Fix linting issues with ruff --fix

Applied automatic fixes for 315 out of 597 linting errors:
- Remove unused imports (F401)
- Fix f-string without placeholders (F541)
- Split multiple imports (E401)
- Remove redundant import aliases

Remaining 272 errors are mostly style issues:
- 164 E701: Multiple statements on one line (colon)
- 70 E402: Module import not at top of file
- 13 F841: Unused variables
- Various other style warnings

Code functionality unchanged - all fixes are cosmetic improvements.

analyze_kik_hash_generation.py

@@ -8,7 +8,6 @@ and trying to reverse engineer the hash generation logic.
 import asyncio
 import json
 import hashlib
-import hmac
 import base64
 from fastmcp import Client
 from mcp_server_main import app
@@ -152,7 +151,7 @@ async def test_hash_generation_comprehensive():
                 
                 # Test with first decision
                 sample_decision = decisions[0]
-                print(f"\n📋 Sample decision for hash analysis:")
+                print("\n📋 Sample decision for hash analysis:")
                 for key, value in sample_decision.items():
                     print(f"   {key}: {value}")
                 
@@ -162,20 +161,20 @@ async def test_hash_generation_comprehensive():
                 all_hashes = {}
                 
                 # Test different hash generation methods
-                print(f"\n🔨 Testing webpack-style hashing...")
+                print("\n🔨 Testing webpack-style hashing...")
                 webpack_hashes = test_webpack_style_hashing(sample_decision)
                 all_hashes.update(webpack_hashes)
                 
-                print(f"🔨 Testing Angular routing hashes...")  
+                print("🔨 Testing Angular routing hashes...")  
                 angular_hashes = test_angular_routing_hashes(sample_decision)
                 all_hashes.update(angular_hashes)
                 
-                print(f"🔨 Testing base64 encoding variants...")
+                print("🔨 Testing base64 encoding variants...")
                 b64_hashes = test_base64_encoding_variants(sample_decision)
                 all_hashes.update(b64_hashes)
                 
                 # Check for matches
-                print(f"\n🎯 Checking for hash matches...")
+                print("\n🎯 Checking for hash matches...")
                 matches_found = []
                 partial_matches = []
                 
@@ -191,13 +190,13 @@ async def test_hash_generation_comprehensive():
                         print(f"   🔍 Partial match (last 8): {hash_name} -> ...{hash_value[-16:]}")
                 
                 if not matches_found and not partial_matches:
-                    print(f"   ❌ No matches found")
-                    print(f"\n📝 Sample generated hashes (first 10):")
+                    print("   ❌ No matches found")
+                    print("\n📝 Sample generated hashes (first 10):")
                     for i, (hash_name, hash_value) in enumerate(list(all_hashes.items())[:10]):
                         print(f"   {hash_name}: {hash_value}")
                 
                 # Try combinations with other decisions
-                print(f"\n🔄 Testing hash combinations with multiple decisions...")
+                print("\n🔄 Testing hash combinations with multiple decisions...")
                 if len(decisions) > 1:
                     for i, decision in enumerate(decisions[1:3]):  # Test 2 more
                         print(f"\n   Testing decision {i+2}: {decision.get('kararNo')}")
@@ -209,7 +208,7 @@ async def test_hash_generation_comprehensive():
                                 matches_found.append((f"decision_{i+2}_{hash_name}", hash_value))
                 
                 # Try composite hashes (combining multiple fields)
-                print(f"\n🔗 Testing composite hash generation...")
+                print("\n🔗 Testing composite hash generation...")
                 composite_tests = [
                     f"{sample_decision.get('gundemMaddesiId')}_{sample_decision.get('kararNo')}",
                     f"{sample_decision.get('kararNo')}_{sample_decision.get('kararTarihi')}",
@@ -224,7 +223,7 @@ async def test_hash_generation_comprehensive():
                         print(f"   🎉 COMPOSITE MATCH FOUND: test_{i} -> {composite_str[:50]}...")
                         matches_found.append((f"composite_{i}", composite_hash))
                         
-                print(f"\n🎯 Hash analysis completed!")
+                print("\n🎯 Hash analysis completed!")
                 print(f"   Total matches found: {len(matches_found)}")
                 print(f"   Partial matches: {len(partial_matches)}")
                 

anayasa_mcp_module/bireysel_client.py

@@ -2,13 +2,13 @@
 # This client is for Bireysel Başvuru: https://kararlarbilgibankasi.anayasa.gov.tr
 
 import httpx
-from bs4 import BeautifulSoup, Tag
-from typing import Dict, Any, List, Optional, Tuple
+from bs4 import BeautifulSoup
+from typing import List, Optional, Tuple
 import logging
 import html
 import re
 import io
-from urllib.parse import urlencode, urljoin, quote
+from urllib.parse import urljoin
 from markitdown import MarkItDown
 import math # For math.ceil for pagination
 

anayasa_mcp_module/client.py

@@ -3,12 +3,12 @@
 
 import httpx
 from bs4 import BeautifulSoup
-from typing import Dict, Any, List, Optional, Tuple
+from typing import List, Optional, Tuple
 import logging
 import html
 import re
 import io
-from urllib.parse import urlencode, urljoin, quote
+from urllib.parse import urljoin
 from markitdown import MarkItDown
 import math # For math.ceil for pagination
 

anayasa_mcp_module/unified_client.py

@@ -2,7 +2,6 @@
 # Unified client for both Norm Denetimi and Bireysel Başvuru
 
 import logging
-from typing import Optional
 from urllib.parse import urlparse
 
 from .models import (

asgi_app.py

@@ -10,16 +10,13 @@ Usage:
 """
 
 import os
-import time
 import logging
 import json
-from datetime import datetime, timedelta
 from fastapi import FastAPI, Request, HTTPException, Query
 from fastapi.responses import JSONResponse, HTMLResponse, Response
 from fastapi.exception_handlers import http_exception_handler
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
-from starlette.middleware.base import BaseHTTPMiddleware
 
 # Import the proper create_app function that includes all middleware
 from mcp_server_main import create_app
@@ -505,14 +502,14 @@ async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
         logger.info("User authenticated successfully via Clerk")
         
         # Return success response
-        return HTMLResponse(f"""
+        return HTMLResponse("""
         <html>
             <head>
                 <title>MCP Connection Successful</title>
                 <style>
-                    body {{ font-family: Arial, sans-serif; text-align: center; padding: 50px; }}
-                    .success {{ color: #28a745; }}
-                    .token {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; word-break: break-all; }}
+                    body { font-family: Arial, sans-serif; text-align: center; padding: 50px; }
+                    .success { color: #28a745; }
+                    .token { background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; word-break: break-all; }
                 </style>
             </head>
             <body>
@@ -525,13 +522,13 @@ async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
                 <p>You can now close this window and return to your MCP client.</p>
                 <script>
                     // Try to close the popup if opened as such
-                    if (window.opener) {{
-                        window.opener.postMessage({{
+                    if (window.opener) {
+                        window.opener.postMessage({
                             type: 'MCP_AUTH_SUCCESS',
                             token: 'use_clerk_jwt_token'
-                        }}, '*');
+                        }, '*');
                         setTimeout(() => window.close(), 3000);
-                    }}
+                    }
                 </script>
             </body>
         </html>

bddk_mcp_module/client.py

@@ -1,13 +1,12 @@
 # bddk_mcp_module/client.py
 
 import httpx
-from typing import List, Optional, Dict, Any
+from typing import Optional
 import logging
 import os
 import re
 import io
 import math
-from urllib.parse import urlparse
 from markitdown import MarkItDown
 
 from .models import (

bddk_mcp_module/models.py

@@ -1,7 +1,7 @@
 # bddk_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import List, Optional
+from typing import List
 
 class BddkSearchRequest(BaseModel):
     """

bedesten_mcp_module/models.py

@@ -1,8 +1,7 @@
 # bedesten_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import List, Optional, Dict, Any, Literal, Union
-from datetime import datetime
+from typing import List, Optional, Dict, Any, Literal
 
