rag-construction

RAG 构建

概述

根据 DDC 方法论（第 2.3 章），此技能用于为建筑知识库构建检索增强生成（RAG）系统，从而实现对建筑文档的语义搜索和 AI 驱动的问答。

书籍参考：《Pandas DataFrame и LLM ChatGPT》/《Pandas DataFrame and LLM ChatGPT》

快速开始

from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional, Any, Callable
from datetime import datetime
import json
import hashlib
import re

class DocumentType(Enum):
    """Types of construction documents"""
    SPECIFICATION = "specification"
    DRAWING = "drawing"
    CONTRACT = "contract"
    RFI = "rfi"
    SUBMITTAL = "submittal"
    CHANGE_ORDER = "change_order"
    MEETING_MINUTES = "meeting_minutes"
    DAILY_REPORT = "daily_report"
    SAFETY_REPORT = "safety_report"
    INSPECTION = "inspection"
    MANUAL = "manual"
    STANDARD = "standard"

class ChunkingStrategy(Enum):
    """Text chunking strategies"""
    FIXED_SIZE = "fixed_size"
    PARAGRAPH = "paragraph"
    SECTION = "section"
    SEMANTIC = "semantic"
    SENTENCE = "sentence"

@dataclass
class DocumentChunk:
    """A chunk of document text"""
    id: str
    document_id: str
    content: str
    metadata: Dict[str, Any]
    embedding: Optional[List[float]] = None
    token_count: int = 0
    position: int = 0

@dataclass
class Document:
    """Construction document"""
    id: str
    title: str
    doc_type: DocumentType
    content: str
    source: str
    metadata: Dict[str, Any] = field(default_factory=dict)
    chunks: List[DocumentChunk] = field(default_factory=list)
    created_at: datetime = field(default_factory=datetime.now)

@dataclass
class SearchResult:
    """Search result from vector store"""
    chunk: DocumentChunk
    score: float
    document_title: str
    doc_type: DocumentType

@dataclass
class RAGResponse:
    """Response from RAG system"""
    query: str
    answer: str
    sources: List[SearchResult]
    confidence: float
    tokens_used: int


class TextChunker:
    """Split documents into chunks for embedding"""

    def __init__(
        self,
        strategy: ChunkingStrategy = ChunkingStrategy.PARAGRAPH,
        chunk_size: int = 500,
        chunk_overlap: int = 50
    ):
        self.strategy = strategy
        self.chunk_size = chunk_size
        self.chunk_overlap = chunk_overlap

    def chunk_document(self, document: Document) -> List[DocumentChunk]:
        """Split document into chunks"""
        if self.strategy == ChunkingStrategy.FIXED_SIZE:
            return self._chunk_fixed_size(document)
        elif self.strategy == ChunkingStrategy.PARAGRAPH:
            return self._chunk_by_paragraph(document)
        elif self.strategy == ChunkingStrategy.SECTION:
            return self._chunk_by_section(document)
        elif self.strategy == ChunkingStrategy.SENTENCE:
            return self._chunk_by_sentence(document)
        else:
            return self._chunk_fixed_size(document)

    def _chunk_fixed_size(self, document: Document) -> List[DocumentChunk]:
        """Chunk by fixed character size with overlap"""
        chunks = []
        text = document.content
        start = 0
        position = 0

        while start < len(text):
            end = start + self.chunk_size

            # Find word boundary
            if end < len(text):
                while end > start and text[end] not in ' \n\t':
                    end -= 1

            chunk_text = text[start:end].strip()
            if chunk_text:
                chunk_id = self._generate_chunk_id(document.id, position)
                chunks.append(DocumentChunk(
                    id=chunk_id,
                    document_id=document.id,
                    content=chunk_text,
                    metadata={
                        "doc_type": document.doc_type.value,
                        "title": document.title,
                        **document.metadata
                    },
                    token_count=len(chunk_text.split()),
                    position=position
                ))
                position += 1

            start = end - self.chunk_overlap
            if start >= len(text):
                break

        return chunks

    def _chunk_by_paragraph(self, document: Document) -> List[DocumentChunk]:
        """Chunk by paragraphs"""
        chunks = []
        paragraphs = document.content.split('\n\n')
        current_chunk = ""
        position = 0

        for para in paragraphs:
            para = para.strip()
            if not para:
                continue

            if len(current_chunk) + len(para) < self.chunk_size:
                current_chunk += "\n\n" + para if current_chunk else para
            else:
                if current_chunk:
                    chunk_id = self._generate_chunk_id(document.id, position)
                    chunks.append(DocumentChunk(
                        id=chunk_id,
                        document_id=document.id,
                        content=current_chunk,
                        metadata={
                            "doc_type": document.doc_type.value,
                            "title": document.title,
                            **document.metadata
                        },
                        token_count=len(current_chunk.split()),
                        position=position
                    ))
                    position += 1
                current_chunk = para

