Comprensione dei documenti con Mistral OCR e Milvus

Questo tutorial mostra come costruire un sistema di comprensione dei documenti utilizzando:

Mistral OCR

Un potente servizio di riconoscimento ottico dei caratteri che:

• elabora PDF, immagini e altri formati di documenti
• Preserva la struttura e la formattazione del documento
• Gestisce documenti a più pagine
• Riconosce tabelle, elenchi e altri elementi complessi.

Incorporamenti Mistral

• Trasforma il testo in rappresentazioni numeriche:
• Converte il testo in vettori a 1024 dimensioni.
• Cattura le relazioni semantiche tra i concetti
• Consente la corrispondenza delle somiglianze in base al significato
• Fornisce le basi per la ricerca semantica

Database vettoriale Milvus

Database specializzato per la ricerca di similarità vettoriale:

• Open-Source
• Esegue una ricerca vettoriale efficiente
• Si adatta a raccolte di documenti di grandi dimensioni
• Supporta la ricerca ibrida (similarità vettoriale + filtraggio dei metadati)
• Ottimizzato per le applicazioni di intelligenza artificiale

Alla fine di questo tutorial, avrete un sistema in grado di:

1. Elaborare documenti (PDF/immagini) tramite URL
2. Estrarre il testo utilizzando l'OCR
3. Memorizzare il testo e le incorporazioni vettoriali in Milvus
4. Eseguire ricerche semantiche nella raccolta di documenti

---

Installazione e dipendenze

Per prima cosa, installiamo i pacchetti necessari:

$ pip install mistralai pymilvus python-dotenv

Configurazione dell'ambiente

Sono necessari

1. Una chiave API di Mistral (si può ottenere da https://console.mistral.ai/).
2. Milvus in esecuzione localmente tramite Docker o con Zilliz Cloud

Configuriamo il nostro ambiente:

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

Impostazione della raccolta Milvus

Ora creiamo una raccolta Milvus per memorizzare i dati dei nostri documenti. La raccolta avrà i seguenti campi:

• id: Chiave primaria (generata automaticamente)
• url: URL di origine del documento
• page_num: Numero di pagina del documento
• content: Contenuto del testo estratto
• embedding: Rappresentazione vettoriale del contenuto (1024 dimensioni)

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.

Funzionalità di base

Implementiamo le funzioni principali del nostro sistema di comprensione dei documenti:

# 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

Funzionalità di ricerca

Implementiamo ora la funzionalità di ricerca per recuperare i contenuti rilevanti dei documenti:

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),
    }

Demo: Elaborazione dei documenti

Elaboriamo alcuni documenti di esempio. È possibile sostituire questi URL con i propri documenti.

# 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

![img-0.jpeg](img-0.jpeg)


#### 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...

Elaboriamo anche un'immagine:

# 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
![img-0.jpeg](img-0.jpeg)
![img-1.jpeg](img-1.jpeg)
![img-2.jpeg](img-2.jpeg)
![img-3.jpeg](img-3.jpeg)
![img-4.jpeg](img-4.jpeg)
![img-5.jpeg](img-5.jpeg)
![img-6.jpeg](img-6.jpeg)
![img-7.jpeg](img-7.jpeg)
![img-8.jpeg](img-8.jpeg)
![img-9.jpeg](img-9.jpeg)
![img-10.jpeg](img-10.jpeg)
![img-11.jpeg](img-11.jpeg)
![img-12.jpeg](img-12.jpeg)
![img-13.jpeg](img-13.jpeg)
![img-14.jpeg](img-14.jpeg)
![img-15.jpeg](img-15.jpeg)
![img-16.jpeg](img-16.jpeg)
![img-17.jpeg](img-17.jpeg)
![img-18.jpeg](img-18.jpeg)
![img-19.jpeg](img-19.jpeg)
![img-20.jpeg](img-20.jpeg)
![img-21.jpeg](img-21.jpeg)
![img-22.jpeg](img-22.jpeg)
![img-23.jpeg](img-23.jpeg)
![img-24.jpeg](img-24.jpeg)
![img-25.jpeg](img-25.jpeg)
![img-26.jpeg](img-26.jpeg)
![img-27.jpeg](img-27.jpeg)
![img-28.jpeg](img-28.jpeg)
![img-29.jpeg](img-29.jpeg)
![img-30.jpeg](img-30.jpeg)

Demo: Ricerca di documenti

Ora che abbiamo elaborato alcuni documenti, cerchiamoli:

# 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

![img-0.jpeg](img-0.jpeg)


#### Abstract

We introduce Mistral 7B, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7...

Provate un'altra query di ricerca:

# 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...

Conclusione

In questo tutorial abbiamo costruito un sistema completo di comprensione dei documenti utilizzando Mistral OCR e Milvus. Questo sistema è in grado di:

1. Elaborare documenti da URL
2. Estrarre il testo utilizzando le funzionalità OCR di Mistral
3. Generare embeddings vettoriali per il contenuto
4. Memorizzare sia il testo che i vettori in Milvus
5. Eseguire ricerche semantiche su tutti i documenti elaborati

Questo approccio permette di ottenere potenti capacità di comprensione dei documenti che vanno oltre la semplice corrispondenza delle parole chiave, consentendo agli utenti di trovare informazioni basate sul significato piuttosto che su corrispondenze testuali esatte.