Integrasi LangExtract + Milvus

Panduan ini mendemonstrasikan cara menggunakan LangExtract dengan Milvus untuk membangun sistem pemrosesan dan pencarian dokumen yang cerdas.

LangExtract adalah pustaka Python yang menggunakan Large Language Models (LLM) untuk mengekstrak informasi terstruktur dari dokumen teks yang tidak terstruktur dengan sumber yang tepat. Sistem ini menggabungkan kemampuan ekstraksi LangExtract dengan penyimpanan vektor Milvus untuk memungkinkan pencarian kemiripan semantik dan pemfilteran metadata yang tepat.

Integrasi ini sangat berharga untuk manajemen konten, pencarian semantik, penemuan pengetahuan, dan membangun sistem rekomendasi berdasarkan atribut dokumen yang diekstrak.

Prasyarat

Sebelum menjalankan notebook ini, pastikan Anda telah menginstal dependensi berikut:

$ pip install --upgrade pymilvus milvus-lite langextract google-genai requests tqdm pandas

Kita akan menggunakan Gemini sebagai LLM dalam contoh ini. Anda harus menyiapkan kunci api GEMINI_API_KEY sebagai variabel lingkungan.

import os

os.environ["GEMINI_API_KEY"] = "AIza*****************"

Mendefinisikan pipeline LangExtract + Milvus

Kita akan mendefinisikan pipeline yang menggunakan LangExtract untuk ekstraksi informasi terstruktur dan Milvus sebagai penyimpan vektor.

import langextract as lx
import textwrap
from google import genai
from google.genai.types import EmbedContentConfig
from pymilvus import MilvusClient, DataType
import uuid

Konfigurasi dan Penyiapan

Mari kita konfigurasikan parameter global kita untuk integrasi. Kita akan menggunakan model penyematan Gemini untuk menghasilkan representasi vektor untuk dokumen kita.

genai_client = genai.Client()

COLLECTION_NAME = "document_extractions"
EMBEDDING_MODEL = "gemini-embedding-001"
EMBEDDING_DIM = 3072  # Default dimension for gemini-embedding-001

Inisialisasi Klien Milvus

Sekarang mari kita inisialisasi klien Milvus kita. Kita akan menggunakan berkas basis data lokal untuk mempermudah, tetapi ini dapat dengan mudah ditingkatkan ke penerapan server Milvus secara penuh.

client = MilvusClient(uri="./milvus_demo.db")

Persiapan Data Sampel

Untuk demonstrasi ini, kita akan menggunakan deskripsi film sebagai dokumen sampel. Ini menunjukkan kemampuan LangExtract untuk mengekstrak informasi terstruktur seperti genre, karakter, dan tema dari teks yang tidak terstruktur.

sample_documents = [
    "John McClane fights terrorists in a Los Angeles skyscraper during Christmas Eve. The action-packed thriller features intense gunfights and explosive scenes.",
    "A young wizard named Harry Potter discovers his magical abilities at Hogwarts School. The fantasy adventure includes magical creatures and epic battles.",
    "Tony Stark builds an advanced suit of armor to become Iron Man. The superhero movie showcases cutting-edge technology and spectacular action sequences.",
    "A group of friends get lost in a haunted forest where supernatural creatures lurk. The horror film creates a terrifying atmosphere with jump scares.",
    "Two detectives investigate a series of mysterious murders in New York City. The crime thriller features suspenseful plot twists and dramatic confrontations.",
    "A brilliant scientist creates artificial intelligence that becomes self-aware. The sci-fi thriller explores the dangers of advanced technology and human survival.",
    "A romantic comedy about two friends who fall in love during a cross-country road trip. The drama explores personal growth and relationship dynamics.",
    "An evil sorcerer threatens to destroy the magical kingdom. A brave hero must gather allies and master ancient magic to save the fantasy world.",
    "Space marines battle alien invaders on a distant planet. The action sci-fi movie features futuristic weapons and intense combat in space.",
    "A detective investigates supernatural crimes in Victorian London. The horror thriller combines period drama with paranormal investigation themes.",
]

print("=== LangExtract + Milvus Integration Demo ===")
print(f"Preparing to process {len(sample_documents)} documents")

=== LangExtract + Milvus Integration Demo ===
Preparing to process 10 documents

Menyiapkan Koleksi Milvus

Sebelum kita dapat menyimpan data yang telah diekstrak, kita perlu membuat koleksi Milvus dengan skema yang sesuai. Koleksi ini akan menyimpan teks dokumen asli, sematan vektor, dan bidang metadata yang diekstrak.

print("\n1. Setting up Milvus collection...")

