Optimize Thai RAG System: Performance & Accuracy Guide

Hey guys! Ever wondered how to make your Thai Retrieval-Augmented Generation (RAG) system faster and more accurate? Well, you're in the right place! This article dives deep into a comprehensive plan to optimize Thai Government Document RAG systems. We're talking serious performance boosts and accuracy enhancements, all while keeping the system rock-solid reliable. So, let's get started on this journey to supercharge your RAG system!

Understanding the Optimization Plan

Before we dive into the nitty-gritty, let's get the big picture. This is a comprehensive optimization plan designed specifically for Thai Government Document RAG systems. Our main goals? To significantly improve both performance and accuracy. But hey, we're not just about speed; we want to make sure the system remains reliable and trustworthy. This plan is broken down into phases, each tackling different aspects of the system. From retrieval inefficiencies to LLM bottlenecks and even state management, we're leaving no stone unturned. Think of it as a holistic health check for your RAG system, ensuring it's in tip-top shape!

Identifying Current Performance Issues

Alright, before we can fix anything, we need to know what's broken, right? Our current system has a few hiccups we need to address. We've pinpointed some key performance issues that are holding us back. These fall into a few main categories:

1. Retrieval System Inefficiencies: Our system is pulling a lot of documents before reranking – we're talking up to 100! Plus, it's doing this sequentially, which is like waiting in line at the DMV. We're also using fixed weights for different retrieval methods, and there's no caching, so we're doing the same work over and over again. Imagine running a marathon, but you keep retracing your steps – not very efficient, huh?

2. LLM Usage Bottlenecks: We're converting data to and from JSON for every single call to the Large Language Model (LLM), which is a real time-suck. We're also making multiple sequential calls and using the same model for every task. And to top it off, we're not batching requests, so each operation is handled individually. It's like sending snail mail when you could be sending an email – way slower!

3. Memory and State Management: Our system is carrying around massive state objects with tons of fields. There's redundant data all over the place, and we're passing the full state through every node. It's like trying to run a marathon with a backpack full of bricks – heavy and unnecessary.

Now that we've diagnosed the problems, let's get to the solutions! We're going to break this down into phases, starting with some quick wins.

Phase 1: Retrieval System Optimization (Quick Wins)

Let's kick things off with some easy wins, guys! We're focusing on the retrieval system first because it's a crucial piece of the puzzle. Think of it as the foundation of our RAG system – if the foundation is shaky, the whole thing suffers. We're going to implement some key changes here that should give us a noticeable performance boost right away.

1.1 Adaptive Retrieval Parameters

One of the first things we'll tackle is adaptive retrieval parameters. Right now, we're using fixed values for K (the number of documents to retrieve), but that's not very smart. Some queries are simple, while others are complex, and we need to adjust accordingly. We'll introduce a system that dynamically adjusts the number of documents retrieved based on the complexity of the query. This is like having a smart gearshift in your car – it adjusts to the terrain and helps you go faster.

We're going to modify config/config.py and corrective_rag_web_search.py:196-213 to make this happen. Here's a sneak peek at the code:

# New adaptive configuration
DENSE_RETRIEVAL_K_MIN = 20
DENSE_RETRIEVAL_K_MAX = 50
BM25_RETRIEVAL_K_MIN = 20
BM25_RETRIEVAL_K_MAX = 50

# Query complexity-based adjustment
def get_adaptive_k_values(query_complexity: str):
    if query_complexity == "simple":
        return 20, 20
    elif query_complexity == "medium":
        return 30, 30
    else:  # complex
        return 50, 50

This code snippet shows how we'll set minimum and maximum values for K and then adjust those values based on the complexity of the query. Simple queries get lower K values, while complex queries get higher ones. It's all about being efficient and getting the right amount of information for the job.

1.2 Parallel Retrieval Execution

Next up, we're going to parallelize the retrieval execution. Currently, our system processes dense and BM25 retrieval sequentially, which means one has to finish before the other can start. That's like having a one-lane highway during rush hour – a recipe for traffic jams! We'll implement parallel processing, allowing both retrieval methods to run simultaneously. This is a huge time-saver and a key step in speeding things up. Think of it as adding more lanes to the highway – more traffic can flow at once.

