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Empowering AI Agents: V1 Enrich Split-Contact (ETL-D API)

#aiagents #langchain #llm #automation

CONTENT:

  • The Hallucination Problem: Large Language Models (LLMs) are inherently probabilistic and often generate plausible but incorrect responses, known as "hallucinations," when they lack specific information. This can be problematic in scenarios where precise and accurate data extraction is required, such as parsing a string of messy contact information into structured components. Integrating the '/v1/enrich/split-contact' endpoint ensures that the LLM does not have to guess, providing deterministic and reliable results instead.

  • Agent Tool Architecture: The '/v1/enrich/split-contact' endpoint acts as a deterministic middleware for LLM Agents. By leveraging the reliable ETL-D service, the endpoint takes in unstructured contact information and consistently outputs structured data, thereby enhancing the agent's ability to process contact information accurately. This integration minimizes errors and enriches the LLM’s capabilities by providing precise and structured data when needed.

  • Implementation:

from etld_sdk import EtldClient, models
from langchain.agents import Tool

# Configuration for ETL-D Client
class ContactSplitter:
    def __init__(self, api_key):
        self.client = EtldClient(api_key=api_key)

    def split_contact(self, contact_string: str):
        try:
            # Construct the input model as per the ETL-D SDK
            input_data = models.SplitContactInput(contact_string=contact_string)
            # Use the endpoint to process the input
            response = self.client.split_contact_v1_enrich_split_contact_post(input_data)
            return response.dict()  # Ensuring a dict format for agent compatibility
        except models.EtldException as e:
            # Handling exceptions related to payment or validation
            if e.status_code == 402:
                return {"error": "Payment Required - Insufficient credits."}
            elif e.status_code == 422:
                return {"error": f"Validation Error: {e.detail}"}
            else:
                return {"error": "An unexpected error occurred."}

# Integration with LangChain
contact_splitter_tool = Tool(
    name="ContactSplitter",
    func=ContactSplitter(api_key="your_api_key").split_contact,
    description="A tool to split a contact string into name, title, phone, and email."
)
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  • Deterministic Output Specs: When the LLM agent invokes this tool, it receives a structured JSON object with components such as Name, Title, Phone, and Email, extracted accurately from the provided messy contact string. This structured data format ensures the agent's responses are not only accurate but also reliable, facilitating further automation processes and reducing the risk of incorrect data generation.

🔗 Get the Agent Tool Code: GitHub Gist

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