        # Add remaining content
        if current_chunk:
            chunk_id = self._generate_chunk_id(document.id, position)
            chunks.append(DocumentChunk(
                id=chunk_id,
                document_id=document.id,
                content=current_chunk,
                metadata={
                    "doc_type": document.doc_type.value,
                    "title": document.title,
                    **document.metadata
                },
                token_count=len(current_chunk.split()),
                position=position
            ))

        return chunks

    def _chunk_by_section(self, document: Document) -> List[DocumentChunk]:
        """Chunk by document sections (headers)"""
        # Split by common section patterns
        section_pattern = r'\n(?=(?:\d+\.|\d+\s|SECTION|ARTICLE|PART)\s+[A-Z])'
        sections = re.split(section_pattern, document.content)

        chunks = []
        for position, section in enumerate(sections):
            section = section.strip()
            if section:
                # If section is too large, further split it
                if len(section) > self.chunk_size * 2:
                    sub_chunker = TextChunker(ChunkingStrategy.PARAGRAPH, self.chunk_size)
                    sub_doc = Document(
                        id=f"{document.id}_sec{position}",
                        title=document.title,
                        doc_type=document.doc_type,
                        content=section,
                        source=document.source,
                        metadata=document.metadata
                    )
                    sub_chunks = sub_chunker.chunk_document(sub_doc)
                    for i, chunk in enumerate(sub_chunks):
                        chunk.id = self._generate_chunk_id(document.id, position * 100 + i)
                        chunk.position = position * 100 + i
                    chunks.extend(sub_chunks)
                else:
                    chunk_id = self._generate_chunk_id(document.id, position)
                    chunks.append(DocumentChunk(
                        id=chunk_id,
                        document_id=document.id,
                        content=section,
                        metadata={
                            "doc_type": document.doc_type.value,
                            "title": document.title,
                            **document.metadata
                        },
                        token_count=len(section.split()),
                        position=position
                    ))

        return chunks

    def _chunk_by_sentence(self, document: Document) -> List[DocumentChunk]:
        """Chunk by sentences, grouping to meet size requirements"""
        # Simple sentence splitting
        sentences = re.split(r'(?<=[.!?])\s+', document.content)

        chunks = []
        current_chunk = ""
        position = 0

        for sentence in sentences:
            if len(current_chunk) + len(sentence) < self.chunk_size:
                current_chunk += " " + sentence if current_chunk else sentence
            else:
                if current_chunk:
                    chunk_id = self._generate_chunk_id(document.id, position)
                    chunks.append(DocumentChunk(
                        id=chunk_id,
                        document_id=document.id,
                        content=current_chunk.strip(),
                        metadata={
                            "doc_type": document.doc_type.value,
                            "title": document.title,
                            **document.metadata
                        },
                        token_count=len(current_chunk.split()),
                        position=position
                    ))
                    position += 1
                current_chunk = sentence

        if current_chunk:
            chunk_id = self._generate_chunk_id(document.id, position)
            chunks.append(DocumentChunk(
                id=chunk_id,
                document_id=document.id,
                content=current_chunk.strip(),
                metadata={
                    "doc_type": document.doc_type.value,
                    "title": document.title,
                    **document.metadata
                },
                token_count=len(current_chunk.split()),
                position=position
            ))

        return chunks

    def _generate_chunk_id(self, doc_id: str, position: int) -> str:
        """Generate unique chunk ID"""
        return hashlib.md5(f"{doc_id}_{position}".encode()).hexdigest()[:12]


class VectorStore:
    """Simple in-memory vector store for RAG"""

    def __init__(self):
        self.chunks: Dict[str, DocumentChunk] = {}
        self.embeddings: Dict[str, List[float]] = {}

    def add_chunks(self, chunks: List[DocumentChunk]):
        """Add chunks to the store"""
        for chunk in chunks:
            self.chunks[chunk.id] = chunk
            if chunk.embedding:
                self.embeddings[chunk.id] = chunk.embedding

    def search(
        self,
        query_embedding: List[float],
        top_k: int = 5,
        filter_metadata: Optional[Dict] = None
    ) -> List[Tuple[DocumentChunk, float]]:
        """Search for similar chunks"""
        results = []

        for chunk_id, chunk in self.chunks.items():
            # Apply metadata filter
            if filter_metadata:
                match = all(
                    chunk.metadata.get(k) == v
                    for k, v in filter_metadata.items()
                )
                if not match:
                    continue

            # Calculate similarity (cosine similarity simulation)
            if chunk_id in self.embeddings:
                score = self._cosine_similarity(query_embedding, self.embeddings[chunk_id])
                results.append((chunk, score))

