Init project

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