 # Import compressed BirimAdiEnum for chamber filtering
 from .enums import BirimAdiEnum

danistay_mcp_module/client.py

@@ -1,11 +1,9 @@
 # danistay_mcp_module/client.py
 
 import httpx
-from bs4 import BeautifulSoup 
-from typing import Dict, Any, List, Optional
+from typing import Dict, List, Optional
 import logging
 import html
-import re
 import io
 from markitdown import MarkItDown
 

emsal_mcp_module/client.py

@@ -2,10 +2,9 @@
 
 import httpx
 # from bs4 import BeautifulSoup # Uncomment if needed for advanced HTML pre-processing
-from typing import Dict, Any, List, Optional
+from typing import Dict, Optional
 import logging
 import html
-import re
 import io
 from markitdown import MarkItDown
 

example_fastapi_app.py

@@ -7,11 +7,9 @@ import os
 from typing import List, Dict, Any, Optional
 from datetime import datetime
 
-from fastapi import FastAPI, HTTPException, Query, Depends, Body
+from fastapi import FastAPI, HTTPException, Query
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field
-import json
 
 # Import the main MCP app
 from mcp_server_main import app as mcp_server

kik_mcp_module/client.py

@@ -10,13 +10,12 @@ from playwright.async_api import (
 )
 from bs4 import BeautifulSoup
 import logging
-from typing import Dict, Any, List, Optional
+from typing import List, Optional
 import urllib.parse 
 import base64 # Base64 için
 import re
 import html as html_parser 
 from markitdown import MarkItDown 
-import os
 import math 
 import io
 import random
@@ -907,7 +906,7 @@ class KikApiClient:
                     event_target_for_submit = self.FIELD_LOCATORS['search_button_id']
                     # Use human-like clicking for search button
                     search_button_selector = f"a[id='{event_target_for_submit}']"
-                    logger.info(f"Performing human-like search button click...")
+                    logger.info("Performing human-like search button click...")
                     
                     try:
                         # Hide datepicker first to prevent interference
@@ -1181,7 +1180,7 @@ class KikApiClient:
             try: 
                 if await current_main_page.locator(self.MODAL_CLOSE_BUTTON_SELECTOR).is_visible(timeout=2000): 
                     await current_main_page.locator(self.MODAL_CLOSE_BUTTON_SELECTOR).click()
-                    await current_main_page.wait_for_selector(f"div#detayPopUp:not(.in)", timeout=5000) 
+                    await current_main_page.wait_for_selector("div#detayPopUp:not(.in)", timeout=5000) 
             except: pass
 
             return KikDocumentMarkdown(

kik_mcp_module/client_v2.py

@@ -1,13 +1,9 @@
 # kik_mcp_module/client_v2.py
 
 import httpx
-import requests
 import logging
 import uuid
-import base64
 import ssl
-from typing import Optional
-from datetime import datetime
 
 from .models_v2 import (
     KikV2DecisionType, KikV2SearchPayload, KikV2SearchPayloadDk, KikV2SearchPayloadMk,

kik_mcp_module/models.py

@@ -1,5 +1,5 @@
 # kik_mcp_module/models.py
-from pydantic import BaseModel, Field, HttpUrl, computed_field, ConfigDict
+from pydantic import BaseModel, Field, computed_field, ConfigDict
 from typing import List, Optional
 from enum import Enum
 import base64 # Base64 encoding/decoding için

kik_mcp_module/models_v2.py

@@ -1,7 +1,6 @@
 # kik_mcp_module/models_v2.py
 from pydantic import BaseModel, Field, ConfigDict
-from typing import List, Optional
-from datetime import datetime
+from typing import List
 from enum import Enum
 
 # New KIK v2 API Models

kvkk_mcp_module/client.py

@@ -2,13 +2,13 @@
 
 import httpx
 from bs4 import BeautifulSoup
-from typing import List, Optional, Dict, Any
+from typing import Optional, Dict, Any
 import logging
 import os
 import re
 import io
 import math
-from urllib.parse import urljoin, urlparse, parse_qs
+from urllib.parse import urlparse
 from markitdown import MarkItDown
 from pydantic import HttpUrl
 

kvkk_mcp_module/models.py

@@ -1,7 +1,7 @@
 # kvkk_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl
-from typing import List, Optional, Any
+from typing import List, Optional
 
 class KvkkSearchRequest(BaseModel):
     """Model for KVKK (Personal Data Protection Authority) search request via Brave API."""

mcp_auth/clerk_config.py

@@ -36,7 +36,7 @@ def create_clerk_oauth_config() -> OAuthConfig:
         scopes=["mcp:tools:read", "mcp:tools:write", "openid", "profile", "email"]
     )
     
-    logger.info(f"Created Clerk OAuth config with adapter endpoints")
+    logger.info("Created Clerk OAuth config with adapter endpoints")
     logger.info(f"Clerk domain: {clerk_domain}")
     logger.debug(f"Authorization endpoint: {config.authorization_endpoint}")
     logger.debug(f"Token endpoint: {config.token_endpoint}")

mcp_auth/oauth.py

@@ -9,7 +9,7 @@ import time
 import logging
 from dataclasses import dataclass
 from datetime import datetime, timedelta
-from typing import Any, Optional
+from typing import Any
 from urllib.parse import urlencode
 
 import httpx

mcp_auth_http_adapter.py

@@ -18,7 +18,7 @@ from fastapi.responses import RedirectResponse, JSONResponse
 try:
     from clerk_backend_api import Clerk
     CLERK_AVAILABLE = True
-except ImportError as e:
+except ImportError:
     CLERK_AVAILABLE = False
     Clerk = None
 

mcp_auth_http_simple.py

@@ -6,7 +6,7 @@ Uses Redis for authorization code storage to support multi-machine deployment
 import os
 import logging
 from typing import Optional
-from urllib.parse import urlencode, quote
+from urllib.parse import urlencode
 
 from fastapi import APIRouter, Request, Query, HTTPException
 from fastapi.responses import RedirectResponse, JSONResponse
@@ -39,7 +39,6 @@ def get_redis_session_store():
     if redis_store is None:
         try:
             import concurrent.futures
-            import functools
             
             # Use thread pool with timeout to prevent hanging
             with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
@@ -206,7 +205,6 @@ async def oauth_callback(
             auth_code = f"clerk_auth_{os.urandom(16).hex()}"
             
             # Prepare code data
-            import time
             code_data = {
                 "user_id": user_id,
                 "session_id": session_id,
@@ -225,7 +223,7 @@ async def oauth_callback(
                 if success:
                     logger.info(f"Stored authorization code {auth_code[:10]}... in Redis with real JWT token")
                 else:
-                    logger.error(f"Failed to store authorization code in Redis, falling back to in-memory")
+                    logger.error("Failed to store authorization code in Redis, falling back to in-memory")
                     # Fall back to in-memory storage
                     if not hasattr(oauth_callback, '_code_storage'):
                         oauth_callback._code_storage = {}
@@ -236,7 +234,7 @@ async def oauth_callback(
                 if not hasattr(oauth_callback, '_code_storage'):
                     oauth_callback._code_storage = {}
                 oauth_callback._code_storage[auth_code] = code_data
-                logger.info(f"Stored authorization code in memory (fallback)")
+                logger.info("Stored authorization code in memory (fallback)")
             
             # Redirect back to client with authorization code
             redirect_params = {

mcp_server_main.py

@@ -8,8 +8,7 @@ import json
 import time
 from collections import defaultdict
 from pydantic import HttpUrl, Field 
-from typing import Optional, Dict, List, Literal, Any, Union
-import urllib.parse
+from typing import Dict, List, Literal, Any
 import tiktoken
 from fastmcp.server.middleware import Middleware, MiddlewareContext
 from fastmcp.server.dependencies import get_access_token, AccessToken
@@ -159,7 +158,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception as e:
+        except Exception:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("tool_call_error", input_tokens, 0, 
                                tool_name, duration_ms)
@@ -189,7 +188,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception as e:
+        except Exception:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("resource_read_error", 0, 0, 
                                resource_uri, duration_ms)
@@ -219,7 +218,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception as e:
+        except Exception:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("prompt_get_error", 0, 0, 
                                prompt_name, duration_ms)
@@ -255,21 +254,18 @@ def create_app(auth=None):
 
