What Is Pre-Retrieval Query Optimization? 🚀

Pre-retrieval query optimization is like training your research assistant before sending them into a library 📚. It ensures they know exactly what to look for to retrieve the most relevant data—no wasted time, no irrelevant books!

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Why Does It Matter? 🤔

1. Improved Precision 🎯

   • Focuses the search to retrieve relevant documents only.

   • Example: Instead of searching for “machine learning,” we refine the query to “applications of machine learning in healthcare.”

2. Enhanced Relevance 🔍

   • Handles multi-intent queries by splitting questions into sub-queries.

   • Example:
     Query: "Explain AI and its impact on climate change."

     • Split Queries:

       • “What is AI?”

       • “How does AI impact climate change?”

3. Reduces Ambiguity 🧩

   • Fixes vague questions using techniques like:

     • Step-Back Prompting: Clarifies intent with follow-up prompts.

     • HyDE (Hypothetical Document Embeddings): Predicts what the ideal document should look like.

   • Example: Query “growth” → Clarify whether it's economic growth or population growth.

4. Leverages External Knowledge 🌎

   • Routes queries to domain-specific databases or external APIs based on context.

   • Example: If the query involves medical data, it routes to PubMed instead of general sources.

5. Foundation for Accurate Outputs 📑

   • Prepares the LLM with clean, focused data, avoiding hallucinations or irrelevant results.

   • Example: Query: “Effects of meditation on stress levels.”

     • Finds research-backed evidence rather than blog opinions.

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Techniques for Optimization 🛠️

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Real-World Use Case 🌍

Scenario: Financial Data Analysis 📈

Query: “How did Tesla’s stock price react to Elon Musk’s announcements?”

Optimization Steps:

1. Multi-Query Retrieval → Break into:

   • “Tesla stock price history.”

   • “Timeline of Elon Musk’s announcements.”

2. Semantic Routing → Route stock prices to Yahoo Finance API and announcements to News APIs.

3. HyDE → Generate hypothetical documents about price trends after major news to refine matching.

4. Final Output → Retrieves charts + commentary + summaries.

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Why Is It Better? 🚀

Without optimization: Scattered results, irrelevant articles, and hallucinated answers.
With optimization: Laser-focused outputs, clear context, and factually accurate insights.

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Multi-Query Techniques for Complex Information Retrieval 🔍

What Is Multi-Query?

Multi-query is a query expansion technique in RAG systems that creates multiple reformulations of the user’s question to enhance document retrieval. This improves relevance by considering different perspectives of the query.

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Why Use Multi-Query? 🤖

1. Better Coverage of Information 🛠️

   • Captures synonyms, variations, and related concepts of the query.

   • Example:

     • Original query: “What is LangSmith?”

     • Expanded queries:

       • “Define LangSmith.”

       • “Purpose of LangSmith.”

       • “Use cases of LangSmith.”

2. Improves Retrieval Accuracy 🎯

   • Reduces missed results due to poor keyword matching.

   • Broader coverage ensures no relevant documents are left out.

3. Ideal for Ambiguous Queries 🌐

   • Handles multi-intent queries by splitting them into sub-questions.

   • Example:

     • Query: “Explain AI and its impact on climate change.”

     • Split into:

       • “What is AI?”

       • “How does AI affect the environment?”

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Step-by-Step Implementation 🚀

1. Import Necessary Modules

from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.prompts import ChatPromptTemplate
from langchain.output_parsers import StrOutputParser
from langchain.chains import LLMChain
from langchain.llms import OpenAI

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2. Set Up API Keys 🔑

import os
os.environ["LANGCHAIN_API_KEY"] = "your_langchain_api_key"
os.environ["OPENAI_API_KEY"] = "your_openai_api_key"

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3. Prepare and Split Data 📄

from langchain.document_loaders import TextLoader

# Load and split documents
loader = TextLoader("example.txt")
documents = loader.load()

splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)
chunks = splitter.split_documents(documents)

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4. Index Documents with Vector Store 📚

from langchain.embeddings.openai import OpenAIEmbeddings

# Generate embeddings and store in Chroma
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)
retriever = vectorstore.as_retriever()

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5. Generate Multi-Query Variations 🧠

from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Multi-query prompt template
template = """
Generate three variations of the following query to capture different aspects:
Query: {query}

Variations:
"""
prompt = ChatPromptTemplate.from_template(template)

# LLM Chain for generating query variations
llm = OpenAI(temperature=0)  # Lower temperature ensures predictable output
query_chain = LLMChain(llm=llm, prompt=prompt)

# Generate variations
query = "What is LangSmith and why do we need it?"
variations = query_chain.run({"query": query})
print(variations)

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6. Retrieve Documents Using Multi-Query 🔄

unique_docs = set()  # Store unique results

for variation in variations.splitlines():
    docs = retriever.get_relevant_documents(variation)  # Fetch relevant docs
    unique_docs.update(docs)  # Add to the unique set

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7. Run the RAG Model 📝

from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain

# Prompt template for final answer generation
rag_prompt = """
Use the context below to answer the question. Be concise and limit to 3 sentences:
Context: {context}

Question: {question}

Answer:
"""
prompt = ChatPromptTemplate.from_template(rag_prompt)

# Final RAG Chain
llm = OpenAI(temperature=0)
rag_chain = LLMChain(llm=llm, prompt=prompt)

# Combine retrieved context
context = "\n".join([doc.page_content for doc in unique_docs])

# Generate final answer
response = rag_chain.run({"context": context, "question": query})
print(response)

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Key Features of Multi-Query Implementation

1. Query Diversification 🌟

   • Automatically generates multiple reformulations of the query.

2. Parallel Retrieval ⚡

   • Runs parallel searches to fetch broader document sets.

3. Duplicate Removal 🧹

   • Ensures retrieved results are unique before generating the response.

4. Dynamic Query Expansion 🧠

   • Captures synonyms, related terms, and perspectives automatically.

5. Configurable LLM Chains 🔗

   • Uses ChatOpenAI with templates for customization.

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Sample Output 📝

Input Query:
“What is LangSmith and why do we need it?”

Generated Queries:

1. “What does LangSmith do?”

2. “Explain the features of LangSmith.”

3. “Why is LangSmith useful in RAG systems?”

Final Answer:
LangSmith is a tool for building and optimizing RAG pipelines. It simplifies query management, improves search accuracy, and supports scalable document retrieval. Thanks for asking! ✅

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Pro Tips 🌟

• Test with Ambiguous Queries: Experiment with multi-intent questions for better outputs.

• Scale Vector Stores: Replace Chroma with Qdrant for high-performance storage if needed.

• Monitor Query Performance: Use tools like LangSmith to analyze how queries are processed.

• Fine-Tune Prompt Templates: Adjust prompts to fit domain-specific requirements.

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