# Drop existing collection if it exists
if client.has_collection(collection_name=COLLECTION_NAME):
    client.drop_collection(collection_name=COLLECTION_NAME)
    print(f"Dropped existing collection: {COLLECTION_NAME}")

# Create collection schema
schema = client.create_schema(
    auto_id=False,
    enable_dynamic_field=True,
    description="Document extraction results and vector storage",
)

# Add fields - simplified to 3 main metadata fields
schema.add_field(
    field_name="id", datatype=DataType.VARCHAR, max_length=100, is_primary=True
)
schema.add_field(
    field_name="document_text", datatype=DataType.VARCHAR, max_length=10000
)
schema.add_field(
    field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=EMBEDDING_DIM
)

# Create collection
client.create_collection(collection_name=COLLECTION_NAME, schema=schema)
print(f"Collection '{COLLECTION_NAME}' created successfully")

# Create vector index
index_params = client.prepare_index_params()
index_params.add_index(
    field_name="embedding",
    index_type="AUTOINDEX",
    metric_type="COSINE",
)
client.create_index(collection_name=COLLECTION_NAME, index_params=index_params)
print("Vector index created successfully")

1. Setting up Milvus collection...
Dropped existing collection: document_extractions
Collection 'document_extractions' created successfully
Vector index created successfully

Mendefinisikan Skema Ekstraksi

LangExtract menggunakan petunjuk dan contoh untuk memandu LLM dalam mengekstraksi informasi terstruktur. Mari kita tentukan skema ekstraksi kita untuk deskripsi film, dengan menentukan informasi apa yang akan diekstrak dan bagaimana mengkategorikannya.

print("\n2. Extracting tags from documents...")

# Define extraction prompt - for movie descriptions, specify attribute value ranges
prompt = textwrap.dedent(
    """\
    Extract movie genre, main characters, and key themes from movie descriptions.
    Use exact text for extractions. Do not paraphrase or overlap entities.
    
    For each extraction, provide attributes with values from these predefined sets:
    
    Genre attributes:
    - primary_genre: ["action", "comedy", "drama", "horror", "sci-fi", "fantasy", "thriller", "crime", "superhero"]
    - secondary_genre: ["action", "comedy", "drama", "horror", "sci-fi", "fantasy", "thriller", "crime", "superhero"]
    
    Character attributes:
    - role: ["protagonist", "antagonist", "supporting"]
    - type: ["hero", "villain", "detective", "military", "wizard", "scientist", "friends", "investigator"]
    
    Theme attributes:
    - theme_type: ["conflict", "investigation", "personal_growth", "technology", "magic", "survival", "romance"]
    - setting: ["urban", "space", "fantasy_world", "school", "forest", "victorian", "america", "future"]
    
    Focus on identifying key elements that would be useful for movie search and filtering."""
)

2. Extracting tags from documents...

Memberikan Contoh untuk Ekstraksi yang Lebih Baik

Untuk meningkatkan kualitas dan konsistensi ekstraksi, kami akan memberikan beberapa contoh kepada LangExtract. Contoh-contoh ini menunjukkan format yang diharapkan dan membantu model memahami persyaratan ekstraksi kami.

