Initial Tool Setup

.env.example

@@ -1,3 +0,0 @@
-ANTHROPIC_API_KEY=...
-TAVILY_API_KEY=...
-OPENAI_API_KEY=...

my_agent/agent.py

@@ -7,7 +7,7 @@
 
 # Define the config
 class GraphConfig(TypedDict):
-    model_name: Literal["anthropic", "openai"]
+    model_name: Literal[ "openai"]
 
 
 # Define a new graph
@@ -50,3 +50,24 @@ class GraphConfig(TypedDict):
 # This compiles it into a LangChain Runnable,
 # meaning you can use it as you would any other runnable
 graph = workflow.compile()
+
+# def stream_graph_updates(user_input: str):
+#     for event in graph.stream({"messages": [("user", user_input)]}):
+#         for value in event.values():
+#             print(value)
+#             print("Assistant:", value["messages"][-1].content)
+
+# while True:
+#     try:
+#         user_input = input("User: ")
+#         if user_input.lower() in ["quit", "exit", "q"]:
+#             print("Goodbye!")
+#             break
+
+#         stream_graph_updates(user_input)
+#     except:
+#         # fallback if input() is not available
+#         user_input = "What do you know about LangGraph?"
+#         print("User: " + user_input)
+#         stream_graph_updates(user_input)
+#         break

my_agent/utils/tools.py

@@ -1,3 +1,80 @@
 from langchain_community.tools.tavily_search import TavilySearchResults
 
-tools = [TavilySearchResults(max_results=1)]
+
+
+import http.client
+import json
+from typing import List, Optional, Dict
+from pydantic import BaseModel, Field
+from langchain.tools import tool
+
+
+class PropertySearchFields(BaseModel):
+    city: Optional[List[str]] = Field(
+        None,
+        description="List of Cities to search properties (e.g., ['Houston', 'Dallas']), CAN NEVER BE A NUMBER",
+    )
+    community: Optional[List[str]] = Field(
+        None,
+        description="List of Communities to search properties on. {Key Communities: cinco ranch, copperfield, eaglewood, canyon gate at brazon, clear lake city}",
+    )
+    zip_code: Optional[str] = Field(
+        None,
+        description="Numeric Zip code to search properties by, (e.g., 77027, 77024). Zip codes usually start with '77'.",
+    )
+    bedrooms: Optional[Dict[str, int]] = Field(
+        None,
+        description="Dictionary with Number of Bedrooms with keys 'min', 'max', or 'equal'",
+    )
+    baths: Optional[Dict[str, int]] = Field(
+        None,
+        description="Dictionary with Number of Bathrooms with keys 'min', 'max', or 'equal'",
+    )
+    price: Optional[Dict[str, int]] = Field(
+        None,
+        description="Dictionary with Price of property with keys 'min', 'max', or 'equal'",
+    )
+    for_sale: Optional[int] = Field(
+        1, description="Indicates if the property is on rent"
+    )
+
+class PropertySearchResultItem(BaseModel):
+    mlsnum: Optional[str]
+    address: Optional[str]
+    price: Optional[float]
+    beds: int
+    baths: int
+    city: str
+    zipCode: str
+    sqft: int
+#args_schema=PropertySearchFields
+class PropertySearchInput(BaseModel):
+    fields: PropertySearchFields = Field(
+        ..., description="Input fields to search properties"
+    )
+@tool(args_schema=PropertySearchInput)
+def search_properties(fields: PropertySearchFields) ->Dict:
+    """Search properties based on input filters."""
+    # Implement your API connection and handling here
+    # Dummy data for the sake of example
+    print("1")
+    properties = [
+        {"property":
+        # PropertySearchResultItem(
+          """  mlsnum="123456",
+            address="123 Main St",
+            price=350000.0,
+            beds=3,
+            baths=2,
+            city="Houston",
+            zipCode="77002",
+            sqft=1500   """} 
+            # )
+    ]
+    return properties
+
+# Example use:
+# Create an instance of PropertySearchFields with the desired search criteria
+# search_criteria = PropertySearchFields(city=["Houston"], max_price=500000)
+# properties = search_properties(fields=search_criteria)
+tools = [search_properties]