Pass default value matching argument types expected by text_search methods

src/khoj/routers/api.py

@@ -24,6 +24,7 @@ from khoj.search_filter.word_filter import WordFilter
 from khoj.utils.helpers import log_telemetry, timer
 from khoj.utils.rawconfig import (
     FullConfig,
+    ProcessorConfig,
     SearchResponse,
     TextContentConfig,
     ConversationProcessorConfig,
@@ -101,7 +102,10 @@ async def set_content_config_data(content_type: str, updated_config: TextContent
 
 @api.post("/config/data/processor/conversation", status_code=200)
 async def set_processor_conversation_config_data(updated_config: ConversationProcessorConfig):
-    state.config.processor.conversation = updated_config
+    if state.config.processor is None:
+        state.config.processor = ProcessorConfig(conversation=updated_config)
+    else:
+        state.config.processor.conversation = updated_config
     try:
         save_config_to_file_updated_state()
         return {"status": "ok"}
@@ -139,6 +143,7 @@ def search(
         return state.query_cache[query_cache_key]
 
     # Encode query with filter terms removed
+    defiltered_query = user_query
     for filter in [DateFilter(), WordFilter(), FileFilter()]:
         defiltered_query = filter.defilter(user_query)
 
@@ -162,9 +167,9 @@ def search(
                     user_query,
                     state.model.org_search,
                     question_embedding=encoded_asymmetric_query,
-                    rank_results=r,
+                    rank_results=r or False,
                     score_threshold=score_threshold,
-                    dedupe=dedupe,
+                    dedupe=dedupe or True,
                 )
             ]
 
@@ -176,9 +181,9 @@ def search(
                     user_query,
                     state.model.markdown_search,
                     question_embedding=encoded_asymmetric_query,
-                    rank_results=r,
+                    rank_results=r or False,
                     score_threshold=score_threshold,
-                    dedupe=dedupe,
+                    dedupe=dedupe or True,
                 )
             ]
 
@@ -190,9 +195,9 @@ def search(
                     user_query,
                     state.model.pdf_search,
                     question_embedding=encoded_asymmetric_query,
-                    rank_results=r,
+                    rank_results=r or False,
                     score_threshold=score_threshold,
-                    dedupe=dedupe,
+                    dedupe=dedupe or True,
                 )
             ]
 
@@ -203,9 +208,9 @@ def search(
                     text_search.query,
                     user_query,
                     state.model.ledger_search,
-                    rank_results=r,
+                    rank_results=r or False,
                     score_threshold=score_threshold,
-                    dedupe=dedupe,
+                    dedupe=dedupe or True,
                 )
             ]
 
@@ -217,9 +222,9 @@ def search(
                     user_query,
                     state.model.music_search,
                     question_embedding=encoded_asymmetric_query,
-                    rank_results=r,
+                    rank_results=r or False,
                     score_threshold=score_threshold,
-                    dedupe=dedupe,
+                    dedupe=dedupe or True,
                 )
             ]
 
@@ -237,16 +242,16 @@ def search(
 
         if (t is None or t in SearchType) and state.model.plugin_search:
             # query specified plugin type
-            search_future[t] += [
+            search_futures[t] += [
                 executor.submit(
                     text_search.query,
                     user_query,
                     # Get plugin search model for specified search type, or the first one if none specified
                     state.model.plugin_search.get(t.value) or next(iter(state.model.plugin_search.values())),
                     question_embedding=encoded_asymmetric_query,
-                    rank_results=r,
+                    rank_results=r or False,
                     score_threshold=score_threshold,
-                    dedupe=dedupe,
+                    dedupe=dedupe or True,
                 )
             ]
 
@@ -262,12 +267,12 @@ def search(
                         image_names=state.model.image_search.image_names,
                         output_directory=output_directory,
                         image_files_url="/static/images",
-                        count=results_count,
+                        count=results_count or 5,
                     )
                 else:
                     hits, entries = search_future.result()
                     # Collate results
-                    results += text_search.collate_results(hits, entries, results_count)
+                    results += text_search.collate_results(hits, entries, results_count or 5)
 
             # Sort results across all content types
             results.sort(key=lambda x: float(x.score), reverse=True)

src/khoj/search_type/text_search.py

@@ -105,7 +105,7 @@ def compute_embeddings(
 def query(
     raw_query: str,
     model: TextSearchModel,
-    question_embedding: torch.Tensor = None,
+    question_embedding: torch.Tensor | None = None,
     rank_results: bool = False,
     score_threshold: float = -math.inf,
     dedupe: bool = True,