mirror of https://github.com/hwchase17/langchain
Harrison/pii chatbot (#12523)
the pii detection in the template is pretty basic, will need to be customized per use case the chain it "protects" can be swapped out for any chainpull/12354/head^2
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# pii-protected-chatbot
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from typing import List, Tuple
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.pydantic_v1 import BaseModel
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from langchain.schema.messages import AIMessage, HumanMessage
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable 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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[tool.poetry]
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name = "pii_protected_chatbot"
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version = "0.0.1"
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description = ""
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authors = []
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readme = "README.md"
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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langchain = ">=0.0.313, <0.1"
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openai = "^0.28.1"
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[tool.poetry.group.dev.dependencies]
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langchain-cli = ">=0.0.4"
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fastapi = "^0.104.0"
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sse-starlette = "^1.6.5"
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[tool.langserve]
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export_module = "pii_protected_chatbot.chain"
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export_attr = "chain"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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