Mistral OCRとmilvusを使った文書理解
このチュートリアルでは、Mistral OCRとMilvusを使用した文書理解システムの構築方法を説明します：
Mistral OCR
強力な光学式文字認識サービス
- PDF、画像、その他のドキュメントフォーマットの処理
- 文書の構造と書式を保持
- 複数ページの文書を処理
- 表、リスト、その他の複雑な要素を認識
ミストラル埋め込み
- テキストを数値表現に変換
- テキストを1024次元のベクトルに変換
- 概念間の意味的関係を捕捉
- 意味に基づいた類似性のマッチングを可能にする
- 意味検索の基盤を提供
Milvusベクトルデータベース
ベクトル類似検索に特化したデータベース
- オープンソース
- 効率的なベクトル検索
- 大規模な文書コレクションに対応
- ハイブリッド検索（ベクトル類似度＋メタデータフィルタリング）をサポート
- AIアプリケーションに最適化
このチュートリアルが終わる頃には、以下のようなシステムが完成しているでしょう：
- URL経由でドキュメント（PDF/画像）を処理する
- OCRを使ってテキストを抽出
- テキストとベクトル埋め込みをmilvusに保存
- ドキュメントコレクション全体のセマンティック検索
セットアップと依存関係
まず、必要なパッケージをインストールしましょう：
$ pip install mistralai pymilvus python-dotenv
環境セットアップ
必要なもの
- Mistral APIキー (https://console.mistral.ai/ で取得)
- DockerまたはZilliz Cloudを使ってローカルで動作するmilvus
環境をセットアップしよう：
import json
import os
import re
from dotenv import load_dotenv
from mistralai import Mistral
from pymilvus import CollectionSchema, DataType, FieldSchema, MilvusClient
from pymilvus.client.types import LoadState
# Load environment variables from .env file
load_dotenv()
# Initialize clients
api_key = os.getenv("MISTRAL_API_KEY")
if not api_key:
api_key = input("Enter your Mistral API key: ")
os.environ["MISTRAL_API_KEY"] = api_key
client = Mistral(api_key=api_key)
# Define models
text_model = "mistral-small-latest" # For chat interactions
ocr_model = "mistral-ocr-latest" # For OCR processing
embedding_model = "mistral-embed" # For generating embeddings
# Connect to Milvus (default: localhost)
milvus_uri = os.getenv("MILVUS_URI", "http://localhost:19530")
milvus_client = MilvusClient(uri=milvus_uri)
# Milvus collection name
COLLECTION_NAME = "document_ocr"
print(f"Connected to Mistral API and Milvus at {milvus_uri}")
Connected to Mistral API and Milvus at http://localhost:19530
Milvusコレクションのセットアップ
それでは、ドキュメントデータを保存するMilvusコレクションを作成しましょう。コレクションは以下のフィールドを持つ：
id:主キー（自動生成）url:ドキュメントのソースURLpage_num:文書内のページ番号content:抽出されたテキスト内容embedding:コンテンツのベクトル表現（1024次元）
def setup_milvus_collection():
"""Create Milvus collection if it doesn't exist."""
# Check if collection already exists
if milvus_client.has_collection(COLLECTION_NAME):
print(f"Collection '{COLLECTION_NAME}' already exists.")
return
# Define collection schema
fields = [
FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name="url", dtype=DataType.VARCHAR, max_length=500),
FieldSchema(name="page_num", dtype=DataType.INT64),
FieldSchema(name="content", dtype=DataType.VARCHAR, max_length=65535),
FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=1024),
]
schema = CollectionSchema(fields=fields)
# Create collection
milvus_client.create_collection(
collection_name=COLLECTION_NAME,
schema=schema,
)
# Create index for vector search
index_params = milvus_client.prepare_index_params()
index_params.add_index(
field_name="embedding",
index_type="IVF_FLAT", # Index type for approximate nearest neighbor search
metric_type="COSINE", # Similarity metric
params={"nlist": 128}, # Number of clusters
)
milvus_client.create_index(
collection_name=COLLECTION_NAME, index_params=index_params
)
print(f"Collection '{COLLECTION_NAME}' created successfully with index.")
setup_milvus_collection()
Collection 'document_ocr' already exists.
コア機能
文書理解システムの中核となる機能を実装してみよう：
# Generate embeddings using Mistral
def generate_embedding(text):
"""Generate embedding for text using Mistral embedding model."""
response = client.embeddings.create(model=embedding_model, inputs=[text])
return response.data[0].embedding
# Store OCR results in Milvus
def store_ocr_in_milvus(url, ocr_result):
"""Process OCR results and store in Milvus."""
