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Fix model retrieval and improve answering by adding summary context (#5)
Browse files- Fix model retrieval and improve answering by adding summary context (681a1427df5b7b3e66349fd6d4caafd23aee82ae)
Co-authored-by: Trương Tấn Cường <tosanoob@users.noreply.huggingface.co>
- chat/model_manage.py +133 -64
chat/model_manage.py
CHANGED
@@ -5,6 +5,25 @@ import json
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model = None
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def create_model():
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with open("apikey.txt","r") as apikey:
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key = apikey.readline()
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@@ -14,7 +33,7 @@ def create_model():
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print(m.name)
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print("He was there")
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config = genai.GenerationConfig(max_output_tokens=2048,
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temperature=0
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safety_settings = [
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{
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"category": "HARM_CATEGORY_DANGEROUS",
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@@ -37,53 +56,71 @@ def create_model():
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"threshold": "BLOCK_NONE",
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},
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]
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global model
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model = genai.GenerativeModel("gemini-pro",
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generation_config=config,
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safety_settings=safety_settings)
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def get_model():
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global model
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if model is None:
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# Khởi tạo model ở đây
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model = create_model() # Giả sử create_model là hàm tạo model của bạn
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return model
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def extract_keyword_prompt(query):
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"""A prompt that return a JSON block as arguments for querying database"""
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prompt =
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return prompt
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def make_answer_prompt(input, contexts):
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"""A prompt that return the final answer, based on the queried context"""
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prompt = (
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"""[INST] You are a library assistant that help
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QUESTION: '{input}'
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INFORMATION: '{contexts}'
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[/INST]
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ANSWER:
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"""
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).format(input=input, contexts=contexts)
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return prompt
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def response(args, db_instance):
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"""Create response context, based on input arguments"""
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keys = list(dict.keys(args))
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@@ -115,41 +152,48 @@ def response(args, db_instance):
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result_string = ""
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if paper_info:
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for i in range(len(paper_info)):
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result_string += "Title: {}, Author: {}, Link: {}".format(paper_info[i][2],paper_info[i][3],paper_info[i][6])
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records.append([paper_info[i][2],paper_info[i][3],paper_info[i][6]])
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else:
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return "Information not found", "Information not found"
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# invoke llm and return result
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if "title" in keys:
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# invoke llm and return result
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def full_chain_single_question(input_prompt, db_instance):
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try:
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first_prompt = extract_keyword_prompt(input_prompt)
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@@ -180,23 +224,48 @@ def format_chat_history_from_web(chat_history: list):
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)
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return temp_chat
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def full_chain_history_question(chat_history: list, db_instance):
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try:
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temp_chat = format_chat_history_from_web(chat_history)
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args = json.loads(utils.trimming(temp_answer))
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contexts, results = response(args, db_instance)
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if not results:
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# print(contexts)
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return "Random question, direct return", contexts
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else:
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answer =
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return
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except Exception as e:
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model = None
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model_retrieval = None
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model_answer = None
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RETRIEVAL_INSTRUCT = """You are an auto chatbot that response with only one action below based on user question.
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1. If the guest question is asking about a science topic, you need to respond the information in JSON schema below:
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{
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"keywords": [a list of string keywords about the topic],
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"description": "a paragraph describing the topic in about 50 to 100 words"
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}
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2. If the guest is not asking for any informations or documents, you need to respond in JSON schema below:
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{
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"answer": "your answer to the user question"
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}"""
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ANSWER_INSTRUCT = """You are a library assistant that help answering customer question based on the information given.
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You always answer in a conversational form naturally and politely.
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You must introduce all the records given, each must contain title, authors and the link to the pdf file."""
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def create_model():
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with open("apikey.txt","r") as apikey:
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key = apikey.readline()
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print(m.name)
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print("He was there")
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config = genai.GenerationConfig(max_output_tokens=2048,
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temperature=1.0)
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safety_settings = [
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{
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"category": "HARM_CATEGORY_DANGEROUS",
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"threshold": "BLOCK_NONE",
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},
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]
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global model, model_retrieval, model_answer
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model = genai.GenerativeModel("gemini-1.5-pro-latest",
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generation_config=config,
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safety_settings=safety_settings)
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model_retrieval = genai.GenerativeModel("gemini-1.5-pro-latest",
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generation_config=config,
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safety_settings=safety_settings,
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system_instruction=RETRIEVAL_INSTRUCT)
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model_answer = genai.GenerativeModel("gemini-1.5-pro-latest",
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generation_config=config,
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safety_settings=safety_settings,
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system_instruction=ANSWER_INSTRUCT)
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return model, model_answer, model_retrieval
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def get_model():
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global model, model_answer, model_retrieval
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if model is None:
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# Khởi tạo model ở đây
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model, model_answer, model_retrieval = create_model() # Giả sử create_model là hàm tạo model của bạn
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return model, model_answer, model_retrieval
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def extract_keyword_prompt(query):
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"""A prompt that return a JSON block as arguments for querying database"""
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prompt = """[INST] SYSTEM: You are an auto chatbot that response with only one action below based on user question.