 # --- Module Imports ---
 from yargitay_mcp_module.client import YargitayOfficialApiClient
-from yargitay_mcp_module.models import (
-    YargitayDetailedSearchRequest, YargitayDocumentMarkdown, CompactYargitaySearchResult,
-    YargitayBirimEnum, CleanYargitayDecisionEntry
-)
 from bedesten_mcp_module.client import BedestenApiClient
 from bedesten_mcp_module.models import (
     BedestenSearchRequest, BedestenSearchData,
     BedestenDocumentMarkdown, BedestenCourtTypeEnum
 )
 from bedesten_mcp_module.enums import BirimAdiEnum
+
+# Semantic Search Module Imports
+from semantic_search.embedder import EmbeddingGemma
+from semantic_search.vector_store import VectorStore
+from semantic_search.processor import DocumentProcessor
 from danistay_mcp_module.client import DanistayApiClient
-from danistay_mcp_module.models import (
-    DanistayKeywordSearchRequest, DanistayDetailedSearchRequest,
-    DanistayDocumentMarkdown, CompactDanistaySearchResult
-)
 from emsal_mcp_module.client import EmsalApiClient
 from emsal_mcp_module.models import (
     EmsalSearchRequest, EmsalDocumentMarkdown, CompactEmsalSearchResult
@@ -283,24 +279,12 @@ from anayasa_mcp_module.client import AnayasaMahkemesiApiClient
 from anayasa_mcp_module.bireysel_client import AnayasaBireyselBasvuruApiClient
 from anayasa_mcp_module.unified_client import AnayasaUnifiedClient
 from anayasa_mcp_module.models import (
-    AnayasaNormDenetimiSearchRequest,
-    AnayasaSearchResult,
-    AnayasaDocumentMarkdown,
-    AnayasaBireyselReportSearchRequest,
-    AnayasaBireyselReportSearchResult,
-    AnayasaBireyselBasvuruDocumentMarkdown,
     AnayasaUnifiedSearchRequest,
-    AnayasaUnifiedSearchResult,
-    AnayasaUnifiedDocumentMarkdown,
     # Removed enum imports - now using Literal strings in models
 )
 # KIK v2 Module Imports (New API)
 from kik_mcp_module.client_v2 import KikV2ApiClient
 from kik_mcp_module.models_v2 import KikV2DecisionType
-from kik_mcp_module.models_v2 import (
-    KikV2SearchResult,
-    KikV2DocumentMarkdown
-)
 
 from rekabet_mcp_module.client import RekabetKurumuApiClient
 from rekabet_mcp_module.models import (
@@ -312,14 +296,9 @@ from rekabet_mcp_module.models import (
 
 from sayistay_mcp_module.client import SayistayApiClient
 from sayistay_mcp_module.models import (
-    GenelKurulSearchRequest, GenelKurulSearchResponse,
-    TemyizKuruluSearchRequest, TemyizKuruluSearchResponse,
-    DaireSearchRequest, DaireSearchResponse,
-    SayistayDocumentMarkdown,
     SayistayUnifiedSearchRequest, SayistayUnifiedSearchResult,
     SayistayUnifiedDocumentMarkdown
 )
-from sayistay_mcp_module.enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum
 from sayistay_mcp_module.unified_client import SayistayUnifiedClient
 
 # KVKK Module Imports
@@ -333,14 +312,11 @@ from kvkk_mcp_module.models import (
 # BDDK Module Imports
 from bddk_mcp_module.client import BddkApiClient
 from bddk_mcp_module.models import (
-    BddkSearchRequest,
-    BddkSearchResult,
-    BddkDocumentMarkdown
+    BddkSearchRequest
 )
 
 
 # Create a placeholder app that will be properly initialized after tools are defined
-from fastmcp import FastMCP
 
 # MCP app for Turkish legal databases with explicit capabilities
 app = FastMCP(
@@ -647,7 +623,7 @@ async def search_emsal_detailed_decisions(
         page_size=page_size
     )
     
-    logger.info(f"Tool 'search_emsal_detailed_decisions' called.")
+    logger.info("Tool 'search_emsal_detailed_decisions' called.")
     try:
         api_response = await emsal_client_instance.search_detailed_decisions(search_query)
         if api_response.data:
@@ -659,8 +635,8 @@ async def search_emsal_detailed_decisions(
             )
         logger.warning("API response for Emsal search did not contain expected data structure.")
         return CompactEmsalSearchResult(decisions=[], total_records=0, requested_page=search_query.page_number, page_size=search_query.page_size)
-    except Exception as e:
-        logger.exception(f"Error in tool 'search_emsal_detailed_decisions'.")
+    except Exception:
+        logger.exception("Error in tool 'search_emsal_detailed_decisions'.")
         raise
 
 @app.tool(
@@ -676,8 +652,8 @@ async def get_emsal_document_markdown(id: str) -> EmsalDocumentMarkdown:
     if not id or not id.strip(): raise ValueError("Document ID required for Emsal.")
     try:
         return await emsal_client_instance.get_decision_document_as_markdown(id)
-    except Exception as e:
-        logger.exception(f"Error in tool 'get_emsal_document_markdown'.")
+    except Exception:
+        logger.exception("Error in tool 'get_emsal_document_markdown'.")
         raise
 
 # --- MCP Tools for Uyusmazlik ---
@@ -745,11 +721,11 @@ async def search_uyusmazlik_decisions(
         not_hepsi=not_hepsi
     )
     
-    logger.info(f"Tool 'search_uyusmazlik_decisions' called.")
+    logger.info("Tool 'search_uyusmazlik_decisions' called.")
     try:
         return await uyusmazlik_client_instance.search_decisions(search_params)
-    except Exception as e:
-        logger.exception(f"Error in tool 'search_uyusmazlik_decisions'.")
+    except Exception:
+        logger.exception("Error in tool 'search_uyusmazlik_decisions'.")
         raise
 
 @app.tool(
@@ -768,8 +744,8 @@ async def get_uyusmazlik_document_markdown_from_url(
         raise ValueError("Document URL (document_url) is required for Uyuşmazlık document retrieval.")
     try:
         return await uyusmazlik_client_instance.get_decision_document_as_markdown(str(document_url))
-    except Exception as e:
-        logger.exception(f"Error in tool 'get_uyusmazlik_document_markdown_from_url'.")
+    except Exception:
+        logger.exception("Error in tool 'get_uyusmazlik_document_markdown_from_url'.")
         raise
 
 # --- DEACTIVATED: MCP Tools for Anayasa Mahkemesi (Individual Tools) ---
@@ -860,8 +836,8 @@ async def search_anayasa_unified(
         result = await anayasa_unified_client_instance.search_unified(request)
         return json.dumps(result.model_dump(), ensure_ascii=False, indent=2)
         
-    except Exception as e:
-        logger.exception(f"Error in tool 'search_anayasa_unified'.")
+    except Exception:
+        logger.exception("Error in tool 'search_anayasa_unified'.")
         raise
 
 @app.tool(
@@ -882,8 +858,8 @@ async def get_anayasa_document_unified(
         result = await anayasa_unified_client_instance.get_document_unified(document_url, page_number)
         return json.dumps(result.model_dump(mode='json'), ensure_ascii=False, indent=2)
         
-    except Exception as e:
-        logger.exception(f"Error in tool 'get_anayasa_document_unified'.")
+    except Exception:
+        logger.exception("Error in tool 'get_anayasa_document_unified'.")
         raise
 
