WIP

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: FastAPI",
+            "type": "debugpy",
+            "request": "launch",
+            "module": "uvicorn",
+            "args": [
+                "app.main:app",
+                "--reload"
+            ],
+            "justMyCode": true
+        }
+    ]
+}

app/agents/invoice_agent.py

@@ -15,11 +15,14 @@ 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_date`: The invoice date. Extract in YYYY-MM-DD format. If unclear, leave as an empty string.
+- `invoice_due_date`: The invoice due 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.
+- `ocr_number`: The OCR number from the invoice. 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.
+- `tax_exclusive_amount`: Extract the the amount excluding taxes 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.
@@ -33,7 +36,7 @@ Carefully analyze the invoice image and extract the following fields according t
 - `bank_giro`: If found, extract the bank giro number. It often follows patterns like 'ddd-dddd', 'dddd-dddd', or 'dddddddd #41#'. If not found or unclear, leave as an empty string.
 - `plus_giro`: If found, extract the plus giro number. It often follows patterns like 'ddddddd-d #16#', 'ddddddd-d', or 'ddd dd dd-d'. If not found or unclear, leave as an empty string.
 - `customer_ssn`: If found, extract the customer social security number (personnummer). It follows the pattern 'YYYYMMDD-XXXX' or 'YYMMDD-XXXX'. 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.
+- `line_items`: Extract all line items from the invoice. For each item, extract the `description`, `quantity`, `unit_price`, and `total_price`. A list of all line items from the invoice. Make sure all of them are extracted. 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:

app/agents/vectorization_agent.py

@@ -1,29 +1,25 @@
 # app/agents/vectorization_agent.py
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_openai import OpenAIEmbeddings
-embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
-import chromadb
-
-client = chromadb.PersistentClient(path="./chroma_db")
-vector_store = client.get_or_create_collection(name="documents")
 
 text_splitter = RecursiveCharacterTextSplitter(
     chunk_size=1000,
     chunk_overlap=100,
 )
 
-def agent_vectorize_and_store(doc_id: str, text: str, category: str, language: str):
+def agent_vectorize_and_store(
-    """
+        doc_id: str,
-    Agent 4: Vectorizes a document and stores it in ChromaDB.
+        text: str,
-    """
+        category: str,
+        language: str,
+        embedding_model,
+        vector_store
+):
     print(f"--- [Background Task] Starting vectorization (ID: {doc_id})...")
 
     try:
-        return
-
         chunks = text_splitter.split_text(text)
         if not chunks:
-            print(f"--- [Background Task] document {doc_id} has no text to vectorize.")
+            print(f"--- [Background task] document is empty, skip vectorization. (ID: {doc_id})")
             return
 
         chunk_ids = [f"{doc_id}_{i}" for i in range(len(chunks))]
@@ -38,6 +34,6 @@ def agent_vectorize_and_store(doc_id: str, text: str, category: str, language: s
             documents=chunks,
             metadatas=metadatas
         )
-        print(f"--- [Background Task] Document {doc_id} vectorized and stored successfully.")
+        print(f"--- [Background Task] Document {doc_id} vectorized。")
     except Exception as e:
-        print(f"--- [background Task] Vectorization failed (ID: {doc_id}): {e}")
+        print(f"--- [Background Task] Document vectorization failed (ID: {doc_id}): {e}")

app/core/ocr.py

@@ -4,20 +4,16 @@ from typing import List
 
 
 def extract_text_from_images(images: List[Image.Image]) -> str:
-    """
+    print("--- [Core OCR] Extracting text...")
-    使用Tesseract OCR从一系列图片中提取并合并所有文本。
-    """
-    print("--- [Core OCR] 正在从图片中提取文本用于向量化...")
     full_text = []
     for img in images:
         try:
-            # lang='chi_sim+eng' 表示同时识别简体中文和英文
             text = pytesseract.image_to_string(img, lang='chi_sim+eng')
             full_text.append(text)
         except Exception as e:
-            print(f"--- [Core OCR] 单页处理失败: {e}")
+            print(f"--- [Core OCR] Processing image failed: {e}")
             continue
 
     combined_text = "\n\n--- Page Break ---\n\n".join(full_text)
-    print("--- [Core OCR] 文本提取成功。")
+    print("--- [Core OCR] Text extraction completed.")
     return combined_text

app/core/vector_store.py

@@ -1,47 +1,28 @@
-# app/core/vector_store.py
 import os
 import chromadb
 from dotenv import load_dotenv
 from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings
 
 load_dotenv()
-
 LLM_PROVIDER = os.getenv("LLM_PROVIDER", "openai").lower()
-
 embedding_model = None
 
 print(f"--- [Core] Initializing Embeddings with provider: {LLM_PROVIDER} ---")
 
 if LLM_PROVIDER == "azure":
-    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":
-    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'.")
+    raise ValueError(f"Unsupported LLM_PROVIDER: {LLM_PROVIDER}.")
-
 
 client = chromadb.PersistentClient(path="./chroma_db")
-vector_store = client.get_or_create_collection(
+vector_store = client.get_or_create_collection(name="documents")
-    name="documents",
-    metadata={"hnsw:space": "cosine"}
-)

app/routers/documents.py

@@ -5,10 +5,10 @@ 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_images
+from ..core.vector_store import embedding_model, vector_store
 
 # Create an APIRouter instance
 router = APIRouter(
@@ -102,10 +102,12 @@ async def upload_and_process_document(
         full_text = await run_in_threadpool(extract_text_from_images, images)
         background_tasks.add_task(
             agents.agent_vectorize_and_store,
-            doc_id,
+            doc_id=doc_id,
-            full_text,
+            text=full_text,
-            category,
+            category=category,
-            language
+            language=language,
+            embedding_model=embedding_model,
+            vector_store=vector_store
         )
         print("--- [Main] Vectorization job added to background tasks.")
 
@@ -118,4 +120,4 @@ async def upload_and_process_document(
 async def get_result(doc_id: str):
     if doc_id in db_results:
         return db_results[doc_id]
-    raise HTTPException(status_code=404, detail="Document not found.")
+    raise HTTPException(status_code=404, detail="Document not found.")

app/schemas.py

@@ -32,11 +32,14 @@ class LineItem(BaseModel):
 
 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_date: Optional[str] = Field("", description="The invoice date. Extract in YYYY-MM-DD format. If unclear, leave as an empty string.")
+    invoice_due_date: Optional[str] = Field("", description="The invoice due date.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.")
+    ocr_number: Optional[str] = Field("", description="The OCR number from the invoice. 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.")
+    tax_exclusive_amount: Optional[float] = Field(None, description="Extract the the amount excluding taxes and format it to a decimal number. 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.")
@@ -50,4 +53,4 @@ class InvoiceInfo(BaseModel):
     bank_giro: Optional[str] = Field("", description="BankGiro number, e.g., '123-4567'. If not found, leave as an empty string.")
     plus_giro: Optional[str] = Field("", description="PlusGiro number, e.g., '123456-7'. If not found, leave as an empty string.")
     customer_ssn: Optional[str] = Field("", description="Customer's social security number, e.g., 'YYYYMMDD-XXXX'. If not found, leave as an empty string.")
-    line_items: List[LineItem] = Field([], description="A list of all line items from the invoice.")
+    line_items: List[LineItem] = Field([], description="A list of all line items from the invoice. Make sure all of them are extracted.")