62 lines
No EOL
2 KiB
Python
62 lines
No EOL
2 KiB
Python
import logging
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from langchain.prompts import ChatPromptTemplate
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import json
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# TO DO, ADD ALL CLASSIFIERS HERE
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from langchain.chains import create_extraction_chain
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from langchain.chat_models import ChatOpenAI
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from ..config import Config
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from ..database.vectordb.loaders.loaders import _document_loader
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config = Config()
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config.load()
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OPENAI_API_KEY = config.openai_key
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from langchain.document_loaders import TextLoader
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from langchain.document_loaders import DirectoryLoader
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async def classify_summary(query, document_summaries):
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llm = ChatOpenAI(temperature=0, model=config.model)
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prompt_classify = ChatPromptTemplate.from_template(
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"""You are a classifier. Determine what document are relevant for the given query: {query}, Document summaries and ids:{document_summaries}"""
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)
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json_structure = [
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{
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"name": "classifier",
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"description": "Classification",
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"parameters": {
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"type": "object",
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"properties": {
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"DocumentSummary": {
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"type": "string",
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"description": "The summary of the document and the topic it deals with.",
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},
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"d_id": {"type": "string", "description": "The id of the document"},
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},
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"required": ["DocumentSummary"],
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},
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}
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]
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chain_filter = prompt_classify | llm.bind(
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function_call={"name": "classifier"}, functions=json_structure
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)
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classifier_output = await chain_filter.ainvoke(
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{"query": query, "document_summaries": document_summaries}
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)
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arguments_str = classifier_output.additional_kwargs["function_call"]["arguments"]
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logging.info("This is the arguments string %s", arguments_str)
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arguments_dict = json.loads(arguments_str)
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logging.info("Relevant summary is %s", arguments_dict.get("DocumentSummary", None))
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classfier_id = arguments_dict.get("d_id", None)
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logging.info("This is the classifier id %s", classfier_id)
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return classfier_id |