Init project

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/AmazingDoc.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="Flask">
+    <option name="enabled" value="true" />
+  </component>
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="GOOGLE" />
+    <option name="myDocStringFormat" value="Google" />
+  </component>
+  <component name="TemplatesService">
+    <option name="TEMPLATE_CONFIGURATION" value="Jinja2" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.13" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/AmazingDoc.iml" filepath="$PROJECT_DIR$/.idea/AmazingDoc.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app/agents.py

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+# app/agents.py
+import asyncio
+import random
+from .schemas import ReceiptInfo, InvoiceInfo, ReceiptItem
+
+# --- Agent核心功能 (占位符/模拟实现) ---
+# 在实际应用中，这些函数将被替换为调用LangChain和LLM的真实逻辑。
+
+async def agent_classify_document(text: str) -> str:
+    """Agent 1: 文件分类 (模拟)"""
+    print("--- [Agent 1] 正在进行文档分类...")
+    await asyncio.sleep(0.5) # 模拟网络延迟
+    doc_types = ["信件", "收据", "发票", "合约"]
+    if "发票" in text: return "发票"
+    if "收据" in text or "小票" in text: return "收据"
+    if "合同" in text or "协议" in text: return "合约"
+    return random.choice(doc_types)
+
+async def agent_extract_receipt_info(text: str) -> ReceiptInfo:
+    """Agent 2: 收据信息提取 (模拟)"""
+    print("--- [Agent 2] 正在提取收据信息...")
+    await asyncio.sleep(1) # 模拟LLM处理时间
+    return ReceiptInfo(
+        merchant_name="模拟超市",
+        transaction_date="2025-08-10",
+        total_amount=198.50,
+        items=[ReceiptItem(name="牛奶", quantity=2, price=11.5)]
+    )
+
+async def agent_extract_invoice_info(text: str) -> InvoiceInfo:
+    """Agent 3: 发票信息提取 (模拟)"""
+    print("--- [Agent 3] 正在提取发票信息...")
+    await asyncio.sleep(1) # 模拟LLM处理时间
+    return InvoiceInfo(
+        invoice_number="INV123456789",
+        issue_date="2025-08-09",
+        seller_name="模拟科技有限公司",
+        total_amount_in_figures=12000.00
+    )
+
+async def agent_vectorize_and_store(doc_id: str, text: str, category: str, vector_db: dict):
+    """Agent 4: 向量化并存储 (模拟)"""
+    print(f"--- [Agent 4] 正在向量化文档 (ID: {doc_id})...")
+    await asyncio.sleep(0.5)
+    chunks = [text[i:i+200] for i in range(0, len(text), 200)]
+    vector_db[doc_id] = {
+        "metadata": {"category": category, "chunk_count": len(chunks)},
+        "content_chunks": chunks,
+        "vectors": [random.random() for _ in range(len(chunks) * 128)]
+    }
+    print(f"--- [Agent 4] 文档 {doc_id} 已存入向量数据库。")

app/agents/__init__.py

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+# app/agents/__init__.py
+
+# This file makes it easy to import all agents from the 'agents' package.
+# We are importing the function with its correct name now.
+from .classification_agent import agent_classify_document_from_text
+from .receipt_agent import agent_extract_receipt_info
+from .invoice_agent import agent_extract_invoice_info

