LangChain の Embedding と CustomAgent で発注書を作る

# Embedding用
from langchain.embeddings.openai import OpenAIEmbeddings
# Vector 格納 / FAISS
from langchain.vectorstores import FAISS
from langchain.document_loaders import DataFrameLoader
import pandas as pd

df = pd.read_csv('Source/SyohinList.csv')
loader = DataFrameLoader(df, page_content_column="商品名")

商品コード,商品名,売価

loader = DataFrameLoader(df, page_content_column="商品コード")

docs = loader.load()

embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(docs, embeddings)

db.save_local("code_index")
#db.save_local("name_index")

from langchain.chat_models import ChatOpenAI
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.prompts import StringPromptTemplate
from langchain import LLMChain
from typing import List, Union
from pydantic import BaseModel, Field
from langchain.schema import AgentAction, AgentFinish
import re

embeddings = OpenAIEmbeddings()
code_db = FAISS.load_local('code_index', embeddings)
name_db = FAISS.load_local('name_index', embeddings)
tools = [
    Tool(
        name = "product search by code",
        func=code_db.similarity_search,
        description="useful for getting product name and price by product_code, like 'XQ-CQ44'.You can get 'unit price' from '売価'."
    ),
    Tool(
        name = "product search by name",
        func=name_db.similarity_search,
        description="useful for getting product name and price by product_name, like 'エクスペリア 5'.You can get unit_price' from '売価'"
    )
]

template = """Give the name, price and quantity of the product the user wants. Access the list of products using the following tools:

{tools}

Use the following format:

Demand: the input requests for which you must provide information
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: You must respond with a new line for each item of information and each item of information separated by a comma as follows,
CODE,name,unit price,quantity

Begin! Remember that you must answer the name, price and quantity for all items included in the request.

Demand: {input}
{agent_scratchpad}"""

# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
    # The template to use
    template: str
    # The list of tools available
    tools: List[Tool]
    
    def format(self, **kwargs) -> str:
        # Agentの中間ステップを取得し、それらをフォーマットする(AgentAction, Observation tuples)
        intermediate_steps = kwargs.pop("intermediate_steps")
        thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log
            thoughts += f"\nObservation: {observation}\nThought: "
        # agent_scratchpad に thoughtsをセット
        kwargs["agent_scratchpad"] = thoughts
        # Toolを展開する
        kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
        # ツールの名前を展開する
        kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
        return self.template.format(**kwargs)

prompt = CustomPromptTemplate(
    template=template,
    tools=tools,
    # `agent_scratchpad`, `tools`, and `tool_names` 変数は通常必要ですが、format内部で代入しているため不要です
    # 一方で、 `intermediate_steps` が必要です
    input_variables=["input", "intermediate_steps"]
)

class CustomOutputParser(AgentOutputParser):
    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
        # Final Answerがあったら最終出力
        if "Final Answer:" in llm_output:
            return AgentFinish(
                # 通常、最終出力は outputキーを一つだけもつ辞書型
                # 現在のところは変更は推奨されない
                return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
                log=llm_output,
            )
        # actionとaction inputをパース
        regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
        match = re.search(regex, llm_output, re.DOTALL)
        if not match:
            # 悪名高い Could not parse LLM output の例外
            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
        action = match.group(1).strip()
        action_input = match.group(2)
        # Return the action and action input
        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
    
output_parser = CustomOutputParser()

llm = ChatOpenAI(temperature=0)

# LLMとpromptから chain を構築
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
    llm_chain=llm_chain, 
    output_parser=output_parser,
    stop=["\nObservation:"], 
    allowed_tools=tool_names
)

agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)

agent_executor.run("エクスペリア5 2台と、ピクセル6a を1台")

XQ-CQ44,エクスペリア 5 IV,90000,2
gglpl6a,Google ピクセル6a,60000,1

from openpyxl import load_workbook

def create_estimate_excel_from_csv(items_csv, name, template_file, output_file):
    # 入力文字列を行ごとに分割
    items = items_csv.strip().split('\n')

    wb = load_workbook(template_file)
    ws = wb.active

    ws.cell(row=4, column=2, value=name)

    # データを書き込み
    for row_num, item in enumerate(items, 2):
        product_code, product_name, price, quantity = item.split(',')
        price = int(price)
        quantity = int(quantity)

        #ws.cell(row=row_num, column=1, value=product_code)
        ws.cell(row=row_num, column=2, value=product_name)
        ws.cell(row=row_num, column=3, value=price)
        ws.cell(row=row_num, column=4, value=quantity)
        #ws.cell(row=row_num, column=5, value=price * quantity)

    # Excelファイルを保存
    wb.save(output_file)