与 LLM 框架集成
学习将向量数据库连接到 LangChain 或 LlamaIndex 等热门 LLM 编排框架。
与 LLM 框架集成 是 CoddyKit 上的免费 Vector Databases: Pinecone, Weaviate & pgvector 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Vector Databases: Pinecone, Weaviate & pgvector 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Vector Databases: Pinecone, Weaviate & pgvector 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
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 upLangChain: 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.
常见问题解答
「与 LLM 框架集成」课时是免费的吗？
是的 — 「与 LLM 框架集成」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Vector Databases: Pinecone, Weaviate & pgvector 课程的其余内容，请升级到 CoddyKit PRO。 Vector Databases: Pinecone, Weaviate & pgvector 课程共包含 4 节课。
「与 LLM 框架集成」这节课中我会学到什么？
学习将向量数据库连接到 LangChain 或 LlamaIndex 等热门 LLM 编排框架。 你通过在浏览器中直接运行的动手代码来练习 Vector Databases: Pinecone, Weaviate & pgvector，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Vector Databases: Pinecone, Weaviate & pgvector 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Vector Databases: Pinecone, Weaviate & pgvector 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「与 LLM 框架集成」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Vector Databases: Pinecone, Weaviate & pgvector 课中编写并运行代码吗？
能。每节 Vector Databases: Pinecone, Weaviate & pgvector 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。
此课程中的所有课时
- RAG 系统架构概览
- 与 LLM 框架集成
- 上下文信息检索
- RAG 的文本块拆分策略