app/agents/classification_agent.py

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+# app/agents/classification_agent.py
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+from ..core.llm import llm
+from ..schemas import ClassificationResult  # 导入新的Schema
+
+# 1. 设置PydanticOutputParser
+parser = PydanticOutputParser(pydantic_object=ClassificationResult)
+
+# 2. 更新Prompt模板以要求语言，并包含格式指令
+classification_template = """
+You are a professional document analysis assistant. Please perform two tasks on the following text:
+1.  Determine its category. The category must be one of: ["LETTER", "INVOICE", "RECEIPT", "CONTRACT", "OTHER"].
+2.  Detect the primary language of the text. Return the language as a two-letter ISO 639-1 code (e.g., "en" for English, "zh" for Chinese, "es" for Spanish).
+
+{format_instructions}
+
+[Document Text Start]
+{document_text}
+[Document Text End]
+"""
+
+classification_prompt = PromptTemplate(
+    template=classification_template,
+    input_variables=["document_text"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+# 3. 创建新的LangChain链
+classification_chain = classification_prompt | llm | parser
+
+
+async def agent_classify_document_from_text(text: str) -> ClassificationResult:
+    """Agent 1: Classify document and detect language from OCR-extracted text."""
+    print("--- [Agent 1] Calling LLM for classification and language detection...")
+    if not text.strip():
+        print("--- [Agent 1] Text content is empty, classifying as 'OTHER'.")
+        return ClassificationResult(category="OTHER", language="unknown")
+
+    # 调用链并返回Pydantic对象
+    result = await classification_chain.ainvoke({"document_text": text})
+    return result

app/agents/invoice_agent.py

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+# app/agents/invoice_agent.py
+from langchain_core.messages import HumanMessage
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain.prompts import PromptTemplate
+from ..core.llm import llm
+from ..schemas import InvoiceInfo
+
+parser = PydanticOutputParser(pydantic_object=InvoiceInfo)
+
+# The prompt now includes the detailed rules for each field using snake_case.
+invoice_template = """
+You are an expert data entry clerk AI. Your primary goal is to extract information from an invoice image with the highest possible accuracy.
+The document's primary language is '{language}'.
+
+## Instructions:
+Carefully analyze the invoice image and extract the following fields according to these specific rules. Do not invent information. If a field is not found or is unclear, follow the specific instruction for that field.
+
+- `date`: Extract in YYYY-MM-DD format. If unclear, leave as an empty string.
+- `invoice_number`: If not found or unclear, leave as an empty string.
+- `supplier_number`: This is the organisation number. If not found or unclear, leave as an empty string.
+- `biller_name`: This is the sender's name. If not found or unclear, leave as an empty string.
+- `amount`: Extract the final total amount and format it to a decimal number. If not present, leave as null.
+- `customer_name`: This is the receiver's name. Ensure it is a name and clear any special characters. If not found or unclear, leave as an empty string.
+- `customer_address`: This is the receiver's full address. Put it in one line. If not found or unclear, leave as an empty string.
+- `customer_address_line`: This is only the street address line from the receiver's address. If not found or unclear, leave as an empty string.
+- `customer_address_city`: This is the receiver's city. If not found, try to find any city in the document. If unclear, leave as an empty string.
+- `customer_address_country`: This is the receiver's country. If not found, find the country of the extracted city. If unclear, leave as an empty string.
+- `customer_address_postal_code`: This is the receiver's postal code. If not found or unclear, leave as an empty string.
+- `customer_address_apartment`: This is the receiver's apartment or suite number. If not found or unclear, leave as an empty string.
+- `customer_address_region`: This is the receiver's region. If not found, find the region of the extracted city or country. If unclear, leave as an empty string.
+- `customer_address_care_of`: This is the receiver's 'care of' (c/o) line. If not found or unclear, leave as an empty string.
+- `billo_id`: To find this, think step-by-step: 1. Find the customer_address. 2. Scan the address for a pattern of three letters, an optional space, three digits, an optional dash, and one alphanumeric character (e.g., 'ABC 123-X' or 'DEF 456Z'). 3. If found, extract it. If not found or unclear, leave as an empty string.
+- `line_items`: Extract all line items from the invoice. For each item, extract the `description`, `quantity`, `unit_price`, and `total_price`. If a value is not present, leave it as null.
+
+## Example:
+If the invoice shows a line item "Consulting Services | 2 hours | $100.00/hr | $200.00", the output for that line item should be:
+```json
+{{
+  "description": "Consulting Services",
+  "quantity": 2,
+  "unit_price": 100.00,
+  "total_price": 200.00
+}}
+
+Your Task:
+Now, analyze the provided image and output the full JSON object according to the format below.
+{format_instructions}
+"""
+
+invoice_prompt = PromptTemplate(
+    template=invoice_template,
+    input_variables=["language"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+async def agent_extract_invoice_info(image_base64: str, language: str) -> InvoiceInfo:
+    """Agent 3: Extracts invoice information from an image, aware of the document's language."""
+    print(f"--- [Agent 3] Calling multimodal LLM to extract invoice info (Language: {language})...")
+
+    prompt_text = await invoice_prompt.aformat(language=language)
+
+    msg = HumanMessage(
+        content=[
+            {"type": "text", "text": prompt_text},
+            {
+                "type": "image_url",
+                "image_url": f"data:image/png;base64,{image_base64}",
+            },
+        ]
+    )
+
+    chain = llm | parser
+    invoice_info = await chain.ainvoke([msg])
+    return invoice_info