We'll be working in corrective_rag_web_search.py:321-393 to make this happen. Here's a glimpse of the code:

import asyncio
from concurrent.futures import ThreadPoolExecutor

async def parallel_retrieve(state: GraphState) -> GraphState:
    """Execute dense and BM25 retrieval in parallel"""
    query = state.get("optimized_query") or state["question"]
    
    with ThreadPoolExecutor(max_workers=2) as executor:
        dense_future = executor.submit(dense_retriever.invoke, query)
        bm25_future = executor.submit(bm25_retriever.invoke, query)
        
        dense_docs = dense_future.result()
        bm25_docs = bm25_future.result()
    
    # Combine and rerank
    ensemble_docs = dense_docs + bm25_docs
    # ... rest of reranking logic

This code uses Python's ThreadPoolExecutor to run the dense and BM25 retrieval processes in parallel. It's like having two engines powering your car instead of one – double the power, double the speed!

1.3 Embedding Caching Layer

Last but not least in Phase 1, we're adding an embedding caching layer. Right now, we're recalculating embeddings for the same queries over and over again. That's like doing the same math problem every time you see it – a total waste of energy! We'll implement a caching mechanism to store embeddings, so we can reuse them when needed. This is a major efficiency boost and will significantly reduce processing time. Think of it as creating a cheat sheet for frequently used formulas – saves you from having to recalculate them every time.

We'll create a new file, cache.py, and modify corrective_rag_web_search.py:192-196. Here's a sneak peek:

# New cache.py
from functools import lru_cache
import hashlib
import pickle
import os

class EmbeddingCache:
    def __init__(self, cache_dir="./embeddings_cache"):
        self.cache_dir = cache_dir
        os.makedirs(cache_dir, exist_ok=True)
    
    def get_query_hash(self, query: str) -> str:
        return hashlib.md5(query.encode()).hexdigest()
    
    def get_cached_retrieval(self, query: str) -> Optional[List[Document]]:
        cache_file = os.path.join(self.cache_dir, f"{self.get_query_hash(query)}.pkl")
        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as f:
                return pickle.load(f)
        return None
    
    def cache_retrieval(self, query: str, docs: List[Document]):
        cache_file = os.path.join(self.cache_dir, f"{self.get_query_hash(query)}.pkl")
        with open(cache_file, 'wb') as f:
            pickle.dump(docs, f)

This code defines an EmbeddingCache class that uses Python's lru_cache decorator to cache embeddings. It's like having a memory bank for your system – it remembers the answers and doesn't have to recalculate them.

Phase 2: LLM Optimization

Alright, Phase 1 down! Now, let's talk about optimizing our Large Language Model (LLM) usage. Our LLM is the brains of the operation, so making it more efficient is crucial. We're going to tackle some bottlenecks and streamline how we interact with the LLM. Think of this phase as giving our LLM a tune-up and making it run like a well-oiled machine.

2.1 Structured Output Optimization

First up, we're tackling structured output optimization. Right now, we're converting data to and from JSON for every LLM call. This is like translating a document back and forth between two languages – it takes time and effort. We'll replace this JSON parsing with Pydantic models, which allow us to work with structured data more efficiently. It's like speaking the same language as the LLM – no translation needed!

We'll be working in corrective_rag_web_search.py:251-297. Here's a sneak peek:

# Replace JSON parsing with Pydantic models for DeepSeek
try:
    # Try native structured output first
    optimization_chain = prompt | llm_small.with_structured_output(OptimizedQuery)
    result = optimization_chain.invoke({"question": question})
except Exception:
    # Fallback to JSON mode
    optimization_chain = prompt | llm_small | StrOutputParser()
    result_str = optimization_chain.invoke({"question": question})
    result = OptimizedQuery.parse_raw(result_str)

This code attempts to use Pydantic models for structured output. If that fails, it falls back to JSON mode. It's like having a backup plan – if the main route is blocked, you've got a detour ready to go.

