adds context log

cognee/infrastructure/llm/openai/adapter.py

@@ -63,8 +63,7 @@ class OpenAIAdapter(LLMInterface):
             messages=[
                 {
                     "role": "user",
-                    "content": f"""Use the given format to
-                extract information from the following input: {text_input}. """,
+                    "content": text_input,
                 },
                 {
                     "role": "system",
@@ -91,8 +90,7 @@ class OpenAIAdapter(LLMInterface):
             messages=[
                 {
                     "role": "user",
-                    "content": f"""Use the given format to
-                extract information from the following input: {text_input}. """,
+                    "content": text_input,
                 },
                 {
                     "role": "system",

cognee/infrastructure/llm/prompts/answer_simple_question.txt

@@ -1 +1,10 @@
 Answer the question using the provided context. Be as brief as possible.
+Please make sure that you answer the question with a correct type.
+
+Rules:
+    •  Minimize words – Use as few as possible
+    •  Yes/no questions → Answer with only "yes" or "no"
+    •  What/Who/Where questions → Answer with a single word or phrase (no full sentences).
+    •  When questions → Give only the relevant date, time, or period.
+    •  How/Why questions → Answer with the shortest possible phrase.
+    •  No punctuation, just the answer, preferably in dry, concise lowercase

cognee/modules/retrieval/graph_completion_retriever.py

@@ -1,13 +1,28 @@
-from typing import Any, Optional
+from typing import Any, Optional, List
 from collections import Counter
 import string
+import logging
 
+from pydantic import BaseModel, Field
 from cognee.infrastructure.engine import DataPoint
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
 from cognee.modules.graph.utils.convert_node_to_data_point import get_all_subclasses
 from cognee.modules.retrieval.base_retriever import BaseRetriever
 from cognee.modules.retrieval.utils.brute_force_triplet_search import brute_force_triplet_search
 from cognee.modules.retrieval.utils.completion import generate_completion
 from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS
+from cognee.modules.search.models.Query import Query
+
+t_logger = logging.getLogger(__name__)
+t_logger.setLevel(logging.INFO)
+
+
+class AnswerValidation(BaseModel):
+    is_valid: bool = Field(
+        ...,
+        description="Indicates whether the answer to the question is fully supported by the context",
+    )
+    reasoning: str = Field("", description="Detailed reasoning of what is missing from the context")
 
 
 class GraphCompletionRetriever(BaseRetriever):
@@ -84,6 +99,7 @@ class GraphCompletionRetriever(BaseRetriever):
 
     async def get_completion(self, query: str, context: Optional[Any] = None) -> Any:
         """Generates a completion using graph connections context."""
+        """
         if context is None:
             context = await self.get_context(query)
 
@@ -93,8 +109,70 @@ class GraphCompletionRetriever(BaseRetriever):
             user_prompt_path=self.user_prompt_path,
             system_prompt_path=self.system_prompt_path,
         )
+
+        """
+
+        completion = await self.get_chain_of_thought(query=query)
         return [completion]
 
+    async def get_chain_of_thought(self, query, max_iter=4):
+        llm_client = get_llm_client()
+        followup_question = ""
+        triplets = []
+
+        for round_idx in range(max_iter + 1):
+            if round_idx == 0:
+                triplets = await self.get_triplets(query)
+                context = await self.resolve_edges_to_text(triplets)
+            else:
+                triplets += await self.get_triplets(followup_question)
+                context = await self.resolve_edges_to_text(list(set(triplets)))
+                t_logger.info(f"Round {round_idx} - context: {context}")
+
+            # Generate answer
+            answer = await generate_completion(
+                query=query,
+                context=context,
+                user_prompt_path=self.user_prompt_path,
+                system_prompt_path=self.system_prompt_path,
+            )
+            t_logger.info(f"Round {round_idx} - answer: {answer}")
+
+            # Prepare validation prompt
+            valid_user_prompt = (
+                f"""\n\n--Question--\n{query}\n\n--ANSWER--\n{answer}\n\n--CONTEXT--\n{context}\n"""
+            )
+
+            valid_system_prompt = "You are a helpful agent who are allowed to use only the provided question answer and context. I want to you find reasoning what is missing from the context or why the answer is not answering the question or not correct strictly based on the context"
+
+            reasoning = await llm_client.acreate_structured_output(
+                text_input=valid_user_prompt,
+                system_prompt=valid_system_prompt,
+                response_model=str,
+            )
+
+            # Ask follow-up question to fill gaps
+            followup_system = (
+                "You are a helpful assistant whose job is to ask exactly one clarifying follow-up question,"
+                " to collect the missing piece of information needed to fully answer the user’s original query."
+                " Respond with the question only (no extra text, no punctuation beyond what’s needed)."
+            )
+
+            followup_prompt = (
+                "Based on the following, ask exactly one question that would directly resolve the gap identified in the validation reasoning and allow a valid answer."
+                "Think in a way that with the followup question you are exploring a knowledge graph which contains entities, entity types and document chunks\n\n"
+                f"Query: {query}\n"
+                f"Answer: {answer}\n"
+                f"Reasoning:\n{reasoning}\n"
+            )
+            followup_question = await llm_client.acreate_structured_output(
+                text_input=followup_prompt, system_prompt=followup_system, response_model=str
+            )
+            t_logger.info(f"Round {round_idx} - follow-up question: {followup_question}")
+
+        # Fallback if no iteration passed validation
+        return answer
+
     def _top_n_words(self, text, stop_words=None, top_n=3, separator=", "):
         """Concatenates the top N frequent words in text."""
         if stop_words is None: