Adaptive RAG 🤹‍♂️

Adaptive RAG introduces a RAG technique that combines query analysis with self-corrective RAG.

For Query Analysis, it uses a small classifier(LLM), to decide the query’s complexity. Query Analysis guides adjustment between different retrieval strategies: No retrieval, Single-shot RAG or Iterative RAG.

Official Paper

Adaptive-RAG: Source

Official Implementation

Here’s a code snippet for query analysis:

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI

class RouteQuery(BaseModel):
    """Route a user query to the most relevant datasource."""

    datasource: Literal["vectorstore", "web_search"] = Field(
        ...,
        description="Given a user question choose to route it to web search or a vectorstore.",
    )


# LLM with function call
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm_router = llm.with_structured_output(RouteQuery)

The following example defines and queries a retriever:

# add documents in LanceDB
vectorstore = LanceDB.from_documents(
    documents=doc_splits,
    embedding=OpenAIEmbeddings(),
)
retriever = vectorstore.as_retriever()

# query using defined retriever
question = "How adaptive RAG works"
docs = retriever.get_relevant_documents(question)