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Vector Databases: Pinecone, Weaviate & pgvector · Pelajaran

Mengintegrasikan dengan Kerangka Kerja LLM

Pelajari cara menghubungkan basis data vektor dengan kerangka kerja orkestrasi LLM populer seperti LangChain atau LlamaIndex.

Mengintegrasikan dengan Kerangka Kerja LLM adalah pelajaran Vector Databases: Pinecone, Weaviate & pgvector gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Vector Databases: Pinecone, Weaviate & pgvector, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Vector Databases: Pinecone, Weaviate & pgvector mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

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.

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Mengintegrasikan dengan Kerangka Kerja LLM” gratis?

Ya — teks lengkap “Mengintegrasikan dengan Kerangka Kerja LLM” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Vector Databases: Pinecone, Weaviate & pgvector, upgrade ke CoddyKit PRO. Kursus Vector Databases: Pinecone, Weaviate & pgvector mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Mengintegrasikan dengan Kerangka Kerja LLM”?

Pelajari cara menghubungkan basis data vektor dengan kerangka kerja orkestrasi LLM populer seperti LangChain atau LlamaIndex. Kamu berlatih Vector Databases: Pinecone, Weaviate & pgvector dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai Vector Databases: Pinecone, Weaviate & pgvector?

Tidak diperlukan pengalaman sebelumnya. Vector Databases: Pinecone, Weaviate & pgvector di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.

Berapa lama pelajaran “Mengintegrasikan dengan Kerangka Kerja LLM” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran Vector Databases: Pinecone, Weaviate & pgvector ini?

Ya. Setiap pelajaran Vector Databases: Pinecone, Weaviate & pgvector menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

  1. Gambaran Umum Arsitektur Sistem RAG
  2. Mengintegrasikan dengan Kerangka Kerja LLM
  3. Pengambilan Informasi Kontekstual
  4. Strategi Pembagian Potongan untuk RAG
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