app/agents/receipt_agent.py

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+# app/agents/receipt_agent.py
+from langchain_core.messages import HumanMessage
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain.prompts import PromptTemplate
+from ..core.llm import llm
+from ..schemas import ReceiptInfo
+
+parser = PydanticOutputParser(pydantic_object=ReceiptInfo)
+
+# 更新Prompt模板以包含语言信息
+receipt_template = """
+You are a highly accurate receipt information extraction robot.
+The document's primary language is '{language}'.
+Please extract all key information from the following receipt image.
+If some information is not present in the image, leave it as null.
+Please strictly follow the JSON format below, without adding any extra explanations or comments.
+
+{format_instructions}
+"""
+
+receipt_prompt = PromptTemplate(
+    template=receipt_template,
+    input_variables=["language"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+
+async def agent_extract_receipt_info(image_base64: str, language: str) -> ReceiptInfo:
+    """Agent 2: Extracts receipt information from an image, aware of the document's language."""
+    print(f"--- [Agent 2] Calling multimodal LLM to extract receipt info (Language: {language})...")
+
+    prompt_text = await receipt_prompt.aformat(language=language)
+
+    msg = HumanMessage(
+        content=[
+            {"type": "text", "text": prompt_text},
+            {
+                "type": "image_url",
+                "image_url": f"data:image/png;base64,{image_base64}",
+            },
+        ]
+    )
+
+    chain = llm | parser
+    receipt_info = await chain.ainvoke([msg])
+    return receipt_info

app/agents/vectorization_agent.py

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+# app/agents/vectorization_agent.py
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from ..core.vector_store import vector_store, embedding_model
+
+# 初始化文本分割器，用于将长文档切成小块
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=500,  # 每个块的大小（字符数）
+    chunk_overlap=50, # 块之间的重叠部分
+)
+
+def agent_vectorize_and_store(doc_id: str, text: str, category: str):
+    """Agent 4: 向量化并存储 (真实实现)"""
+    print(f"--- [Agent 4] 正在向量化文档 (ID: {doc_id})...")
+
+    # 1. 将文档文本分割成块
+    chunks = text_splitter.split_text(text)
+    print(f"--- [Agent 4] 文档被切分为 {len(chunks)} 个块。")
+
+    if not chunks:
+        print(f"--- [Agent 4] 文档内容为空，跳过向量化。")
+        return
+
+    # 2. 为每个块创建唯一的ID和元数据
+    chunk_ids = [f"{doc_id}_{i}" for i in range(len(chunks))]
+    metadatas = [{"doc_id": doc_id, "category": category, "chunk_number": i} for i in range(len(chunks))]
+
+    # 3. 使用嵌入模型为所有块生成向量
+    embeddings = embedding_model.embed_documents(chunks)
+
+    # 4. 将ID、向量、元数据和文本块本身添加到ChromaDB
+    vector_store.add(
+        ids=chunk_ids,
+        embeddings=embeddings,
+        documents=chunks,
+        metadatas=metadatas
+    )
+
+    print(f"--- [Agent 4] 文档 {doc_id} 的向量已存入ChromaDB。")

