mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
from typing import List, Tuple
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from langchain_community.chat_models import ChatOpenAI
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnablePassthrough
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from presidio_analyzer import AnalyzerEngine
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# Formatting for chat history
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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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# Prompt we will use
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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 who speaks like a pirate",
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),
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MessagesPlaceholder(variable_name="chat_history"),
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("human", "{text}"),
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]
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)
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# Model we will use
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_model = ChatOpenAI()
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# Standard conversation chain.
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chat_chain = (
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{
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"chat_history": lambda x: _format_chat_history(x["chat_history"]),
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"text": lambda x: x["text"],
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}
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| _prompt
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| _model
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| StrOutputParser()
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)
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# PII Detection logic
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analyzer = AnalyzerEngine()
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# You can customize this to detect any PII
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def _detect_pii(inputs: dict) -> bool:
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analyzer_results = analyzer.analyze(text=inputs["text"], language="en")
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return bool(analyzer_results)
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# Add logic to route on whether PII has been detected
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def _route_on_pii(inputs: dict):
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if inputs["pii_detected"]:
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# Response if PII is detected
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return "Sorry, I can't answer questions that involve PII"
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else:
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return chat_chain
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# Final chain
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chain = RunnablePassthrough.assign(
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# First detect PII
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pii_detected=_detect_pii
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) | {
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# Then use this information to generate the response
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"response": _route_on_pii,
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# Return boolean of whether PII is detected so client can decided
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# whether or not to include in chat history
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"pii_detected": lambda x: x["pii_detected"],
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}
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# Add typing for playground
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class ChainInput(BaseModel):
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text: str
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chat_history: List[Tuple[str, str]]
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chain = chain.with_types(input_type=ChainInput)
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