LangChain の Embeddings を利用してスマートに発注書を作る

# slack bolt
from slack_bolt import App
# socket mode での動作
from slack_bolt.adapter.socket_mode import SocketModeHandler
# GPT-3.5-turbo
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain

from langchain.memory import ConversationBufferMemory

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS

from openpyxl import Workbook, load_workbook
import tempfile
import uuid

# 環境変数にAPIキーを設定
import os
SLACK_APP_TOKEN = os.environ["SLACK_APP_TOKEN"] # "xapp-XXXXXXXXXXXX"
SLACK_BOT_TOKEN = os.environ["SLACK_BOT_TOKEN"] # "xoxb-XXXXXXXXXXXX"

# Slack Bot の作成
app = App(token=SLACK_BOT_TOKEN)

embeddings = OpenAIEmbeddings()
name_db = FAISS.load_local("name_index", embeddings)
code_db = FAISS.load_local("code_index", embeddings)


# モデル作成
llm = ChatOpenAI(temperature=0)

template = """Give the name and quantity of the product the user wants. You must'nt translate the name.

You must respond with a new line for each item of information and each item of information separated by a comma as follows,
name,quantity 

Begin! Remember that you must answer the name,and quantity for all items included in the request. You MUST NOT translate the name.

Human: {input}
AI Assistant: """
prompt = PromptTemplate(template=template, input_variables=["input"])

@app.command("/create_order")
def command_handler(ack, body, logger, client):
    ack()
    trigger_id = body["trigger_id"]
    open_modal(client, trigger_id)

@app.shortcut("create_order")
def shortcut_handler(ack, body, logger, client):
    ack()
    trigger_id = body["trigger_id"]
    open_modal(client, trigger_id)

def open_modal(client, trigger_id):
    client.views_open(
        trigger_id=trigger_id,
        view={
            "type": "modal",
            "callback_id": "order_modal",
            "title": {"type": "plain_text", "text": "order"},
            "blocks": [
                {
                    "type": "input",
                    "block_id": "destination",
                    "element": {
                        "type": "plain_text_input",
                        "action_id": "destination_input",
                        "multiline": True
                    },
                    "label": {"type": "plain_text", "text": "宛先"},
                },
                {
                    "type": "input",
                    "block_id": "content",
                    "element": {
                        "type": "plain_text_input",
                        "multiline": True,
                        "action_id": "content_input"
                    },
                    "label": {"type": "plain_text", "text": "内容"},
                },
            ],
            "submit": {"type": "plain_text", "text": "送信"},
        },
    )

def check_input(input_str):
    lines = input_str.split('\n')
    items = []
    for line in lines:
        parts = line.split(',')
        if len(parts) != 2:
            return False
        name, quantity = parts
        try:
            quantity = int(quantity)
        except ValueError:
            return False
    return True

def parse_user_input(input):
    chain = LLMChain(prompt=prompt, llm=llm, verbose=True)
    return chain.run(input=input)

if not check_input(content):
    content = parse_user_input(content)
    
content = search_product_code(content)

def get_product_code_or_page_content(document_tuple):
    document = document_tuple[0]
    metadata = document.metadata

    if "商品コード" in metadata:
        return metadata["商品コード"]
    else:
        return document.page_content
def search_product_code(input):
    lines = input.split('\n')
    result = ''
    for line in lines:
        parts = line.split(',')
        name, quantity = parts
        n = name_db.similarity_search_with_score(name, k=1)
        c = code_db.similarity_search_with_score(name, k=1)
        best_name_result, best_code_result = n[0], c[0]
        best_result = min(best_name_result, best_code_result, key=lambda x: x[1])
        best_code = get_product_code_or_page_content(best_result)
        result += f"{best_code},{quantity}\n"
    return result.strip()

@app.view("order_modal")