Retrieval Augmented Generation (RAG) with LangChain
Meri Nova
Machine Learning Engineer
vector_store = Chroma.from_documents( documents=chunks, embedding=embedding_model )
retriever = vector_store.as_retriever( search_type="similarity", search_kwargs={"k": 2} )
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_template(""" Use the following pieces of context to answer the question at the end. If you don't know the answer, say that you don't know. Context: {context} Question: {question} """)
llm = ChatOpenAI(model="gpt-4o-mini", api_key="...", temperature=0)
from langchain_core.runnables import RunnablePassthrough from langchain_core.output_parsers import StrOutputParser
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
result = chain.invoke({"question": "What are the key findings or results presented in the paper?"})
print(result)
- Top Performance: RAG models set new records on open-domain question answering tasks...
- Better Generation: RAG models produce more specific, diverse, and factual language...
- Dynamic Knowledge Use: The non-parametric memory allows RAG models to access and ...
Retrieval Augmented Generation (RAG) with LangChain