app/core/llm.py

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+# app/core/llm.py
+import os
+from dotenv import load_dotenv
+from langchain_openai import AzureChatOpenAI, ChatOpenAI
+
+# 加载.env文件中的环境变量
+load_dotenv()
+
+# 获取配置的LLM供应商
+LLM_PROVIDER = os.getenv("LLM_PROVIDER", "openai").lower()
+
+llm = None
+
+print(f"--- [Core] Initializing LLM with provider: {LLM_PROVIDER} ---")
+
+if LLM_PROVIDER == "azure":
+    # --- Azure OpenAI 配置 ---
+    required_vars = [
+        "AZURE_OPENAI_ENDPOINT", "AZURE_OPENAI_API_KEY",
+        "OPENAI_API_VERSION", "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"
+    ]
+    if not all(os.getenv(var) for var in required_vars):
+        raise ValueError("One or more Azure OpenAI environment variables for chat are not set.")
+
+    llm = AzureChatOpenAI(
+        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+        api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+        api_version=os.getenv("OPENAI_API_VERSION"),
+        azure_deployment=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"),
+        temperature=0,
+    )
+
+elif LLM_PROVIDER == "openai":
+    # --- 标准 OpenAI 配置 ---
+    if not os.getenv("OPENAI_API_KEY"):
+        raise ValueError("OPENAI_API_KEY is not set for the 'openai' provider.")
+
+    llm = ChatOpenAI(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        model_name=os.getenv("OPENAI_MODEL_NAME", "gpt-4o"),
+        temperature=0,
+    )
+
+else:
+    raise ValueError(f"Unsupported LLM_PROVIDER: {LLM_PROVIDER}. Please use 'azure' or 'openai'.")

app/core/ocr.py

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+# app/core/ocr.py
+import pytesseract
+from PIL import Image
+
+
+# 注意: 您需要先在您的系统中安装Google的Tesseract OCR引擎。
+# 详情请参考之前的安装说明。
+
+def extract_text_from_image(image: Image.Image) -> str:
+    """
+    使用Tesseract OCR从Pillow Image对象中提取文本。
+
+    参数:
+        image: Pillow Image对象。
+
+    返回:
+        从图片中提取出的字符串文本。
+    """
+    try:
+        print("--- [Core OCR] 正在从图片中提取文本用于分类...")
+        # lang='chi_sim+eng' 表示同时识别简体中文和英文
+        text = pytesseract.image_to_string(image, lang='chi_sim+eng')
+        print("--- [Core OCR] 文本提取成功。")
+        return text
+    except Exception as e:
+        print(f"--- [Core OCR] OCR处理失败: {e}")
+        raise IOError(f"OCR processing failed: {e}")

