refactor: pre-commit

libs/kotaemon/kotaemon/indices/qa/citation_qa.py

@@ -201,7 +201,11 @@ class AnswerWithContextPipeline(BaseComponent):
 
         def mindmap_call():
             nonlocal mindmap
-            mindmap = self.create_mindmap_pipeline(context=evidence, question=question)
+
+            if self.create_mindmap_pipeline is not None:
+                mindmap = self.create_mindmap_pipeline(
+                    context=evidence, question=question
+                )
 
         citation_thread = None
         mindmap_thread = None
@@ -306,4 +310,3 @@ class AnswerWithContextPipeline(BaseComponent):
 
             # print("Matched citation:", quote, matched_excerpts),
         return spans
-

libs/kotaemon/kotaemon/indices/qa/citation_qa_inline.py

@@ -214,7 +214,10 @@ class AnswerWithInlineCitation(AnswerWithContextPipeline):
 
         def mindmap_call():
             nonlocal mindmap
-            mindmap = self.create_mindmap_pipeline(context=evidence, question=question)
+            if self.create_mindmap_pipeline is not None:
+                mindmap = self.create_mindmap_pipeline(
+                    context=evidence, question=question
+                )
 
         mindmap_thread = None
 

libs/kotaemon/kotaemon/indices/rankings/cohere.py

@@ -18,8 +18,7 @@ class CohereReranking(BaseReranking):
             import cohere
         except ImportError:
             raise ImportError(
-                "Please install Cohere "
-                "`pip install cohere` to use Cohere Reranking"
+                "Please install Cohere " "`pip install cohere` to use Cohere Reranking"
             )
 
         if not self.cohere_api_key:

libs/ktem/ktem/index/file/pipelines.py

@@ -364,9 +364,7 @@ class IndexPipeline(BaseComponent):
             vector_store=self.VS,
             doc_store=self.DS,
             embedding=self.embedding,
-            cache_dir=getattr(
-                settings, "KH_CHUNKS_OUTPUT_DIR", None
-            ),
+            cache_dir=getattr(settings, "KH_CHUNKS_OUTPUT_DIR", None),
         )
 
     def handle_docs(self, docs, file_id, file_name) -> Generator[Document, None, int]:

libs/ktem/ktem/reasoning/citation_display.py

@@ -10,14 +10,14 @@ from __future__ import annotations
 
 import logging
 
+from ktem.utils.render import Render
+
 from kotaemon.base import Document
 from kotaemon.indices.qa.citation_qa import (
     CONTEXT_RELEVANT_WARNING_SCORE,
     AnswerWithContextPipeline,
 )
 
-from ktem.utils.render import Render
-
 logger = logging.getLogger(__name__)
 
 
@@ -34,9 +34,7 @@ def prepare_citations(
     """
     with_citation: list[Document] = []
     without_citation: list[Document] = []
-    has_llm_score = any(
-        "llm_trulens_score" in doc.metadata for doc in docs
-    )
+    has_llm_score = any("llm_trulens_score" in doc.metadata for doc in docs)
 
     spans = pipeline.match_evidence_with_context(answer, docs)
     id2docs = {doc.doc_id: doc for doc in docs}
@@ -60,9 +58,7 @@ def prepare_citations(
             to_highlight = cur_doc.text[span_start:span_end]
             last_end = span_end
 
-            highlight_text += (
-                (" " if highlight_text else "") + to_highlight
-            )
+            highlight_text += (" " if highlight_text else "") + to_highlight
 
             span_idx = span.get("idx", None)
             if span_idx is not None:
@@ -70,14 +66,10 @@ def prepare_citations(
 
             text += Render.highlight(
                 to_highlight,
-                elem_id=(
-                    str(span_idx) if span_idx is not None else None
-                ),
+                elem_id=(str(span_idx) if span_idx is not None else None),
             )
             if idx < len(ss) - 1:
-                text += cur_doc.text[
-                    span["end"] : ss[idx + 1]["start"]
-                ]
+                text += cur_doc.text[span["end"] : ss[idx + 1]["start"]]
 
