refactor: unify structured and str completion

cognee/modules/retrieval/graph_completion_cot_retriever.py

@@ -6,7 +6,10 @@ from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge
 from cognee.shared.logging_utils import get_logger
 
 from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever
-from cognee.modules.retrieval.utils.completion import generate_completion, summarize_text
+from cognee.modules.retrieval.utils.completion import (
+    generate_structured_completion,
+    summarize_text,
+)
 from cognee.modules.retrieval.utils.session_cache import (
     save_conversation_history,
     get_conversation_history,
@@ -82,12 +85,20 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
         self,
         query: str,
         context: Optional[List[Edge]] = None,
-        session_id: Optional[str] = None,
+        conversation_history: str = "",
         max_iter: int = 4,
         response_model: Type = str,
     ) -> tuple[Any, str, List[Edge]]:
         """
-        Run chain-of-thought completion with optional structured output and session caching.
+        Run chain-of-thought completion with optional structured output.
+
+        Parameters:
+        -----------
+            - query: User query
+            - context: Optional pre-fetched context edges
+            - conversation_history: Optional conversation history string
+            - max_iter: Maximum CoT iterations
+            - response_model: Type for structured output (str for plain text)
 
         Returns:
         --------
@@ -99,16 +110,6 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
         triplets = []
         completion = ""
 
-        # Retrieve conversation history if session saving is enabled
-        cache_config = CacheConfig()
-        user = session_user.get()
-        user_id = getattr(user, "id", None)
-        session_save = user_id and cache_config.caching
-
-        conversation_history = ""
-        if session_save:
-            conversation_history = await get_conversation_history(session_id=session_id)
-
         for round_idx in range(max_iter + 1):
             if round_idx == 0:
                 if context is None:
@@ -120,29 +121,15 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
                 triplets += await self.get_context(followup_question)
                 context_text = await self.resolve_edges_to_text(list(set(triplets)))
 
-            if response_model is str:
-                completion = await generate_completion(
-                    query=query,
-                    context=context_text,
-                    user_prompt_path=self.user_prompt_path,
-                    system_prompt_path=self.system_prompt_path,
-                    system_prompt=self.system_prompt,
-                    conversation_history=conversation_history if session_save else None,
-                )
-            else:
-                args = {"question": query, "context": context_text}
-                user_prompt = render_prompt(self.user_prompt_path, args)
-                system_prompt = (
-                    self.system_prompt
-                    if self.system_prompt
-                    else read_query_prompt(self.system_prompt_path)
-                )
-
-                completion = await LLMGateway.acreate_structured_output(
-                    text_input=user_prompt,
-                    system_prompt=system_prompt,
-                    response_model=response_model,
-                )
+            completion = await generate_structured_completion(
+                query=query,
+                context=context_text,
+                user_prompt_path=self.user_prompt_path,
+                system_prompt_path=self.system_prompt_path,
+                system_prompt=self.system_prompt,
+                conversation_history=conversation_history if conversation_history else None,
+                response_model=response_model,
+            )
 
             logger.info(f"Chain-of-thought: round {round_idx} - answer: {completion}")
 
@@ -176,16 +163,6 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
                     f"Chain-of-thought: round {round_idx} - follow-up question: {followup_question}"
                 )
 
-        # Save to session cache
-        if session_save:
-            context_summary = await summarize_text(context_text)
-            await save_conversation_history(
-                query=query,
-                context_summary=context_summary,
-                answer=str(completion),
-                session_id=session_id,
-            )
-
         return completion, context_text, triplets
 
     async def get_structured_completion(
@@ -217,10 +194,21 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
         --------
             - Any: The generated structured completion based on the response model.
         """
+        # Check if session saving is enabled
+        cache_config = CacheConfig()
+        user = session_user.get()
+        user_id = getattr(user, "id", None)
+        session_save = user_id and cache_config.caching
+
+        # Load conversation history if enabled
+        conversation_history = ""
+        if session_save:
+            conversation_history = await get_conversation_history(session_id=session_id)
+
         completion, context_text, triplets = await self._run_cot_completion(
             query=query,
             context=context,
-            session_id=session_id,
+            conversation_history=conversation_history,
             max_iter=max_iter,
             response_model=response_model,
         )
@@ -230,6 +218,16 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
                 question=query, answer=str(completion), context=context_text, triplets=triplets
             )
 
+        # Save to session cache if enabled
+        if session_save:
+            context_summary = await summarize_text(context_text)
+            await save_conversation_history(
+                query=query,
+                context_summary=context_summary,
+                answer=str(completion),
+                session_id=session_id,
+            )
+
         return completion
 
     async def get_completion(
@@ -263,7 +261,7 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
 
             - List[str]: A list containing the generated answer to the user's query.
         """
-        completion, context_text, triplets = await self._run_cot_completion(
+        completion = await self.get_structured_completion(
             query=query,
             context=context,
             session_id=session_id,
@@ -271,9 +269,4 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
             response_model=str,
         )
 
-        if self.save_interaction and context and triplets and completion:
-            await self.save_qa(
-                question=query, answer=completion, context=context_text, triplets=triplets
-            )
-
         return [completion]

cognee/modules/retrieval/utils/completion.py

@@ -1,17 +1,18 @@
-from typing import Optional
+from typing import Optional, Type, Any
 from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.infrastructure.llm.prompts import render_prompt, read_query_prompt
 
 
-async def generate_completion(
+async def generate_structured_completion(
     query: str,
     context: str,
     user_prompt_path: str,
     system_prompt_path: str,
     system_prompt: Optional[str] = None,
     conversation_history: Optional[str] = None,
-) -> str:
-    """Generates a completion using LLM with given context and prompts."""
+    response_model: Type = str,
+) -> Any:
+    """Generates a structured completion using LLM with given context and prompts."""
     args = {"question": query, "context": context}
     user_prompt = render_prompt(user_prompt_path, args)
     system_prompt = system_prompt if system_prompt else read_query_prompt(system_prompt_path)
@@ -23,6 +24,26 @@ async def generate_completion(
     return await LLMGateway.acreate_structured_output(
         text_input=user_prompt,
         system_prompt=system_prompt,
+        response_model=response_model,
+    )
+
+
+async def generate_completion(
+    query: str,
+    context: str,
+    user_prompt_path: str,
+    system_prompt_path: str,
+    system_prompt: Optional[str] = None,
+    conversation_history: Optional[str] = None,
+) -> str:
+    """Generates a completion using LLM with given context and prompts."""
+    return await generate_structured_completion(
+        query=query,
+        context=context,
+        user_prompt_path=user_prompt_path,
+        system_prompt_path=system_prompt_path,
+        system_prompt=system_prompt,
+        conversation_history=conversation_history,
         response_model=str,
     )