Pipeline RAG multi-stage
Progetti e implementi flussi di lavoro RAG complessi, con più fasi di retrieval e generazione, per risposte più articolate.
Pipeline RAG multi-stage è una lezione Vector Databases: Pinecone, Weaviate & pgvector gratuita su CoddyKit. Questa è la lezione 2 di 4. Puoi leggere la lezione completa qui gratuitamente — poi esercitati direttamente nel browser con un editor di codice integrato e un tutor IA disponibile 24/7. Fa parte del percorso di apprendimento Vector Databases: Pinecone, Weaviate & pgvector, e i tuoi progressi si sincronizzano tra il web e l'app CoddyKit. Il corso Vector Databases: Pinecone, Weaviate & pgvector include 4 lezioni in totale.
Parti di questa lezione non sono ancora state tradotte e vengono mostrate in inglese.
What is Multi-Stage RAG?
Traditional RAG (Retrieval Augmented Generation) works well for straightforward questions. However, for complex or ambiguous queries, a single retrieval and generation step can often fall short.
Multi-stage RAG pipelines address this by breaking down the problem into several sequential steps, refining the search and generation process at each stage to produce more accurate and nuanced answers.
Why Go Multi-Stage?
A basic RAG setup might struggle with:
- Multi-part questions: "Who founded Apple and when was their first product released?"
- Ambiguous queries: Needing iterative clarification.
- Deep contextual understanding: Requiring information from disparate sources or multiple 'hops' in knowledge.
Multi-stage RAG enhances the system's ability to handle these challenges by processing information more thoroughly.
The Core Idea: Iterative Refinement
The essence of a multi-stage RAG pipeline is iterative refinement. Instead of one pass, the system performs multiple passes, where:
- Early stages generate intermediate results or refined queries.
- Later stages use these intermediate outputs to perform more targeted retrieval or generation.
Each step builds upon the previous one, leading to a more precise and comprehensive final answer.
Step 1: Query Decomposition
For intricate user questions, the first step often involves query decomposition. This means breaking down a complex query into a set of simpler, more focused sub-questions.
- Example: "Tell me about the founder of Python and when was it first released?"
- Decomposed: "Who founded Python?", "When was Python first released?"
Each sub-question can then be processed individually for more effective retrieval.
Step 2: Initial Context Retrieval
Once you have decomposed the original query into sub-questions, the next step is to perform an initial retrieval for each of these sub-queries.
This involves querying your vector database (or other knowledge sources) with each sub-question to gather a broad set of potentially relevant documents or text chunks. The goal is to collect all initial pieces of the puzzle.
Step 3: Intermediate Generation & Refinement
With the initial context retrieved, an LLM (Large Language Model) can be used to process this information. This intermediate step can involve:
- Generating intermediate answers: Providing partial answers to sub-questions.
- Formulating follow-up questions: Using initial context to generate new, more specific queries for a second retrieval pass.
- Summarizing initial findings: Condensing retrieved information to guide subsequent steps.
This feedback loop helps in refining the search.
Step 4: Re-ranking & Aggregation
After potentially multiple retrieval passes and intermediate generations, you'll have various pieces of context and potentially partial answers. The final stages involve:
- Re-ranking: Using a more powerful model or a different relevance score to select the most pertinent chunks from all retrieved documents.
- Aggregation: Combining all relevant information and intermediate answers to synthesize a single, comprehensive, and coherent final response to the original user query.
Multi-Hop Q&A Example
Consider a 'multi-hop' question: "What is the capital of the country where the Eiffel Tower is located?"
A multi-stage pipeline could:
- Hop 1: Retrieve information about the "Eiffel Tower" to identify its location (Paris, France).
- Hop 2: Use "France" as a new query to retrieve information about its capital (Paris).
- Final Answer: Combine to answer "Paris".
This chaining of retrieval steps is a powerful application of multi-stage RAG.
