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Vector Databases: Pinecone, Weaviate & pgvector · Lektion

Integration mit LLM-Frameworks

Lernen Sie, Vektordatenbanken mit beliebten Orchestrierungs-Frameworks für LLMs wie LangChain oder LlamaIndex zu verbinden.

Integration mit LLM-Frameworks ist eine kostenlose Vector Databases: Pinecone, Weaviate & pgvector-Lektion auf CoddyKit. Dies ist Lektion 2 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.

LLM Frameworks for RAG

Welcome! In this lesson, we'll learn how to connect vector databases with powerful LLM orchestration frameworks. These frameworks simplify building complex AI applications like Retrieval-Augmented Generation (RAG).

Think of them as tools that help your language model talk to your vector database efficiently.

Why Use LLM Frameworks?

Building RAG applications involves many steps: loading data, splitting text, generating embeddings, storing them in a vector database, retrieving relevant chunks, and finally, feeding them to an LLM.

LLM frameworks streamline this process by:

  • Abstracting complexity: Providing a unified interface for various components.
  • Modularity: Allowing you to easily swap out different models or databases.
  • Workflow management: Helping chain together different operations.

Introducing LangChain

LangChain is a popular framework for developing applications powered by language models. It's known for its modular design, making it easy to build complex LLM workflows.

Key concepts in LangChain include:

  • Chains: Sequences of calls to LLMs or other utilities.
  • Agents: LLMs that decide which tools to use and in what order.
  • Retrievers: Components that fetch documents from a data source.
  • Vector Stores: Integrations with various vector databases.

Introducing LlamaIndex

LlamaIndex (formerly GPT Index) is another leading data framework for LLM applications. It focuses heavily on data ingestion, indexing, and retrieval to augment LLMs.

LlamaIndex is particularly strong in:

  • Data connectors: Easily loading data from many sources.
  • Data indexes: Structuring data for efficient retrieval (e.g., VectorStoreIndex).
  • Query engines: Providing an interface to query your indexed data.

LangChain: Setting up a Vector Store

LangChain allows you to easily integrate with various vector databases. Here, we'll use an in-memory Chroma database to demonstrate the setup.

Notice how `Chroma.from_documents` handles both embedding and storage.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.documents import Document

# Dummy Embedding Model for demonstration
class DummyEmbeddings(OpenAIEmbeddings):
    def embed_documents(self, texts):
        return [[0.1] * 1536 for _ in texts]
    def embed_query(self, text):
        return [0.1] * 1536

embeddings = DummyEmbeddings()

docs = [
    Document(page_content="The quick brown fox."),
    Document(page_content="AI is transforming industries."),
    Document(page_content="Vector databases are key."),
]

# Create an in-memory Chroma vector store
db = Chroma.from_documents(docs, embeddings)

print("Chroma vector store initialized.")
print(f"Number of documents: {len(docs)}")

LangChain: Document Handling

Before storing data in a vector database, it often needs to be loaded and processed. LangChain provides DocumentLoaders to read data and TextSplitters to break it into manageable chunks.

This ensures optimal retrieval and prompt length for LLMs.

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
import os

# Create a dummy text file
with open("sample.txt", "w") as f:
    f.write("This is a long text about RAG. "
            "It combines retrieval with generation. "
            "Vector databases are essential here.")

# Load documents
loader = TextLoader("sample.txt")
documents = loader.load()

# Split documents into chunks
text_splitter = CharacterTextSplitter(
    chunk_size=50, chunk_overlap=0
)
split_docs = text_splitter.split_documents(documents)

print(f"Original doc content: {documents[0].page_content[:40]}...")
print(f"Number of split chunks: {len(split_docs)}")
print(f"First chunk: {split_docs[0].page_content}")

os.remove("sample.txt") # Clean up

LangChain: Creating a Retriever

Once your vector store is set up, you can turn it into a Retriever. This component is responsible for fetching relevant documents based on a query, which is crucial for the 'Retrieval' part of RAG.

