Fix CI ruff check and format issues

Author: laxmanclo

vektori/ingestion/extractor.py

@@ -32,7 +32,6 @@
 - Specific product features or capabilities the prospect expressed interest in
 - ICP qualification signals (company size, industry, tech stack)
 Deprioritise: general greetings, scheduling logistics unrelated to deal timeline, small talk.""",
-
     "sales": """
 Domain focus (sales agent):
 Prioritise extracting:
@@ -45,7 +44,6 @@
 - Legal, procurement, or security review concerns raised
 - Champion and economic buyer identification
 Deprioritise: general greetings, small talk.""",
-
     "account_management": """
 Domain focus (account management agent):
 Prioritise extracting:
@@ -57,7 +55,6 @@
 - Feature requests and product feedback from the customer
 - Commitments made by the account team
 Deprioritise: general pleasantries, unrelated small talk.""",
-
     # ── Customer experience ───────────────────────────────────────────────────
     "support": """
 Domain focus (customer support agent):
@@ -70,7 +67,6 @@
 - Escalation triggers or SLA/ticket references
 - Follow-up commitments and deadlines
 Deprioritise: scripted greetings, hold messages, boilerplate closing pleasantries.""",
-
     "onboarding": """
 Domain focus (customer onboarding agent):
 Prioritise extracting:
@@ -82,7 +78,6 @@
 - Go-live dates and success criteria defined
 - Configuration choices and setup decisions made
 Deprioritise: generic greetings, off-topic conversation.""",
-
     # ── People & talent ───────────────────────────────────────────────────────
     "hr": """
 Domain focus (HR agent):
@@ -95,7 +90,6 @@
 - Leave, absence, or schedule-related details
 - Compensation or benefits questions raised
 Deprioritise: casual small talk, water-cooler conversation, off-topic personal chat.""",
-
     "recruiting": """
 Domain focus (recruiting / talent acquisition agent):
 Prioritise extracting:
@@ -107,7 +101,6 @@
 - Red flags or concerns noted by the recruiter
 - Next steps and scheduled interviews
 Deprioritise: small talk, generic introductions.""",
-
     # ── Finance & legal ───────────────────────────────────────────────────────
     "finance": """
 Domain focus (finance / accounting agent):
@@ -120,7 +113,6 @@
 - Vendor names and contract financial terms
 - Reporting deadlines and period-close milestones
 Deprioritise: general chat, non-financial pleasantries.""",
-
     "legal": """
 Domain focus (legal agent):
 Prioritise extracting:
@@ -132,7 +124,6 @@
 - Dispute details, allegations, or litigation references
 - Legal advice or guidance provided
 Deprioritise: casual conversation, non-legal pleasantries.""",
-
     # ── Technical ────────────────────────────────────────────────────────────
     "coding": """
 Domain focus (software engineering / coding agent):
@@ -145,7 +136,6 @@
 - Performance or security concerns raised
 - Task assignments, PR numbers, and deadlines
 Deprioritise: generic greetings, non-technical small talk.""",
-
     "data_analytics": """
 Domain focus (data / analytics agent):
 Prioritise extracting:
@@ -157,7 +147,6 @@
 - Data pipeline or ETL issues mentioned
 - Deadlines for reports or model deliverables
 Deprioritise: general pleasantries, off-topic conversation.""",
-
     "research": """
 Domain focus (research agent):
 Prioritise extracting:
@@ -169,7 +158,6 @@
 - Next research steps and open questions
 - Authors, institutions, or publications mentioned
 Deprioritise: administrative filler, generic acknowledgements.""",
-
     "cybersecurity": """
 Domain focus (cybersecurity agent):
 Prioritise extracting:
@@ -181,7 +169,6 @@
 - Compliance frameworks referenced (ISO 27001, NIST, SOC 2)
 - Incident timeline and containment actions
 Deprioritise: routine greetings, non-security small talk.""",
-
     # ── Operations & industry ────────────────────────────────────────────────
     "healthcare": """
 Domain focus (healthcare / clinical agent):
@@ -195,7 +182,6 @@
 - Follow-up instructions and care commitments
 Deprioritise: general small talk, non-clinical pleasantries.
 Note: treat all health information with appropriate sensitivity.""",
-
     "supply_chain": """
 Domain focus (supply chain / logistics agent):
 Prioritise extracting:
@@ -207,7 +193,6 @@
 - Disruption signals (port delays, weather, supplier issues)
 - Cost-per-unit or freight cost changes mentioned
 Deprioritise: general pleasantries, unrelated conversation.""",
-
     "retail": """
 Domain focus (retail / e-commerce agent):
 Prioritise extracting:
@@ -219,7 +204,6 @@
 - Loyalty programme status and points
 - Competitor products or pricing mentioned
 Deprioritise: generic greetings, unrelated small talk.""",
-
     "operations": """
 Domain focus (operations / scheduling agent):
 Prioritise extracting:
@@ -433,7 +417,7 @@ def __init__(
         max_facts: int = 8,
         max_input_tokens: int = 4000,
         max_output_tokens: int = 8192,
-        extraction_config: "ExtractionConfig | None" = None,
+        extraction_config: ExtractionConfig | None = None,
     ) -> None:
         self.db = db
         self.embedder = embedder

vektori/integrations/pipecat/__init__.py

@@ -14,6 +14,4 @@
 
     __all__ = ["VektoriMemoryProcessor", "VektoriStorageProcessor"]
 except ImportError as exc:  # pipecat not installed
-    raise ImportError(
-        "Pipecat is not installed. Run: pip install 'vektori[pipecat]'"
-    ) from exc
+    raise ImportError("Pipecat is not installed. Run: pip install 'vektori[pipecat]'") from exc

vektori/integrations/pipecat/processor.py

@@ -101,9 +101,7 @@ def _last_message(context: OpenAILLMContext, role: str) -> str | None:
             content = m.get("content", "")
             if isinstance(content, list):
                 # Handle multi-part content (e.g. OpenAI vision format)
-                content = " ".join(
-                    p.get("text", "") for p in content if isinstance(p, dict)
-                )
+                content = " ".join(p.get("text", "") for p in content if isinstance(p, dict))
             return content or None
     return None
 
@@ -144,7 +142,7 @@ class VektoriMemoryProcessor(FrameProcessor):
 
     def __init__(
         self,
-        vektori: "Vektori",
+        vektori: Vektori,
         user_id: str,
         *,
         base_system_prompt: str = "",
@@ -205,10 +203,7 @@ async def _inject_memory(self, context: OpenAILLMContext) -> None:
     async def process_frame(self, frame: Frame, direction: FrameDirection) -> None:
         await super().process_frame(frame, direction)
 
-        if (
-            isinstance(frame, OpenAILLMContextFrame)
-            and direction == FrameDirection.DOWNSTREAM
-        ):
+        if isinstance(frame, OpenAILLMContextFrame) and direction == FrameDirection.DOWNSTREAM:
             await self._inject_memory(frame.context)
 
         await self.push_frame(frame, direction)
@@ -251,7 +246,7 @@ class VektoriStorageProcessor(FrameProcessor):
 
     def __init__(
         self,
-        vektori: "Vektori",
+        vektori: Vektori,
         user_id: str,
         session_id: str,
         context: OpenAILLMContext | None = None,