Retrieval Strategies from Vector DB

Retrievers are available from Vector DB, default one is Cosine similarity and it represented as search_type="similarity"

This is the Basic retriever and there are few others like "MMR"

Is this same as cosine similarity

In most common configurations, yes, it is the same as or highly related to cosine similarity, but the exact mathematical metric depends on your Vector Database settings.

from langchain_community.vectorstores import FAISS

from langchain_openai import OpenAIEmbeddings

from langchain_core.documents import Document

# Create sample vectorstore

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

docs = [

    Document(page_content="LangChain is a framework for LLM applications", metadata={"topic": "langchain"}),

    Document(page_content="RAG combines retrieval with generation", metadata={"topic": "rag"}),

    Document(page_content="Vector databases store embeddings", metadata={"topic": "vectors"}),

    Document(page_content="Transformers use attention mechanisms", metadata={"topic": "transformers"}),

    Document(page_content="FAISS is a similarity search library", metadata={"topic": "vectors"}),

]

vectorstore = FAISS.from_documents(docs, embeddings)

print("✅ Vector store created")

# Create retriever

retriever = vectorstore.as_retriever(

    search_type="similarity",

    search_kwargs={"k": 3}  # Return top 3 results

)

# Use retriever

query = "How does RAG work?"

results = retriever.invoke(query)

print(f"Query: {query}\n")

print("Results:")

for i, doc in enumerate(results, 1):

    print(f"{i}. {doc.page_content}")

    print(f"   Topic: {doc.metadata['topic']}\n")

Sample code is under: https://github.com/LeelaPrasadG/rag_langchain/blob/main/06_Retrieval_Strategies.ipynb