┌─────────────────────────────────────────────────────────────────┐
│                     TRADITIONAL RAG ❌                           │
│                                                                 │
│  Query ───► Retrieve ───► Generate ───► May Hallucinate ⚠️      │
│                                                                 │
│  (No quality check - bad documents → bad answers)               │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│                     CORRECTIVE RAG ✅                            │
│                                                                 │
│  Query ───► Retrieve ───► Evaluator                             │
│                               │                                 │
│                 ┌─────────────┼─────────────┐                   │
│                 │             │             │                   │
│                 ▼             ▼             ▼                   │
│            [Correct]    [Ambiguous]   [Incorrect]               │
│                 │             │             │                   │
│                 ▼             ▼             ▼                   │
│            Refine       Both Sources   Web Search               │
│            Knowledge         │             │                    │
│                 │             │             │                   │
│                 └─────────────┼─────────────┘                   │
│                               │                                 │
│                               ▼                                 │
│                           Generate                              │
│                               │                                 │
│                               ▼                                 │
│                       Grounded Answer ✓                         │
└─────────────────────────────────────────────────────────────────┘

corrective-rag/
├── config.py                 # Configuration
├── retriever.py             # Document retrieval
├── evaluator.py             # Retrieval quality evaluation
├── refiner.py               # Knowledge refinement
├── web_search.py            # Web search fallback
├── corrective_rag.py        # Main orchestration
├── app.py                   # FastAPI application
└── requirements.txt

# config.py
from pydantic_settings import BaseSettings
from pydantic import Field
from functools import lru_cache
from enum import Enum


class RetrievalAction(str, Enum):
    """Actions based on retrieval evaluation."""
    CORRECT = "correct"       # Retrieval is good - refine and use
    INCORRECT = "incorrect"   # Retrieval is bad - use web search
    AMBIGUOUS = "ambiguous"   # Uncertain - combine both


class Settings(BaseSettings):
    """Application configuration."""

    openai_api_key: str
    searxng_url: str = "http://localhost:8080"  # SearXNG instance URL

    # Model settings
    embedding_model: str = "text-embedding-3-small"
    llm_model: str = "gpt-4o-mini"
    evaluator_model: str = "gpt-4o-mini"

    # Retrieval settings
    retrieval_k: int = 5

    # Evaluation thresholds
    correct_threshold: float = 0.7    # Above = CORRECT
    incorrect_threshold: float = 0.3  # Below = INCORRECT
    # Between = AMBIGUOUS

    # Refinement settings
    max_sentences_per_doc: int = 5

    # Web search settings
    web_search_max_results: int = 3

    # ChromaDB
    chroma_persist_dir: str = "./chroma_db"
    collection_name: str = "corrective_rag_docs"

    class Config:
        env_file = ".env"


@lru_cache
def get_settings() -> Settings:
    return Settings()

# evaluator.py
from openai import OpenAI
from pydantic import BaseModel, Field
from config import get_settings, RetrievalAction


class DocumentRelevance(BaseModel):
    """Relevance assessment for a single document."""
    doc_index: int
    is_relevant: bool
    relevance_score: float = Field(ge=0, le=1)
    key_information: list[str]
    irrelevant_parts: list[str]


class RetrievalEvaluation(BaseModel):
    """Overall evaluation of retrieval quality."""
    action: RetrievalAction
    confidence: float = Field(ge=0, le=1)
    overall_score: float = Field(ge=0, le=1)
    document_evaluations: list[DocumentRelevance]
    reasoning: str


class RetrievalEvaluator:
    """Evaluates retrieval quality and determines corrective action."""

    def __init__(self):
        settings = get_settings()
        self.client = OpenAI(api_key=settings.openai_api_key)
        self.model = settings.evaluator_model
        self.correct_threshold = settings.correct_threshold
        self.incorrect_threshold = settings.incorrect_threshold

    def evaluate(
        self,
        query: str,
        documents: list[dict]
    ) -> RetrievalEvaluation:
        """
        Evaluate retrieval quality and determine action.

