Intégrer une base de données vectorielle
Apprenez à connecter votre application RAG à une base de données vectorielle comme Pinecone, Weaviate ou Chroma, puis à effectuer l’indexation et des requêtes.
Intégrer une base de données vectorielle est une leçon LLM Apps in Production (RAG + Vector DB + Caching) gratuite sur CoddyKit. Ceci est la leçon 3 sur 4. Tu peux lire la leçon complète ci-dessous gratuitement — puis la pratiquer en direct dans le navigateur avec un éditeur de code intégré et un tuteur IA 24/7. Elle fait partie du parcours d'apprentissage LLM Apps in Production (RAG + Vector DB + Caching), et ta progression se synchronise sur le web et l'application CoddyKit. Le cours LLM Apps in Production (RAG + Vector DB + Caching) comprend 4 leçons au total.
Certaines parties de cette leçon n'ont pas encore été traduites et s'affichent en anglais.
Connect RAG & Vector DBs
Welcome to the final lesson in our 'Deep Dive into Vector Databases' course! You've learned why vector databases are essential for RAG systems and what vector embeddings are.
Now, let's bring it all together. This lesson focuses on the practical steps of integrating a vector database into your RAG application.
We'll cover:
- Connecting to a vector database client.
- Preparing your data for storage.
- Indexing (adding) your data.
- Querying (searching) for relevant information.
Vector DBs in RAG: A Quick Recap
Before we dive into integration, let's quickly recall the role of vector databases in RAG.
They are specialized databases designed to store and efficiently search vector embeddings. These embeddings are numerical representations of text, images, or other data, capturing their semantic meaning.
When a user asks a question, we convert it into an embedding, search the vector database for similar document embeddings, and retrieve the most relevant chunks of information. This context is then fed to the LLM.
Choosing a Client Library
To interact with a vector database, you'll use its official client library. These libraries provide methods to connect, add data, query, and manage your index.
Popular choices include:
- Pinecone Client: For Pinecone's cloud-native vector database.
- ChromaDB Client: For Chroma, an open-source vector database often used locally or self-hosted.
- Weaviate Client: For Weaviate, another popular open-source, cloud-native vector database.
Each client has a similar pattern for connecting and performing operations.
Setting Up a Connection
The first step is always to establish a connection to your vector database. This usually involves initializing a client object with your API key, environment details, or host address.
For demonstration, we'll use a simplified SimpleVectorDB class that mimics real vector database operations. Try running this example to see how a client might be initialized.
import java.util.Map;
import java.util.HashMap;
import java.util.List;
import java.util.ArrayList;
import java.util.Arrays;
class SimpleVectorDB {
private String apiKey;
private String environment;
public SimpleVectorDB(String apiKey, String environment) {
this.apiKey = apiKey;
this.environment = environment;
System.out.println("SimpleVectorDB client initialized!");
System.out.println("API Key (masked): ****" + apiKey.substring(apiKey.length() - 4));
System.out.println("Environment: " + environment);
}
// Placeholder for other methods like upsert, query
}
public class Main {
public static void main(String[] args) {
String myApiKey = "sk_your_actual_api_key";
String myEnvironment = "gcp-starter";
// Initialize the vector database client
SimpleVectorDB dbClient = new SimpleVectorDB(myApiKey, myEnvironment);
}
}Understanding Data Structure
When you add data to a vector database, it typically expects three main components for each item:
- ID: A unique identifier for your document chunk (e.g., "doc123-chunk4").
- Vector: The numerical embedding (a list of floating-point numbers) of your text chunk.
- Metadata: Optional, but highly useful, key-value pairs (e.g., source document, page number, author) that provide additional context and allow for filtering during queries.
This structure helps the database manage and retrieve your information efficiently.
Preparing Data for Indexing
Before you can index data, you need to prepare it. This involves:
- Loading Data: Getting your raw text from various sources (PDFs, web pages, databases).
- Chunking: Breaking down long documents into smaller, semantically meaningful chunks.
- Embedding: Converting each text chunk into its corresponding vector embedding using an embedding model.
For this lesson, we'll assume you already have your text chunks and their embeddings ready to be indexed. The focus here is on the interaction with the vector database itself.
Indexing Documents (Upsert)
The process of adding or updating vectors and their associated metadata in a vector database is often called upserting. It's like inserting if the ID is new, or updating if the ID already exists.
