Update app.py

app.py

@@ -22,6 +22,14 @@ from langchain.tools.retriever import create_retriever_tool
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import datetime
 
+# ------------------------------
+# State Schema Definition
+# ------------------------------
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[AIMessage | HumanMessage | ToolMessage], add_messages]
+    context: Dict[str, Any]
+    metadata: Dict[str, Any]
+
 # ------------------------------
 # Configuration
 # ------------------------------
@@ -31,7 +39,7 @@ class ResearchConfig:
     CHUNK_SIZE = 512
     CHUNK_OVERLAP = 64
     MAX_CONCURRENT_REQUESTS = 5
-    EMBEDDING_DIMENSIONS = 1536  # New config parameter
+    EMBEDDING_DIMENSIONS = 1536
     DOCUMENT_MAP = {
         "Research Report: Results of a New AI Model Improving Image Recognition Accuracy to 98%": 
             "CV-Transformer Hybrid Architecture",
@@ -62,14 +70,14 @@ if not ResearchConfig.DEEPSEEK_API_KEY:
     st.stop()
 
 # ------------------------------
-# Quantum Document Processing (Corrected Embeddings)
+# Quantum Document Processing
 # ------------------------------
 class QuantumDocumentManager:
     def __init__(self):
         self.client = chromadb.PersistentClient(path=ResearchConfig.CHROMA_PATH)
         self.embeddings = OpenAIEmbeddings(
             model="text-embedding-3-large",
-            dimensions=ResearchConfig.EMBEDDING_DIMENSIONS  # Correct parameter usage
+            dimensions=ResearchConfig.EMBEDDING_DIMENSIONS
         )
         
     def create_collection(self, documents: List[str], collection_name: str) -> Chroma:
@@ -200,11 +208,6 @@ class ResearchWorkflow:
         self.workflow = StateGraph(AgentState)
         self._build_workflow()
 
-    class AgentState(TypedDict):
-        messages: Annotated[Sequence[AIMessage | HumanMessage | ToolMessage], add_messages]
-        context: Dict[str, Any]
-        metadata: Dict[str, Any]
-
     def _build_workflow(self):
         self.workflow.add_node("ingest", self.ingest_query)
         self.workflow.add_node("retrieve", self.retrieve_documents)