agents using grok

examples/multimodal/Agents/medical insurance claim rejection based on medical codes.py

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+from langchain.agents import initialize_agent, Tool, AgentType
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+from langchain_xai import ChatXAI
+
+llm = ChatXAI(
+    model="grok-2-1212",#"grok-beta",
+    temperature=0.7,
+    max_tokens=None,
+    timeout=None,
+    max_retries=2,
+    api_key="your Grok api key")
+    # other params...
+
+
+# Create a function to query ICD/CPT codes from an LLM
+def get_medical_code_response(query: str) -> str:
+    """Fetches ICD/CPT codes based on the user query."""
+    # Change to your preferred model
+    messages=[
+            {"role": "system", "content": "You are a helpful medical coding assistant."},
+            {"role": "human", "content": query}
+        ]
+
+
+    response=llm.invoke(messages)
+    return response["choices"][0]["message"]["content"]
+
+# Create a tool to use in the LangChain agent
+medical_code_tool = Tool(
+    name="MedicalCodeLookup",
+    func=get_medical_code_response,
+    description="Use this tool to fetch ICD/CPT codes for medical conditions or procedures."
+)
+
+# Create a tool for claim rejection prediction (optional)
+def predict_claim_rejection(codes: str) -> str:
+    """Predict claim rejection risks based on medical codes."""
+    query = f"Predict claim rejection risks for the following medical codes: {codes}"
+    return get_medical_code_response(query)
+
+claim_rejection_tool = Tool(
+    name="ClaimRejectionPrediction",
+    func=predict_claim_rejection,
+    description="Use this tool to analyze claim rejection risks based on provided medical codes."
+)
+
+# Set up conversation memory
+memory = ConversationBufferMemory(memory_key="chat_history")
+
+# Initialize the agent with the tools
+tools = [medical_code_tool, claim_rejection_tool]
+agent = initialize_agent(
+    tools,
+    ChatXAI(model="grok-2-1212",api_key="your grok api key"),  # Choose your preferred model
+    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    memory=memory,
+    verbose=True
+ )
+
+# Start the chatbot loop
+print("Medical Coding Agent (Type 'exit' to quit)\n")
+while True:
+    user_query = input("You: ")
+
+    if user_query.lower() == 'exit':
+        print("Goodbye! ")
+        break
+
+    # Pass the user input to the agent
+    response = agent.run(user_query)
+
+    print(f"Agent: {response}\n")