75 lines
4.2 KiB
Python
75 lines
4.2 KiB
Python
# app/agents/invoice_agent.py
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from langchain_core.messages import HumanMessage
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from langchain_core.output_parsers import PydanticOutputParser
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from langchain.prompts import PromptTemplate
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from ..core.llm import llm
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from ..schemas import InvoiceInfo
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parser = PydanticOutputParser(pydantic_object=InvoiceInfo)
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# The prompt now includes the detailed rules for each field using snake_case.
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invoice_template = """
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You are an expert data entry clerk AI. Your primary goal is to extract information from an invoice image with the highest possible accuracy.
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The document's primary language is '{language}'.
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## Instructions:
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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.
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- `date`: Extract in YYYY-MM-DD format. If unclear, leave as an empty string.
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- `invoice_number`: If not found or unclear, leave as an empty string.
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- `supplier_number`: This is the organisation number. If not found or unclear, leave as an empty string.
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- `biller_name`: This is the sender's name. If not found or unclear, leave as an empty string.
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- `amount`: Extract the final total amount and format it to a decimal number. If not present, leave as null.
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- `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.
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- `customer_address`: This is the receiver's full address. Put it in one line. If not found or unclear, leave as an empty string.
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- `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.
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- `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.
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- `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.
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- `customer_address_postal_code`: This is the receiver's postal code. If not found or unclear, leave as an empty string.
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- `customer_address_apartment`: This is the receiver's apartment or suite number. If not found or unclear, leave as an empty string.
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- `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.
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- `customer_address_care_of`: This is the receiver's 'care of' (c/o) line. If not found or unclear, leave as an empty string.
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- `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.
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- `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.
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## Example:
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If the invoice shows a line item "Consulting Services | 2 hours | $100.00/hr | $200.00", the output for that line item should be:
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```json
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{{
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"description": "Consulting Services",
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"quantity": 2,
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"unit_price": 100.00,
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"total_price": 200.00
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}}
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Your Task:
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Now, analyze the provided image and output the full JSON object according to the format below.
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{format_instructions}
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"""
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invoice_prompt = PromptTemplate(
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template=invoice_template,
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input_variables=["language"],
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partial_variables={"format_instructions": parser.get_format_instructions()},
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)
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async def agent_extract_invoice_info(image_base64: str, language: str) -> InvoiceInfo:
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"""Agent 3: Extracts invoice information from an image, aware of the document's language."""
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print(f"--- [Agent 3] Calling multimodal LLM to extract invoice info (Language: {language})...")
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prompt_text = await invoice_prompt.aformat(language=language)
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msg = HumanMessage(
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content=[
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{"type": "text", "text": prompt_text},
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{
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"type": "image_url",
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"image_url": f"data:image/png;base64,{image_base64}",
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},
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]
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)
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chain = llm | parser
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invoice_info = await chain.ainvoke([msg])
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return invoice_info
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