使用 Mistral OCR 和 Milvus 理解文档
本教程演示如何使用以下工具构建文档理解系统：
Mistral OCR
功能强大的光学字符识别服务
- 处理 PDF、图像和其他文档格式
- 保留文档结构和格式
- 处理多页文档
- 识别表格、列表和其他复杂元素
Mistral Embeddings
- 将文本转换为数字表示：
- 将文本转换为 1024 维向量
- 捕捉概念之间的语义关系
- 实现基于意义的相似性匹配
- 为语义搜索奠定基础
Milvus 向量数据库
用于向量相似性搜索的专用数据库：
- 开源
- 执行高效的向量搜索
- 可扩展至大型文档 Collections
- 支持混合搜索（向量相似性 + 元数据过滤）
- 针对人工智能应用进行了优化
本教程结束时，您将拥有一个可以实现以下功能的系统
- 通过 URL 处理文档（PDF/图片
- 使用 OCR 提取文本
- 将文本和向量嵌入存储在 Milvus 中
- 在文档 Collections 中执行语义搜索
设置和依赖
首先，让我们安装所需的软件包：
$ pip install mistralai pymilvus python-dotenv
环境设置
你需要
- 一个 Mistral API 密钥（从 https://console.mistral.ai/ 获取一个）
- 通过Docker或Zilliz Cloud在本地运行 Milvus
让我们来设置环境：
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
设置 Milvus Collections
现在，让我们创建一个 Milvus Collection 来存储我们的文档数据。Collections 将包含以下字段：
id:主键（自动生成）url:文档的源 URLpage_num:文件的页码content:提取的文本内容embedding:内容的向量表示（1024 维）
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.
核心功能
让我们来实现文档理解系统的核心功能：
# 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
搜索功能
现在，让我们来实现搜索功能，以检索相关文档内容：
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),
}
演示：处理文档
让我们处理一些示例文档。您可以用自己的文档替换这些 URL。
# 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...
我们也来处理一张图片：
# 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
演示：搜索文档
现在我们已经处理了一些文档，让我们来搜索它们：
# 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...
尝试另一个搜索查询：
# 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...
结论
在本教程中，我们使用 Mistral OCR 和 Milvus 构建了一个完整的文档理解系统。这个系统可以
- 处理来自 URL 的文档
- 使用 Mistral 的 OCR 功能提取文本
- 为内容生成向量 Embeddings
- 在 Milvus 中存储文本和向量
- 在所有处理过的文档中进行语义搜索
这种方法实现了强大的文档理解能力，超越了简单的关键字匹配，使用户能够根据意义而不是精确的文本匹配来查找信息。