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from langchain_core.pydantic_v1 import BaseModel, Field |
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from typing import List |
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from typing import Literal |
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from langchain.prompts import ChatPromptTemplate |
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from langchain_core.utils.function_calling import convert_to_openai_function |
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from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser |
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class IntentCategorizer(BaseModel): |
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"""Analyzing the user message input""" |
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language: str = Field( |
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description="Find the language of the message input in full words (ex: French, English, Spanish, ...), defaults to English", |
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default="English", |
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) |
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intent: str = Field( |
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enum=[ |
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"ai_impact", |
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"geo_info", |
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"esg", |
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"search", |
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"chitchat", |
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], |
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description=""" |
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Categorize the user input in one of the following category |
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Any question |
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Examples: |
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- ai_impact = Environmental impacts of AI: "What are the environmental impacts of AI", "How does AI affect the environment" |
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- geo_info = Geolocated info about climate change: Any question where the user wants to know localized impacts of climate change, eg: "What will be the temperature in Marseille in 2050" |
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- esg = Any question about the ESG regulation, frameworks and standards like the CSRD, TCFD, SASB, GRI, CDP, etc. |
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- search = Searching for any quesiton about climate change, energy, biodiversity, nature, and everything we can find the IPCC or IPBES reports or scientific papers, |
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- chitchat = Any general question that is not related to the environment or climate change or just conversational, or if you don't think searching the IPCC or IPBES reports would be relevant |
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""", |
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) |
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def make_intent_categorization_chain(llm): |
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openai_functions = [convert_to_openai_function(IntentCategorizer)] |
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llm_with_functions = llm.bind(functions = openai_functions,function_call={"name":"IntentCategorizer"}) |
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prompt = ChatPromptTemplate.from_messages([ |
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("system", "You are a helpful assistant, you will analyze, translate and reformulate the user input message using the function provided"), |
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("user", "input: {input}") |
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]) |
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chain = prompt | llm_with_functions | JsonOutputFunctionsParser() |
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return chain |
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def make_intent_categorization_node(llm): |
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categorization_chain = make_intent_categorization_chain(llm) |
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def categorize_message(state): |
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output = categorization_chain.invoke({"input":state["user_input"]}) |
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if "language" not in output: output["language"] = "English" |
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output["query"] = state["user_input"] |
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return output |
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return categorize_message |
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