app/core/pdf_processor.py

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+# app/core/pdf_processor.py
+from pdf2image import convert_from_bytes
+from PIL import Image
+from io import BytesIO
+from typing import List
+import base64
+
+
+# 注意: 您需要安装Poppler。
+# - macOS: brew install poppler
+# - Ubuntu/Debian: sudo apt-get install poppler-utils
+# - Windows: 下载Poppler并将其bin目录添加到系统PATH。
+
+def convert_pdf_to_images(pdf_bytes: bytes) -> List[Image.Image]:
+    """将PDF文件的字节流转换为Pillow Image对象列表。"""
+    try:
+        print("--- [Core PDF] 正在将PDF转换为图片...")
+
+        # --- 新增代码开始 ---
+        # 在这里直接指定您电脑上Poppler的bin目录路径
+        # 请确保将下面的示例路径替换为您的真实路径
+        poppler_path = r"C:\ProgramData\chocolatey\lib\poppler\tools\Library\bin"
+        # --- 新增代码结束 ---
+
+        # --- 修改的代码开始 ---
+        # 在调用时传入poppler_path参数
+        images = convert_from_bytes(pdf_bytes)
+        # --- 修改的代码结束 ---
+
+        print(f"--- [Core PDF] 转换成功，共 {len(images)} 页。")
+        return images
+    except Exception as e:
+        print(f"--- [Core PDF] PDF转换失败: {e}")
+        # 增加一个更友好的错误提示
+        print("--- [Core PDF] 请确认您已在系统中正确安装Poppler，并在上面的代码中指定了正确的poppler_path。")
+        raise IOError(f"PDF to image conversion failed: {e}")
+
+
+def image_to_base64_str(image: Image.Image) -> str:
+    """将Pillow Image对象转换为Base64编码的字符串。"""
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode('utf-8')

app/core/vector_store.py

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+# app/core/vector_store.py
+import os
+import chromadb
+from dotenv import load_dotenv
+from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings
+
+# 加载.env文件中的环境变量
+load_dotenv()
+
+# 获取配置的LLM供应商
+LLM_PROVIDER = os.getenv("LLM_PROVIDER", "openai").lower()
+
+embedding_model = None
+
+print(f"--- [Core] Initializing Embeddings with provider: {LLM_PROVIDER} ---")
+
+if LLM_PROVIDER == "azure":
+    # --- Azure OpenAI 配置 ---
+    required_vars = [
+        "AZURE_OPENAI_ENDPOINT", "AZURE_OPENAI_API_KEY",
+        "OPENAI_API_VERSION", "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME"
+    ]
+    if not all(os.getenv(var) for var in required_vars):
+        raise ValueError("One or more Azure OpenAI environment variables for embeddings are not set.")
+
+    embedding_model = AzureOpenAIEmbeddings(
+        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+        api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+        api_version=os.getenv("OPENAI_API_VERSION"),
+        azure_deployment=os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME"),
+    )
+
+elif LLM_PROVIDER == "openai":
+    # --- 标准 OpenAI 配置 ---
+    if not os.getenv("OPENAI_API_KEY"):
+        raise ValueError("OPENAI_API_KEY is not set for the 'openai' provider.")
+
+    embedding_model = OpenAIEmbeddings(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        model=os.getenv("OPENAI_EMBEDDING_MODEL_NAME", "text-embedding-3-small")
+    )
+
+else:
+    raise ValueError(f"Unsupported LLM_PROVIDER: {LLM_PROVIDER}. Please use 'azure' or 'openai'.")
+
+
+# 初始化ChromaDB客户端 (无变化)
+client = chromadb.PersistentClient(path="./chroma_db")
+vector_store = client.get_or_create_collection(
+    name="documents",
+    metadata={"hnsw:space": "cosine"}
+)

app/main.py

@@ -0,0 +1,21 @@
+# app/main.py
+import uvicorn
+from fastapi import FastAPI
+from .routers import documents # 导入我们新的文档路由
+
+app = FastAPI(
+    title="混合模式文档处理AI Agent",
+    description="一个用于自动分类、提取和处理文档的AI应用框架。",
+    version="0.9.0", # 版本升级: 模块化API路由
+)
+
+# 将文档路由包含到主应用中
+app.include_router(documents.router)
+
+@app.get("/", tags=["Root"])
+async def read_root():
+    """一个简单的根端点，用于检查服务是否正在运行。"""
+    return {"message": "Welcome to the Document Processing AI Agent API!"}
+
+if __name__ == "__main__":
+    uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True)

