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Vector Databases: Pinecone, Weaviate & pgvector · Lektion

Semantische Suche und hybride Suche

Implementieren Sie fortgeschrittene Suchtechniken, die Vektorähnlichkeit mit Schlüsselwortabgleich für bessere Ergebnisse verbinden.

Semantische Suche und hybride Suche ist eine kostenlose Vector Databases: Pinecone, Weaviate & pgvector-Lektion auf CoddyKit. Dies ist Lektion 1 von 4. Du kannst die komplette Lektion unten kostenlos lesen – dann übst du sie direkt im Browser mit einem integrierten Code-Editor und einem KI-Tutor rund um die Uhr. Sie ist Teil des Vector Databases: Pinecone, Weaviate & pgvector-Lernpfads, und dein Fortschritt wird über Web und CoddyKit-App synchronisiert. Der Vector Databases: Pinecone, Weaviate & pgvector-Kurs umfasst insgesamt 4 Lektionen.

Teile dieser Lektion wurden noch nicht übersetzt und werden auf Englisch angezeigt.

Beyond Basic Searches

Welcome! In this lesson, we'll dive into advanced search techniques in Weaviate. Moving past simple vector searches, we'll explore how to combine different methods for incredibly precise results.

We'll cover:

  • Pure semantic search
  • Traditional keyword (BM25) search
  • The power of hybrid search

Semantic Search: Meaning First

Semantic search finds items based on their meaning, not just exact words. It uses vector embeddings to represent data, measuring "distance" to find similar concepts. Weaviate uses .with_near_text() for this.

Try this example:

import weaviate
import os

# Connect to your Weaviate instance
# Ensure WEAVIATE_URL is set (e.g., "http://localhost:8080")
client = weaviate.Client(
    url=os.getenv("WEAVIATE_URL", "http://localhost:8080")
)

# Make sure you have an 'Article' class with 'title' and 'content' properties
# and some data imported for this to work!

query_concept = "latest advancements in technology"

response = client.query.get(
    "Article", # Your class name
    ["title", "content"]
).with_near_text(
    {"concepts": [query_concept]}
).with_limit(2).do()

print("Semantic Search Results:")
for item in response["data"]["Get"]["Article"]:
    print(f"- {item['title']}")

Keyword Search Fundamentals

While semantic search is powerful, sometimes you need to find exact keywords. This is where traditional keyword search comes in. Weaviate supports this using the BM25 algorithm.

BM25 (Best Match 25) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It's great for precision when you know exactly what words you're looking for.

Keyword Search with BM25

You can perform keyword searches in Weaviate by combining a .with_where() filter with a text search, and asking for the _additional {score} to see BM25 relevance.

Here's how to search for articles containing specific keywords:

import weaviate
import os

# Connect to your Weaviate instance
client = weaviate.Client(
    url=os.getenv("WEAVIATE_URL", "http://localhost:8080")
)

# Make sure you have an 'Article' class with 'title' and 'content' properties
# and some data imported for this to work!

keyword_query = "AI" # Search for articles containing "AI"

response = client.query.get(
    "Article",
    ["title", "content", "_additional {score}"] # Request BM25 score
).with_where({
    "path": ["content"], # Search in the 'content' field
    "operator": "Like",
    "valueText": f"*{keyword_query}*" # Wildcard search
}).with_limit(2).do()

print("Keyword Search Results:")
for item in response["data"]["Get"]["Article"]:
    print(f"- {item['title']} (BM25 Score: {item['_additional']['score']:.2f})")

Why Hybrid? Limitations

Both semantic and keyword searches have strengths and weaknesses:

  • Semantic: Great for conceptual understanding, but can miss exact terms.
  • Keyword: Excellent for exact matches, but struggles with synonyms or nuanced meaning.

Imagine searching for "best car for family trips." Semantic search might show SUVs, while keyword search might only show articles with "family" and "trip." What if you want both?

Introducing Hybrid Search

Hybrid search combines the strengths of semantic (vector) search and keyword (BM25) search. It retrieves results based on both conceptual similarity and exact term matching, then intelligently fuses them.

This leads to more comprehensive and relevant results, especially for complex or ambiguous queries.