 # --- MCP Tools for KIK v2 (Kamu İhale Kurulu - New API) ---
@@ -1057,7 +1033,7 @@ async def search_rekabet_kurumu_decisions(
     try:
        
         return await rekabet_client_instance.search_decisions(search_query)
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'search_rekabet_kurumu_decisions'.")
         return RekabetSearchResult(decisions=[], retrieved_page_number=page, total_records_found=0, total_pages=0)
 
@@ -1080,7 +1056,7 @@ async def get_rekabet_kurumu_document(
     try:
       
         return await rekabet_client_instance.get_decision_document(karar_id, page_number=current_page_to_fetch)
-    except Exception as e:
+    except Exception:
         logger.exception(f"Error in tool 'get_rekabet_kurumu_document'. Karar ID: {karar_id}")
         raise 
 
@@ -1191,7 +1167,7 @@ For best results, use exact phrases with quotes for legal terms."""),
             "page_size": pageSize,
             "searched_courts": court_types
         }
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'search_bedesten_unified'")
         raise
 
@@ -1213,7 +1189,7 @@ async def get_bedesten_document_markdown(
     
     try:
         return await bedesten_client_instance.get_document_as_markdown(documentId)
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'get_kyb_bedesten_document_markdown'")
         raise
 
@@ -1402,7 +1378,7 @@ async def search_sayistay_unified(
             web_karar_metni=web_karar_metni
         )
         return await sayistay_unified_client_instance.search_unified(search_request)
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'search_sayistay_unified'")
         raise
 
@@ -1426,7 +1402,7 @@ async def get_sayistay_document_unified(
     
     try:
         return await sayistay_unified_client_instance.get_document_unified(decision_id, decision_type)
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'get_sayistay_document_unified'")
         raise
 
@@ -1839,6 +1815,234 @@ async def get_bddk_document_markdown(
             "error": str(e)
         }
 
+# --- Semantic Search Tool ---
+
+@app.tool(
+    description="Semantic search for Turkish legal decisions using EmbeddingGemma for intelligent ranking",
+    annotations={
+        "readOnlyHint": True,
+        "openWorldHint": True,
+        "idempotentHint": True
+    }
+)
+async def search_bedesten_semantic(
+    query: str = Field(..., description="Search query in Turkish for semantic matching"),
+    initial_keyword: str = Field(..., description="Initial keyword for Bedesten API search (broad term)"),
+    court_types: List[BedestenCourtTypeEnum] = Field(
+        default=["YARGITAYKARARI", "DANISTAYKARAR", "YERELHUKUK", "ISTINAFHUKUK", "KYB"],
+        description="Court types to search: YARGITAYKARARI, DANISTAYKARAR, YERELHUKUK, ISTINAFHUKUK, KYB (default: all)"
+    ),
+    top_k: int = Field(10, ge=1, le=50, description="Number of top results to return (1-50)")
+) -> Dict[str, Any]:
+    """
+    Perform semantic search on Turkish legal decisions using EmbeddingGemma.
+    
+    This tool:
+    1. Searches Bedesten API with initial keyword (retrieves 100 results)
+    2. Fetches full document content for each result
+    3. Generates embeddings using Google's EmbeddingGemma model
+    4. Performs semantic similarity search with the query
+    5. Returns re-ranked results based on semantic relevance
+    
+    Benefits over keyword search:
+    - Better understanding of context and meaning
+    - Finds semantically similar documents even with different wording
+    - More accurate ranking based on relevance
+    - Supports multilingual queries (100+ languages)
+    """
+    logger.info(f"Semantic search tool called with query: {query}, keyword: {initial_keyword}")
+    
+    try:
+        # Initialize components
+        embedder = EmbeddingGemma()
+        vector_store = VectorStore(dimension=256)  # Always use 256 for optimal speed/quality balance
+        processor = DocumentProcessor(chunk_size=1500, chunk_overlap=300)
+        
+        # Step 1: Initial keyword search to get document IDs
+        logger.info(f"Step 1: Searching Bedesten API with keyword: {initial_keyword}")
+        
+        all_decisions = []
+        
+        # Search each court type
+        for court_type in court_types:
+            try:
+                # Calculate page size per court type to get 100 total
+                per_court_limit = max(20, 100 // len(court_types))
+                
+                search_results = await bedesten_client_instance.search_documents(
+                    BedestenSearchRequest(
+                        data=BedestenSearchData(
+                            phrase=initial_keyword,
+                            itemTypeList=[court_type],
+                            pageSize=per_court_limit,  # Distribute 100 across court types
+                            pageNumber=1
+                        )
+                    )
+                )
+                
+                if search_results.data and search_results.data.emsalKararList:
+                    all_decisions.extend(search_results.data.emsalKararList)
+                    logger.info(f"Found {len(search_results.data.emsalKararList)} results from {court_type}")
+                
+            except Exception as e:
+                logger.warning(f"Error searching {court_type}: {e}")
+        
+        if not all_decisions:
+            logger.warning("No documents found from initial search")
+            return {
+                "status": "no_results",
+                "message": "No documents found matching the initial keyword",
+                "results": []
+            }
+        
+        logger.info(f"Total documents found: {len(all_decisions)}")
+        
+        # Step 2: Fetch document content and process
+        logger.info("Step 2: Fetching and processing document content...")
+        
+        documents_data = []
+        failed_fetches = 0
+        
+        # Process up to 100 documents total
+        decisions_to_process = all_decisions[:100]
+        
+        for i, decision in enumerate(decisions_to_process):
+            try:
+                # Fetch document content
+                doc = await bedesten_client_instance.get_document_as_markdown(decision.documentId)
+                
+                if doc.markdown_content:
+                    # Process document into chunks
+                    metadata = {
+                        "document_id": decision.documentId,
+                        "birim_adi": decision.birimAdi,
+                        "esas_no": decision.esasNo,
+                        "karar_no": decision.kararNo,
+                        "karar_tarihi": decision.kararTarihiStr,
+                        "court_type": decision.itemType.name if decision.itemType else None
+                    }
+                    
+                    chunks = processor.process_document(
+                        document_id=decision.documentId,
+                        text=doc.markdown_content,
+                        metadata=metadata
+                    )
+                    
+                    # For now, use the full document as one chunk (can be optimized later)
+                    if chunks:
+                        full_text = " ".join([chunk.text for chunk in chunks])
+                        documents_data.append({
+                            "id": decision.documentId,
+                            "text": full_text[:3000],  # Limit text for embedding
+                            "metadata": metadata
+                        })
+                
+                # Log progress every 10 documents
+                if (i + 1) % 10 == 0:
+                    logger.info(f"Processed {i + 1}/{len(decisions_to_process)} documents")
+                    
+            except Exception as e:
+                logger.warning(f"Failed to fetch document {decision.documentId}: {e}")
+                failed_fetches += 1
+        
+        if not documents_data:
+            logger.warning("No documents could be processed")
+            return {
+                "status": "processing_error",
+                "message": "Could not process any documents",
+                "results": []
+            }
+        
+        logger.info(f"Successfully processed {len(documents_data)} documents, {failed_fetches} failed")
+        
+        # Step 3: Generate embeddings
+        logger.info("Step 3: Generating embeddings...")
+        
+        # Generate query embedding
+        query_embedding = embedder.encode_query(query, task="search result")
+        
+        # Generate document embeddings
+        doc_texts = [doc["text"] for doc in documents_data]
+        doc_titles = [doc["metadata"].get("birim_adi", "none") for doc in documents_data]
+        doc_embeddings = embedder.encode_documents(doc_texts, titles=doc_titles)
+        
+        # Always reduce to 256 dimensions for optimal speed/quality balance
+        query_embedding = embedder.reduce_dimensions(query_embedding, 256)
+        doc_embeddings = embedder.reduce_dimensions(doc_embeddings, 256)
+        
+        # Step 4: Add to vector store and search
+        logger.info("Step 4: Performing semantic search...")
+        
+        # Add documents to vector store
+        doc_ids = [doc["id"] for doc in documents_data]
+        doc_metadatas = [doc["metadata"] for doc in documents_data]
+        
+        vector_store.add_documents(
+            ids=doc_ids,
+            texts=doc_texts,
+            embeddings=doc_embeddings,
+            metadata=doc_metadatas
+        )
+        
+        # Perform semantic search
+        search_results = vector_store.search(
+            query_embedding=query_embedding,
+            top_k=top_k,
+            threshold=0.3  # Minimum similarity threshold
+        )
+        
+        # Step 5: Format results
+        logger.info(f"Step 5: Formatting {len(search_results)} results")
+        
+        formatted_results = []
+        for doc, score in search_results:
+            # Build title from metadata
+            title_parts = []
+            if doc.metadata.get("birim_adi"):
+                title_parts.append(doc.metadata["birim_adi"])
+            if doc.metadata.get("esas_no"):
+                title_parts.append(f"Esas: {doc.metadata['esas_no']}")
+            if doc.metadata.get("karar_no"):
+                title_parts.append(f"Karar: {doc.metadata['karar_no']}")
+            if doc.metadata.get("karar_tarihi"):
+                title_parts.append(f"Tarih: {doc.metadata['karar_tarihi']}")
+            
+            title = " - ".join(title_parts) if title_parts else f"Document {doc.id}"
+            
+            formatted_results.append({
+                "document_id": doc.id,
+                "title": title,
+                "similarity_score": float(score),
+                "preview": doc.text[:500] + "..." if len(doc.text) > 500 else doc.text,
+                "metadata": doc.metadata,
+                "source_url": f"https://mevzuat.adalet.gov.tr/ictihat/{doc.id}"
+            })
+        
+        # Get vector store stats
+        stats = vector_store.get_stats()
+        
+        return {
+            "status": "success",
+            "query": query,
+            "initial_keyword": initial_keyword,
+            "total_documents_processed": len(documents_data),
+            "embedding_dimension": 256,  # Fixed at 256 for optimal performance
+            "results": formatted_results,
+            "stats": {
+                "documents_in_store": stats["num_documents"],
+                "memory_usage_mb": round(stats["memory_usage_mb"], 2),
+                "failed_fetches": failed_fetches
+            }
+        }
+        
+    except Exception as e:
+        logger.exception(f"Error in semantic search: {e}")
+        return {
+            "status": "error",
+            "message": str(e),
+            "results": []
+        }
+
 # --- ChatGPT Deep Research Compatible Tools ---
 