Python Workflow Illustration
This conceptual Python code illustrates the high-level orchestration of a multi-stage RAG pipeline. It focuses on the flow rather than specific external API calls.
class MultiStageRAG:
def __init__(self, retriever, llm_model):
self.retriever = retriever
self.llm = llm_model
def run_pipeline(self, user_query):
# Stage 1: Decompose query into sub-questions
sub_queries = self.llm.decompose_query(user_query)
print(f"Decomposed queries: {sub_queries}")
all_retrieved_docs = []
intermediate_answers = []
for sq in sub_queries:
# Stage 2: Initial Retrieval for each sub-query
docs = self.retriever.retrieve(sq)
all_retrieved_docs.extend(docs)
print(f"Retrieved for '{sq}': {len(docs)} docs")
# Stage 3: Intermediate Generation (e.g., summarizing, refining)
intermediate_ans = self.llm.generate_answer(sq, docs)
intermediate_answers.append(intermediate_ans)
# Stage 4: Re-rank all retrieved context and aggregate
final_context = self.retriever.re_rank(all_retrieved_docs)
print(f"Final context length: {len(final_context)}")
final_answer = self.llm.generate_final_answer(user_query, final_context)
return final_answer
# --- Mock Implementations for Demonstration ---
class MockRetriever:
def retrieve(self, query):
# Simulate retrieving documents based on query
return [f"Doc for '{query}' part A", f"Doc for '{query}' part B"]
def re_rank(self, docs):
# Simulate re-ranking, just returns the first few for simplicity
return docs[:3]
class MockLLM:
def decompose_query(self, query):
# Simple decomposition for example
if " and " in query:
parts = query.split(" and ")
return [p.strip() + "?" for p in parts]
return [query + "?"]
def generate_answer(self, query, docs):
# Simulate generating an intermediate answer
return f"Intermediate answer for '{query}' based on {len(docs)} docs."
def generate_final_answer(self, original_query, context):
# Simulate generating a final answer
return f"Final answer to '{original_query}' based on context: {context}."
# --- Main Execution ---
if __name__ == "__main__":
mock_retriever = MockRetriever()
mock_llm = MockLLM()
pipeline = MultiStageRAG(mock_retriever, mock_llm)
query = "What is the capital of France and who painted the Mona Lisa?"
result = pipeline.run_pipeline(query)
print(f"\nResult: {result}")
Multi-Stage Benefits
Which of the following is a primary benefit of using a multi-stage RAG pipeline compared to a single-pass RAG?
Recap: Mastering Complex RAG
We've explored multi-stage RAG pipelines, understanding how they tackle complex queries through iterative steps:
- Query Decomposition: Breaking down complex questions into simpler sub-queries.
- Iterative Retrieval: Performing multiple passes to gather and refine context.
- LLM Refinement: Using LLMs to generate intermediate answers or guide subsequent search steps.
- Aggregation: Combining all insights for a comprehensive and coherent final answer.
By orchestrating these steps, you can build RAG systems capable of delivering much more precise and thorough responses to even the most challenging user prompts.
Domande Frequenti
La lezione «Pipeline RAG multi-stage» è gratuita?
Sì — il testo completo di «Pipeline RAG multi-stage» è gratuito qui sul web. Per esercitarvi in modo interattivo (un editor di codice integrato e un tutor IA 24/7) e sbloccare il resto del corso Vector Databases: Pinecone, Weaviate & pgvector, passa a CoddyKit PRO. Il corso Vector Databases: Pinecone, Weaviate & pgvector include 4 lezioni in totale.
Cosa imparerò in «Pipeline RAG multi-stage»?
Progetti e implementi flussi di lavoro RAG complessi, con più fasi di retrieval e generazione, per risposte più articolate. Eserciti Vector Databases: Pinecone, Weaviate & pgvector con codice pratico che esegui direttamente nel browser, e un tutor IA 24/7 risponde alle tue domande mentre lavori sulla lezione.
Ho bisogno di esperienza per iniziare Vector Databases: Pinecone, Weaviate & pgvector?
Non è richiesta alcuna esperienza precedente. Vector Databases: Pinecone, Weaviate & pgvector su CoddyKit è strutturato per principianti e studenti avanzati, quindi puoi iniziare da qui o dall'inizio e procedere al tuo ritmo. Questa è la lezione 2 di 4.
Quanto tempo richiede la lezione «Pipeline RAG multi-stage»?
La maggior parte delle lezioni CoddyKit richiede circa 5–10 minuti. Ogni lezione è breve e interattiva, quindi fai progressi costanti e riprendi esattamente da dove hai lasciato su web e app.
Posso scrivere ed eseguire codice in questa lezione Vector Databases: Pinecone, Weaviate & pgvector?
Sì. Ogni lezione Vector Databases: Pinecone, Weaviate & pgvector include un editor di codice integrato, quindi scrivi ed esegui codice reale direttamente nel tuo browser e ricevi feedback istantaneo dall'IA — nessuna configurazione locale necessaria.
Tutte le lezioni di questo corso
- Tecniche di trasformazione delle query
- Pipeline RAG multi-stage
- Valutare le prestazioni di un sistema RAG
- Riordinare i risultati recuperati