app/routers/documents.py

@@ -0,0 +1,99 @@
+# app/routers/documents.py
+import uuid
+import mimetypes
+import base64
+from fastapi import APIRouter, UploadFile, File, HTTPException
+from typing import Dict, Any, List
+from fastapi.concurrency import run_in_threadpool
+from PIL import Image
+from io import BytesIO
+
+from .. import agents
+from ..core.pdf_processor import convert_pdf_to_images, image_to_base64_str
+from ..core.ocr import extract_text_from_image
+
+# 创建一个APIRouter实例
+router = APIRouter(
+    prefix="/documents",  # 为这个路由下的所有路径添加前缀
+    tags=["Document Processing"],  # 在API文档中为这组端点添加标签
+)
+
+# 模拟一个SQL数据库来存储最终结果
+db_results: Dict[str, Any] = {}
+
+
+async def hybrid_process_pipeline(doc_id: str, image: Image.Image, page_num: int):
+    """混合处理流水线"""
+    ocr_text = await run_in_threadpool(extract_text_from_image, image)
+
+    classification_result = await agents.agent_classify_document_from_text(ocr_text)
+    category = classification_result.category
+    language = classification_result.language
+    print(f"Document page {page_num} classified as: {category}, Language: {language}")
+
+    extraction_result = None
+    if category in ["RECEIPT", "INVOICE"]:
+        image_base64 = await run_in_threadpool(image_to_base64_str, image)
+        if category == "RECEIPT":
+            # 将语言传递给提取Agent
+            extraction_result = await agents.agent_extract_receipt_info(image_base64, language)
+        elif category == "INVOICE":
+            # 将语言传递给提取Agent
+            extraction_result = await agents.agent_extract_invoice_info(image_base64, language)
+    else:
+        print(f"Document classified as '{category}', skipping high-precision extraction.")
+
+    final_result = {
+        "doc_id": f"{doc_id}_page_{page_num}",
+        "category": category,
+        "language": language,
+        "ocr_text_for_classification": ocr_text,
+        "extraction_data": extraction_result.dict() if extraction_result else None,
+        "status": "Processed"
+    }
+    db_results[final_result["doc_id"]] = final_result
+    return final_result
+
+
+@router.post("/process", summary="上传并处理单个文档(混合模式)")
+async def upload_and_process_document(file: UploadFile = File(...)):
+    """处理上传的文档文件 (PDF, PNG, JPG)"""
+    if not file.filename:
+        raise HTTPException(status_code=400, detail="No file provided.")
+
+    doc_id = str(uuid.uuid4())
+    print(f"\n接收到新文件: {file.filename} (分配ID: {doc_id})")
+    contents = await file.read()
+
+    try:
+        file_type = mimetypes.guess_type(file.filename)[0]
+        print(f"检测到文件类型: {file_type}")
+
+        images: List[Image.Image] = []
+        if file_type == 'application/pdf':
+            images = await run_in_threadpool(convert_pdf_to_images, contents)
+        elif file_type in ['image/png', 'image/jpeg']:
+            images.append(Image.open(BytesIO(contents)))
+        else:
+            raise HTTPException(status_code=400, detail=f"Unsupported file type: {file_type}.")
+
+        if not images:
+            raise HTTPException(status_code=400, detail="Could not extract images from document.")
+
+        all_page_results = []
+        for i, img in enumerate(images):
+            page_result = await hybrid_process_pipeline(doc_id, img, i + 1)
+            all_page_results.append(page_result)
+
+        return all_page_results
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+
+@router.get("/results/{doc_id}", summary="根据ID获取处理结果")
+async def get_result(doc_id: str):
+    """根据文档处理后返回的 doc_id 获取其详细处理结果。"""
+    if doc_id in db_results:
+        return db_results[doc_id]
+    raise HTTPException(status_code=404, detail="Document not found.")

