomaz Bratanic <tomazbratanic@Tomazs-MacBook-Pro.local> Co-authored-by: Erick Friis <erick@langchain.dev>
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
from typing import List, Tuple
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_to_openai_function_messages
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from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.pydantic_v1 import BaseModel, Field
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from langchain.schema import AIMessage, HumanMessage
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from langchain.tools.render import format_tool_to_openai_function
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from langchain_community.chat_models import ChatOpenAI
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from neo4j_semantic_layer.information_tool import InformationTool
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from neo4j_semantic_layer.memory_tool import MemoryTool
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from neo4j_semantic_layer.recommendation_tool import RecommenderTool
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llm = ChatOpenAI(temperature=0, model="gpt-4")
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tools = [InformationTool(), RecommenderTool(), MemoryTool()]
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llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful assistant that finds information about movies "
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" and recommends them. If tools require follow up questions, "
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"make sure to ask the user for clarification. Make sure to include any "
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"available options that need to be clarified in the follow up questions",
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),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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)
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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agent = (
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{
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"input": lambda x: x["input"],
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"chat_history": lambda x: _format_chat_history(x["chat_history"])
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if x.get("chat_history")
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else [],
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"agent_scratchpad": lambda x: format_to_openai_function_messages(
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x["intermediate_steps"]
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),
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}
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| prompt
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| llm_with_tools
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| OpenAIFunctionsAgentOutputParser()
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)
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# Add typing for input
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class AgentInput(BaseModel):
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input: str
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chat_history: List[Tuple[str, str]] = Field(
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..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
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)
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agent_executor = AgentExecutor(agent=agent, tools=tools).with_types(
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input_type=AgentInput
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)
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