        Args:
            query: User query
            documents: List of retrieved documents with 'content' and 'source'

        Returns:
            Evaluation with recommended action
        """
        # Format documents for evaluation
        docs_text = ""
        for i, doc in enumerate(documents):
            docs_text += f"\n[Document {i}]\n{doc['content']}\n"

        system_prompt = """You are a retrieval quality evaluator. Assess whether
the retrieved documents are relevant and sufficient to answer the query.

For each document, determine:
1. Is it relevant to the query? (true/false)
2. Relevance score (0.0-1.0)
3. What key information does it contain for answering the query?
4. What parts are irrelevant or noise?

Then provide an overall assessment:
- overall_score: Average relevance (0.0-1.0)
- action: "correct" (good retrieval), "incorrect" (bad retrieval), or "ambiguous" (mixed)
- confidence: How confident you are in this assessment (0.0-1.0)

Return JSON:
{
    "document_evaluations": [
        {
            "doc_index": 0,
            "is_relevant": true,
            "relevance_score": 0.85,
            "key_information": ["fact1", "fact2"],
            "irrelevant_parts": ["noise1"]
        }
    ],
    "overall_score": 0.75,
    "action": "correct",
    "confidence": 0.8,
    "reasoning": "Why this action was chosen"
}"""

        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": f"Query: {query}\n\nDocuments:{docs_text}"}
            ],
            response_format={"type": "json_object"}
        )

        import json
        result = json.loads(response.choices[0].message.content)

        # Determine action based on thresholds
        overall_score = result.get("overall_score", 0.5)
        if overall_score >= self.correct_threshold:
            action = RetrievalAction.CORRECT
        elif overall_score <= self.incorrect_threshold:
            action = RetrievalAction.INCORRECT
        else:
            action = RetrievalAction.AMBIGUOUS

        return RetrievalEvaluation(
            action=action,
            confidence=result.get("confidence", 0.5),
            overall_score=overall_score,
            document_evaluations=[
                DocumentRelevance(**doc_eval)
                for doc_eval in result.get("document_evaluations", [])
            ],
            reasoning=result.get("reasoning", "")
        )


class LightweightEvaluator:
    """Fast rule-based evaluator for latency-sensitive applications."""

    def __init__(self):
        settings = get_settings()
        self.correct_threshold = settings.correct_threshold
        self.incorrect_threshold = settings.incorrect_threshold

    def evaluate(
        self,
        query: str,
        documents: list[dict],
        distances: list[float]
    ) -> RetrievalEvaluation:
        """
        Evaluate using retrieval distances as proxy for relevance.

        Lower distance = higher relevance in vector search.
        """
        # Convert distances to relevance scores
        relevance_scores = [max(0, 1 - dist) for dist in distances]
        overall_score = sum(relevance_scores) / len(relevance_scores) if relevance_scores else 0

        # Determine action
        if overall_score >= self.correct_threshold:
            action = RetrievalAction.CORRECT
        elif overall_score <= self.incorrect_threshold:
            action = RetrievalAction.INCORRECT
        else:
            action = RetrievalAction.AMBIGUOUS

        # Create document evaluations
        doc_evals = [
            DocumentRelevance(
                doc_index=i,
                is_relevant=score >= 0.5,
                relevance_score=score,
                key_information=[],
                irrelevant_parts=[]
            )
            for i, score in enumerate(relevance_scores)
        ]

        return RetrievalEvaluation(
            action=action,
            confidence=0.6,  # Lower confidence for rule-based
            overall_score=overall_score,
            document_evaluations=doc_evals,
            reasoning=f"Distance-based evaluation: avg score {overall_score:.2f}"
        )