2.2 Specialized Model Assignment

Next, we're implementing specialized model assignment. Currently, we're using the same LLM for every task. That's like using a Swiss Army knife for everything – it works, but it's not always the best tool for the job. We'll assign specific models to different tasks based on their strengths. For example, we might use a fast model for query optimization and a more powerful model for document generation. It's like having a toolbox full of specialized tools – you can choose the perfect one for each task.

We'll be modifying config/config.py:17-21. Here's a glimpse:

# Specialized models for different tasks
QUERY_OPTIMIZATION_MODEL = "deepseek-chat"  # Fast, accurate
DOCUMENT_GRADING_MODEL = "deepseek-chat"    # Consistent grading
GENERATION_MODEL = "deepseek-chat"         # High-quality output
REFLECTION_MODEL = "deepseek-chat"         # Quality assessment

# Model-specific configurations
MODEL_CONFIGS = {
    "query_optimization": {"temperature": 0.1, "max_tokens": 200},
    "document_grading": {"temperature": 0, "max_tokens": 150},
    "generation": {"temperature": 0, "max_tokens": 2000},
    "reflection": {"temperature": 0.1, "max_tokens": 300}
}

This code configures different models for query optimization, document grading, generation, and reflection. It's like having a team of experts, each specializing in a different area.

2.3 Request Batching

Finally, we're implementing request batching. Right now, we're making individual API calls for each operation. That's like buying groceries one item at a time – super inefficient! We'll batch requests together, so we can send multiple operations in a single API call. This significantly reduces overhead and speeds things up. Think of it as doing one big grocery run instead of multiple small trips – saves time and energy!

We'll be working in corrective_rag_web_search.py:425-468. Here's a sneak peek:

def batch_grade_documents(state: GraphState) -> GraphState:
    """Grade multiple documents in a single API call"""
    documents = state.get("reranked_docs", [])
    question = state.get("optimized_query") or state["question"]
    
    # Prepare batch input
    batch_input = []
    for i, doc in enumerate(documents[:app_config.FINAL_CONTEXT_K]):
        batch_input.append({
            "index": i,
            "content": doc.page_content[:1500],
            "question": question
        })
    
    # Single API call for all documents
    batch_prompt = ChatPromptTemplate.from_messages([
        ("system", app_config.BATCH_GRADING_SYSTEM_PROMPT),
        ("human", "Documents to grade: {batch_input}")
    ])
    
    batch_chain = batch_prompt | llm_small | StrOutputParser()
    result_str = batch_chain.invoke({"batch_input": json.dumps(batch_input)})
    
    # Parse batch results
    results = json.loads(result_str)
    # ... process results

This code batches document grading requests into a single API call. It's like sending one big email instead of a bunch of individual ones – much more efficient!

Phase 3: State Management Optimization

Okay, guys, let's talk state management. In this phase, we're focusing on streamlining how our system handles and stores data throughout the process. Think of it as organizing your workspace – a clean and efficient workspace leads to better productivity. We'll be reducing clutter, optimizing data flow, and making sure everything is running smoothly behind the scenes.

3.1 Streamlined State Schema

First up, we're tackling the state schema. Currently, our state objects are carrying around a lot of fields – many of which are redundant or unnecessary. This is like carrying a backpack full of things you don't need – it just weighs you down. We'll reduce the number of fields in our state schema, keeping only the essentials. This will make our system lighter, faster, and easier to manage.

We'll be working in corrective_rag_web_search.py:121-159. Here's a sneak peek:

class OptimizedGraphState(TypedDict):
    """Reduced state schema for better performance"""
    # Core fields only
    question: str
    optimized_query: Optional[str]
    documents: List[Document]  # Single source of truth
    generation: Optional[str]
    quality_score: Optional[float]
    
    # Control flags (reduced)
    iteration: Annotated[int, operator.add]
    max_iterations: int
    
    # Metadata (compact)
    retrieval_method: Optional[str]
    warnings: Annotated[List[str], operator.add]

This code defines a new OptimizedGraphState TypedDict with a reduced set of fields. It's like Marie Kondo-ing your data – keeping only what sparks joy (or, in this case, is actually necessary).

