Fix to the workflow. Old flow did not make sense with our current logic.

RAGManager/app/agents/graph.py

@@ -7,7 +7,6 @@
     context_builder,
     fallback_final,
     fallback_inicial,
-    generator,
     guard_final,
     guard_inicial,
     parafraseo,
@@ -18,25 +17,24 @@
     route_after_guard_inicial,
 )
 from app.agents.state import AgentState
-from app.agents.routing import route_after_guard
 
 def create_agent_graph() -> StateGraph:
     """
     Create and configure the LangGraph agent graph.
 
     The graph implements the following flow:
     1. START -> agent_host (Nodo 1) - Prepares state, no DB operations
-    2. agent_host -> guard (Nodo 2) - Validates for malicious content
-    3. guard -> [conditional]:
-       - malicious -> fallback -> END (stops processing, no DB save)
+    2. agent_host -> guard_inicial (Nodo 2) - Validates for malicious content
+    3. guard_inicial -> [conditional]:
+       - malicious -> fallback_inicial -> END (stops processing, no DB save)
        - continue -> parafraseo (Nodo 4)
     4. parafraseo -> Saves message to DB, retrieves chat history, paraphrases
-    5. parafraseo -> retriever (Nodo 5)
-    6. retriever -> context_builder (Nodo 6)
-    7. context_builder -> guard (validates response)
-    8. guard -> [conditional]:
-       - malicious -> fallback -> END
-       - continue -> END (success)
+    5. parafraseo -> retriever (Nodo 5) - Retrieves relevant chunks from vector DB
+    6. retriever -> context_builder (Nodo 6) - Builds enriched query and generates response
+    7. context_builder -> guard_final (Nodo 8) - Validates response for risky content
+    8. guard_final -> [conditional]:
+       - risky -> fallback_final -> END
+       - continue -> END (success, returns generated message)
 
     Returns:
         Configured StateGraph instance ready for execution
@@ -51,7 +49,6 @@ def create_agent_graph() -> StateGraph:
     workflow.add_node("parafraseo", parafraseo)
     workflow.add_node("retriever", retriever)
     workflow.add_node("context_builder", context_builder)
-    workflow.add_node("generator", generator)
     workflow.add_node("guard_final", guard_final)
     workflow.add_node("fallback_final", fallback_final)
 
@@ -81,11 +78,8 @@ def create_agent_graph() -> StateGraph:
     # retriever -> context_builder
     workflow.add_edge("retriever", "context_builder")
 
-    # context_builder -> guard
-    workflow.add_edge("context_builder", "guard")
-
-    # generator -> guard_final
-    workflow.add_edge("generator", "guard_final")
+    # context_builder -> guard_final
+    workflow.add_edge("context_builder", "guard_final")
 
     # guard_final -> conditional routing
     workflow.add_conditional_edges(

RAGManager/app/agents/nodes/retriever.py

@@ -105,33 +105,35 @@ def retriever(state: AgentState) -> AgentState:
     Retriever node - Performs semantic search in vector database using LangChain PGVector.
 
     This node:
-    1. Takes 3 paraphrased phrases from parafraseo node
-    2. For each phrase, uses PGVector's similarity_search to query the database
-    3. Retrieves top 3 most relevant chunks per phrase
+    1. Takes 3 paraphrased statements from parafraseo node
+    2. For each statement, uses PGVector's similarity_search to query the database
+    3. Retrieves top 3 most relevant chunks per statement
     4. Creates a unique union of all retrieved chunks (no duplicates)
     5. Stores the chunk contents in relevant_chunks
 
     Args:
-        state: Agent state containing paraphrased_phrases (list of 3 phrases)
+        state: Agent state containing paraphrased_statements (list of 3 statements)
 
     Returns:
         Updated state with relevant_chunks set (list of unique chunk contents)
     """
     updated_state = state.copy()
 
-    # Get the 3 paraphrased phrases from state
-    paraphrased_phrases = state.get("paraphrased_phrases")
+    # Get the 3 paraphrased statements from state
+    paraphrased_statements = state.get("paraphrased_statements")
 
-    if not paraphrased_phrases or len(paraphrased_phrases) == 0:
+    if not paraphrased_statements or len(paraphrased_statements) == 0:
         logger.warning(
-            "No paraphrased phrases found in state. Parafraseo node may not have been executed yet."
+            "No paraphrased statements found in state. Parafraseo node may not have been executed yet."
         )
         updated_state["relevant_chunks"] = []
         return updated_state
 
-    # Ensure we have exactly 3 phrases (or at least handle what we get)
-    phrases_to_process = paraphrased_phrases[:3] if len(paraphrased_phrases) >= 3 else paraphrased_phrases
-    logger.info(f"Retrieving documents for {len(phrases_to_process)} phrases")
+    # Ensure we have exactly 3 statements (or at least handle what we get)
+    statements_to_process = (
+        paraphrased_statements[:3] if len(paraphrased_statements) >= 3 else paraphrased_statements
+    )
+    logger.info(f"Retrieving documents for {len(statements_to_process)} statements")
 
     # Use a set to track unique chunk IDs to avoid duplicates
     seen_chunk_ids: set[str] = set()
@@ -141,10 +143,10 @@ def retriever(state: AgentState) -> AgentState:
         # Get PGVector instance
         vector_store = _get_vector_store()
 
-        # For each phrase, retrieve top 3 chunks using PGVector's similarity_search
-        for phrase in phrases_to_process:
-            logger.debug(f"Retrieving chunks for phrase: {phrase[:50]}...")
-            chunks = _retrieve_chunks_for_phrase(vector_store, phrase, top_k=3)
+        # For each statement, retrieve top 3 chunks using PGVector's similarity_search
+        for statement in statements_to_process:
+            logger.debug(f"Retrieving chunks for statement: {statement[:50]}...")
+            chunks = _retrieve_chunks_for_phrase(vector_store, statement, top_k=3)
 
             # Add chunks to unique list (avoiding duplicates by chunk ID)
             for chunk_id, chunk_content in chunks:
@@ -153,7 +155,7 @@ def retriever(state: AgentState) -> AgentState:
                     unique_chunks.append(chunk_content)
                     logger.debug(f"Added chunk {chunk_id} to results")
 
-        logger.info(f"Retrieved {len(unique_chunks)} unique chunks from {len(phrases_to_process)} phrases")
+        logger.info(f"Retrieved {len(unique_chunks)} unique chunks from {len(statements_to_process)} statements")
     except Exception as e:
         logger.error(f"Error during retrieval: {e}", exc_info=True)
         unique_chunks = []