┌─────────────────────────────────────────────────────────────┐
│ Query: "What is machine learning?"                          │
│                                                             │
│ Retrieved Documents:                                        │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Doc 0: "ML enables computers to learn from data..."     │ │
│ │        Score: 0.92 ✓ Highly relevant                    │ │
│ ├─────────────────────────────────────────────────────────┤ │
│ │ Doc 1: "Common algorithms include neural networks..."   │ │
│ │        Score: 0.85 ✓ Relevant                           │ │
│ ├─────────────────────────────────────────────────────────┤ │
│ │ Doc 2: "The weather in Paris is mild..."                │ │
│ │        Score: 0.15 ✗ Irrelevant                         │ │
│ └─────────────────────────────────────────────────────────┘ │
│                                                             │
│ Overall Score: (0.92 + 0.85 + 0.15) / 3 = 0.64              │
│ Action: AMBIGUOUS (between 0.3 and 0.7)                     │
└─────────────────────────────────────────────────────────────┘

# refiner.py
from openai import OpenAI
from pydantic import BaseModel
from config import get_settings
from evaluator import DocumentRelevance


class RefinedKnowledge(BaseModel):
    """Refined knowledge extracted from documents."""
    key_facts: list[str]
    supporting_quotes: list[str]
    source_documents: list[int]
    refinement_ratio: float  # How much was filtered out


class KnowledgeRefiner:
    """
    Refines retrieved documents using decompose-then-recompose.

    This removes irrelevant information while preserving key knowledge.
    """

    def __init__(self):
        settings = get_settings()
        self.client = OpenAI(api_key=settings.openai_api_key)
        self.model = settings.llm_model
        self.max_sentences = settings.max_sentences_per_doc

    def refine(
        self,
        query: str,
        documents: list[dict],
        evaluations: list[DocumentRelevance]
    ) -> RefinedKnowledge:
        """
        Refine documents by extracting only relevant information.

        Steps:
        1. Decompose each document into atomic facts
        2. Filter facts by relevance to query
        3. Recompose into coherent knowledge
        """
        # Step 1 & 2: Decompose and filter
        all_facts = []
        all_quotes = []
        source_docs = []

        for doc, eval_result in zip(documents, evaluations):
            if not eval_result.is_relevant:
                continue

            # Extract facts from this document
            facts = self._extract_facts(query, doc['content'])
            for fact in facts:
                all_facts.append(fact)
                source_docs.append(eval_result.doc_index)

            # Keep key quotes
            if eval_result.key_information:
                all_quotes.extend(eval_result.key_information[:2])

        # Step 3: Recompose - deduplicate and organize
        unique_facts = list(dict.fromkeys(all_facts))  # Preserve order, remove dupes

        # Calculate refinement ratio
        original_length = sum(len(d['content']) for d in documents)
        refined_length = sum(len(f) for f in unique_facts)
        ratio = 1 - (refined_length / original_length) if original_length > 0 else 0

        return RefinedKnowledge(
            key_facts=unique_facts[:10],  # Top 10 facts
            supporting_quotes=all_quotes[:5],
            source_documents=list(set(source_docs)),
            refinement_ratio=ratio
        )

    def _extract_facts(self, query: str, content: str) -> list[str]:
        """Extract atomic facts relevant to the query."""

        system_prompt = """Extract atomic facts from the document that are relevant
to answering the query. Each fact should be:
1. Self-contained (understandable without context)
2. Directly relevant to the query
3. Concise (one sentence)

Return JSON: {"facts": ["fact1", "fact2", ...]}

Only include facts that help answer the query. Omit background info, examples,
and tangential information."""

        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": f"Query: {query}\n\nDocument:\n{content}"}
            ],
            response_format={"type": "json_object"}
        )

        import json
        result = json.loads(response.choices[0].message.content)

        return result.get("facts", [])[:self.max_sentences]

ORIGINAL DOCUMENT:
"Machine learning is a subset of AI. It was coined by Arthur Samuel in 1959.
ML algorithms learn from data. Popular libraries include TensorFlow, PyTorch,
and scikit-learn. The weather today is sunny. Training requires GPUs for
large models. Arthur Samuel worked at IBM."