 def get_preview_text(markdown_content: str, skip_chars: int = 100, preview_chars: int = 200) -> str:
@@ -2012,7 +2216,7 @@ async def search(
         logger.info(f"ChatGPT Deep Research search completed. Found {len(results)} results via Bedesten API.")
         return {"results": results}
         
-    except Exception as e:
+    except Exception:
         logger.exception("Error in ChatGPT Deep Research search tool")
         # Return partial results if any were found
         if results:
@@ -2141,7 +2345,7 @@ async def fetch(
             doc = await bedesten_client_instance.get_document_as_markdown(doc_id)
         """
         
-    except Exception as e:
+    except Exception:
         logger.exception(f"Error fetching ChatGPT Deep Research document {id}")
         raise
 
@@ -2192,7 +2396,7 @@ def main():
         app.run()
     except KeyboardInterrupt: 
         logger.info("Server shut down by user (KeyboardInterrupt).")
-    except Exception as e: 
+    except Exception: 
         logger.exception("Server failed to start or crashed.")
     finally:
         logger.info(f"{app.name} server has shut down.")

pyproject.toml

@@ -31,6 +31,11 @@ dependencies = [
     "fastapi>=0.115.14",
     "PyJWT>=2.8.0",
     "tiktoken>=0.5.0",
+    "sentence-transformers>=3.0.0",
+    "torch>=2.0.0",
+    "numpy>=1.24.0",
+    "scikit-learn>=1.3.0",
+    "transformers>=4.57.0.dev0",
 ]
 
 [project.optional-dependencies]

redis_session_store.py

@@ -11,8 +11,8 @@ import os
 import json
 import time
 import logging
-from typing import Optional, Dict, Any, Union
-from datetime import datetime, timedelta
+from typing import Optional, Dict, Any
+from datetime import datetime
 
 logger = logging.getLogger(__name__)
 

rekabet_mcp_module/client.py

@@ -4,10 +4,9 @@ import httpx
 from bs4 import BeautifulSoup
 from typing import List, Optional, Tuple, Dict, Any
 import logging
-import html
 import re
 import io # For io.BytesIO
-from urllib.parse import urlencode, urljoin, quote, parse_qs, urlparse
+from urllib.parse import urljoin, parse_qs, urlparse
 from markitdown import MarkItDown
 import math
 
@@ -18,8 +17,7 @@ from .models import (
     RekabetKurumuSearchRequest,
     RekabetDecisionSummary,
     RekabetSearchResult,
-    RekabetDocument,
-    RekabetKararTuruGuidEnum
+    RekabetDocument
 )
 from pydantic import HttpUrl # Ensure HttpUrl is imported from pydantic
 

rekabet_mcp_module/models.py

@@ -1,7 +1,7 @@
 # rekabet_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl
-from typing import List, Optional, Any
+from typing import List, Optional
 from enum import Enum
 
 # Enum for decision type GUIDs (used by the client and expected by the website)

run_asgi.py

@@ -93,7 +93,7 @@ def main():
         config["workers"] = args.workers
     
     # Print startup information
-    print(f"Starting Yargı MCP server...")
+    print("Starting Yargı MCP server...")
     print(f"Host: {args.host}")
     print(f"Port: {args.port}")
     print(f"Transport: {args.transport}")

saidsurucu-yargi-mcp-f5fa007/anayasa_mcp_module/bireysel_client.py

@@ -2,13 +2,13 @@
 # This client is for Bireysel Başvuru: https://kararlarbilgibankasi.anayasa.gov.tr
 
 import httpx
-from bs4 import BeautifulSoup, Tag
-from typing import Dict, Any, List, Optional, Tuple
+from bs4 import BeautifulSoup
+from typing import List, Optional, Tuple
 import logging
 import html
 import re
 import io
-from urllib.parse import urlencode, urljoin, quote
+from urllib.parse import urljoin
 from markitdown import MarkItDown
 import math # For math.ceil for pagination
 

saidsurucu-yargi-mcp-f5fa007/anayasa_mcp_module/client.py

@@ -3,12 +3,12 @@
 
 import httpx
 from bs4 import BeautifulSoup
-from typing import Dict, Any, List, Optional, Tuple
+from typing import List, Optional, Tuple
 import logging
 import html
 import re
 import io
-from urllib.parse import urlencode, urljoin, quote
+from urllib.parse import urljoin
 from markitdown import MarkItDown
 import math # For math.ceil for pagination
 

saidsurucu-yargi-mcp-f5fa007/anayasa_mcp_module/unified_client.py

@@ -2,7 +2,6 @@
 # Unified client for both Norm Denetimi and Bireysel Başvuru
 
 import logging
-from typing import Optional
 from urllib.parse import urlparse
 
 from .models import (

saidsurucu-yargi-mcp-f5fa007/asgi_app.py

@@ -10,16 +10,12 @@ Usage:
 """
 
 import os
-import time
 import logging
-from datetime import datetime, timedelta
 from fastapi import FastAPI, Request, HTTPException, Query
 from fastapi.responses import JSONResponse, HTMLResponse
 from fastapi.exception_handlers import http_exception_handler
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
-from starlette.responses import Response
-from starlette.requests import Request as StarletteRequest
 
 # Import the MCP app creator function
 from mcp_server_main import create_app
@@ -103,7 +99,6 @@ mcp_app = mcp_server.http_app(path="/")
 
 # Configure JSON encoder for proper Turkish character support
 import json
-from fastapi.responses import JSONResponse
 
 class UTF8JSONResponse(JSONResponse):
     def __init__(self, content=None, status_code=200, headers=None, **kwargs):
@@ -461,14 +456,14 @@ async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
         logger.info("User authenticated successfully via Clerk")
         