Let's extend our SimpleVectorDB to include an upsert method and add some sample data. Notice how each item has an ID, a vector, and metadata.
import java.util.Map;
import java.util.HashMap;
import java.util.List;
import java.util.ArrayList;
import java.util.Arrays;
class SimpleVectorDB {
private String apiKey;
private String environment;
// In-memory store for demonstration
private Map<String, Map<String, Object>> index = new HashMap<>();
public SimpleVectorDB(String apiKey, String environment) {
this.apiKey = apiKey;
this.environment = environment;
System.out.println("SimpleVectorDB client initialized!");
}
public void upsert(List<Map<String, Object>> vectorsToUpsert) {
System.out.println("Upserting " + vectorsToUpsert.size() + " vectors...");
for (Map<String, Object> item : vectorsToUpsert) {
String id = (String) item.get("id");
index.put(id, item);
System.out.println(" Upserted ID: " + id + ", Metadata: " + item.get("metadata"));
}
System.out.println("Upsert complete. Total indexed items: " + index.size());
}
}
public class Main {
public static void main(String[] args) {
String myApiKey = "sk_your_actual_api_key";
String myEnvironment = "gcp-starter";
SimpleVectorDB dbClient = new SimpleVectorDB(myApiKey, myEnvironment);
// Sample data for indexing
List<Map<String, Object>> data = new ArrayList<>();
data.add(new HashMap<String, Object>() {{
put("id", "doc1-chunk1");
put("vector", Arrays.asList(0.1f, 0.2f, 0.3f, 0.4f));
put("metadata", new HashMap<String, String>() {{ put("source", "report.pdf"); put("page", "1"); }});
}});
data.add(new HashMap<String, Object>() {{
put("id", "doc1-chunk2");
put("vector", Arrays.asList(0.5f, 0.6f, 0.7f, 0.8f));
put("metadata", new HashMap<String, String>() {{ put("source", "report.pdf"); put("page", "2"); }});
}});
dbClient.upsert(data);
}
}Performing a Similarity Search
Once your data is indexed, you can perform queries. A query typically involves:
- Converting the user's question into a query embedding.
- Sending this query embedding to the vector database.
- The database finds the 'k' most similar vectors (document chunks) based on their embeddings.
- It returns these document chunks along with their associated metadata.
This is the core of how RAG retrieves relevant context!
Querying Code Example
Let's add a query method to our SimpleVectorDB and simulate a search. For simplicity, our mock query will just return the most 'similar' item based on a very basic matching logic (in a real DB, this is a complex similarity algorithm).
Imagine a user asks a question, and its embedding is our queryVector.
import java.util.Map;
import java.util.HashMap;
import java.util.List;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Comparator;
class SimpleVectorDB {
private String apiKey;
private String environment;
private Map<String, Map<String, Object>> index = new HashMap<>();
public SimpleVectorDB(String apiKey, String environment) {
this.apiKey = apiKey;
this.environment = environment;
}
public void upsert(List<Map<String, Object>> vectorsToUpsert) {
for (Map<String, Object> item : vectorsToUpsert) {
index.put((String) item.get("id"), item);
}
System.out.println("Upsert complete. Total indexed items: " + index.size());
}
// Simple similarity (Euclidean distance for demo)
private double calculateSimilarity(List<Float> vec1, List<Float> vec2) {
double sumSqDiff = 0;
for (int i = 0; i < vec1.size(); i++) {
sumSqDiff += Math.pow(vec1.get(i) - vec2.get(i), 2);
}
return -Math.sqrt(sumSqDiff); // Smaller distance = higher similarity
}
public List<Map<String, Object>> query(List<Float> queryVector, int topK) {
System.out.println("Querying for top " + topK + " similar vectors...");
List<Map<String, Object>> results = new ArrayList<>();
List<Map.Entry<String, Map<String, Object>>> sortedEntries = new ArrayList<>(index.entrySet());
sortedEntries.sort(new Comparator<Map.Entry<String, Map<String, Object>>>() {
@Override
public int compare(Map.Entry<String, Map<String, Object>> e1, Map.Entry<String, Map<String, Object>> e2) {
List<Float> vec1 = (List<Float>) e1.getValue().get("vector");
List<Float> vec2 = (List<Float>) e2.getValue().get("vector");
return Double.compare(calculateSimilarity(queryVector, vec2), calculateSimilarity(queryVector, vec1));
}
});
for (int i = 0; i < Math.min(topK, sortedEntries.size()); i++) {
results.add(sortedEntries.get(i).getValue());
}
return results;
}
}
public class Main {
public static void main(String[] args) {
String myApiKey = "sk_your_actual_api_key";
String myEnvironment = "gcp-starter";
SimpleVectorDB dbClient = new SimpleVectorDB(myApiKey, myEnvironment);
List<Map<String, Object>> data = new ArrayList<>();