3.2 Document Management Optimization

Next, we're optimizing document management. Right now, we have multiple document lists floating around (documents, filtered_docs, reranked_docs). This is like having multiple copies of the same file on your computer – confusing and inefficient. We'll centralize document management with a single source of truth and add metadata to track confidence, relevance, and source. It's like having a well-organized filing system – everything has its place and is easy to find.

We'll be working in corrective_rag_web_search.py:364-500. Here's a sneak peek:

class DocumentManager:
    """Centralized document management with metadata"""
    
    def __init__(self):
        self.documents = []
        self.metadata = {}  # confidence, relevance, source
    
    def add_documents(self, docs: List[Document], source: str, confidence: float = None):
        for doc in docs:
            doc_id = len(self.documents)
            self.documents.append(doc)
            self.metadata[doc_id] = {
                "source": source,
                "confidence": confidence,
                "index": doc_id
            }
    
    def get_top_documents(self, k: int, min_confidence: float = 0.0) -> List[Document]:
        """Get top K documents by confidence"""
        filtered = [
            (doc, meta) for doc, meta in zip(self.documents, self.metadata.values())
            if meta["confidence"] and meta["confidence"] >= min_confidence
        ]
        filtered.sort(key=lambda x: x[1]["confidence"], reverse=True)
        return [doc for doc, _ in filtered[:k]]

This code defines a DocumentManager class that centralizes document management and adds metadata. It's like having a librarian for your documents – keeping everything organized and accessible.

Phase 4: Advanced Optimizations

Alright, let's crank things up a notch! In Phase 4, we're diving into some advanced optimizations that will really push our system to the next level. We're talking about techniques that can significantly improve both performance and accuracy. Think of this phase as adding turbo boosters to your system – we're going for maximum performance!

4.1 Query Expansion with Multiple Strategies

First up, we're implementing query expansion with multiple strategies. Right now, we're relying on a single query to retrieve documents. That's like asking a question in only one way – you might miss some important information. We'll expand our query by using multiple strategies, such as government terminology expansion, HyDE (Hypothetical Document), and query decomposition. It's like asking the same question in multiple ways – you're more likely to get a complete answer.

We'll be working in corrective_rag_web_search.py:266-317. Here's a sneak peek:

def advanced_query_optimization(state: GraphState) -> GraphState:
    """Multi-strategy query optimization"""
    question = state["question"]
    
    # Strategy 1: Original query
    queries = [question]
    
    # Strategy 2: Government terminology expansion
    gov_terms = expand_government_terminology(question)
    queries.extend(gov_terms)
    
    # Strategy 3: HyDE (Hypothetical Document)
    hypothetical_doc = generate_hypothetical_document(question)
    queries.append(hypothetical_doc)
    
    # Strategy 4: Query decomposition (for complex queries)
    if is_complex_query(question):
        sub_queries = decompose_query(question)
        queries.extend(sub_queries)
    
    # Execute all queries in parallel and merge results
    return execute_multiple_queries(queries)

This code implements multiple query expansion strategies. It's like casting a wider net – you're more likely to catch the information you need.

4.2 Thai Language Specific Optimizations

Next, we're implementing Thai language-specific optimizations. Our system is designed to work with Thai documents, so we need to make sure it's optimized for the nuances of the Thai language. We'll use a better Thai tokenizer, incorporate Thai stopwords, and expand government terminology using Thai-specific knowledge. It's like tailoring a suit to fit perfectly – it's much more effective than wearing something off the rack.

We'll be working in config/config.py:136-246. Here's a sneak peek:

# Thai-specific optimizations
THAI_TOKENIZER = "deepseek-tokenizer"  # Better Thai tokenization
THAI_STOPWORDS = ["และ", "ของ", "ที่", "ใน", "กับ", "เพื่อ"]  # Common stopwords
THAI_GOVERNMENT_TERMS = {
    "บันทึกข้อความ": ["หนังสือบันทึก", "เอกสารภายใน", "บันทึก"],
    "เชิญประชุม": ["การประชุม", "การนัดหมาย", "ประชุม"],
    "ขออนุมัติ": ["คำขอ", "การอนุมัติ", "เรื่องขออนุมัติ"]
}

This code configures Thai-specific optimizations. It's like speaking the language fluently – you're better able to understand and communicate.

