Advanced RAG Techniques: Implementing Sparse Vector Retrieval with Qdrant and LangChain
Advanced RAG Techniques: Implementing Sparse Vector Retrieval with Qdrant and LangChain
In the world of Retrieval Augmented Generation (RAG), the quality of document retrieval significantly impacts the performance of the entire system. While dense vector embeddings have been the standard approach, sparse vector retrieval offers complementary benefits that can enhance retrieval accuracy. In this article, we’ll explore how to implement sparse vector retrieval using Qdrant and LangChain to build more robust RAG systems.
Understanding Sparse Vector Retrieval
Before diving into implementation, let’s briefly understand what sparse vector retrieval is and why it matters.
Dense vectors (like those from embedding models) capture semantic meaning but may miss exact keyword matches. Sparse vectors, on the other hand, excel at preserving term-specific information, making them particularly effective for keyword-based searches.
By combining both approaches, we can create a more robust retrieval system that captures both semantic meaning and keyword precision.
Introducing QdrantSparseVectorRetriever
LangChain offers the QdrantSparseVectorRetriever class, which enables sparse vector retrieval with Qdrant, a vector database. This retriever implements the standard Runnable Interface, making it easy to integrate into LangChain pipelines.
Note: The QdrantSparseVectorRetriever is deprecated since version 0.2.16. The recommended approach is to use the sparse vector search functionality directly from Qdrant’s integration. It will be removed in langchain-community==0.5.0.
Setting Up the Retriever
Let’s start by setting up our sparse vector retriever:
from langchain_community.retrievers import QdrantSparseVectorRetriever
from qdrant_client import QdrantClient
# Initialize Qdrant client
client = QdrantClient(url="http://localhost:6333")
# Define a sparse encoder function
def sparse_encoder(text):
# This is a simplified example
# In practice, you would use a proper sparse encoding method
# like BM25, SPLADE, or another sparse encoder
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
vectorizer.fit([text])
vector = vectorizer.transform([text]).toarray()[0]
return vector
# Initialize the retriever
retriever = QdrantSparseVectorRetriever(
qdrant_client=client,
collection_name="my_documents",
content_payload_key="content",
metadata_payload_key="metadata",
sparse_encoder=sparse_encoder,
sparse_vector_name="sparse_vector"
)
Adding Documents to the Retriever
To populate our retriever with documents, we can use the add_documents method:
from langchain_core.documents import Document
# Create some sample documents
documents = [
Document(page_content="Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.", metadata={"source": "wiki", "topic": "ML"}),
Document(page_content="Natural language processing is a subfield of linguistics, computer science, and artificial intelligence.", metadata={"source": "textbook", "topic": "NLP"}),
Document(page_content="Vector databases are specialized database systems designed to store and search vector embeddings efficiently.", metadata={"source": "blog", "topic": "Databases"})
]
# Add documents to the retriever
doc_ids = retriever.add_documents(documents)
print(f"Added documents with IDs: {doc_ids}")
Retrieving Documents
Now that we have documents in our system, we can retrieve relevant ones based on a query:
# Basic retrieval
query = "How do vector databases work?"
retrieved_docs = retriever.invoke(query)
print(f"Retrieved {len(retrieved_docs)} documents")
for i, doc in enumerate(retrieved_docs):
print(f"Document {i+1}:")
print(f"Content: {doc.page_content[:100]}...")
print(f"Metadata: {doc.metadata}")
print("---")
Configuring the Retriever
The QdrantSparseVectorRetriever offers several configuration options to fine-tune retrieval:
from qdrant_client.models import Filter, FieldCondition, MatchValue
# Create a filter to narrow down search results
filter_condition = Filter(
must=[
FieldCondition(
key="metadata.topic",
match=MatchValue(value="Databases")
)
]
)
# Create a retriever with custom search parameters
advanced_retriever = QdrantSparseVectorRetriever(
qdrant_client=client,
collection_name="my_documents",
content_payload_key="content",
metadata_payload_key="metadata",
sparse_encoder=sparse_encoder,
sparse_vector_name="sparse_vector",
search_options={"filter": filter_condition},
k=2 # Number of documents to retrieve
)
# Retrieve documents with the advanced configuration
filtered_docs = advanced_retriever.invoke("vector databases")
Integration with LangChain Chains
One of the benefits of using LangChain’s retriever classes is their seamless integration with other components. Let’s see how to use our sparse vector retriever in a RAG chain:
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
# Initialize the language model
llm = ChatOpenAI(model_name="gpt-3.5-turbo")
# Create a prompt template
prompt_template = """You are an assistant that answers questions based on the provided context.
Context:
{context}
Question: {question}
Answer:"""
prompt = ChatPromptTemplate.from_template(prompt_template)
# Define a function to format documents
def format_docs(docs):
return "\n\n".join([doc.page_content for doc in docs])
# Create the RAG chain
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
# Use the chain
response = rag_chain.invoke("What are vector databases used for?")
print(response)
Advanced Usage: Streaming Results
The QdrantSparseVectorRetriever also supports streaming results, which can be useful for real-time applications:
# Stream results
async def process_stream():
query = "vector database applications"
async for chunk in retriever.astream(query):
# In a real application, you might send each chunk to a frontend
print(f"Received chunk: {chunk}")
# In an async context
import asyncio
asyncio.run(process_stream())
Error Handling with Fallbacks
We can also implement fallback mechanisms to ensure our RAG system remains robust:
from langchain_core.runnables import RunnableLambda
# Define a fallback retriever (could be a different type)
fallback_retriever = QdrantSparseVectorRetriever(
qdrant_client=client,
collection_name="backup_collection",
sparse_encoder=sparse_encoder,
sparse_vector_name="sparse_vector"
)
# Create a retriever with fallback
def retrieve_with_fallback(query):
try:
docs = retriever.invoke(query)
if docs:
return docs
else:
return fallback_retriever.invoke(query)
except Exception as e:
print(f"Primary retriever failed: {e}")
return fallback_retriever.invoke(query)
# Use the fallback mechanism
robust_retriever = RunnableLambda(retrieve_with_fallback)
Migrating from QdrantSparseVectorRetriever
As mentioned earlier, the QdrantSparseVectorRetriever is deprecated. Here’s how to migrate to the recommended approach using Qdrant’s direct sparse vector search functionality:
from langchain_community.vectorstores import Qdrant
# Create a vector store with sparse vector support
vectorstore = Qdrant(
client=client,
collection_name="my_documents",
embeddings=your_embedding_model, # For dense vectors
content_payload_key="content",
metadata_payload_key="metadata",
)
# Use sparse vector search
sparse_docs = vectorstore.similarity_search_with_score(
query="vector databases",
k=4,
search_type="sparse" # Specify sparse search
)
Conclusion
Sparse vector retrieval offers a powerful complement to dense vector embeddings in RAG systems. By implementing sparse vector retrieval with Qdrant and LangChain, you can enhance the accuracy and robustness of your document retrieval process.
While the QdrantSparseVectorRetriever is being deprecated, the core functionality remains available through Qdrant’s direct sparse vector search capabilities. By understanding both approaches, you can build more effective RAG systems that leverage the best of both dense and sparse vector representations.
For production systems, consider implementing hybrid search approaches that combine both dense and sparse vectors, potentially with re-ranking mechanisms, to achieve optimal retrieval performance for your specific use case.
This post was originally written in my native language and then translated using an LLM. I apologize if there are any grammatical inconsistencies.