Query: "What is machine learning?"
                    │
                    ▼
┌─────────────────────────────────────────────────────────────┐
│ DECOMPOSE: Extract atomic facts                             │
│                                                             │
│ 1. "Machine learning is a subset of AI"               ✓     │
│ 2. "It was coined by Arthur Samuel in 1959"           ✓     │
│ 3. "ML algorithms learn from data"                    ✓     │
│ 4. "Popular libraries: TensorFlow, PyTorch, sklearn"  ~     │
│ 5. "The weather today is sunny"                       ✗     │
│ 6. "Training requires GPUs for large models"          ~     │
│ 7. "Arthur Samuel worked at IBM"                      ~     │
└─────────────────────────────────────────────────────────────┘
                    │
                    ▼
┌─────────────────────────────────────────────────────────────┐
│ FILTER: Keep only relevant facts                            │
│                                                             │
│ ✓ "Machine learning is a subset of AI"                      │
│ ✓ "It was coined by Arthur Samuel in 1959"                  │
│ ✓ "ML algorithms learn from data"                           │
└─────────────────────────────────────────────────────────────┘
                    │
                    ▼
┌─────────────────────────────────────────────────────────────┐
│ RECOMPOSE: Deduplicate and organize                         │
│                                                             │
│ Refinement ratio: 70% filtered out                          │
│ (Removed weather, tangential details)                       │
└─────────────────────────────────────────────────────────────┘

docker run -d --name searxng \
  -p 8080:8080 \
  -e SEARXNG_SECRET="your-secret-key" \
  searxng/searxng:latest

# docker-compose.searxng.yml
version: '3.8'
services:
  searxng:
    image: searxng/searxng:latest
    container_name: searxng
    ports:
      - "8080:8080"
    environment:
      - SEARXNG_SECRET=your-secret-key
    volumes:
      - ./searxng:/etc/searxng:rw
    restart: unless-stopped

docker-compose -f docker-compose.searxng.yml up -d

# web_search.py
import httpx
from pydantic import BaseModel
from config import get_settings


class WebSearchResult(BaseModel):
    """Result from web search."""
    title: str
    content: str
    url: str
    relevance_score: float


class WebSearchResults(BaseModel):
    """Collection of web search results."""
    query: str
    results: list[WebSearchResult]
    search_successful: bool


class WebSearcher:
    """Web search fallback using SearXNG (self-hosted metasearch)."""

    def __init__(self):
        settings = get_settings()
        self.base_url = settings.searxng_url
        self.max_results = settings.web_search_max_results

    def search(self, query: str) -> WebSearchResults:
        """
        Perform web search using SearXNG.

        SearXNG aggregates results from multiple search engines
        without tracking, making it ideal for learning projects.
        """
        try:
            # SearXNG JSON API endpoint
            response = httpx.get(
                f"{self.base_url}/search",
                params={
                    "q": query,
                    "format": "json",
                    "categories": "general",
                    "language": "en",
                },
                timeout=10.0
            )
            response.raise_for_status()
            data = response.json()

            results = []
            for i, item in enumerate(data.get("results", [])[:self.max_results]):
                results.append(WebSearchResult(
                    title=item.get("title", ""),
                    content=item.get("content", ""),
                    url=item.get("url", ""),
                    # SearXNG doesn't provide scores, use position-based ranking
                    relevance_score=1.0 - (i * 0.1)
                ))

            return WebSearchResults(
                query=query,
                results=results,
                search_successful=True
            )

        except httpx.ConnectError:
            print("SearXNG not available. Start it with: docker run -p 8080:8080 searxng/searxng")
            return WebSearchResults(
                query=query,
                results=[],
                search_successful=False
            )
        except Exception as e:
            print(f"Web search failed: {e}")
            return WebSearchResults(
                query=query,
                results=[],
                search_successful=False
            )


class MockWebSearcher:
    """Mock web searcher for testing without SearXNG running."""

    def search(self, query: str) -> WebSearchResults:
        """Return mock results for testing."""
        return WebSearchResults(
            query=query,
            results=[
                WebSearchResult(
                    title=f"Web result for: {query}",
                    content=f"This is simulated web content about {query}. "
                            "In production, this would be real SearXNG results.",
                    url="https://example.com/result",
                    relevance_score=0.8
                )
            ],
            search_successful=True
        )

