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85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
from utils import prompts
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from env import settings
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import requests
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from llama_index.readers.database import DatabaseReader
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from llama_index import GPTSimpleVectorIndex
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from langchain.memory.chat_memory import BaseChatMemory
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class RequestsGet:
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@prompts(
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name="requests_get",
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description="A portal to the internet. "
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"Use this when you need to get specific content from a website."
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"Input should be a url (i.e. https://www.google.com)."
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"The output will be the text response of the GET request.",
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)
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def inference(self, url: str) -> str:
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"""Run the tool."""
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text = requests.get(url).text
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if len(text) > 100:
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text = text[:100] + "..."
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return text
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class WineDB:
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def __init__(self):
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db = DatabaseReader(
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scheme="postgresql", # Database Scheme
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host=settings["WINEDB_HOST"], # Database Host
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port="5432", # Database Port
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user="alphadom", # Database User
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password=settings["WINEDB_PASSWORD"], # Database Password
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dbname="postgres", # Database Name
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)
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self.columns = ["nameEn", "nameKo", "description"]
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concat_columns = str(",'-',".join([f'"{i}"' for i in self.columns]))
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query = f"""
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SELECT
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Concat({concat_columns})
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FROM wine
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"""
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# CAST(type AS VARCHAR), 'nameEn', 'nameKo', vintage, nationality, province, CAST(size AS VARCHAR), 'grapeVariety', price, image, description, code, winery, alcohol, pairing
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documents = db.load_data(query=query)
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self.index = GPTSimpleVectorIndex(documents)
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@prompts(
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name="Wine Recommendataion",
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description="A tool to recommend wines based on a user's input. "
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"Inputs are necessary factors for wine recommendations, such as the user's mood today, side dishes to eat with wine, people to drink wine with, what things you want to do, the scent and taste of their favorite wine."
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"The output will be a list of recommended wines."
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"The tool is based on a database of wine reviews, which is stored in a database.",
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)
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def inference(self, query: str) -> str:
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"""Run the tool."""
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results = self.index.query(query)
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wine = "\n".join(
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[
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f"{i}:{j}"
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for i, j in zip(
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self.columns, results.source_nodes[0].source_text.split("-")
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)
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]
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)
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return results.response + "\n\n" + wine
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class ExitConversation:
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def __init__(self, memory: BaseChatMemory):
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self.memory = memory
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@prompts(
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name="exit_conversation",
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description="A tool to exit the conversation. "
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"Use this when you want to end the conversation. "
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"The output will be a message that the conversation is over.",
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)
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def inference(self, query: str) -> str:
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"""Run the tool."""
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self.memory.chat_memory.messages = []
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return ""
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