Update art_explorer.py

art_explorer.py

@@ -1,9 +1,7 @@
-# art_explorer.py
-import instructor
+from typing import Dict, Any, List, Optional
 from openai import OpenAI
-from pydantic import BaseModel
-from typing import List, Optional
 import json
+from pydantic import BaseModel
 
 class ExplorationNode(BaseModel):
   title: str
@@ -13,32 +11,82 @@ class ExplorationNode(BaseModel):
 
 class ExplorationPath(BaseModel):
   nodes: List[ExplorationNode]
-  context: str
+  query: str
+  domain: Optional[str] = None
 
 class ExplorationPathGenerator:
   def __init__(self, api_key: str):
-      # Initialize OpenAI client with GROQ's base URL
       self.client = OpenAI(
           api_key=api_key,
-          base_url="https://api.groq.com/openai/v1"
+          base_url="https://api.groq.com/v1"
       )
-      # Patch the client with instructor
-      self.client = instructor.patch(self.client)
-
-  def generate_exploration_path(self, query: str, max_depth: int = 3) -> ExplorationPath:
+      
+  def generate_exploration_path(
+      self,
+      query: str,  # Changed from user_query to query to match app.py
+      selected_path: List[Dict[str, Any]] = None,
+      exploration_parameters: Dict[str, Any] = None
+  ) -> Dict[str, Any]:
+      """
+      Generate an exploration path based on the query and parameters
+      
+      Args:
+          query: The user's exploration query
+          selected_path: Previously selected exploration path nodes
+          exploration_parameters: Additional parameters for exploration
+      
+      Returns:
+          Dict containing the exploration path and metadata
+      """
       try:
-          system_prompt = """You are an art history expert creating exploration paths. 
-          Generate a structured exploration path based on the query, with connected concepts."""
+          # Set defaults
+          selected_path = selected_path or []
+          exploration_parameters = exploration_parameters or {}
           
+          # Extract parameters
+          depth = exploration_parameters.get("depth", 5)
+          domain = exploration_parameters.get("domain")
+          
+          # Prepare system prompt
+          system_prompt = """You are an expert art historian assistant. Generate structured exploration paths 
+          that help users discover connections and insights in art history. Format your response as a JSON object 
+          with 'nodes' containing exploration points."""
+
+          # Prepare user message
+          user_message = {
+              "query": query,
+              "depth": depth,
+              "domain": domain,
+              "previous_explorations": selected_path
+          }
+
+          # Make API call
           response = self.client.chat.completions.create(
               model="mixtral-8x7b-32768",
-              response_model=ExplorationPath,
               messages=[
                   {"role": "system", "content": system_prompt},
-                  {"role": "user", "content": f"Generate an exploration path for: {query}. Maximum depth: {max_depth}"}
-              ]
+                  {"role": "user", "content": json.dumps(user_message)}
+              ],
+              temperature=0.7,
+              max_tokens=2000
           )
-          return response
+
+          # Parse and validate response
+          result = json.loads(response.choices[0].message.content)
+          
+          # Validate with Pydantic
+          exploration_path = ExplorationPath(
+              nodes=[ExplorationNode(**node) for node in result["nodes"]],
+              query=query,
+              domain=domain
+          )
+          
+          return exploration_path.model_dump()
+
       except Exception as e:
-          print(f"Error generating exploration path: {str(e)}")
-          raise
+          print(f"Error generating exploration path: {e}")
+          return {
+              "error": str(e),
+              "status": "failed",
+              "message": "Failed to generate exploration path"
+          }