# Provide examples to guide the model - n-shot examples for movie descriptions
# Unify attribute keys to ensure consistency in extraction results
examples = [
    lx.data.ExampleData(
        text="A space marine battles alien creatures on a distant planet. The sci-fi action movie features futuristic weapons and intense combat scenes.",
        extractions=[
            lx.data.Extraction(
                extraction_class="genre",
                extraction_text="sci-fi action",
                attributes={"primary_genre": "sci-fi", "secondary_genre": "action"},
            ),
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="space marine",
                attributes={"role": "protagonist", "type": "military"},
            ),
            lx.data.Extraction(
                extraction_class="theme",
                extraction_text="battles alien creatures",
                attributes={"theme_type": "conflict", "setting": "space"},
            ),
        ],
    ),
    lx.data.ExampleData(
        text="A detective investigates supernatural murders in Victorian London. The horror thriller film combines period drama with paranormal elements.",
        extractions=[
            lx.data.Extraction(
                extraction_class="genre",
                extraction_text="horror thriller",
                attributes={"primary_genre": "horror", "secondary_genre": "thriller"},
            ),
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="detective",
                attributes={"role": "protagonist", "type": "detective"},
            ),
            lx.data.Extraction(
                extraction_class="theme",
                extraction_text="supernatural murders",
                attributes={"theme_type": "investigation", "setting": "victorian"},
            ),
        ],
    ),
    lx.data.ExampleData(
        text="Two friends embark on a road trip adventure across America. The comedy drama explores friendship and self-discovery through humorous situations.",
        extractions=[
            lx.data.Extraction(
                extraction_class="genre",
                extraction_text="comedy drama",
                attributes={"primary_genre": "comedy", "secondary_genre": "drama"},
            ),
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="two friends",
                attributes={"role": "protagonist", "type": "friends"},
            ),
            lx.data.Extraction(
                extraction_class="theme",
                extraction_text="friendship and self-discovery",
                attributes={"theme_type": "personal_growth", "setting": "america"},
            ),
        ],
    ),
]

# Extract from each document
extraction_results = []
for doc in sample_documents:
    result = lx.extract(
        text_or_documents=doc,
        prompt_description=prompt,
        examples=examples,
        model_id="gemini-2.0-flash",
    )
    extraction_results.append(result)
    print(f"Successfully extracted from document: {doc[:50]}...")

print(f"Completed tag extraction, processed {len(extraction_results)} documents")

Memproses dan Membuat Vektor Hasil

Sekarang kita perlu memproses hasil ekstraksi dan menghasilkan penyematan vektor untuk setiap dokumen. Kita juga akan meratakan atribut yang diekstrak ke dalam bidang yang terpisah agar mudah dicari di Milvus.

print("\n3. Processing extraction results and generating vectors...")

processed_data = []

for result in extraction_results:
    # Generate vectors for documents
    embedding_response = genai_client.models.embed_content(
        model=EMBEDDING_MODEL,
        contents=[result.text],
        config=EmbedContentConfig(
            task_type="RETRIEVAL_DOCUMENT",
            output_dimensionality=EMBEDDING_DIM,
        ),
    )
    embedding = embedding_response.embeddings[0].values
    print(f"Successfully generated vector: {result.text[:30]}...")

    # Initialize data structure, flatten attributes into separate fields
    data_entry = {
        "id": result.document_id or str(uuid.uuid4()),
        "document_text": result.text,
        "embedding": embedding,
        # Initialize all possible fields with default values
        "genre": "unknown",
        "primary_genre": "unknown",
        "secondary_genre": "unknown",
        "character_role": "unknown",
        "character_type": "unknown",
        "theme_type": "unknown",
        "theme_setting": "unknown",
    }

    # Process extraction results, flatten attributes
    for extraction in result.extractions:
        if extraction.extraction_class == "genre":
            # Flatten genre attributes
            data_entry["genre"] = extraction.extraction_text
            attrs = extraction.attributes or {}
            data_entry["primary_genre"] = attrs.get("primary_genre", "unknown")
            data_entry["secondary_genre"] = attrs.get("secondary_genre", "unknown")

        elif extraction.extraction_class == "character":
            # Flatten character attributes (take first main character's attributes)
            attrs = extraction.attributes or {}
            if (
                data_entry["character_role"] == "unknown"
            ):  # Only take first character's attributes
                data_entry["character_role"] = attrs.get("role", "unknown")
                data_entry["character_type"] = attrs.get("type", "unknown")

        elif extraction.extraction_class == "theme":
            # Flatten theme attributes (take first main theme's attributes)
            attrs = extraction.attributes or {}
            if (
                data_entry["theme_type"] == "unknown"
            ):  # Only take first theme's attributes
                data_entry["theme_type"] = attrs.get("theme_type", "unknown")
                data_entry["theme_setting"] = attrs.get("setting", "unknown")

    processed_data.append(data_entry)

print(f"Completed data processing, ready to insert {len(processed_data)} records")

