2026년 최고의 로컬 RAG 도구: Open WebUI, LlamaIndex, LangChain

# 1. Run Open WebUI with Docker
docker run -d -p 3000:8080 \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  ghcr.io/open-webui/open-webui:latest

# 2. Open http://localhost:3000
# 3. Click "+" next to message input → "Upload files"
# 4. Select PDFs or text files
# 5. Ask questions -- Open WebUI retrieves relevant chunks
# 6. Model answers based on documents, with citations

# 1. Install
pip install llama-index
pip install llama-index-embeddings-ollama  # use local Ollama embeddings
pip install llama-index-vector-stores-chroma  # use Chroma for storage

# 2. Simple RAG pipeline
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.embeddings.ollama import OllamaEmbedding

# Load documents
documents = SimpleDirectoryReader("./documents").load_data()

# Create index with local embeddings
embedding_model = OllamaEmbedding(model_name="nomic-embed-text")
index = VectorStoreIndex.from_documents(
  documents,
  embed_model=embedding_model
)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What does the document say about X?")
print(response)

# pip install langchain langchain-community langchain-chroma

from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OllamaEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOllama
from langchain.chains import RetrievalQA

# Load documents
loader = DirectoryLoader("./documents")
docs = loader.load()

# Split into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks = splitter.split_documents(docs)

# Create embeddings and vector store
embeddings = OllamaEmbeddings(model="nomic-embed-text")
vectorstore = Chroma.from_documents(chunks, embeddings)

# Create QA chain
llm = ChatOllama(model="llama3.2:8b")
qa = RetrievalQA.from_chain_type(
  llm=llm,
  chain_type="stuff",
  retriever=vectorstore.as_retriever()
)

# Answer questions
result = qa.run("What does the document say about X?")
print(result)