         # Return success response
-        return HTMLResponse(f"""
+        return HTMLResponse("""
         <html>
             <head>
                 <title>MCP Connection Successful</title>
                 <style>
-                    body {{ font-family: Arial, sans-serif; text-align: center; padding: 50px; }}
-                    .success {{ color: #28a745; }}
-                    .token {{ background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; word-break: break-all; }}
+                    body { font-family: Arial, sans-serif; text-align: center; padding: 50px; }
+                    .success { color: #28a745; }
+                    .token { background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; word-break: break-all; }
                 </style>
             </head>
             <body>
@@ -481,13 +476,13 @@ async def mcp_oauth_callback(request: Request, clerk_token: str = Query(None)):
                 <p>You can now close this window and return to your MCP client.</p>
                 <script>
                     // Try to close the popup if opened as such
-                    if (window.opener) {{
-                        window.opener.postMessage({{
+                    if (window.opener) {
+                        window.opener.postMessage({
                             type: 'MCP_AUTH_SUCCESS',
                             token: 'use_clerk_jwt_token'
-                        }}, '*');
+                        }, '*');
                         setTimeout(() => window.close(), 3000);
-                    }}
+                    }
                 </script>
             </body>
         </html>

saidsurucu-yargi-mcp-f5fa007/bddk_mcp_module/client.py

@@ -1,13 +1,12 @@
 # bddk_mcp_module/client.py
 
 import httpx
-from typing import List, Optional, Dict, Any
+from typing import Optional
 import logging
 import os
 import re
 import io
 import math
-from urllib.parse import urlparse
 from markitdown import MarkItDown
 
 from .models import (

saidsurucu-yargi-mcp-f5fa007/bddk_mcp_module/models.py

@@ -1,7 +1,7 @@
 # bddk_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import List, Optional
+from typing import List
 
 class BddkSearchRequest(BaseModel):
     """

saidsurucu-yargi-mcp-f5fa007/bedesten_mcp_module/models.py

@@ -1,8 +1,7 @@
 # bedesten_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import List, Optional, Dict, Any, Literal, Union
-from datetime import datetime
+from typing import List, Optional, Dict, Any, Literal
 
 # Import compressed BirimAdiEnum for chamber filtering
 from .enums import BirimAdiEnum

saidsurucu-yargi-mcp-f5fa007/danistay_mcp_module/client.py

@@ -1,11 +1,9 @@
 # danistay_mcp_module/client.py
 
 import httpx
-from bs4 import BeautifulSoup 
-from typing import Dict, Any, List, Optional
+from typing import Dict, List, Optional
 import logging
 import html
-import re
 import io
 from markitdown import MarkItDown
 

saidsurucu-yargi-mcp-f5fa007/emsal_mcp_module/client.py

@@ -2,10 +2,9 @@
 
 import httpx
 # from bs4 import BeautifulSoup # Uncomment if needed for advanced HTML pre-processing
-from typing import Dict, Any, List, Optional
+from typing import Dict, Optional
 import logging
 import html
-import re
 import io
 from markitdown import MarkItDown
 

saidsurucu-yargi-mcp-f5fa007/example_fastapi_app.py

@@ -7,11 +7,9 @@ import os
 from typing import List, Dict, Any, Optional
 from datetime import datetime
 
-from fastapi import FastAPI, HTTPException, Query, Depends, Body
+from fastapi import FastAPI, HTTPException, Query
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field
-import json
 
 # Import the main MCP app
 from mcp_server_main import app as mcp_server

saidsurucu-yargi-mcp-f5fa007/kik_mcp_module/client.py

@@ -10,13 +10,12 @@ from playwright.async_api import (
 )
 from bs4 import BeautifulSoup
 import logging
-from typing import Dict, Any, List, Optional
+from typing import List, Optional
 import urllib.parse 
 import base64 # Base64 için
 import re
 import html as html_parser 
 from markitdown import MarkItDown 
-import os
 import math 
 import io
 import random
@@ -907,7 +906,7 @@ class KikApiClient:
                     event_target_for_submit = self.FIELD_LOCATORS['search_button_id']
                     # Use human-like clicking for search button
                     search_button_selector = f"a[id='{event_target_for_submit}']"
-                    logger.info(f"Performing human-like search button click...")
+                    logger.info("Performing human-like search button click...")
                     
                     try:
                         # Hide datepicker first to prevent interference
@@ -1181,7 +1180,7 @@ class KikApiClient:
             try: 
                 if await current_main_page.locator(self.MODAL_CLOSE_BUTTON_SELECTOR).is_visible(timeout=2000): 
                     await current_main_page.locator(self.MODAL_CLOSE_BUTTON_SELECTOR).click()
-                    await current_main_page.wait_for_selector(f"div#detayPopUp:not(.in)", timeout=5000) 
+                    await current_main_page.wait_for_selector("div#detayPopUp:not(.in)", timeout=5000) 
             except: pass
 
             return KikDocumentMarkdown(

saidsurucu-yargi-mcp-f5fa007/kik_mcp_module/models.py

@@ -1,5 +1,5 @@
 # kik_mcp_module/models.py
-from pydantic import BaseModel, Field, HttpUrl, computed_field, ConfigDict
+from pydantic import BaseModel, Field, computed_field, ConfigDict
 from typing import List, Optional
 from enum import Enum
 import base64 # Base64 encoding/decoding için

saidsurucu-yargi-mcp-f5fa007/kvkk_mcp_module/client.py

@@ -2,13 +2,13 @@
 
 import httpx
 from bs4 import BeautifulSoup
-from typing import List, Optional, Dict, Any
+from typing import Optional, Dict, Any
 import logging
 import os
 import re
 import io
 import math
-from urllib.parse import urljoin, urlparse, parse_qs
+from urllib.parse import urlparse
 from markitdown import MarkItDown
 from pydantic import HttpUrl
 

saidsurucu-yargi-mcp-f5fa007/kvkk_mcp_module/models.py

@@ -1,7 +1,7 @@
 # kvkk_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl
-from typing import List, Optional, Any
+from typing import List, Optional
 
 class KvkkSearchRequest(BaseModel):
     """Model for KVKK (Personal Data Protection Authority) search request via Brave API."""

saidsurucu-yargi-mcp-f5fa007/mcp_auth/clerk_config.py

@@ -36,7 +36,7 @@ def create_clerk_oauth_config() -> OAuthConfig:
         scopes=["mcp:tools:read", "mcp:tools:write", "openid", "profile", "email"]
     )
     
-    logger.info(f"Created Clerk OAuth config with adapter endpoints")
+    logger.info("Created Clerk OAuth config with adapter endpoints")
     logger.info(f"Clerk domain: {clerk_domain}")
     logger.debug(f"Authorization endpoint: {config.authorization_endpoint}")
     logger.debug(f"Token endpoint: {config.token_endpoint}")

saidsurucu-yargi-mcp-f5fa007/mcp_auth/oauth.py

@@ -9,7 +9,7 @@ import time
 import logging
 from dataclasses import dataclass
 from datetime import datetime, timedelta
-from typing import Any, Optional
+from typing import Any
 from urllib.parse import urlencode
 
 import httpx

saidsurucu-yargi-mcp-f5fa007/mcp_auth_http_adapter.py

@@ -18,7 +18,7 @@ from fastapi.responses import RedirectResponse, JSONResponse
 try:
     from clerk_backend_api import Clerk
     CLERK_AVAILABLE = True
-except ImportError as e:
+except ImportError:
     CLERK_AVAILABLE = False
     Clerk = None
 

saidsurucu-yargi-mcp-f5fa007/mcp_auth_http_simple.py

@@ -6,7 +6,7 @@ Uses Redis for authorization code storage to support multi-machine deployment
 import os
 import logging
 from typing import Optional
-from urllib.parse import urlencode, quote
+from urllib.parse import urlencode
 
 from fastapi import APIRouter, Request, Query, HTTPException
 from fastapi.responses import RedirectResponse, JSONResponse
@@ -39,7 +39,6 @@ def get_redis_session_store():
     if redis_store is None:
         try:
             import concurrent.futures
-            import functools
             
