Embeddings speichern und abrufen
Implementieren Sie den Prozess, Embeddings aus Dokumentabschnitten zu erzeugen und für den späteren Abruf in einer Vektordatenbank zu speichern.
Embeddings speichern und abrufen ist eine kostenlose LangChain / RAG / Vector DBs-Lektion auf CoddyKit. Dies ist Lektion 3 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 LangChain / RAG / Vector DBs-Lernpfads, und dein Fortschritt wird über Web und CoddyKit-App synchronisiert. Der LangChain / RAG / Vector DBs-Kurs umfasst insgesamt 4 Lektionen.
Teile dieser Lektion wurden noch nicht übersetzt und werden auf Englisch angezeigt.
Storing & Retrieving Embeddings
Welcome to Lesson 3! In this lesson, we'll connect the dots between text chunks and vector databases.
You'll learn how to generate numerical representations (embeddings) from your document chunks and then store them efficiently in a vector database for quick and accurate retrieval.
Recap: Chunks & Embeddings
Before we dive in, let's quickly recap. From previous lessons, you know:
- Document Chunks: Large documents are split into smaller, manageable pieces to fit LLM context windows and improve retrieval granularity.
- Text Embeddings: These are numerical vectors that capture the semantic meaning of text. Similar texts have similar embeddings.
Our goal now is to turn those chunks into embeddings and make them searchable!
The Storage & Retrieval Flow
Here's the typical workflow for getting your data ready for RAG:
- Load & Split: Ingest raw documents and break them into chunks.
- Embed: Convert each text chunk into an embedding vector using an embedding model.
- Store: Save these embedding vectors (along with their original text chunks and metadata) in a vector database.
- Retrieve: When a user asks a question, embed the query, search the vector database for similar embeddings, and retrieve the most relevant chunks.
Initializing an Embedding Model
First, we need an embedding model. LangChain provides interfaces for many models, including those from OpenAI, Cohere, and local models like those from Hugging Face.
For this example, we'll use a local Hugging Face model to avoid needing an API key. This model turns text into a vector of numbers.
from langchain_community.embeddings import HuggingFaceEmbeddings
# Initialize a local embedding model
# This might download the model the first time
embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
print("Embedding model initialized successfully!")Generating Embeddings from Text
Once our embedding model is ready, we can feed it text chunks. The model will then output a list of numbers (our embedding vector) for each chunk.
These vectors are what the vector database will use to find similar pieces of information.
from langchain_community.embeddings import HuggingFaceEmbeddings
embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
texts_to_embed = [
"The quick brown fox jumps over the lazy dog.",
"A canine named Fido is taking a nap."
]
# Generate embeddings for the texts
vectors = embeddings_model.embed_documents(texts_to_embed)
print(f"Number of vectors generated: {len(vectors)}")
print(f"Dimension of each vector: {len(vectors[0])}")
# print(f"First vector (partial): {vectors[0][:5]}...") # Too long for mobile
Introducing Vector Stores
A vector store is a specialized database designed to efficiently store and query high-dimensional vectors. It's built to quickly find vectors that are 'close' to a given query vector.
Think of it as a super-fast index for semantic meaning. When you search, it doesn't look for keywords; it looks for meaning.
LangChain supports many vector stores, from simple in-memory ones like FAISS to robust cloud services like Pinecone or Chroma.
Storing Embeddings with FAISS
Let's use FAISS, an in-memory vector store, to demonstrate storage. We'll take our text chunks and their embeddings and add them to FAISS. FAISS handles the indexing for fast search.
In a real application, you'd load chunks from documents first, then embed them, and finally store them. Here, we'll create simple documents directly.
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.documents import Document
embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# Create some example documents (text chunks with optional metadata)
documents = [
Document(page_content="The capital of France is Paris.", metadata={"source": "geo"}),
Document(page_content="Eiffel Tower is a landmark in Paris.", metadata={"source": "tourism"}),
Document(page_content="Python is a popular programming language.", metadata={"source": "tech"}),
Document(page_content="Coding with Python is fun and versatile.", metadata={"source": "tech"})
]
# Create a FAISS vector store from the documents and embeddings model
vectorstore = FAISS.from_documents(documents, embeddings_model)
print("Documents successfully stored in FAISS vector store!")Performing a Similarity Search
Now that our documents are embedded and stored, we can query the vector store to find the most semantically similar documents to our question.
The vector store will embed your query, compare its vector to all stored vectors, and return the top 'k' (e.g., top 4) most similar documents.
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.documents import Document
embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
documents = [
Document(page_content="The capital of France is Paris.", metadata={"source": "geo"}),
Document(page_content="Eiffel Tower is a landmark in Paris.", metadata={"source": "tourism"}),
Document(page_content="Python is a popular programming language.", metadata={"source": "tech"}),
Document(page_content="Coding with Python is fun and versatile.", metadata={"source": "tech"})
]
vectorstore = FAISS.from_documents(documents, embeddings_model)
query = "What is the main city of France?"
# Perform a similarity search
retrieved_docs = vectorstore.similarity_search(query, k=2)
print(f"Query: '{query}'\n")
print("Top 2 retrieved documents:")
for i, doc in enumerate(retrieved_docs):
print(f"{i+1}. Content: '{doc.page_content}' (Source: {doc.metadata['source']})")Metadata is Your Friend
Notice in the previous example how we included metadata with our documents? This is incredibly powerful!
- Filtering: You can filter searches based on metadata (e.g., only search documents from a specific author or date).
- Context: Metadata helps the LLM understand the source and relevance of the retrieved chunk, improving answer quality.
- Debugging: It's easier to trace where information came from.
Always consider what metadata is useful to store alongside your text chunks.
Quick Check
You've learned about the steps to prepare your data for a RAG system. What is the correct sequence for storing and retrieving information?
Recap: Storing & Retrieving
Great job! In this lesson, you've mastered the critical steps of preparing your data for a RAG system:
- Initializing an embedding model to convert text into numerical vectors.
- Understanding the role of a vector store for efficient storage and similarity search.
- Implementing the process of generating embeddings and storing them (e.g., using FAISS).
- Performing similarity searches to retrieve relevant document chunks based on a query.
- Recognizing the importance of metadata for richer context and filtering.
These skills are fundamental to building effective RAG applications!
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Häufig gestellte Fragen
Ist die Lektion „Embeddings speichern und abrufen“ kostenlos?
Ja — der vollständige Text von „Embeddings speichern und abrufen“ ist hier im Web kostenlos zu lesen. Um sie interaktiv zu üben (integrierter Code-Editor und 24/7 KI-Tutor) und den Rest des LangChain / RAG / Vector DBs-Kurses freizuschalten, upgrade auf CoddyKit PRO. Der LangChain / RAG / Vector DBs-Kurs umfasst insgesamt 4 Lektionen.
Was lerne ich in „Embeddings speichern und abrufen“?
Implementieren Sie den Prozess, Embeddings aus Dokumentabschnitten zu erzeugen und für den späteren Abruf in einer Vektordatenbank zu speichern. Du übst LangChain / RAG / Vector DBs 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 LangChain / RAG / Vector DBs zu starten?
Keine Vorkenntnisse erforderlich. LangChain / RAG / Vector DBs 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 3 von 4.
Wie lange dauert die Lektion „Embeddings speichern und abrufen“?
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 LangChain / RAG / Vector DBs-Lektion Code schreiben und ausführen?
Ja. Jede LangChain / RAG / Vector DBs-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
- Text-Embeddings verstehen
- Einführung in Vektordatenbanken
- Embeddings speichern und abrufen
- Ähnlichkeit von Embeddings messen