Phase 5: Performance Monitoring

Alright, we've made some serious improvements! But our work isn't done yet. In Phase 5, we're setting up performance monitoring to ensure our system continues to run smoothly and efficiently. Think of this phase as regular check-ups for your system – we want to catch any potential problems before they become major issues.

5.1 Comprehensive Metrics

We'll be tracking comprehensive metrics to get a clear picture of our system's performance. This includes timing metrics (total time, retrieval time, generation time), quality metrics (retrieval precision, generation quality), resource metrics (tokens used, API calls), and cache hit rate. It's like having a dashboard for your system – you can see all the key information at a glance.

We'll create a new file, performance_monitor.py. Here's a sneak peek:

import time
from dataclasses import dataclass
from typing import Dict, List

@dataclass
class PerformanceMetrics:
    query_id: str
    timestamp: float
    
    # Timing metrics
    total_time: float
    retrieval_time: float
    generation_time: float
    reflection_time: float
    
    # Quality metrics
    retrieval_precision: float
    generation_quality: float
    num_iterations: int
    
    # Resource metrics
    tokens_used: int
    api_calls: int
    cache_hits: int

class PerformanceMonitor:
    def __init__(self):
        self.metrics: List[PerformanceMetrics] = []
    
    def track_execution(self, query_id: str, execution_func):
        """Track execution time and performance"""
        start_time = time.time()
        result = execution_func()
        end_time = time.time()
        
        # Record metrics
        metrics = PerformanceMetrics(
            query_id=query_id,
            timestamp=start_time,
            total_time=end_time - start_time,
            # ... other metrics
        )
        self.metrics.append(metrics)
        return result, metrics

This code defines a PerformanceMonitor class that tracks various metrics. It's like having a fitness tracker for your system – you can see how it's performing and identify areas for improvement.

Implementation Priority

To ensure a smooth and efficient rollout, we'll be implementing these phases in a specific order:

Phase 1 (Immediate - Day 1):

1. Implement embedding cache
2. Reduce retrieval K values
3. Add parallel retrieval

Phase 2 (Day 2-3):

1. Optimize structured output
2. Implement batch document grading
3. Add specialized model configurations

Phase 3 (Day 4):

1. Streamline state management
2. Optimize document handling
3. Add performance monitoring

Phase 4 (Day 5-6):

1. Advanced query optimization
2. Thai language enhancements
3. Comprehensive testing

Phase 5 (Ongoing):

1. Performance monitoring
2. A/B testing
3. Continuous optimization

This phased approach allows us to see results quickly and make adjustments as needed. It's like building a house – you start with the foundation and then build up from there.

Expected Performance Improvements

So, what kind of results can we expect from all this hard work? We're anticipating some significant improvements:

• Retrieval speed: 40-60% faster with parallel execution and caching
• Overall latency: 30-50% reduction in total response time
• Accuracy: 10-15% improvement with better query optimization
• Resource usage: 25-40% reduction in API calls and tokens
• Thai language handling: 20% improvement in document relevance

These are some serious gains! It's like turning your system from a bicycle into a motorcycle – much faster and more efficient.

Success Metrics

To measure our success, we'll be tracking the following key metrics:

1. Performance: Average query time < 10 seconds (currently ~15-20 seconds)
2. Accuracy: RAGAS scores > 0.85 across all metrics
3. Efficiency: < 5 API calls per query (currently 6-8 calls)
4. Cache hit rate: > 30% for similar queries
5. User satisfaction: Quality scores > 0.8 on 90% of queries

These metrics will give us a clear picture of how well our optimizations are working. It's like having a report card for your system – you can see where you're excelling and where you need to improve.

Conclusion

So there you have it, guys! A comprehensive plan to optimize your Thai RAG system for performance and accuracy. By implementing these strategies, we can expect significant improvements in speed, efficiency, and the quality of results. It's a journey, but one that's well worth taking to ensure our RAG system is the best it can be. Let's get to work and make this happen!