        # Sort by score descending
        results.sort(key=lambda x: x[1], reverse=True)
        return results[:top_k]

    def _cosine_similarity(self, a: List[float], b: List[float]) -> float:
        """Calculate cosine similarity between two vectors"""
        if len(a) != len(b):
            return 0.0

        dot_product = sum(x * y for x, y in zip(a, b))
        norm_a = sum(x * x for x in a) ** 0.5
        norm_b = sum(x * x for x in b) ** 0.5

        if norm_a == 0 or norm_b == 0:
            return 0.0

        return dot_product / (norm_a * norm_b)

    def get_stats(self) -> Dict:
        """Get store statistics"""
        doc_types = {}
        for chunk in self.chunks.values():
            doc_type = chunk.metadata.get("doc_type", "unknown")
            doc_types[doc_type] = doc_types.get(doc_type, 0) + 1

        return {
            "total_chunks": len(self.chunks),
            "chunks_with_embeddings": len(self.embeddings),
            "chunks_by_type": doc_types
        }


class EmbeddingModel:
    """Simulated embedding model (replace with actual model in production)"""

    def __init__(self, model_name: str = "text-embedding-ada-002"):
        self.model_name = model_name
        self.dimension = 1536

    def embed(self, text: str) -> List[float]:
        """Generate embedding for text"""
        # Simulation: generate deterministic embedding based on text hash
        text_hash = hashlib.sha256(text.encode()).digest()
        embedding = []
        for i in range(self.dimension):
            byte_idx = i % len(text_hash)
            embedding.append((text_hash[byte_idx] - 128) / 128.0)
        return embedding

    def embed_batch(self, texts: List[str]) -> List[List[float]]:
        """Generate embeddings for multiple texts"""
        return [self.embed(text) for text in texts]


class ConstructionRAG:
    """
    RAG system for construction knowledge bases.
    Based on DDC methodology Chapter 2.3.
    """

    def __init__(
        self,
        embedding_model: Optional[EmbeddingModel] = None,
        chunking_strategy: ChunkingStrategy = ChunkingStrategy.PARAGRAPH,
        chunk_size: int = 500
    ):
        self.embedding_model = embedding_model or EmbeddingModel()
        self.chunker = TextChunker(chunking_strategy, chunk_size)
        self.vector_store = VectorStore()
        self.documents: Dict[str, Document] = {}

    def add_document(self, document: Document) -> int:
        """
        Add a document to the knowledge base.

        Args:
            document: Document to add

        Returns:
            Number of chunks created
        """
        # Store document
        self.documents[document.id] = document

        # Chunk document
        chunks = self.chunker.chunk_document(document)

        # Generate embeddings
        for chunk in chunks:
            chunk.embedding = self.embedding_model.embed(chunk.content)

        # Add to vector store
        self.vector_store.add_chunks(chunks)

        # Update document with chunks
        document.chunks = chunks

        return len(chunks)

    def add_documents(self, documents: List[Document]) -> Dict[str, int]:
        """Add multiple documents"""
        results = {}
        for doc in documents:
            results[doc.id] = self.add_document(doc)
        return results

    def search(
        self,
        query: str,
        top_k: int = 5,
        doc_type: Optional[DocumentType] = None
    ) -> List[SearchResult]:
        """
        Search the knowledge base.

        Args:
            query: Search query
            top_k: Number of results to return
            doc_type: Filter by document type

        Returns:
            List of search results
        """
        # Generate query embedding
        query_embedding = self.embedding_model.embed(query)

        # Build filter
        filter_metadata = None
        if doc_type:
            filter_metadata = {"doc_type": doc_type.value}

        # Search vector store
        results = self.vector_store.search(
            query_embedding,
            top_k=top_k,
            filter_metadata=filter_metadata
        )

        # Build search results
        search_results = []
        for chunk, score in results:
            doc = self.documents.get(chunk.document_id)
            search_results.append(SearchResult(
                chunk=chunk,
                score=score,
                document_title=doc.title if doc else "Unknown",
                doc_type=doc.doc_type if doc else DocumentType.MANUAL
            ))

        return search_results

    def query(
        self,
        question: str,
        top_k: int = 5,
        doc_type: Optional[DocumentType] = None
    ) -> RAGResponse:
        """
        Answer a question using RAG.