3. Processing extraction results and generating vectors...
Successfully generated vector: John McClane fights terrorists...
Successfully generated vector: A young wizard named Harry Pot...
Successfully generated vector: Tony Stark builds an advanced ...
Successfully generated vector: A group of friends get lost in...
Successfully generated vector: Two detectives investigate a s...
Successfully generated vector: A brilliant scientist creates ...
Successfully generated vector: A romantic comedy about two fr...
Successfully generated vector: An evil sorcerer threatens to ...
Successfully generated vector: Space marines battle alien inv...
Successfully generated vector: A detective investigates super...
Completed data processing, ready to insert 10 records

Memasukkan Data ke dalam Milvus

Dengan data yang telah diproses, mari kita masukkan ke dalam koleksi Milvus. Hal ini akan memungkinkan kita untuk melakukan pencarian semantik dan pemfilteran metadata yang tepat.

print("\n4. Inserting data into Milvus...")

if processed_data:
    res = client.insert(collection_name=COLLECTION_NAME, data=processed_data)
    print(f"Successfully inserted {len(processed_data)} documents into Milvus")
    print(f"Insert result: {res}")
else:
    print("No data to insert")

4. Inserting data into Milvus...
Successfully inserted 10 documents into Milvus
Insert result: {'insert_count': 10, 'ids': ['doc_f8797155', 'doc_78c7e586', 'doc_fa3a3ab5', 'doc_64981815', 'doc_3ab18cb2', 'doc_1ea42b18', 'doc_f0779243', 'doc_386590b7', 'doc_3b3ae1ab', 'doc_851089d6']}

Mendemonstrasikan Pemfilteran Metadata

Salah satu keuntungan utama dari menggabungkan LangExtract dengan Milvus adalah kemampuan untuk melakukan pemfilteran yang tepat berdasarkan metadata yang diekstrak. Mari kita tunjukkan hal ini dengan beberapa pencarian ekspresi filter.

print("\n=== Filter Expression Search Examples ===")

# Load collection into memory for querying
print("Loading collection into memory...")
client.load_collection(collection_name=COLLECTION_NAME)
print("Collection loaded successfully")

# Search for thriller movies
print("\n1. Searching for thriller movies:")
results = client.query(
    collection_name=COLLECTION_NAME,
    filter='secondary_genre == "thriller"',
    output_fields=["document_text", "genre", "primary_genre", "secondary_genre"],
    limit=5,
)

for result in results:
    print(f"- {result['document_text'][:100]}...")
    print(
        f"  Genre: {result['genre']} ({result.get('primary_genre')}-{result.get('secondary_genre')})"
    )

# Search for movies with military characters
print("\n2. Searching for movies with military characters:")
results = client.query(
    collection_name=COLLECTION_NAME,
    filter='character_type == "military"',
    output_fields=["document_text", "genre", "character_role", "character_type"],
    limit=5,
)

for result in results:
    print(f"- {result['document_text'][:100]}...")
    print(f"  Genre: {result['genre']}")
    print(
        f"  Character: {result.get('character_role')} ({result.get('character_type')})"
    )

=== Filter Expression Search Examples ===
Loading collection into memory...
Collection loaded successfully

1. Searching for thriller movies:
- A brilliant scientist creates artificial intelligence that becomes self-aware. The sci-fi thriller e...
  Genre: sci-fi thriller (sci-fi-thriller)
- Two detectives investigate a series of mysterious murders in New York City. The crime thriller featu...
  Genre: crime thriller (crime-thriller)
- A detective investigates supernatural crimes in Victorian London. The horror thriller combines perio...
  Genre: horror thriller (horror-thriller)
- John McClane fights terrorists in a Los Angeles skyscraper during Christmas Eve. The action-packed t...
  Genre: action-packed thriller (action-thriller)