The retriever abstracts the underlying search logic of the vector database.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.documents import Document

# Dummy Embeddings & Documents
class DummyEmbeddings(OpenAIEmbeddings):
    def embed_documents(self, texts): return [[0.1] * 1536 for _ in texts]
    def embed_query(self, text): return [0.1] * 1536
embeddings = DummyEmbeddings()
docs = [
    Document(page_content="Apples are red."),
    Document(page_content="Bananas are yellow."),
    Document(page_content="Grapes are purple."),
]
db = Chroma.from_documents(docs, embeddings)

# Convert the vector store into a retriever
retriever = db.as_retriever()

query = "What color are grapes?"
retrieved_docs = retriever.invoke(query)

print(f"Query: '{query}'")
print(f"Retrieved {len(retrieved_docs)} documents.")
for i, doc in enumerate(retrieved_docs):
    print(f"  Doc {i+1}: {doc.page_content}")

LlamaIndex: Indexing Documents

LlamaIndex uses the concept of 'Indexes' to structure your data for efficient retrieval. The VectorStoreIndex is a common type, leveraging a vector database for similarity search.

This example shows how to create an in-memory Chroma-backed index with LlamaIndex.

from llama_index.core import VectorStoreIndex, Document
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb
from llama_index.core import Settings

# Initialize an in-memory Chroma client
db = chromadb.Client()
chroma_collection = db.get_or_create_collection("my_ll_docs")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)

# Dummy documents
documents = [
    Document(text="LlamaIndex builds LLM apps."),
    Document(text="It helps with data indexing."),
    Document(text="Vector dbs are core for retrieval."),
]

# Dummy embedding model for runnable example
class DummyLlamaIndexEmbeddings(OpenAIEmbedding):
    def _get_query_embedding(self, query): return [0.2] * 1536
    def _get_text_embedding(self, text): return [0.2] * 1536

Settings.embed_model = DummyLlamaIndexEmbeddings()

# Create a VectorStoreIndex
index = VectorStoreIndex.from_documents(
    documents, vector_store=vector_store
)

print("LlamaIndex VectorStoreIndex created.")

LlamaIndex: Querying the Index

Once an index is created in LlamaIndex, you can use a QueryEngine to perform searches. The query engine handles the retrieval from the underlying vector store and can optionally interact with an LLM to synthesize a response.

Here, we focus on the retrieval aspect.

from llama_index.core import VectorStoreIndex, Document
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb
from llama_index.core import Settings

# Initialize in-memory Chroma client & store
db = chromadb.Client()
chroma_collection = db.get_or_create_collection("my_ll_docs_query")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)

# Dummy documents
documents = [
    Document(text="LlamaIndex helps build context-augmented LLM apps."),
    Document(text="It provides tools for data ingestion."),
    Document(text="Retrieval is key for RAG."),
]

# Dummy embedding model
class DummyLlamaIndexEmbeddings(OpenAIEmbedding):
    def _get_query_embedding(self, query): return [0.2] * 1536
    def _get_text_embedding(self, text): return [0.2] * 1536

Settings.embed_model = DummyLlamaIndexEmbeddings()

# Create and populate index
index = VectorStoreIndex.from_documents(
    documents, vector_store=vector_store
)

# Create a query engine
query_engine = index.as_query_engine()

# Perform a query
query = "What does LlamaIndex do?"
response = query_engine.query(query)

print(f"Query: '{query}'")
print(f"Response (partial): {str(response)[:80]}...")

Connecting Frameworks & VDBs

Both LangChain and LlamaIndex offer robust integrations with various vector databases (like Pinecone, Weaviate, pgvector, etc.). They provide a layer of abstraction, allowing you to switch between VDBs with minimal code changes.

This simplifies developing and maintaining RAG applications by decoupling your application logic from the specific database implementation.

Framework Components Check

Which of the following are common components or concepts found in LLM orchestration frameworks (like LangChain or LlamaIndex) when building RAG applications?

Recap: Integrating with Frameworks

Great job! You've learned how LLM orchestration frameworks like LangChain and LlamaIndex simplify building RAG applications by integrating with vector databases.

  • They provide abstractions for VDBs.
  • They offer tools for document loading, splitting, and indexing.
  • They enable efficient retrieval through retrievers and query engines.

These frameworks are essential for managing the complexity of modern AI applications.

Häufig gestellte Fragen

Ist die Lektion „Integration mit LLM-Frameworks“ kostenlos?

Ja — der vollständige Text von „Integration mit LLM-Frameworks“ 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 „Integration mit LLM-Frameworks“?

Lernen Sie, Vektordatenbanken mit beliebten Orchestrierungs-Frameworks für LLMs wie LangChain oder LlamaIndex zu 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 2 von 4.

Wie lange dauert die Lektion „Integration mit LLM-Frameworks“?

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. Überblick über die RAG-Systemarchitektur
  2. Integration mit LLM-Frameworks
  3. Kontextbezogener Informationsabruf
  4. Chunking-Strategien für RAG
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