Update app.py

app.py

@@ -31,6 +31,7 @@ class ResearchConfig:
     CHUNK_SIZE = 512
     CHUNK_OVERLAP = 64
     MAX_CONCURRENT_REQUESTS = 5
+    EMBEDDING_DIMENSIONS = 1536  # New config parameter
     DOCUMENT_MAP = {
         "Research Report: Results of a New AI Model Improving Image Recognition Accuracy to 98%": 
             "CV-Transformer Hybrid Architecture",
@@ -61,14 +62,14 @@ if not ResearchConfig.DEEPSEEK_API_KEY:
     st.stop()
 
 # ------------------------------
-# Quantum Document Processing
+# Quantum Document Processing (Corrected Embeddings)
 # ------------------------------
 class QuantumDocumentManager:
     def __init__(self):
         self.client = chromadb.PersistentClient(path=ResearchConfig.CHROMA_PATH)
         self.embeddings = OpenAIEmbeddings(
             model="text-embedding-3-large",
-            model_kwargs={"dimensions": 1536}
+            dimensions=ResearchConfig.EMBEDDING_DIMENSIONS  # Correct parameter usage
         )
         
     def create_collection(self, documents: List[str], collection_name: str) -> Chroma:
@@ -185,12 +186,9 @@ class CognitiveProcessor:
             return {"error": str(e)}
     
     def _consensus_check(self, results: List[Dict]) -> Dict:
-        # Implement consensus algorithm
         valid = [r for r in results if "error" not in r]
         if not valid:
             return {"error": "All API requests failed"}
-        
-        # Select longest valid response
         return max(valid, key=lambda x: len(x.get('choices', [{}])[0].get('message', {}).get('content', '')))
 
 # ------------------------------
@@ -208,14 +206,12 @@ class ResearchWorkflow:
         metadata: Dict[str, Any]
 
     def _build_workflow(self):
-        # Define workflow nodes
         self.workflow.add_node("ingest", self.ingest_query)
         self.workflow.add_node("retrieve", self.retrieve_documents)
         self.workflow.add_node("analyze", self.analyze_content)
         self.workflow.add_node("validate", self.validate_output)
         self.workflow.add_node("refine", self.refine_results)
 
-        # Configure workflow edges
         self.workflow.set_entry_point("ingest")
         self.workflow.add_edge("ingest", "retrieve")
         self.workflow.add_edge("retrieve", "analyze")
@@ -227,7 +223,6 @@ class ResearchWorkflow:
         self.workflow.add_edge("validate", END)
         self.workflow.add_edge("refine", "retrieve")
 
-        # Compile workflow
         self.app = self.workflow.compile()
 
     def ingest_query(self, state: AgentState) -> Dict:
@@ -389,7 +384,7 @@ class ResearchInterface:
             
             st.subheader("Analysis Metrics")
             st.metric("Vector Collections", 2)
-            st.metric("Embedding Dimensions", 1536)
+            st.metric("Embedding Dimensions", ResearchConfig.EMBEDDING_DIMENSIONS)
 
     def _build_main_interface(self):
         st.title("🧠 NeuroResearch AI")