LangChain vs Semantic Kernel: which LLM framework should you choose?

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
import os

# Initialize embeddings and vector store
embeddings = OpenAIEmbeddings(api_key=os.environ["OPENAI_API_KEY"])
vectorstore = Chroma(collection_name="docs", embedding_function=embeddings)

# Create retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Create LLM and prompt
llm = ChatOpenAI(model="gpt-4o", api_key=os.environ["OPENAI_API_KEY"])
prompt = ChatPromptTemplate.from_template(
    "Answer this question using the provided context: {context}\n\nQuestion: {input}"
)

# Create RAG chain
docs_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, docs_chain)  # LangChain's fluent chain composition

# Generate answer
result = rag_chain.invoke({"input": "How do I use LangChain for RAG?"})
print(result["answer"])

from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.connectors.ai.embeddings import OpenAITextEmbedding
from semantic_kernel.functions import kernel_function
import os

# Initialize kernel and service
kernel = Kernel()
kernel.add_service(
    OpenAIChatCompletion(
        model_id="gpt-4o",
        api_key=os.environ["OPENAI_API_KEY"]
    )
)

# Add embedding service (note: separate from chat service in SK)
embedding_service = OpenAITextEmbedding(
    api_key=os.environ["OPENAI_API_KEY"]
)

# Define retrieval plugin (manual: SK doesn't bundle vector stores like LangChain)
class RetrievalPlugin:
    @kernel_function(description="Retrieve documents from vector store")
    async def retrieve(self, query: str) -> str:
        # Your vector store call (Pinecone, etc.)
        return "retrieved context..."

kernel.add_plugin(RetrievalPlugin(), "retrieval")  # SK uses explicit plugin registration

# Create RAG prompt and invoke
prompt = """Answer this question using context: {{retrieval.retrieve(input)}}
Question: {{input}}"""
result = await kernel.invoke_prompt_function(prompt, input="How do I use Semantic Kernel for RAG?")
print(result.value)