Vector Databases: Pinecone, Weaviate & pgvector · Ders

Anlamsal Arama ve Melez Arama

Daha üstün sonuçlar elde etmek için vektör benzerliğini anahtar sözcük eşleştirmeyle birleştiren gelişmiş arama tekniklerini uygulayın.

Anlamsal Arama ve Melez Arama, CoddyKit'te ücretsiz bir Vector Databases: Pinecone, Weaviate & pgvector dersidir. Bu, 4 dersinin 1. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Vector Databases: Pinecone, Weaviate & pgvector öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Vector Databases: Pinecone, Weaviate & pgvector kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

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!

Sıkça Sorulan Sorular

“Anlamsal Arama ve Melez Arama” dersi ücretsiz mi?

Evet — “Anlamsal Arama ve Melez Arama” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Vector Databases: Pinecone, Weaviate & pgvector kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Vector Databases: Pinecone, Weaviate & pgvector kursu toplamda 4 dersten oluşur.

“Anlamsal Arama ve Melez Arama” dersinde ne öğreneceğim?

Daha üstün sonuçlar elde etmek için vektör benzerliğini anahtar sözcük eşleştirmeyle birleştiren gelişmiş arama tekniklerini uygulayın. Vector Databases: Pinecone, Weaviate & pgvector ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

Vector Databases: Pinecone, Weaviate & pgvector öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te Vector Databases: Pinecone, Weaviate & pgvector, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 1. dersidir.

“Anlamsal Arama ve Melez Arama” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu Vector Databases: Pinecone, Weaviate & pgvector dersinde kod yazıp çalıştırabilir miyim?

Evet. Her Vector Databases: Pinecone, Weaviate & pgvector dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

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