Weaviate's `with_hybrid`

Hybrid search combines semantic and keyword strengths. Weaviate's .with_hybrid() operator makes this easy. It takes both a query and an alpha parameter to control the blend:

  • alpha = 0: Pure keyword
  • alpha = 1: Pure semantic
  • alpha = 0.5: Equal blend (default)

Experiment with this:

import weaviate
import os

# Connect to your Weaviate instance
client = weaviate.Client(
    url=os.getenv("WEAVIATE_URL", "http://localhost:8080")
)

# Make sure you have an 'Article' class with 'title' and 'content' properties
# and some data imported for this to work!

query = "AI tools for data analysis" # Hybrid query text
alpha_value = 0.7 # 0.7 for more semantic weighting

response = client.query.get(
    "Article",
    ["title", "content", "_additional {score, id}"] # Request score & ID
).with_hybrid(
    query=query,
    alpha=alpha_value
).with_limit(3).do()

print(f"Hybrid Search Results (alpha={alpha_value}):")
for item in response["data"]["Get"]["Article"]:
    # The 'score' here is the hybrid score
    print(f"- {item['title']} (Score: {item['_additional']['score']:.2f})")

Understanding Result Fusion (RRF)

When you perform a hybrid search, Weaviate needs a way to combine the rankings from both the semantic and keyword searches into a single, unified list. This is often done using an algorithm like Reciprocal Rank Fusion (RRF).

RRF is a clever method that assigns a score to each document based on its rank in the individual search results. Documents that rank highly in both semantic and keyword searches will get a significantly boosted final score.

Benefits of Hybrid Search

Hybrid search offers several advantages:

  • Improved Relevance: Catches both exact matches and conceptually similar items.
  • Robustness: Performs well even with short, ambiguous, or rare queries.
  • User Satisfaction: Leads to more comprehensive and helpful search results.

It's a crucial technique for building advanced search experiences in AI applications.

Test Your Knowledge

Hybrid search combines semantic and keyword search. Which parameter in Weaviate's .with_hybrid() operator controls the balance between these two search types?

Summary of Advanced Search

Great job! You've mastered advanced search techniques in Weaviate. We explored:

  • Semantic Search: Based on meaning and vector similarity.
  • Keyword Search: Using BM25 for exact term matching.
  • Hybrid Search: Combining both for superior relevance, controlled by the alpha parameter.

These powerful tools will help you build more intelligent and robust search applications. Keep experimenting with different query types and alpha values!

Häufig gestellte Fragen

Ist die Lektion „Semantische Suche und hybride Suche“ kostenlos?

Ja — der vollständige Text von „Semantische Suche und hybride Suche“ ist hier im Web kostenlos zu lesen. Um sie interaktiv zu üben (integrierter Code-Editor und 24/7 KI-Tutor) und den Rest des Vector Databases: Pinecone, Weaviate & pgvector-Kurses freizuschalten, upgrade auf CoddyKit PRO. Der Vector Databases: Pinecone, Weaviate & pgvector-Kurs umfasst insgesamt 4 Lektionen.

Was lerne ich in „Semantische Suche und hybride Suche“?

Implementieren Sie fortgeschrittene Suchtechniken, die Vektorähnlichkeit mit Schlüsselwortabgleich für bessere Ergebnisse verbinden. Du übst Vector Databases: Pinecone, Weaviate & pgvector mit praktischem Code, den du direkt im Browser ausführst, und ein 24/7 KI-Tutor beantwortet deine Fragen während du die Lektion bearbeitest.

Brauche ich Erfahrung, um Vector Databases: Pinecone, Weaviate & pgvector zu starten?

Keine Vorkenntnisse erforderlich. Vector Databases: Pinecone, Weaviate & pgvector auf CoddyKit ist für Anfänger bis fortgeschrittene Lernende strukturiert, sodass du hier starten oder von Anfang an beginnen und in deinem eigenen Tempo voranschreiten kannst. Dies ist Lektion 1 von 4.

Wie lange dauert die Lektion „Semantische Suche und hybride Suche“?

Die meisten CoddyKit-Lektionen dauern etwa 5–10 Minuten. Jede ist kompakt und interaktiv, sodass du stetig Fortschritte machst und genau dort weitermachst, wo du aufgehört hast – im Web und in der App.

Kann ich in dieser Vector Databases: Pinecone, Weaviate & pgvector-Lektion Code schreiben und ausführen?

Ja. Jede Vector Databases: Pinecone, Weaviate & pgvector-Lektion enthält einen integrierten Code-Editor, sodass du echten Code direkt in deinem Browser schreibst und ausführst und sofort KI-Feedback erhältst — ohne lokale Einrichtung erforderlich.

Alle Lektionen in diesem Kurs

  1. Semantische Suche und hybride Suche
  2. Weaviate-Module verwenden
  3. Strategien für Sicherung und Wiederherstellung
  4. Multi-Tenancy in Weaviate
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