        Args:
            question: Question to answer
            top_k: Number of context chunks to use
            doc_type: Filter by document type

        Returns:
            RAG response with answer and sources
        """
        # Search for relevant context
        search_results = self.search(question, top_k=top_k, doc_type=doc_type)

        if not search_results:
            return RAGResponse(
                query=question,
                answer="I couldn't find relevant information to answer this question.",
                sources=[],
                confidence=0.0,
                tokens_used=0
            )

        # Build context from search results
        context_parts = []
        for i, result in enumerate(search_results):
            context_parts.append(
                f"[Source {i+1}: {result.document_title}]\n{result.chunk.content}"
            )

        context = "\n\n".join(context_parts)

        # Generate answer (simulated - in production, call LLM)
        answer = self._generate_answer(question, context, search_results)

        # Calculate confidence
        avg_score = sum(r.score for r in search_results) / len(search_results)

        return RAGResponse(
            query=question,
            answer=answer,
            sources=search_results,
            confidence=avg_score,
            tokens_used=len(context.split()) + len(question.split())
        )

    def _generate_answer(
        self,
        question: str,
        context: str,
        sources: List[SearchResult]
    ) -> str:
        """
        Generate answer from context.
        In production, this would call an LLM API.
        """
        # Simulated answer generation
        answer_parts = [
            f"Based on the available construction documentation:\n"
        ]

        # Extract key information from sources
        for source in sources[:3]:
            # Take first sentence of each relevant chunk
            first_sentence = source.chunk.content.split('.')[0] + '.'
            answer_parts.append(f"- {first_sentence}")

        answer_parts.append(
            f"\n\nThis information comes from {len(sources)} source documents "
            f"including: {', '.join(set(s.document_title for s in sources[:3]))}."
        )

        return "\n".join(answer_parts)

    def get_document_summary(self, document_id: str) -> Optional[Dict]:
        """Get summary of a document"""
        doc = self.documents.get(document_id)
        if not doc:
            return None

        return {
            "id": doc.id,
            "title": doc.title,
            "type": doc.doc_type.value,
            "chunks": len(doc.chunks),
            "total_tokens": sum(c.token_count for c in doc.chunks),
            "source": doc.source,
            "created_at": doc.created_at.isoformat()
        }

    def get_stats(self) -> Dict:
        """Get system statistics"""
        return {
            "total_documents": len(self.documents),
            "vector_store": self.vector_store.get_stats(),
            "embedding_model": self.embedding_model.model_name,
            "chunking_strategy": self.chunker.strategy.value
        }

    def export_knowledge_base(self) -> Dict:
        """Export knowledge base for backup/transfer"""
        return {
            "documents": [
                {
                    "id": doc.id,
                    "title": doc.title,
                    "type": doc.doc_type.value,
                    "content": doc.content,
                    "source": doc.source,
                    "metadata": doc.metadata
                }
                for doc in self.documents.values()
            ],
            "stats": self.get_stats(),
            "exported_at": datetime.now().isoformat()
        }

常见用例

构建建筑知识库

rag = ConstructionRAG(
    chunking_strategy=ChunkingStrategy.SECTION,
    chunk_size=500
)

# Add specifications
spec_doc = Document(
    id="spec-03300",
    title="Cast-in-Place Concrete Specification",
    doc_type=DocumentType.SPECIFICATION,
    content="""
    SECTION 03 30 00 - CAST-IN-PLACE CONCRETE

    PART 1 - GENERAL
    1.1 SUMMARY
    A. Section includes cast-in-place concrete for foundations,
       slabs, walls, and other structural elements.

    1.2 RELATED SECTIONS
    A. Section 03 10 00 - Concrete Forming
    B. Section 03 20 00 - Concrete Reinforcing

    PART 2 - PRODUCTS
    2.1 CONCRETE MATERIALS
    A. Portland Cement: ASTM C150, Type I or II
    B. Aggregates: ASTM C33, graded
    C. Water: Clean, potable
    """,
    source="project_specs.pdf",
    metadata={"division": "03", "project": "Building A"}
)

chunks_created = rag.add_document(spec_doc)
print(f"Created {chunks_created} chunks")

搜索知识库

# Search for concrete requirements
results = rag.search(
    query="concrete strength requirements",
    top_k=5,
    doc_type=DocumentType.SPECIFICATION
)

for result in results:
    print(f"Score: {result.score:.3f}")
    print(f"Document: {result.document_title}")
    print(f"Content: {result.chunk.content[:200]}...")
    print()

使用 RAG 回答问题

response = rag.query(
    question="What type of cement should be used for foundations?",
    top_k=3
)

print(f"Answer: {response.answer}")
print(f"Confidence: {response.confidence:.0%}")
print(f"Sources: {len(response.sources)}")

快速参考

组件	用途
ConstructionRAG	RAG 主系统
TextChunker	文档分块
VectorStore	嵌入存储
EmbeddingModel	文本嵌入
DocumentChunk	带元数据的分块
RAGResponse	查询响应

资源

• 书籍：《Data-Driven Construction》，作者 Artem Boiko，第 2.3 章
• 网站：https://datadrivenconstruction.io

后续步骤

• 使用 llm-data-automation 实现自动化
• 使用 vector-search 进行高级搜索
• 使用 document-classification-nlp 进行分类