# Extract pages from OCR result
pages = []
current_page = ""
page_num = 0
for line in ocr_result.split("\n"):
if line.startswith("### Page "):
if current_page:
pages.append((page_num, current_page.strip()))
page_num = int(line.replace("### Page ", ""))
current_page = ""
else:
current_page += line + "\n"
# Add the last page
if current_page:
pages.append((page_num, current_page.strip()))
# Prepare data for Milvus
entities = []
for page_num, content in pages:
# Generate embedding for the page content
embedding = generate_embedding(content)
# Create entity
entity = {
"url": url,
"page_num": page_num,
"content": content,
"embedding": embedding,
}
entities.append(entity)
# Insert into Milvus
if entities:
milvus_client.insert(collection_name=COLLECTION_NAME, data=entities)
print(f"Stored {len(entities)} pages from {url} in Milvus.")
return len(entities)
# Define OCR function
def perform_ocr(url):
"""Apply OCR to a URL (PDF or image)."""
try:
# Try PDF OCR first
response = client.ocr.process(
model=ocr_model, document={"type": "document_url", "document_url": url}
)
except Exception:
try:
# If PDF OCR fails, try Image OCR
response = client.ocr.process(
model=ocr_model, document={"type": "image_url", "image_url": url}
)
except Exception as e:
return str(e) # Return error message
# Format the OCR results
ocr_result = "\n\n".join(
[
f"### Page {i + 1}\n{response.pages[i].markdown}"
for i in range(len(response.pages))
]
)
# Store in Milvus
store_ocr_in_milvus(url, ocr_result)
return ocr_result
# Process URLs
def process_document(url):
"""Process a document URL and return its contents."""
print(f"Processing document: {url}")
ocr_result = perform_ocr(url)
return ocr_result
検索機能
それでは、関連する文書コンテンツを検索するための検索機能を実装してみましょう：
def search_documents(query, limit=5):
"""Search Milvus for similar content to the query."""
# Check if collection exists
if not milvus_client.has_collection(COLLECTION_NAME):
return "No documents have been processed yet."
# Load collection if not already loaded
if milvus_client.get_load_state(COLLECTION_NAME) != LoadState.Loaded:
milvus_client.load_collection(COLLECTION_NAME)
print(f"Searching for: {query}")
query_embedding = generate_embedding(query)
search_params = {"metric_type": "COSINE", "params": {"nprobe": 10}}
search_results = milvus_client.search(
collection_name=COLLECTION_NAME,
data=[query_embedding],
anns_field="embedding",
search_params=search_params,
limit=limit,
output_fields=["url", "page_num", "content"],
)
results = []
if not search_results or not search_results[0]:
return "No matching documents found."
for i, hit in enumerate(search_results[0]):
url = hit["entity"]["url"]
page_num = hit["entity"]["page_num"]
content = hit["entity"]["content"]
score = hit["distance"]
results.append(
{
"rank": i + 1,
"score": score,
"url": url,
"page": page_num,
"content": content[:500] + "..." if len(content) > 500 else content,
}
)
return results
# Get statistics about stored documents
def get_document_stats():
"""Get statistics about documents stored in Milvus."""
if not milvus_client.has_collection(COLLECTION_NAME):
return "No documents have been processed yet."
# Get collection stats
stats = milvus_client.get_collection_stats(COLLECTION_NAME)
row_count = stats["row_count"]
# Get unique URLs
results = milvus_client.query(
collection_name=COLLECTION_NAME, filter="", output_fields=["url"], limit=10000
)
unique_urls = set()
for result in results:
unique_urls.add(result["url"])
return {
"total_pages": row_count,
"unique_documents": len(unique_urls),
"documents": list(unique_urls),
}
デモ文書の処理
いくつかのサンプル・ドキュメントを処理してみよう。これらのURLはあなた自身の文書に置き換えることができます。
# Example PDF URL (Mistral AI paper)
pdf_url = "https://arxiv.org/pdf/2310.06825.pdf"
# Process the document
ocr_result = process_document(pdf_url)
# Display a preview of the OCR result
print("\nOCR Result Preview:")
print("====================")
print(ocr_result[:1000] + "...")
Processing document: https://arxiv.org/pdf/2310.06825.pdf
Stored 9 pages from https://arxiv.org/pdf/2310.06825.pdf in Milvus.
OCR Result Preview:
====================
### Page 1
# Mistral 7B
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed
#### Abstract
We introduce Mistral 7B, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms the best open 13B model (Llama 2) across all evaluated benchmarks, and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B - Instruct, that surpasses Llama 2 13B - chat mod...
画像も処理してみましょう：
# Example image URL (replace with your own)
image_url = "https://s3.eu-central-1.amazonaws.com/readcoop.cis.public-assets.prod/hero/old-german-scripts.png"
# Process the image
try:
ocr_result = process_document(image_url)
print("\nImage OCR Result:")
print("=================")
print(ocr_result)
except Exception as e:
print(f"Error processing image: {e}")
Processing document: https://s3.eu-central-1.amazonaws.com/readcoop.cis.public-assets.prod/hero/old-german-scripts.png
Stored 1 pages from https://s3.eu-central-1.amazonaws.com/readcoop.cis.public-assets.prod/hero/old-german-scripts.png in Milvus.
