AzureからGPTを使ってみる | RAG（データベース検索編）

前の記事で、独自データとして利用したいドキュメントをチャンクに分割するというところまで書きました。今回は、その分割したドキュメントをベクトル化して、データベースへ格納、類似度から検索するところまで書いてみようと思います。
Vector DBとしてChromaを、embeddingにはAzureにあるOpenAIのtext-embedding-ada-002を利用します。
テストには、前回利用した文章を使います。

# 昔々
あるところにおじいさんとおばあさんがいました。
## おじいさんとおばあさん
おじいさんは山へ芝刈りに、おばあさんは川へ洗濯に行きました。

## 桃が流れてくる
おばあさんが川で洗濯をしていると、大きな桃がどんぶらこどんぶらこと流れてきました。
## 桃太郎誕生
おじいさんとおばあさんが桃を割ると、中から元気な男の子が出てきました。
その子は桃から生まれたので「桃太郎」と名付けられました。

・MarkdownHeaderTextSplitterで分割後、embedding,vectorstoreへ格納まで

#langchain-chroma=0.1.3   
#langchain-openai=0.1.15
#langchain-text-splitters=0.2.2  

from langchain_chroma import Chroma
from langchain_openai import AzureOpenAIEmbeddings
from langchain_text_splitters import MarkdownHeaderTextSplitter

from dotenv import load_dotenv
import os

# OpenAI APIキーの設定
dotenv_path = ".env"
load_dotenv(dotenv_path)

OPENAI_API_BASE = os.getenv('OPENAI_API_BASE')
OPENAI_API_VERSION = os.getenv('OPENAI_API_VERSION')
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')

os.environ["AZURE_OPENAI_API_KEY"] = OPENAI_API_KEY
os.environ["AZURE_OPENAI_ENDPOINT"] = OPENAI_API_BASE

# Load example document
with open("momo01.md") as f:
    state_of_the_union = f.read()

# Markdown見出しで文章を分割します    
headers_to_split_on = [
    ("#", "Header 1"),
    ("##", "Header 2"),
    ("###", "Header 3"),
]    

markdown_splitter = MarkdownHeaderTextSplitter(
    headers_to_split_on=headers_to_split_on,
    strip_headers = False #コンテンツび分割されるヘッダーを含めるかどうか
    )

md_header_splits = markdown_splitter.split_text(state_of_the_union)

#embeddingには、Azureからtext-embedding-ada-002を使用します。
embed = AzureOpenAIEmbeddings(
    model = 'text-embedding-ada-002',
)
documents = md_header_splits

# VectorStoreの準備
vectorstore = Chroma.from_documents(
    documents,
    embedding=embed,
    #collection_name="example_collection",
    #persist_directory="./chroma_langchain_db",  # Where to save data locally, remove if not neccesary
)    

vectorstoreの中身を確認してみます。

num_docs = vectorstore._collection.count()
print(f"Number of documents: {num_docs}")

stored_data = vectorstore._collection.get(include=["metadatas", "documents", "embeddings"])

# Print out or inspect the data
print(stored_data["documents"])
print(stored_data["metadatas"])
print(stored_data["embeddings"])

Number of documents: 4
['# 昔々\nあるところにおじいさんとおばあさんがいました。', '## 桃太郎誕生\nおじいさんとおばあさんが桃を割ると、中から元気な男の子が出てきました。\nその子は桃から生まれたので「桃太郎」と名付けられました。', '## おじいさんとおばあさん\nおじいさんは山へ芝刈りに、おばあさんは川へ洗濯に行きました。', '## 桃が流れてくる\nおばあさんが川で洗濯をしていると、大きな桃がどんぶらこどんぶらこと流れてきました。']
[{'Header 1': '昔々'}, {'Header 1': '昔々', 'Header 2': '桃太郎誕生'}, {'Header 1': '昔々', 'Header 2': 'おじいさんとおばあさん'}, {'Header 1': '昔々', 'Header 2': '桃が流れてくる'}]
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・類似度検索

ベクトルストアから情報を検索する際に、以下の3つのメソッドがよく使われます：

・similarity_search

similarity_searchは、テキストクエリをベクトル化し、そのベクトルとベクトルストア内のドキュメントのベクトルとの類似度を計算します。一般的にはコサイン類似度が使われます。その結果、類似度の高い順に上位k個のドキュメントが返されます。
機能：指定したテキストクエリに最も類似したドキュメントを検索します。
返り値：類似したドキュメントのリスト（類似度スコアは含まれない

・similarity_search_with_score

similarity_search_with_scoreは、similarity_searchと同様にドキュメントを検索しますが、各ドキュメントに対する類似度スコアも一緒に返します。これにより、各ドキュメントがクエリにどの程度類似しているかを定量的に評価できます。
機能：指定したテキストクエリに最も類似したドキュメントと、その類似度スコアを取得します。
返り値：ドキュメントと類似度スコアのタプルのリスト。

・similarity_search_by_vector

similarity_search_by_vectorは、既にベクトル化されたクエリを使って検索を行います。これにより、カスタムのベクトルや複数のベクトルを一度に使って検索することが可能です。
機能：クエリテキストではなく、クエリベクトルを直接使って検索します。
返り値：類似したドキュメントのリスト（またはスコア付き）。

実際のcode

query = "桃"

・similarity_search

vectorstore.similarity_search(query,k=1)

[Document(metadata={'Header 1': '昔々', 'Header 2': '桃が流れてくる'}, page_content='## 桃が流れてくる\nおばあさんが川で洗濯をしていると、大きな桃がどんぶらこどんぶらこと流れてきました。')]

・similarity_search_with_score

vectorstore.similarity_search_with_score(query,k=1)

[(Document(metadata={'Header 1': '昔々', 'Header 2': '桃が流れてくる'}, page_content='## 桃が流れてくる\nおばあさんが川で洗濯をしていると、大きな桃がどんぶらこどんぶらこと流れてきました。'),
  0.3337773084640503)]

・similarity_search_by_vector

query_vector = embed.embed_query(query)
vectorstore.similarity_search_by_vector(query_vector, k=1)

[Document(metadata={'Header 1': '昔々', 'Header 2': '桃が流れてくる'}, page_content='## 桃が流れてくる\nおばあさんが川で洗濯をしていると、大きな桃がどんぶらこどんぶらこと流れてきました。')]