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Vector Databases: Pinecone, Weaviate & pgvector · 강의

LLM 프레임워크와 연동하기

LangChain이나 LlamaIndex 같은 인기 LLM 오케스트레이션 프레임워크에 벡터 데이터베이스를 연결하는 방법을 배웁니다.

LLM 프레임워크와 연동하기은(는) CoddyKit의 무료 Vector Databases: Pinecone, Weaviate & pgvector 강의입니다. 이것은 4개 중 2번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Vector Databases: Pinecone, Weaviate & pgvector 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Vector Databases: Pinecone, Weaviate & pgvector 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

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.

자주 묻는 질문

“LLM 프레임워크와 연동하기” 강의는 무료인가요?

네 — “LLM 프레임워크와 연동하기” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 Vector Databases: Pinecone, Weaviate & pgvector 강의 전체를 잠금 해제할 수 있습니다. Vector Databases: Pinecone, Weaviate & pgvector 강의에는 총 4개의 강의가 포함되어 있습니다.

“LLM 프레임워크와 연동하기”에서 뭘 배우나요?

LangChain이나 LlamaIndex 같은 인기 LLM 오케스트레이션 프레임워크에 벡터 데이터베이스를 연결하는 방법을 배웁니다. 브라우저에서 직접 실행하는 실습 코드로 Vector Databases: Pinecone, Weaviate & pgvector을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

Vector Databases: Pinecone, Weaviate & pgvector을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 Vector Databases: Pinecone, Weaviate & pgvector은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 2번째 강의입니다.

“LLM 프레임워크와 연동하기” 강의는 얼마나 걸리나요?

대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

이 Vector Databases: Pinecone, Weaviate & pgvector 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 Vector Databases: Pinecone, Weaviate & pgvector 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

이 강의의 모든 강의

  1. RAG 시스템 아키텍처 개요
  2. LLM 프레임워크와 연동하기
  3. 컨텍스트 기반 정보 검색
  4. RAG를 위한 청킹 전략
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