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| import os | |
| import platform | |
| import openai | |
| import chromadb | |
| import langchain | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import Chroma | |
| from langchain.text_splitter import TokenTextSplitter | |
| from langchain.llms import OpenAI | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.chains import ChatVectorDBChain | |
| from langchain.document_loaders import GutenbergLoader | |
| from langchain.embeddings import LlamaCppEmbeddings | |
| from langchain.llms import LlamaCpp | |
| from langchain.output_parsers import StructuredOutputParser, ResponseSchema | |
| from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate | |
| from langchain.llms import OpenAI | |
| from langchain.chains import LLMChain | |
| from langchain.chains import SimpleSequentialChain | |
| from langchain.output_parsers import PydanticOutputParser | |
| from pydantic import BaseModel, Field, validator | |
| from typing import List, Dict | |
| # class AnswerTemplate(BaseModel): | |
| # type: List[str] = Field(description="What is the type of the trip: business, family, vactions. And with whom are you travelling? If can't anwser then leave it empty") | |
| # where: str = Field(description="Where is the user going? If can't anwser then leave it empty") | |
| # start_date: str = Field(description="What is the start date? If can't anwser then leave it empty") | |
| # end_date: str = Field(description="What is the end date? If can't anwser then leave it empty") | |
| # time_constrains: str = Field(description="Is there any time constrains? If can't anwser then leave it empty") | |
| # # dates: Dict[str, str] = Field(description="What are the importante dates and times? If can't anwser then leave it empty") | |
| # preferences: List[str] = Field(description="What does the user want to visit? If can't anwser then leave it empty") | |
| # conditions: str = Field(description="Does the user has any special medical condition? If can't anwser then leave it empty") | |
| # dist_range: str = Field(description="Max distance from a place? If can't anwser then leave it empty") | |
| # # missing: str = Field(description="Is any more information needed?") | |
| class AnswerTemplate(BaseModel): | |
| answer: str = Field(description="Response") | |
| class Gather_Agent(): | |
| def __init__(self): | |
| self.model_name = "gpt-4" | |
| self.model = OpenAI(model_name=self.model_name, temperature=0) | |
| self.output_parser = PydanticOutputParser(pydantic_object=AnswerTemplate) | |
| self.format_instructions = self.output_parser.get_format_instructions() | |
| # self.prompt = PromptTemplate( | |
| # template="""\ | |
| # ### Instruction | |
| # You are Trainline Mate an helpful assistant that plans tours for people at trainline.com. | |
| # As a smart itinerary planner with extensive knowledge of places around the | |
| # world, your task is to determine the user's travel destinations and any specific interests or preferences from | |
| # their message. Here is the history that you have so far: {history} \n### User: \n{input} | |
| # \n### Response: {format_instructions} | |
| # """, | |
| # input_variables=["input", "history", "format_instructions"] | |
| # ) | |
| self.prompt = PromptTemplate( | |
| template="""\ | |
| ### Instruction | |
| You are Trainline Mate an helpful assistant that plans tours for people at trainline.com. | |
| As a smart itinerary planner with extensive knowledge of places around the | |
| world, your task is to determine the user's travel destinations and any specific interests or preferences from | |
| their message. | |
| ### Task | |
| From the following history and user input you should be able to retrieve and resume all the following information: | |
| Where is the trip to, start and end dates for the trip, is there any time constrain, activity preferences, | |
| is there any medical condition and is there a maximum distance range in which the activities have to be. | |
| ### History | |
| Here is the history that you have so far: {history} | |
| ### User: \n{input} | |
| \n### Response: | |
| """, | |
| input_variables=["input", "history"] | |
| ) | |
| def format_prompt(self, input, history): | |
| # return self.prompt.format_prompt(history=history, input=input, format_instructions=self.format_instructions) | |
| return self.prompt.format_prompt(input=input, history=history) | |
| def get_parsed_result(self, input): | |
| result = self.model(input.to_string()) | |
| # return self.output_parser.parse(result) | |
| return result | |