feat: Added node scoring limit to be 0.4!

bot/retrievers/custom_retriever.py

@@ -10,6 +10,7 @@
 )
 from llama_index.core.schema import Node, NodeWithScore, ObjectType
 from llama_index.core.vector_stores.types import VectorStoreQueryResult
+from utils.globals import RETRIEVER_THRESHOLD
 
 
 class CustomVectorStoreRetriever(VectorIndexRetriever):
@@ -50,10 +51,12 @@ def _build_node_list_from_query_result(
             score: float | None = None
             if query_result.similarities is not None:
                 score = query_result.similarities[ind]
-            # This is the part we updated
-            node_new = Node.from_dict(node.to_dict())
-            node_with_score = NodeWithScore(node=node_new, score=score)
+
+            if score is not None and score >= RETRIEVER_THRESHOLD:
+                # This is the part we updated
+                node_new = Node.from_dict(node.to_dict())
+                node_with_score = NodeWithScore(node=node_new, score=score)
 
-            node_with_scores.append(node_with_score)
+                node_with_scores.append(node_with_score)
 
         return node_with_scores

utils/globals.py

@@ -0,0 +1,2 @@
+# the theshold to skip nodes of being included in an answer
+RETRIEVER_THRESHOLD = .4

utils/query_engine/dual_qdrant_retrieval_engine.py

@@ -12,6 +12,7 @@
 from schema.type import DataType
 from tc_hivemind_backend.qdrant_vector_access import QDrantVectorAccess
 from utils.query_engine.qdrant_query_engine_utils import QdrantEngineUtils
+from utils.globals import RETRIEVER_THRESHOLD
 
 qa_prompt = PromptTemplate(
     "Context information is below.\n"
@@ -176,15 +177,17 @@ def _setup_vector_store_index(
 
     def _process_basic_query(self, query_str: str) -> Response:
         nodes: list[NodeWithScore] = self.retriever.retrieve(query_str)
-        context_str = "\n\n".join([n.node.get_content() for n in nodes])
+        nodes_filtered = [node for node in nodes if node.score >= RETRIEVER_THRESHOLD]
+        context_str = "\n\n".join([n.node.get_content() for n in nodes_filtered])
         prompt = self.qa_prompt.format(context_str=context_str, query_str=query_str)
         response = self.llm.complete(prompt)
 
         # return final_response
-        return Response(response=str(response), source_nodes=nodes)
+        return Response(response=str(response), source_nodes=nodes_filtered)
 
     def _process_summary_query(self, query_str: str) -> Response:
         summary_nodes = self.summary_retriever.retrieve(query_str)
+        summary_nodes_filtered = [node for node in summary_nodes if node.score >= RETRIEVER_THRESHOLD]
         utils = QdrantEngineUtils(
             metadata_date_key=self.metadata_date_key,
             metadata_date_format=self.metadata_date_format,
@@ -193,7 +196,7 @@ def _process_summary_query(self, query_str: str) -> Response:
 
         dates = [
             node.metadata[self.metadata_date_summary_key]
-            for node in summary_nodes
+            for node in summary_nodes_filtered
             if self.metadata_date_summary_key in node.metadata
         ]
 
@@ -208,8 +211,10 @@ def _process_summary_query(self, query_str: str) -> Response:
         )
         raw_nodes = retriever.retrieve(query_str)
 
-        context_str = utils.combine_nodes_for_prompt(summary_nodes, raw_nodes)
+        raw_nodes_filtered = [node for node in raw_nodes if node.score >= RETRIEVER_THRESHOLD]
+
+        context_str = utils.combine_nodes_for_prompt(summary_nodes_filtered, raw_nodes_filtered)
         prompt = self.qa_prompt.format(context_str=context_str, query_str=query_str)
         response = self.llm.complete(prompt)
 
-        return Response(response=str(response), source_nodes=raw_nodes)
+        return Response(response=str(response), source_nodes=raw_nodes_filtered)

utils/query_engine/level_based_platform_query_engine.py

@@ -16,6 +16,7 @@
 from llama_index.llms.openai import OpenAI
 from utils.query_engine.base_pg_engine import BasePGEngine
 from utils.query_engine.level_based_platforms_util import LevelBasedPlatformUtils
+from utils.globals import RETRIEVER_THRESHOLD
 
 qa_prompt = PromptTemplate(
     "Context information is below.\n"
@@ -46,13 +47,14 @@ def custom_query(self, query_str: str):
         similar_nodes = retriever.query_db(
             query=query_str, filters=self._filters, date_interval=self._d
         )
+        similar_nodes_filtered = [node for node in similar_nodes if node.score >= RETRIEVER_THRESHOLD]
 
-        context_str = self._prepare_context_str(similar_nodes, summary_nodes=None)
+        context_str = self._prepare_context_str(similar_nodes_filtered, summary_nodes=None)
         fmt_qa_prompt = qa_prompt.format(context_str=context_str, query_str=query_str)
         response = self.llm.complete(fmt_qa_prompt)
         logging.debug(f"fmt_qa_prompt:\n{fmt_qa_prompt}")
 
-        return Response(response=str(response), source_nodes=similar_nodes)
+        return Response(response=str(response), source_nodes=similar_nodes_filtered)
 
     @classmethod
     def prepare_platform_engine(