# corrective_rag.py
import chromadb
from chromadb.utils import embedding_functions
from openai import OpenAI
from pydantic import BaseModel
from config import get_settings, RetrievalAction
from evaluator import RetrievalEvaluator, RetrievalEvaluation
from refiner import KnowledgeRefiner, RefinedKnowledge
from web_search import WebSearcher, WebSearchResults


class CRAGResponse(BaseModel):
    """Response from Corrective RAG."""
    answer: str
    action_taken: RetrievalAction
    evaluation: RetrievalEvaluation
    refined_knowledge: RefinedKnowledge | None
    web_search_used: bool
    web_results: WebSearchResults | None
    sources: list[str]
    confidence: float


class CorrectiveRAG:
    """Corrective RAG with retrieval evaluation and fallback."""

    def __init__(self):
        settings = get_settings()

        # Initialize components
        self.client = OpenAI(api_key=settings.openai_api_key)
        self.llm_model = settings.llm_model

        # ChromaDB
        self.chroma = chromadb.PersistentClient(
            path=settings.chroma_persist_dir
        )
        self.embedding_fn = embedding_functions.OpenAIEmbeddingFunction(
            api_key=settings.openai_api_key,
            model_name=settings.embedding_model
        )
        self.collection = self.chroma.get_or_create_collection(
            name=settings.collection_name,
            embedding_function=self.embedding_fn
        )

        # CRAG components
        self.evaluator = RetrievalEvaluator()
        self.refiner = KnowledgeRefiner()
        self.web_searcher = WebSearcher()

        self.settings = settings

    def query(self, question: str) -> CRAGResponse:
        """
        Process query with corrective retrieval.

        Pipeline:
        1. Retrieve documents
        2. Evaluate retrieval quality
        3. Take corrective action based on evaluation
        4. Generate answer from corrected knowledge
        """
        # Step 1: Retrieve
        results = self.collection.query(
            query_texts=[question],
            n_results=self.settings.retrieval_k,
            include=["documents", "metadatas", "distances"]
        )

        documents = [
            {
                "content": results["documents"][0][i],
                "source": results["metadatas"][0][i].get("source", f"doc_{i}")
            }
            for i in range(len(results["documents"][0]))
        ]

        # Step 2: Evaluate
        evaluation = self.evaluator.evaluate(question, documents)

        # Step 3: Take corrective action
        refined_knowledge = None
        web_results = None
        context = ""
        sources = []

        if evaluation.action == RetrievalAction.CORRECT:
            # Good retrieval - refine and use
            refined_knowledge = self.refiner.refine(
                question, documents, evaluation.document_evaluations
            )
            context = self._format_refined_knowledge(refined_knowledge)
            sources = [documents[i]["source"] for i in refined_knowledge.source_documents]

        elif evaluation.action == RetrievalAction.INCORRECT:
            # Bad retrieval - use web search
            web_results = self.web_searcher.search(question)
            context = self._format_web_results(web_results)
            sources = [r.url for r in web_results.results]

        else:  # AMBIGUOUS
            # Mixed - combine both
            refined_knowledge = self.refiner.refine(
                question, documents, evaluation.document_evaluations
            )
            web_results = self.web_searcher.search(question)

            context = "From knowledge base:\n"
            context += self._format_refined_knowledge(refined_knowledge)
            context += "\n\nFrom web search:\n"
            context += self._format_web_results(web_results)

            sources = [documents[i]["source"] for i in refined_knowledge.source_documents]
            sources += [r.url for r in web_results.results]

