0Pricing
Vector Databases: Pinecone, Weaviate & pgvector · レッスン

多段階RAGパイプライン

複数回の検索と生成を行い、ニュアンスに富んだ応答を生成する複雑なRAGワークフローを設計・実装します。

「多段階RAGパイプライン」はCoddyKit上の無料Vector Databases: Pinecone, Weaviate & pgvectorレッスンです。 これはレッスン2/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはVector Databases: Pinecone, Weaviate & pgvector学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Vector Databases: Pinecone, Weaviate & pgvectorコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

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:

  1. Hop 1: Retrieve information about the "Eiffel Tower" to identify its location (Paris, France).
  2. Hop 2: Use "France" as a new query to retrieve information about its capital (Paris).
  3. 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.

よくある質問

「多段階RAGパイプライン」レッスンは無料ですか？

はい。「多段階RAGパイプライン」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Vector Databases: Pinecone, Weaviate & pgvectorコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Vector Databases: Pinecone, Weaviate & pgvectorコースには全4レッスンが含まれています。

「多段階RAGパイプライン」で何を学びますか？

複数回の検索と生成を行い、ニュアンスに富んだ応答を生成する複雑なRAGワークフローを設計・実装します。 ブラウザで直接実行するハンズオンコードでVector Databases: Pinecone, Weaviate & pgvectorを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

Vector Databases: Pinecone, Weaviate & pgvectorを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのVector Databases: Pinecone, Weaviate & pgvectorは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン2/4です。

「多段階RAGパイプライン」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このVector Databases: Pinecone, Weaviate & pgvectorレッスンでコードを書いて実行できますか？

はい。すべてのVector Databases: Pinecone, Weaviate & pgvectorレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. クエリ変換の技術
  2. 多段階RAGパイプライン
  3. RAGシステムの性能評価
  4. 取得結果を再ランキングする
← Vector Databases: Pinecone, Weaviate & pgvectorに戻る