Empowering AI Agents: V1 Parse Bank-Statement (ETL-D API)

CONTENT:

• The Hallucination Problem: Large language models (LLMs), while powerful, can exhibit unpredictable behavior, often referred to as hallucination, where the AI generates incorrect or misleading results. This is particularly critical in financial contexts like parsing bank statements, where accuracy is paramount. The /v1/parse/bank-statement endpoint from the ETL-D provides a deterministic, precise, and structured transformation of Spanish Norma 43 (N43) bank statement files into JSON. By integrating this endpoint, we ensure that our LLM agent can reliably parse bank statement data without making erroneous assumptions or guesses.

• Agent Tool Architecture: In the context of an LLM agent like LangChain or AutoGPT, acting as a deterministic middleware, the /v1/parse/bank-statement endpoint plays a crucial role. It serves as a tool that takes raw N43 bank statements as input and processes them into structured data that the LLM can leverage for further analysis or decision-making, eliminating ambiguity and errors in data interpretation.

• Implementation: Below is a Python block that demonstrates how to wrap the ETL-D SDK endpoint as a LangChain @tool, incorporating robust error handling.
  

  from etld_sdk import ETLDClient, BankStatementInput
  from langchain.tools import tool

  # Initialize the ETL-D client
  client = ETLDClient(api_key="your_api_key_here")

  @tool
  def parse_bank_statement(raw_text: str) -> dict:
      """Tool for parsing N43 bank statements using ETL-D endpoint."""
      try:
          # Prepare the input data
          input_data = BankStatementInput(raw_text=raw_text)

          # Call the ETL-D endpoint
          response = client.parse_bank_statement_v1_parse_bank_statement_post(input_data)

          # Handle response
          if response.status_code == 200:
              return response.json()
          elif response.status_code == 402:
              raise Exception("Payment Required: Insufficient credits.")
          elif response.status_code == 422:
              raise ValueError("Validation Error: The input was invalid.")
          else:
              raise Exception("Unexpected error occurred.")

      except Exception as e:
          # Log the error
          print(f"An error occurred: {str(e)}")
          return {}

  # Example usage as a callable function
  def agent_parsing_bank_statements(raw_n43_text):
      return parse_bank_statement(raw_n43_text)

• Deterministic Output Specs: When the LLM agent uses this tool to parse a bank statement, it receives back a structured JSON output. This JSON includes detailed account information, balances, and transaction details such as multi-line concepts, providing the LLM a reliable basis for structured data analysis. This process ensures clarity, accuracy, and consistency, critical factors when handling financial data within AI systems.

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🔗 Get the Agent Tool Code: GitHub Gist