             # Use thread pool with timeout to prevent hanging
             with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
@@ -206,7 +205,6 @@ async def oauth_callback(
             auth_code = f"clerk_auth_{os.urandom(16).hex()}"
             
             # Prepare code data
-            import time
             code_data = {
                 "user_id": user_id,
                 "session_id": session_id,
@@ -225,7 +223,7 @@ async def oauth_callback(
                 if success:
                     logger.info(f"Stored authorization code {auth_code[:10]}... in Redis with real JWT token")
                 else:
-                    logger.error(f"Failed to store authorization code in Redis, falling back to in-memory")
+                    logger.error("Failed to store authorization code in Redis, falling back to in-memory")
                     # Fall back to in-memory storage
                     if not hasattr(oauth_callback, '_code_storage'):
                         oauth_callback._code_storage = {}
@@ -236,7 +234,7 @@ async def oauth_callback(
                 if not hasattr(oauth_callback, '_code_storage'):
                     oauth_callback._code_storage = {}
                 oauth_callback._code_storage[auth_code] = code_data
-                logger.info(f"Stored authorization code in memory (fallback)")
+                logger.info("Stored authorization code in memory (fallback)")
             
             # Redirect back to client with authorization code
             redirect_params = {

saidsurucu-yargi-mcp-f5fa007/mcp_server_main.py

@@ -8,8 +8,7 @@ import json
 import time
 from collections import defaultdict
 from pydantic import HttpUrl, Field 
-from typing import Optional, Dict, List, Literal, Any, Union
-import urllib.parse
+from typing import Dict, List, Literal, Any
 import tiktoken
 from fastmcp.server.middleware import Middleware, MiddlewareContext
 from fastmcp.server.dependencies import get_access_token, AccessToken
@@ -159,7 +158,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception as e:
+        except Exception:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("tool_call_error", input_tokens, 0, 
                                tool_name, duration_ms)
@@ -189,7 +188,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception as e:
+        except Exception:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("resource_read_error", 0, 0, 
                                resource_uri, duration_ms)
@@ -219,7 +218,7 @@ class TokenCountingMiddleware(Middleware):
             
             return result
             
-        except Exception as e:
+        except Exception:
             duration_ms = (time.perf_counter() - start_time) * 1000
             self.log_token_usage("prompt_get_error", 0, 0, 
                                prompt_name, duration_ms)
@@ -260,10 +259,6 @@ def create_app(auth=None):
 
 # --- Module Imports ---
 from yargitay_mcp_module.client import YargitayOfficialApiClient
-from yargitay_mcp_module.models import (
-    YargitayDetailedSearchRequest, YargitayDocumentMarkdown, CompactYargitaySearchResult,
-    YargitayBirimEnum, CleanYargitayDecisionEntry
-)
 from bedesten_mcp_module.client import BedestenApiClient
 from bedesten_mcp_module.models import (
     BedestenSearchRequest, BedestenSearchData,
@@ -271,10 +266,6 @@ from bedesten_mcp_module.models import (
 )
 from bedesten_mcp_module.enums import BirimAdiEnum
 from danistay_mcp_module.client import DanistayApiClient
-from danistay_mcp_module.models import (
-    DanistayKeywordSearchRequest, DanistayDetailedSearchRequest,
-    DanistayDocumentMarkdown, CompactDanistaySearchResult
-)
 from emsal_mcp_module.client import EmsalApiClient
 from emsal_mcp_module.models import (
     EmsalSearchRequest, EmsalDocumentMarkdown, CompactEmsalSearchResult
@@ -288,15 +279,7 @@ from anayasa_mcp_module.client import AnayasaMahkemesiApiClient
 from anayasa_mcp_module.bireysel_client import AnayasaBireyselBasvuruApiClient
 from anayasa_mcp_module.unified_client import AnayasaUnifiedClient
 from anayasa_mcp_module.models import (
-    AnayasaNormDenetimiSearchRequest,
-    AnayasaSearchResult,
-    AnayasaDocumentMarkdown,
-    AnayasaBireyselReportSearchRequest,
-    AnayasaBireyselReportSearchResult,
-    AnayasaBireyselBasvuruDocumentMarkdown,
     AnayasaUnifiedSearchRequest,
-    AnayasaUnifiedSearchResult,
-    AnayasaUnifiedDocumentMarkdown,
     # Removed enum imports - now using Literal strings in models
 )
 # KIK Module Imports
@@ -318,14 +301,9 @@ from rekabet_mcp_module.models import (
 
 from sayistay_mcp_module.client import SayistayApiClient
 from sayistay_mcp_module.models import (
-    GenelKurulSearchRequest, GenelKurulSearchResponse,
-    TemyizKuruluSearchRequest, TemyizKuruluSearchResponse,
-    DaireSearchRequest, DaireSearchResponse,
-    SayistayDocumentMarkdown,
     SayistayUnifiedSearchRequest, SayistayUnifiedSearchResult,
     SayistayUnifiedDocumentMarkdown
 )
-from sayistay_mcp_module.enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum
 from sayistay_mcp_module.unified_client import SayistayUnifiedClient
 
 # KVKK Module Imports
@@ -339,14 +317,11 @@ from kvkk_mcp_module.models import (
 # BDDK Module Imports
 from bddk_mcp_module.client import BddkApiClient
 from bddk_mcp_module.models import (
-    BddkSearchRequest,
-    BddkSearchResult,
-    BddkDocumentMarkdown
+    BddkSearchRequest
 )
 
 
 # Create a placeholder app that will be properly initialized after tools are defined
-from fastmcp import FastMCP
 
 # Placeholder app for decorators - will be replaced in create_app() after all tools are defined
 app = FastMCP("Yargı MCP Server Placeholder")
@@ -1372,7 +1347,7 @@ async def search_emsal_detailed_decisions(
         page_size=page_size
     )
     
-    logger.info(f"Tool 'search_emsal_detailed_decisions' called.")
+    logger.info("Tool 'search_emsal_detailed_decisions' called.")
     try:
         api_response = await emsal_client_instance.search_detailed_decisions(search_query)
         if api_response.data:
@@ -1384,8 +1359,8 @@ async def search_emsal_detailed_decisions(
             )
         logger.warning("API response for Emsal search did not contain expected data structure.")
         return CompactEmsalSearchResult(decisions=[], total_records=0, requested_page=search_query.page_number, page_size=search_query.page_size)
-    except Exception as e:
-        logger.exception(f"Error in tool 'search_emsal_detailed_decisions'.")
+    except Exception:
+        logger.exception("Error in tool 'search_emsal_detailed_decisions'.")
         raise
 
 @app.tool(
@@ -1401,8 +1376,8 @@ async def get_emsal_document_markdown(id: str) -> EmsalDocumentMarkdown:
     if not id or not id.strip(): raise ValueError("Document ID required for Emsal.")
     try:
         return await emsal_client_instance.get_decision_document_as_markdown(id)
-    except Exception as e:
-        logger.exception(f"Error in tool 'get_emsal_document_markdown'.")
+    except Exception:
+        logger.exception("Error in tool 'get_emsal_document_markdown'.")
         raise
 
 # --- MCP Tools for Uyusmazlik ---
@@ -1470,11 +1445,11 @@ async def search_uyusmazlik_decisions(
         not_hepsi=not_hepsi
     )
     
-    logger.info(f"Tool 'search_uyusmazlik_decisions' called.")
+    logger.info("Tool 'search_uyusmazlik_decisions' called.")
     try:
         return await uyusmazlik_client_instance.search_decisions(search_params)
-    except Exception as e:
-        logger.exception(f"Error in tool 'search_uyusmazlik_decisions'.")
+    except Exception:
+        logger.exception("Error in tool 'search_uyusmazlik_decisions'.")
         raise
 
