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services/raggr/blueprints/conversation/agents.py

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+from langchain.agents import create_agent
+from langchain.tools import tool
+from langchain_openai import ChatOpenAI
+
+from blueprints.rag.logic import query_vector_store
+
+openai_gpt_5_mini = ChatOpenAI(model="gpt-5-mini")
+
+
+@tool(response_format="content_and_artifact")
+async def simba_search(query: str):
+    """Search through Simba's medical records, veterinary documents, and personal information.
+
+    Use this tool whenever the user asks questions about:
+    - Simba's health history, medical records, or veterinary visits
+    - Medications, treatments, or diagnoses
+    - Weight, diet, or physical characteristics over time
+    - Veterinary recommendations or advice
+    - Ryan's (the owner's) information related to Simba
+    - Any factual information that would be found in documents
+
+    Args:
+        query: The user's question or information need about Simba
+
+    Returns:
+        Relevant information from Simba's documents
+    """
+    print(f"[SIMBA SEARCH] Tool called with query: {query}")
+    serialized, docs = await query_vector_store(query=query)
+    print(f"[SIMBA SEARCH] Found {len(docs)} documents")
+    print(f"[SIMBA SEARCH] Serialized result length: {len(serialized)}")
+    print(f"[SIMBA SEARCH] First 200 chars: {serialized[:200]}")
+    return serialized, docs
+
+
+main_agent = create_agent(model=openai_gpt_5_mini, tools=[simba_search])