app/schemas.py

@@ -0,0 +1,51 @@
+# app/schemas.py
+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+# --- 分类结果模型 ---
+class ClassificationResult(BaseModel):
+    """Defines the structured output for the classification agent."""
+    category: str = Field(description="The category of the document, must be one of ['LETTER', 'INVOICE', 'RECEIPT', 'CONTRACT', 'OTHER']")
+    language: str = Field(description="The detected primary language of the document as a two-letter code (e.g., 'en', 'zh', 'es').")
+
+# --- 现有模型 (无变化) ---
+class ReceiptItem(BaseModel):
+    name: str = Field(description="购买的项目或服务名称")
+    quantity: float = Field(description="项目数量")
+    price: float = Field(description="项目单价")
+
+class ReceiptInfo(BaseModel):
+    merchant_name: Optional[str] = Field(None, description="商户或店铺的名称")
+    transaction_date: Optional[str] = Field(None, description="交易日期，格式为 YYYY-MM-DD")
+    total_amount: Optional[float] = Field(None, description="收据上的总金额")
+    items: Optional[List[ReceiptItem]] = Field(None, description="购买的所有项目列表")
+
+
+# --- 新增: 发票行项目模型 ---
+class LineItem(BaseModel):
+    """Defines a single line item from an invoice."""
+    description: Optional[str] = Field("", description="The description of the product or service.")
+    quantity: Optional[float] = Field(None, description="The quantity of the item.")
+    unit_price: Optional[float] = Field(None, description="The price for a single unit of the item.")
+    total_price: Optional[float] = Field(None, description="The total price for this line item (quantity * unit_price).")
+
+
+# --- 发票模型 (已更新) ---
+class InvoiceInfo(BaseModel):
+    """Defines the detailed, structured information to be extracted from an invoice."""
+    date: Optional[str] = Field("", description="Extract in YYYY-MM-DD format. If unclear, leave as an empty string.")
+    invoice_number: Optional[str] = Field("", description="If not found or unclear, leave as an empty string.")
+    supplier_number: Optional[str] = Field("", description="It's the organisation number. If not found or unclear, leave as an empty string.")
+    biller_name: Optional[str] = Field("", description="It's the sender's name. If not found or unclear, leave as an empty string.")
+    amount: Optional[float] = Field(None, description="Extract and format to decimal. If not present, leave as null.")
+    customer_name: Optional[str] = Field("", description="It's the receiver's name. Clean any special chars from the name. If not found or unclear, leave as an empty string.")
+    customer_address: Optional[str] = Field("", description="It's the receiver's address. Put it in one line. If not found or unclear, leave as an empty string.")
+    customer_address_line: Optional[str] = Field("", description="It's the receiver's address line, not the whole address. If not found or unclear, leave as an empty string.")
+    customer_address_city: Optional[str] = Field("", description="It's the receiver's address city. If not found, find a city in the document. If unclear, leave as an empty string.")
+    customer_address_country: Optional[str] = Field("", description="It's the receiver's address country. If not found, find the country of the extracted city. If unclear, leave as an empty string.")
+    customer_address_postal_code: Optional[str] = Field("", description="It's the receiver's address postal code. If not found or unclear, leave as an empty string.")
+    customer_address_apartment: Optional[str] = Field("", description="It's the receiver's address apartment or suite. If not found or unclear, leave as an empty string.")
+    customer_address_region: Optional[str] = Field("", description="It's the receiver's address region. If not found, find the region of the extracted city or country. If unclear, leave as an empty string.")
+    customer_address_care_of: Optional[str] = Field("", description="It's the receiver's address care of. If not found or unclear, leave as an empty string.")
+    billo_id: Optional[str] = Field("", description="Extract from customer_address if it exists, following the format 'LLL NNN-A'. If not found or unclear, leave as an empty string.")
+    line_items: List[LineItem] = Field([], description="A list of all line items from the invoice.")

requirements.txt

@@ -0,0 +1,11 @@
+fastapi
+uvicorn[standard]
+python-dotenv
+langchain
+langchain-openai
+chromadb
+tiktoken
+pdf2image
+python-multipart
+pytesseract
+Pillow