2. Searching for movies with military characters:
- Space marines battle alien invaders on a distant planet. The action sci-fi movie features futuristic...
  Genre: action sci-fi
  Character: protagonist (military)

Menggabungkan Pencarian Semantik dengan Pemfilteran Metadata

Kekuatan sebenarnya dari integrasi ini berasal dari penggabungan pencarian vektor semantik dengan pemfilteran metadata yang tepat. Hal ini memungkinkan kita untuk menemukan konten yang mirip secara semantik sambil menerapkan batasan tertentu berdasarkan atribut yang diekstrak.

print("\n=== Semantic Search Examples ===")

# 1. Search for action-related content + only thriller genre
print("\n1. Searching for action-related content + only thriller genre:")
query_text = "action fight combat battle explosion"

query_embedding_response = genai_client.models.embed_content(
    model=EMBEDDING_MODEL,
    contents=[query_text],
    config=EmbedContentConfig(
        task_type="RETRIEVAL_QUERY",
        output_dimensionality=EMBEDDING_DIM,
    ),
)
query_embedding = query_embedding_response.embeddings[0].values

results = client.search(
    collection_name=COLLECTION_NAME,
    data=[query_embedding],
    anns_field="embedding",
    limit=3,
    filter='secondary_genre == "thriller"',
    output_fields=["document_text", "genre", "primary_genre", "secondary_genre"],
    search_params={"metric_type": "COSINE"},
)

if results:
    for result in results[0]:
        print(f"- Similarity: {result['distance']:.4f}")
        print(f"  Text: {result['document_text'][:100]}...")
        print(
            f"  Genre: {result.get('genre')} ({result.get('primary_genre')}-{result.get('secondary_genre')})"
        )

# 2. Search for magic-related content + fantasy genre + conflict theme
print("\n2. Searching for magic-related content + fantasy genre + conflict theme:")
query_text = "magic wizard spell fantasy magical"

query_embedding_response = genai_client.models.embed_content(
    model=EMBEDDING_MODEL,
    contents=[query_text],
    config=EmbedContentConfig(
        task_type="RETRIEVAL_QUERY",
        output_dimensionality=EMBEDDING_DIM,
    ),
)
query_embedding = query_embedding_response.embeddings[0].values

results = client.search(
    collection_name=COLLECTION_NAME,
    data=[query_embedding],
    anns_field="embedding",
    limit=3,
    filter='primary_genre == "fantasy" and theme_type == "conflict"',
    output_fields=[
        "document_text",
        "genre",
        "primary_genre",
        "theme_type",
        "theme_setting",
    ],
    search_params={"metric_type": "COSINE"},
)

if results:
    for result in results[0]:
        print(f"- Similarity: {result['distance']:.4f}")
        print(f"  Text: {result['document_text'][:100]}...")
        print(f"  Genre: {result.get('genre')} ({result.get('primary_genre')})")
        print(f"  Theme: {result.get('theme_type')} ({result.get('theme_setting')})")

print("\n=== Demo Complete ===")

=== Semantic Search Examples ===

1. Searching for action-related content + only thriller genre:
- Similarity: 0.6947
  Text: John McClane fights terrorists in a Los Angeles skyscraper during Christmas Eve. The action-packed t...
  Genre: action-packed thriller (action-thriller)
- Similarity: 0.6128
  Text: Two detectives investigate a series of mysterious murders in New York City. The crime thriller featu...
  Genre: crime thriller (crime-thriller)
- Similarity: 0.5889
  Text: A brilliant scientist creates artificial intelligence that becomes self-aware. The sci-fi thriller e...
  Genre: sci-fi thriller (sci-fi-thriller)

2. Searching for magic-related content + fantasy genre + conflict theme:
- Similarity: 0.6986
  Text: An evil sorcerer threatens to destroy the magical kingdom. A brave hero must gather allies and maste...
  Genre: fantasy (fantasy)
  Theme: conflict (fantasy_world)

=== Demo Complete ===