Image OCR Result:
=================
### Page 1
デモドキュメントの検索
ドキュメントの処理が終わったので、検索してみましょう：
# Get document statistics
stats = get_document_stats()
print(f"Total pages stored: {stats['total_pages']}")
print(f"Unique documents: {stats['unique_documents']}")
print("\nProcessed documents:")
for i, url in enumerate(stats["documents"]):
print(f"{i + 1}. {url}")
Total pages stored: 58
Unique documents: 3
Processed documents:
1. https://arxiv.org/pdf/2310.06825.pdf
2. https://s3.eu-central-1.amazonaws.com/readcoop.cis.public-assets.prod/hero/old-german-scripts.png
3. https://arxiv.org/pdf/2410.07073
# Search for information
query = "What is Mistral 7B?"
results = search_documents(query, limit=3)
print(f"Search results for: '{query}'\n")
if isinstance(results, str):
print(results)
else:
for result in results:
print(f"Result {result['rank']} (Score: {result['score']:.2f})")
print(f"Source: {result['url']} (Page {result['page']})")
print(f"Content: {result['content']}\n")
Searching for: What is Mistral 7B?
Search results for: 'What is Mistral 7B?'
Result 1 (Score: 0.83)
Source: https://arxiv.org/pdf/2310.06825.pdf (Page 2)
Content: Mistral 7B is released under the Apache 2.0 license. This release is accompanied by a reference implementation ${ }^{1}$ facilitating easy deployment either locally or on cloud platforms such as AWS, GCP, or Azure using the vLLM [17] inference server and SkyPilot ${ }^{2}$. Integration with Hugging Face ${ }^{3}$ is also streamlined for easier integration. Moreover, Mistral 7B is crafted for ease of fine-tuning across a myriad of tasks. As a demonstration of its adaptability and superior perform...
Result 2 (Score: 0.83)
Source: https://arxiv.org/pdf/2310.06825.pdf (Page 2)
Content: Mistral 7B is released under the Apache 2.0 license. This release is accompanied by a reference implementation ${ }^{1}$ facilitating easy deployment either locally or on cloud platforms such as AWS, GCP, or Azure using the vLLM [17] inference server and SkyPilot ${ }^{2}$. Integration with Hugging Face ${ }^{3}$ is also streamlined for easier integration. Moreover, Mistral 7B is crafted for ease of fine-tuning across a myriad of tasks. As a demonstration of its adaptability and superior perform...
Result 3 (Score: 0.82)
Source: https://arxiv.org/pdf/2310.06825.pdf (Page 1)
Content: # Mistral 7B
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed
#### Abstract
We introduce Mistral 7B, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7...
別の検索クエリを試してみましょう：
# Search for more specific information
query = "What are the capabilities of Mistral's language models?"
results = search_documents(query, limit=3)
print(f"Search results for: '{query}'\n")
if isinstance(results, str):
print(results)
else:
for result in results:
print(f"Result {result['rank']} (Score: {result['score']:.2f})")
print(f"Source: {result['url']} (Page {result['page']})")
print(f"Content: {result['content']}\n")
Searching for: What are the capabilities of Mistral's language models?
Search results for: 'What are the capabilities of Mistral's language models?'
Result 1 (Score: 0.85)
Source: https://arxiv.org/pdf/2310.06825.pdf (Page 2)
Content: Mistral 7B is released under the Apache 2.0 license. This release is accompanied by a reference implementation ${ }^{1}$ facilitating easy deployment either locally or on cloud platforms such as AWS, GCP, or Azure using the vLLM [17] inference server and SkyPilot ${ }^{2}$. Integration with Hugging Face ${ }^{3}$ is also streamlined for easier integration. Moreover, Mistral 7B is crafted for ease of fine-tuning across a myriad of tasks. As a demonstration of its adaptability and superior perform...
Result 2 (Score: 0.85)
Source: https://arxiv.org/pdf/2310.06825.pdf (Page 2)
Content: Mistral 7B is released under the Apache 2.0 license. This release is accompanied by a reference implementation ${ }^{1}$ facilitating easy deployment either locally or on cloud platforms such as AWS, GCP, or Azure using the vLLM [17] inference server and SkyPilot ${ }^{2}$. Integration with Hugging Face ${ }^{3}$ is also streamlined for easier integration. Moreover, Mistral 7B is crafted for ease of fine-tuning across a myriad of tasks. As a demonstration of its adaptability and superior perform...
Result 3 (Score: 0.84)
Source: https://arxiv.org/pdf/2310.06825.pdf (Page 6)
Content: | Model | Answer |
| :--: | :--: |
| Mistral 7B - Instruct with Mistral system prompt | To kill a Linux process, you can use the `kill' command followed by the process ID (PID) of the process you want to terminate. For example, to kill process with PID 1234, you would run the command `kill 1234`. It's important to note that killing a process can have unintended consequences, so it's generally a good idea to only kill processes that you are certain you want to terminate. Additionally, it's genera...
まとめ
このチュートリアルでは、Mistral OCRとMilvusを使って完全な文書理解システムを構築しました。このシステムでできること
- URLから文書を処理する
- MistralのOCR機能を使ったテキストの抽出
- コンテンツの埋め込みベクトルを生成
- テキストとベクトルの両方をMilvusに保存
- 処理されたすべての文書に対してセマンティック検索を実行
このアプローチにより、単純なキーワードマッチングを超えた強力な文書理解機能が実現し、ユーザーはテキストの完全一致ではなく、意味に基づいて情報を見つけることができます。