data.add(new HashMap<String, Object>() {{
put("id", "doc1-chunk1");
put("vector", Arrays.asList(0.1f, 0.2f, 0.3f, 0.4f));
put("metadata", new HashMap<String, String>() {{ put("source", "report.pdf"); put("page", "1"); }});
}});
data.add(new HashMap<String, Object>() {{
put("id", "doc1-chunk2");
put("vector", Arrays.asList(0.5f, 0.6f, 0.7f, 0.8f));
put("metadata", new HashMap<String, String>() {{ put("source", "report.pdf"); put("page", "2"); }});
}});
data.add(new HashMap<String, Object>() {{
put("id", "doc2-chunk1");
put("vector", Arrays.asList(0.15f, 0.25f, 0.35f, 0.45f)); // Similar to chunk1
put("metadata", new HashMap<String, String>() {{ put("source", "article.txt"); put("topic", "AI"); }});
}});
dbClient.upsert(data);
// Simulate a query vector (from a user's question)
List<Float> queryVector = Arrays.asList(0.12f, 0.22f, 0.32f, 0.42f);
List<Map<String, Object>> queryResults = dbClient.query(queryVector, 2);
System.out.println("\nQuery Results:");
for (Map<String, Object> result : queryResults) {
System.out.println(" ID: " + result.get("id") + ", Metadata: " + result.get("metadata"));
}
}
}Handling Query Results
The results from a vector database query are typically a list of document chunks, ranked by similarity to the query. Each result usually includes:
- The original ID of the chunk.
- The text content (if stored in metadata or retrieved separately using the ID).
- The metadata associated with that chunk.
- A similarity score or distance metric.
Your RAG application then takes these top-k retrieved chunks, formats them, and passes them as context to the Large Language Model to generate an informed response.
Quick Check: Vector DB Integration
You've learned the key steps to integrate a vector database. Which of the following is the correct order of operations when adding new text data to a RAG system's vector database?
Recap: Integrating Vector DBs
Great job! In this lesson, we demystified the process of integrating a vector database into your RAG application. You learned:
- How to initialize a vector database client.
- The essential data structure (ID, vector, metadata) required for indexing.
- The steps to prepare and upsert your document chunks and their embeddings.
- How to perform a similarity search (query) to retrieve relevant context.
With this knowledge, you're ready to connect your RAG application to powerful vector databases like Pinecone, Chroma, or Weaviate!
Questions Fréquemment Posées
La leçon « Intégrer une base de données vectorielle » est-elle gratuite ?
Oui — le texte complet de « Intégrer une base de données vectorielle » est gratuit à lire ici sur le web. Pour la pratiquer de manière interactive (un éditeur de code intégré et un tuteur IA 24/7) et déverrouiller le reste du cours LLM Apps in Production (RAG + Vector DB + Caching), passe à CoddyKit PRO. Le cours LLM Apps in Production (RAG + Vector DB + Caching) comprend 4 leçons au total.
Qu'est-ce que j'apprendrai dans « Intégrer une base de données vectorielle » ?
Apprenez à connecter votre application RAG à une base de données vectorielle comme Pinecone, Weaviate ou Chroma, puis à effectuer l’indexation et des requêtes. Tu pratiques LLM Apps in Production (RAG + Vector DB + Caching) avec du code pratique que tu exécutes directement dans le navigateur, et un tuteur IA 24/7 répond à tes questions au fur et à mesure que tu avances dans la leçon.
Dois-je avoir de l'expérience pour commencer LLM Apps in Production (RAG + Vector DB + Caching) ?
Aucune expérience préalable n'est requise. LLM Apps in Production (RAG + Vector DB + Caching) sur CoddyKit est structuré pour les débutants jusqu'aux apprenants avancés, donc tu peux commencer ici ou depuis le début et avancer à ton rythme. Ceci est la leçon 3 sur 4.
Combien de temps prend la leçon « Intégrer une base de données vectorielle » ?
La plupart des leçons CoddyKit prennent environ 5–10 minutes. Chacune est courte et interactive, tu progresses régulièrement et tu repiques exactement où tu t'es arrêté sur le web et l'app.
Peux-tu écrire et exécuter du code dans cette leçon LLM Apps in Production (RAG + Vector DB + Caching) ?
Oui. Chaque leçon LLM Apps in Production (RAG + Vector DB + Caching) inclut un éditeur de code intégré, tu écris et exécutes du vrai code directement dans ton navigateur et tu reçois des retours IA instantanés — aucune configuration locale requise.
Toutes les leçons de ce cours
- La nécessité des bases de données vectorielles
- Représentations vectorielles et recherche par similarité
- Intégrer une base de données vectorielle
- Indexation, filtrage et recherche hybride