        # Step 4: Generate answer
        answer = self._generate_answer(question, context)

        return CRAGResponse(
            answer=answer,
            action_taken=evaluation.action,
            evaluation=evaluation,
            refined_knowledge=refined_knowledge,
            web_search_used=web_results is not None,
            web_results=web_results,
            sources=sources,
            confidence=evaluation.confidence
        )

    def _format_refined_knowledge(self, knowledge: RefinedKnowledge) -> str:
        """Format refined knowledge for generation."""
        lines = ["Key facts:"]
        for fact in knowledge.key_facts:
            lines.append(f"- {fact}")

        if knowledge.supporting_quotes:
            lines.append("\nSupporting evidence:")
            for quote in knowledge.supporting_quotes:
                lines.append(f'- "{quote}"')

        return "\n".join(lines)

    def _format_web_results(self, results: WebSearchResults) -> str:
        """Format web search results for generation."""
        if not results.results:
            return "No web results found."

        lines = []
        for r in results.results:
            lines.append(f"[{r.title}]")
            lines.append(r.content)
            lines.append("")

        return "\n".join(lines)

    def _generate_answer(self, question: str, context: str) -> str:
        """Generate answer from context."""

        system_prompt = """Answer the question based on the provided context.
Be accurate and cite sources when possible.
If the context doesn't contain enough information, say so."""

        response = self.client.chat.completions.create(
            model=self.llm_model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {question}"}
            ]
        )

        return response.choices[0].message.content

    def add_documents(self, documents: list[str], sources: list[str]):
        """Add documents to the knowledge base."""
        ids = [f"doc_{i}" for i in range(len(documents))]
        self.collection.add(
            documents=documents,
            ids=ids,
            metadatas=[{"source": src} for src in sources]
        )

# app.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from contextlib import asynccontextmanager

from corrective_rag import CorrectiveRAG, CRAGResponse


# Global
crag: CorrectiveRAG | None = None


@asynccontextmanager
async def lifespan(app: FastAPI):
    global crag
    crag = CorrectiveRAG()

    # Add sample documents (some relevant, some not)
    sample_docs = [
        "Python is a high-level programming language known for readability. It supports multiple paradigms including procedural, object-oriented, and functional programming.",
        "Machine learning enables computers to learn from data without explicit programming. Common algorithms include decision trees, neural networks, and support vector machines.",
        "The weather in Paris is generally mild. Summers are warm and winters are cool. The city receives moderate rainfall throughout the year.",
        "RAG (Retrieval-Augmented Generation) combines retrieval with generation to ground LLM responses in external knowledge, reducing hallucination.",
        "Coffee is a popular beverage made from roasted coffee beans. It contains caffeine which acts as a stimulant.",
        "Vector databases store embeddings for similarity search. Popular options include Pinecone, Weaviate, Milvus, and ChromaDB.",
        "The Eiffel Tower is a famous landmark in Paris, built in 1889. It stands 330 meters tall and attracts millions of visitors annually.",
        "Fine-tuning adapts pre-trained language models to specific tasks. Techniques like LoRA make this more efficient by updating only low-rank matrices."
    ]
    sources = [
        "python_docs", "ml_intro", "paris_weather", "rag_overview",
        "coffee_wiki", "vector_db_guide", "eiffel_tower", "finetuning_guide"
    ]
    crag.add_documents(sample_docs, sources)

    yield

    crag = None


app = FastAPI(
    title="Corrective RAG API",
    description="Self-correcting RAG with retrieval evaluation and web search fallback",
    lifespan=lifespan
)


class QueryRequest(BaseModel):
    query: str


class DocumentsRequest(BaseModel):
    documents: list[str]
    sources: list[str]


@app.post("/query", response_model=CRAGResponse)
async def query(request: QueryRequest):
    """Query with Corrective RAG."""
    if not crag:
        raise HTTPException(status_code=503, detail="Service not initialized")

    result = crag.query(request.query)
    return result


@app.post("/documents")
async def add_documents(request: DocumentsRequest):
    """Add documents to the knowledge base."""
    if not crag:
        raise HTTPException(status_code=503, detail="Service not initialized")

    if len(request.documents) != len(request.sources):
        raise HTTPException(
            status_code=400,
            detail="Documents and sources must have same length"
        )

    crag.add_documents(request.documents, request.sources)
    return {"status": "success", "documents_added": len(request.documents)}


@app.get("/health")
async def health():
    return {"status": "healthy", "service": "corrective-rag"}

