多阶段 RAG 流水线
设计并实现包含多次检索和生成步骤的复杂 RAG 工作流，以生成更细致的响应。
多阶段 RAG 流水线 是 CoddyKit 上的免费 Vector Databases: Pinecone, Weaviate & pgvector 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Vector Databases: Pinecone, Weaviate & pgvector 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Vector Databases: Pinecone, Weaviate & pgvector 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
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.
常见问题解答
「多阶段 RAG 流水线」课时是免费的吗？
是的 — 「多阶段 RAG 流水线」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Vector Databases: Pinecone, Weaviate & pgvector 课程的其余内容，请升级到 CoddyKit PRO。 Vector Databases: Pinecone, Weaviate & pgvector 课程共包含 4 节课。
「多阶段 RAG 流水线」这节课中我会学到什么？
设计并实现包含多次检索和生成步骤的复杂 RAG 工作流，以生成更细致的响应。 你通过在浏览器中直接运行的动手代码来练习 Vector Databases: Pinecone, Weaviate & pgvector，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Vector Databases: Pinecone, Weaviate & pgvector 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Vector Databases: Pinecone, Weaviate & pgvector 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「多阶段 RAG 流水线」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Vector Databases: Pinecone, Weaviate & pgvector 课中编写并运行代码吗？
能。每节 Vector Databases: Pinecone, Weaviate & pgvector 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。
此课程中的所有课时
- 查询转换技术
- 多阶段 RAG 流水线
- 评估 RAG 系统性能
- 对检索结果重新排序