         text += cur_doc.text[ss[-1]["end"] :]
         with_citation.append(
@@ -96,18 +88,14 @@ def prepare_citations(
 
     sorted_not_detected = sorted(
         not_detected,
-        key=lambda id_: id2docs[id_].metadata.get(
-            "llm_trulens_score", 0.0
-        ),
+        key=lambda id_: id2docs[id_].metadata.get("llm_trulens_score", 0.0),
         reverse=True,
     )
 
     for id_ in sorted_not_detected:
         doc = id2docs[id_]
         doc_score = doc.metadata.get("llm_trulens_score", 0.0)
-        is_open = not has_llm_score or (
-            doc_score > CONTEXT_RELEVANT_WARNING_SCORE
-        )
+        is_open = not has_llm_score or (doc_score > CONTEXT_RELEVANT_WARNING_SCORE)
         without_citation.append(
             Document(
                 channel="info",

libs/ktem/ktem/reasoning/simple.py

@@ -6,14 +6,16 @@ from typing import Generator
 from decouple import config
 from ktem.embeddings.manager import embedding_models_manager as embeddings
 from ktem.llms.manager import llms
-from theflow.settings import settings as flowsettings
+from ktem.reasoning.citation_display import prepare_citations
 from ktem.reasoning.prompt_optimization import (
     DecomposeQuestionPipeline,
     RewriteQuestionPipeline,
 )
+from ktem.reasoning.prompt_optimization.mindmap import CreateMindmapPipeline
 from ktem.utils.render import Render
 from ktem.utils.visualize_cited import CreateCitationVizPipeline
 from plotly.io import to_json
+from theflow.settings import settings as flowsettings
 
 from kotaemon.base import (
     AIMessage,
@@ -30,11 +32,6 @@ from kotaemon.indices.qa.citation_qa import (
     DEFAULT_QA_TEXT_PROMPT,
     AnswerWithContextPipeline,
 )
-
-from ktem.reasoning.citation_display import prepare_citations
-from ktem.reasoning.prompt_optimization.mindmap import (
-    CreateMindmapPipeline,
-)
 from kotaemon.indices.qa.citation_qa_inline import AnswerWithInlineCitation
 from kotaemon.indices.qa.format_context import PrepareEvidencePipeline
 from kotaemon.indices.qa.utils import replace_think_tag_with_details
@@ -374,9 +371,7 @@ class FullQAPipeline(BaseReasoning):
 
         answer_pipeline.llm = llm
         answer_pipeline.citation_pipeline = CitationPipeline(llm=llm)
-        answer_pipeline.create_mindmap_pipeline = CreateMindmapPipeline(
-            llm=llm
-        )
+        answer_pipeline.create_mindmap_pipeline = CreateMindmapPipeline(llm=llm)
         answer_pipeline.n_last_interactions = settings[f"{prefix}.n_last_interactions"]
         answer_pipeline.enable_citation = (
             settings[f"{prefix}.highlight_citation"] != "off"
@@ -384,9 +379,7 @@ class FullQAPipeline(BaseReasoning):
         answer_pipeline.enable_mindmap = settings[f"{prefix}.create_mindmap"]
         answer_pipeline.enable_citation_viz = settings[f"{prefix}.create_citation_viz"]
         answer_pipeline.use_multimodal = settings[f"{prefix}.use_multimodal"]
-        answer_pipeline.vlm_endpoint = getattr(
-            flowsettings, "KH_VLM_ENDPOINT", ""
-        )
+        answer_pipeline.vlm_endpoint = getattr(flowsettings, "KH_VLM_ENDPOINT", "")
         answer_pipeline.system_prompt = settings[f"{prefix}.system_prompt"]
         answer_pipeline.qa_template = settings[f"{prefix}.qa_prompt"]
         answer_pipeline.lang = SUPPORTED_LANGUAGE_MAP.get(