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1. If the guest question is asking about a science topic, you need to respond the information in JSON schema below:
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{
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"keywords": [a list of string keywords about the topic],
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"description": "a paragraph describing the topic in about 50 to 100 words"
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}
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2. If the guest is not asking for any informations or documents, you need to respond in JSON schema below:
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{
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"answer": "your answer to the user question"
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}
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QUESTION: """ + query + """[/INST]
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ANSWER: """
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return prompt
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def make_answer_prompt(input, contexts):
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"""A prompt that return the final answer, based on the queried context"""
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prompt = (
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"""[INST] You are a library assistant that help answering customer QUESTION based on the INFORMATION given.
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You always answer in a conversational form naturally and politely.
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You must introduce all the records given, each must contain title, authors and the link to the pdf file.
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QUESTION: {input}
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INFORMATION: '{contexts}'
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[/INST]
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ANSWER:
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"""
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).format(input=input, contexts=contexts)
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return prompt
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def retrieval_chat_template(question):
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return {
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"role":"user",
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"parts":[f"QUESTION: {question} \n ANSWER:"]
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}
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def answer_chat_template(question, contexts):
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return {
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"role":"user",
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"parts":[f"QUESTION: {question} \n INFORMATION: {contexts} \n ANSWER:"]
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}
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def response(args, db_instance):
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"""Create response context, based on input arguments"""
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keys = list(dict.keys(args))
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result_string = ""
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if paper_info:
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for i in range(len(paper_info)):
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result_string += "Record no.{} - Title: {}, Author: {}, Link: {}, ".format(i+1,paper_info[i][2],paper_info[i][3],paper_info[i][6])
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id = paper_info[i][0]
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selected_document = utils.db.query_exact(id)["documents"]
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doc_str = "Summary:"
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for doc in selected_document:
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doc_str+= doc + " "
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result_string += doc_str
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records.append([paper_info[i][2],paper_info[i][3],paper_info[i][6]])
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return result_string, records
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else:
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return "Information not found", "Information not found"
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# invoke llm and return result
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# if "title" in keys:
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# title = args['title']
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# authors = utils.authors_str_to_list(args['author'])
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# paper_info = db_instance.query(title = title,author = authors)
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# # if query not found then go crawl brh
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# # print(paper_info)
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# if len(paper_info) == 0:
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# new_records = utils.crawl_exact_paper(title=title,author=authors)
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# print("Got new records: ",len(new_records))
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# if type(new_records) == str:
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# # print(new_records)
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# return "Error occured, information not found", "Information not found"
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# utils.db.add(new_records)
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# db_instance.add(new_records)
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# paper_info = db_instance.query(title = title,author = authors)
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# print("Re-queried on chromadb, results: ",paper_info)
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# # -------------------------------------
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# records = [] # get title (2), author (3), link (6)
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# result_string = ""
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# for i in range(len(paper_info)):
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# result_string += "Title: {}, Author: {}, Link: {}".format(paper_info[i][2],paper_info[i][3],paper_info[i][6])
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# records.append([paper_info[i][2],paper_info[i][3],paper_info[i][6]])
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# # process results:
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# if len(result_string) == 0:
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# return "Information not found", "Information not found"
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# return result_string, records
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# invoke llm and return result
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def full_chain_single_question(input_prompt, db_instance):
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try:
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first_prompt = extract_keyword_prompt(input_prompt)
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)
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return temp_chat
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# def full_chain_history_question(chat_history: list, db_instance):
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# try:
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# temp_chat = format_chat_history_from_web(chat_history)
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# print('Extracted temp chat: ',temp_chat)
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# first_prompt = extract_keyword_prompt(temp_chat[-1]["parts"][0])
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# temp_answer = model.generate_content(first_prompt).text
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# args = json.loads(utils.trimming(temp_answer))
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# contexts, results = response(args, db_instance)
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# print('Context extracted: ',contexts)
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# if not results:
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# return "Random question, direct return", contexts
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# else:
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# QA_Prompt = make_answer_prompt(temp_chat[-1]["parts"][0], contexts)
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# temp_chat[-1]["parts"] = QA_Prompt
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# print(temp_chat)
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# answer = model.generate_content(temp_chat).text
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# return temp_answer, answer
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# except Exception as e:
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# # print(e)
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# return temp_answer, "Error occured: " + str(e)
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def full_chain_history_question(chat_history: list, db_instance):
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try:
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temp_chat = format_chat_history_from_web(chat_history)
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question = temp_chat[-1]['parts'][0]
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first_answer = model_retrieval.generate_content(temp_chat).text
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print(first_answer)
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args = json.loads(utils.trimming(first_answer))
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contexts, results = response(args, db_instance)
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if not results:
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return "Random question, direct return", contexts
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else:
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print('Context to answers: ',contexts)
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answer_chat = answer_chat_template(question, contexts)
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temp_chat[-1] = answer_chat
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answer = model_answer.generate_content(temp_chat).text
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return first_answer, answer
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except Exception as e:
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if first_answer:
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return first_answer, "Error occured: " + str(e)
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else:
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return "No answer", "Error occured: " + str(e)
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