LangChain / RAG / Vector DBs · درس

استراتيجيات الاسترجاع متعدد الاستعلامات

حسّن استدعاء الاسترجاع عبر إنشاء وجهات نظر متعددة لاستعلام المستخدم ودمج نتائج عمليات بحث متنوعة.

استراتيجيات الاسترجاع متعدد الاستعلامات درس مجاني في LangChain / RAG / Vector DBs على CoddyKit. هذا هو الدرس 1 من أصل 4. يمكنك قراءة الدرس كاملاً أدناه مجاناً — ثم تمرن عليه مباشرة في المتصفح باستخدام محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7. هذا الدرس جزء من مسار التعلم في LangChain / RAG / Vector DBs، وتقدمك يتزامن عبر الويب وتطبيق CoddyKit. تتضمن دورة LangChain / RAG / Vector DBs 4 دروس في المجموع.

بعض أجزاء هذا الدرس لم تُترجم بعد وتظهر باللغة الإنجليزية.

Boosting RAG with Multi-Query

Sometimes, a single search query isn't enough to find all the relevant information. Multi-query retrieval is a technique that helps your RAG system cast a wider net.

It generates several different versions of your original question. This helps ensure you don't miss important context, leading to more comprehensive answers.

Why One Query Isn't Enough

Imagine asking "What are the benefits of RAG?". A single search might only pick up documents directly matching those exact words. This can limit the context available to your LLM.

It might miss documents using terms like "advantages of RAG" or "why use RAG".
It could also miss related concepts that provide crucial background.

This limitation can lead to incomplete or less accurate answers from the LLM.

Expanding Your Search Horizon

Before advanced tools, people would manually brainstorm related queries to ensure a broader search. For example:

Original: "How does RAG improve LLM accuracy?"
Expanded: "What are RAG benefits for LLMs?", "RAG's impact on factual correctness", "How RAG reduces hallucinations".

While effective, this manual process is tedious and hard to scale for complex systems. We need an automated solution!

LLMs as Query Generators

Large Language Models (LLMs) are excellent at understanding context and generating variations of text. We can leverage an LLM to automatically create several alternative questions from an initial user query.

This uses the LLM's natural language understanding abilities.
It automates the query expansion process efficiently.

These diverse queries then provide multiple 'angles' for searching your knowledge base.

LangChain's MultiQueryRetriever

LangChain provides a powerful component called MultiQueryRetriever. This tool automates the entire multi-query process for you, making it easy to integrate into your RAG pipeline.

It uses an LLM to generate diverse queries from your initial input.
It then runs these multiple queries against your vector store.
Finally, it combines the results into a single, comprehensive set of documents for the LLM.

MultiQueryRetriever in Action

Let's see how to set up MultiQueryRetriever in Python. You'll need an LLM and an existing retriever (e.g., from a vector store).

This example mocks a vector store for demonstration. In a real application, your vector store would be pre-populated with your documents.

from langchain_community.chat_models import ChatOpenAI
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.documents import Document

# This is a mock setup for demonstration
# In a real app, your vector store would be populated
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(
    [
        Document(page_content="RAG improves LLM factual accuracy."),
        Document(page_content="Retrieval Augmented Generation reduces hallucinations."),
        Document(page_content="The advantages of RAG include up-to-date information."),
        Document(page_content="RAG systems combine retrieval with generation."),
    ], embeddings
)
retriever = vectorstore.as_retriever()

# Initialize a chat model (replace with your actual LLM)
# For local testing, consider using a local LLM or mock
llm = ChatOpenAI(temperature=0) # Placeholder

# Create the MultiQueryRetriever
multi_query_retriever = MultiQueryRetriever.from_llm(
    retriever=retriever, llm=llm
)

print("MultiQueryRetriever initialized successfully!")
# Example usage would follow: multi_query_retriever.get_relevant_documents(query)

The Multi-Query Workflow

Here's a simplified breakdown of what MultiQueryRetriever does behind the scenes:

Initial Query: You provide one query, e.g., "What are RAG's advantages?"
Query Generation: The LLM takes your query and generates 2-4 alternative queries (e.g., "Benefits of RAG", "Why use RAG?", "How RAG improves LLMs").
Parallel Retrieval: Each generated query is sent to your underlying retriever (e.g., vector store) simultaneously.
Result Combination: All retrieved documents from these multiple searches are collected and often de-duplicated.
Final Context: This combined set of documents is then passed to your main LLM for generating the final answer.

Merging Retrieved Documents

After multiple queries fetch documents, how do we best combine them to form the final context?

Unique Documents: The simplest approach is to gather all documents and remove duplicates. This ensures variety without redundancy.
Re-ranking: For more advanced control, you can apply a re-ranking model to the combined set. This model scores documents based on their relevance to the original query, ensuring the most important ones are prioritized (a topic for a future lesson!).

Why Use Multi-Query Retrieval?

Implementing multi-query strategies offers significant benefits for your RAG system:

Improved Recall: You're more likely to find all relevant pieces of information, even if they use different phrasing.
Richer Context: The LLM receives a broader and more diverse set of documents, leading to more comprehensive and accurate answers.
Reduced Hallucinations: With better context, the LLM is less likely to "make things up" due to lack of information.
Handles Ambiguity: Helps when the user's initial query is slightly ambiguous or could have multiple interpretations.

Multi-Query Check

Multi-query retrieval aims to improve retrieval by generating multiple perspectives of a user's query. Which of the following best describes the core problem it solves?

Multi-Query Recap

In this lesson, we explored Multi-Query Retrieval. We learned that a single query can often miss valuable context, and how Large Language Models (LLMs) can generate multiple, diverse queries to overcome this.

LangChain's MultiQueryRetriever automates this entire process, significantly improving the recall and richness of the context provided to your RAG system. This ultimately leads to more comprehensive and accurate answers.

الأسئلة الشائعة

هل درس «استراتيجيات الاسترجاع متعدد الاستعلامات» مجاني؟

نعم — نص درس «استراتيجيات الاسترجاع متعدد الاستعلامات» كامل متاح مجاناً هنا على الويب. لتمرينه بشكل تفاعلي (محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7) وفتح باقي دورة LangChain / RAG / Vector DBs، انتقل إلى CoddyKit PRO. تتضمن دورة LangChain / RAG / Vector DBs 4 دروس في المجموع.

ماذا ستتعلم في «استراتيجيات الاسترجاع متعدد الاستعلامات»؟

حسّن استدعاء الاسترجاع عبر إنشاء وجهات نظر متعددة لاستعلام المستخدم ودمج نتائج عمليات بحث متنوعة. تتمرن على LangChain / RAG / Vector DBs مع أكواد عملية تشغلها مباشرة في المتصفح، ومدرس ذكاء اصطناعي متاح 24/7 يجيب على أسئلتك أثناء عملك.

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معظم دروس CoddyKit تستغرق حوالي 5–10 دقائق. كل منها موجز وتفاعلي، لذا تحرز تقدماً مستمراً وتستأنف من حيث توقفت عبر الويب والتطبيق.

هل يمكنني كتابة وتشغيل أكواد في درس LangChain / RAG / Vector DBs هذا؟

نعم. كل درس في LangChain / RAG / Vector DBs يتضمن محرر أكواد مدمج، لذا تكتب وتشغل أكواداً حقيقية مباشرة في متصفحك وتحصل على تعليقات فورية من الذكاء الاصطناعي — بدون إعداد محلي.

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