# requirements.txt
openai>=1.12.0
chromadb>=0.4.22
httpx>=0.25.0
pydantic>=2.0.0
pydantic-settings>=2.0.0
fastapi>=0.109.0
uvicorn>=0.27.0
python-dotenv>=1.0.0

from corrective_rag import CorrectiveRAG

# Initialize
crag = CorrectiveRAG()

# Add documents
crag.add_documents(
    documents=["Your content here..."],
    sources=["source_name"]
)

# Query - system automatically evaluates and corrects
result = crag.query("What is RAG?")

print(f"Answer: {result.answer}")
print(f"Action taken: {result.action_taken}")
print(f"Confidence: {result.confidence:.2f}")
print(f"Web search used: {result.web_search_used}")

# Check evaluation details
print(f"\nEvaluation: {result.evaluation.reasoning}")
for doc_eval in result.evaluation.document_evaluations:
    print(f"  Doc {doc_eval.doc_index}: {doc_eval.relevance_score:.2f}")

# Good retrieval - uses refined knowledge
result = crag.query("Explain Python programming")
assert result.action_taken == "correct"
assert result.refined_knowledge is not None

# Bad retrieval - falls back to web search
result = crag.query("Latest news about quantum computing")
assert result.action_taken == "incorrect"
assert result.web_search_used == True

# Ambiguous - combines both sources
result = crag.query("How do vector databases work in RAG?")
if result.action_taken == "ambiguous":
    print("Used both knowledge base and web search")

# Start server
uvicorn app:app --reload

# Query
curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"query": "What is machine learning?"}'

# Response shows action taken
# {
#   "answer": "...",
#   "action_taken": "correct",
#   "evaluation": {...},
#   "web_search_used": false,
#   ...
# }

┌─────────────────────────────────────────────────────────────────┐
│                    RETRIEVAL EVALUATION                         │
│                                                                 │
│                       ┌───────────┐                             │
│                       │ Evaluator │                             │
│                       └─────┬─────┘                             │
│                             │                                   │
│           ┌─────────────────┼─────────────────┐                 │
│           │                 │                 │                 │
│     Score > 0.7       0.3 ≤ Score ≤ 0.7   Score < 0.3           │
│           │                 │                 │                 │
│           ▼                 ▼                 ▼                 │
│      ┌─────────┐      ┌───────────┐     ┌───────────┐           │
│      │ CORRECT │      │ AMBIGUOUS │     │ INCORRECT │           │
│      └────┬────┘      └─────┬─────┘     └─────┬─────┘           │
│           │                 │                 │                 │
└───────────┼─────────────────┼─────────────────┼─────────────────┘
            │                 │                 │
┌───────────┼─────────────────┼─────────────────┼─────────────────┐
│           ▼                 ▼                 ▼                 │
│     ┌──────────┐      ┌──────────┐      ┌──────────┐            │
│     │ Refine   │      │   Both   │      │ Web      │            │
│     │ Knowledge│      │ Sources  │      │ Search   │            │
│     └────┬─────┘      └─────┬────┘      └────┬─────┘            │
│          │                  │                │                  │
│          └──────────────────┼────────────────┘                  │
│                             │                                   │
│                             ▼                                   │
│                    ┌──────────────┐                             │
│                    │   Generate   │                             │
│                    └──────────────┘                             │
│                                                                 │
│                    CORRECTIVE ACTIONS                           │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│                   DECOMPOSE-THEN-RECOMPOSE                      │
│                                                                 │
│  Document ───► Decompose ───► Filter ───► Recompose             │
│     │         into Facts     Relevant     Knowledge             │
│     │              │         Facts             │                │
│     ▼              ▼           ▼               ▼                │
│  "Long text    [Fact 1]    [Fact 1] ✓    "Relevant              │
│   with noise    [Fact 2]    [Fact 3] ✓     facts only"          │
│   and extra     [Fact 3]                                        │
│   content..."   [Fact 4]                                        │
│                                                                 │
│  Removes noise while preserving key information                 │
└─────────────────────────────────────────────────────────────────┘