 @app.tool(
@@ -1493,8 +1468,8 @@ async def get_uyusmazlik_document_markdown_from_url(
         raise ValueError("Document URL (document_url) is required for Uyuşmazlık document retrieval.")
     try:
         return await uyusmazlik_client_instance.get_decision_document_as_markdown(str(document_url))
-    except Exception as e:
-        logger.exception(f"Error in tool 'get_uyusmazlik_document_markdown_from_url'.")
+    except Exception:
+        logger.exception("Error in tool 'get_uyusmazlik_document_markdown_from_url'.")
         raise
 
 # --- DEACTIVATED: MCP Tools for Anayasa Mahkemesi (Individual Tools) ---
@@ -1585,8 +1560,8 @@ async def search_anayasa_unified(
         result = await anayasa_unified_client_instance.search_unified(request)
         return json.dumps(result.model_dump(), ensure_ascii=False, indent=2)
         
-    except Exception as e:
-        logger.exception(f"Error in tool 'search_anayasa_unified'.")
+    except Exception:
+        logger.exception("Error in tool 'search_anayasa_unified'.")
         raise
 
 @app.tool(
@@ -1607,8 +1582,8 @@ async def get_anayasa_document_unified(
         result = await anayasa_unified_client_instance.get_document_unified(document_url, page_number)
         return json.dumps(result.model_dump(mode='json'), ensure_ascii=False, indent=2)
         
-    except Exception as e:
-        logger.exception(f"Error in tool 'get_anayasa_document_unified'.")
+    except Exception:
+        logger.exception("Error in tool 'get_anayasa_document_unified'.")
         raise
 
 # --- MCP Tools for KIK (Kamu İhale Kurulu) ---
@@ -1654,15 +1629,15 @@ async def search_kik_decisions(
         page=page
     )
     
-    logger.info(f"Tool 'search_kik_decisions' called.")
+    logger.info("Tool 'search_kik_decisions' called.")
     try:
         api_response = await kik_client_instance.search_decisions(search_query)
         page_param_for_log = search_query.page if hasattr(search_query, 'page') else 1
         if not api_response.decisions and api_response.total_records == 0 and page_param_for_log == 1:
-             logger.warning(f"KIK search returned no decisions for query.")
+             logger.warning("KIK search returned no decisions for query.")
         return api_response
-    except Exception as e:
-        logger.exception(f"Error in KIK search tool 'search_kik_decisions'.")
+    except Exception:
+        logger.exception("Error in KIK search tool 'search_kik_decisions'.")
         current_page_val = search_query.page if hasattr(search_query, 'page') else 1
         return KikSearchResult(decisions=[], total_records=0, current_page=current_page_val)
 
@@ -1758,7 +1733,7 @@ async def search_rekabet_kurumu_decisions(
     try:
        
         return await rekabet_client_instance.search_decisions(search_query)
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'search_rekabet_kurumu_decisions'.")
         return RekabetSearchResult(decisions=[], retrieved_page_number=page, total_records_found=0, total_pages=0)
 
@@ -1781,7 +1756,7 @@ async def get_rekabet_kurumu_document(
     try:
       
         return await rekabet_client_instance.get_decision_document(karar_id, page_number=current_page_to_fetch)
-    except Exception as e:
+    except Exception:
         logger.exception(f"Error in tool 'get_rekabet_kurumu_document'. Karar ID: {karar_id}")
         raise 
 
@@ -1876,7 +1851,7 @@ For best results, use exact phrases with quotes for legal terms."""),
             "page_size": pageSize,
             "searched_courts": court_types
         }
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'search_bedesten_unified'")
         raise
 
@@ -1898,7 +1873,7 @@ async def get_bedesten_document_markdown(
     
     try:
         return await bedesten_client_instance.get_document_as_markdown(documentId)
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'get_kyb_bedesten_document_markdown'")
         raise
 
@@ -2087,7 +2062,7 @@ async def search_sayistay_unified(
             web_karar_metni=web_karar_metni
         )
         return await sayistay_unified_client_instance.search_unified(search_request)
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'search_sayistay_unified'")
         raise
 
@@ -2111,7 +2086,7 @@ async def get_sayistay_document_unified(
     
     try:
         return await sayistay_unified_client_instance.get_document_unified(decision_id, decision_type)
-    except Exception as e:
+    except Exception:
         logger.exception("Error in tool 'get_sayistay_document_unified'")
         raise
 
@@ -2698,7 +2673,7 @@ async def search(
         logger.info(f"ChatGPT Deep Research search completed. Found {len(results)} results via Bedesten API.")
         return {"results": results}
         
-    except Exception as e:
+    except Exception:
         logger.exception("Error in ChatGPT Deep Research search tool")
         # Return partial results if any were found
         if results:
@@ -2827,7 +2802,7 @@ async def fetch(
             doc = await bedesten_client_instance.get_document_as_markdown(doc_id)
         """
         
-    except Exception as e:
+    except Exception:
         logger.exception(f"Error fetching ChatGPT Deep Research document {id}")
         raise
 
@@ -2874,7 +2849,7 @@ def main():
         app.run()
     except KeyboardInterrupt: 
         logger.info("Server shut down by user (KeyboardInterrupt).")
-    except Exception as e: 
+    except Exception: 
         logger.exception("Server failed to start or crashed.")
     finally:
         logger.info(f"{app.name} server has shut down.")

saidsurucu-yargi-mcp-f5fa007/redis_session_store.py

@@ -11,8 +11,8 @@ import os
 import json
 import time
 import logging
-from typing import Optional, Dict, Any, Union
-from datetime import datetime, timedelta
+from typing import Optional, Dict, Any
+from datetime import datetime
 
 logger = logging.getLogger(__name__)
 

saidsurucu-yargi-mcp-f5fa007/rekabet_mcp_module/client.py

@@ -4,10 +4,9 @@ import httpx
 from bs4 import BeautifulSoup
 from typing import List, Optional, Tuple, Dict, Any
 import logging
-import html
 import re
 import io # For io.BytesIO
-from urllib.parse import urlencode, urljoin, quote, parse_qs, urlparse
+from urllib.parse import urljoin, parse_qs, urlparse
 from markitdown import MarkItDown
 import math
 
@@ -18,8 +17,7 @@ from .models import (
     RekabetKurumuSearchRequest,
     RekabetDecisionSummary,
     RekabetSearchResult,
-    RekabetDocument,
-    RekabetKararTuruGuidEnum
+    RekabetDocument
 )
 from pydantic import HttpUrl # Ensure HttpUrl is imported from pydantic
 

saidsurucu-yargi-mcp-f5fa007/rekabet_mcp_module/models.py

@@ -1,7 +1,7 @@
 # rekabet_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl
-from typing import List, Optional, Any
+from typing import List, Optional
 from enum import Enum
 
 # Enum for decision type GUIDs (used by the client and expected by the website)

saidsurucu-yargi-mcp-f5fa007/run_asgi.py

@@ -93,7 +93,7 @@ def main():
         config["workers"] = args.workers
     
     # Print startup information
-    print(f"Starting Yargı MCP server...")
+    print("Starting Yargı MCP server...")
     print(f"Host: {args.host}")
     print(f"Port: {args.port}")
     print(f"Transport: {args.transport}")

saidsurucu-yargi-mcp-f5fa007/sayistay_mcp_module/client.py

@@ -1,13 +1,11 @@
 # sayistay_mcp_module/client.py
 
 import httpx
-import re
 from bs4 import BeautifulSoup
-from typing import Dict, Any, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 import logging
-import html
 import io
-from urllib.parse import urlencode, urljoin
+from urllib.parse import urlencode
 from markitdown import MarkItDown
 
 from .models import (
@@ -16,7 +14,7 @@ from .models import (
     DaireSearchRequest, DaireSearchResponse, DaireDecision,
     SayistayDocumentMarkdown
 )
-from .enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum, WEB_KARAR_KONUSU_MAPPING
+from .enums import WEB_KARAR_KONUSU_MAPPING
 
 logger = logging.getLogger(__name__)
 if not logger.hasHandlers():

saidsurucu-yargi-mcp-f5fa007/sayistay_mcp_module/models.py

@@ -1,7 +1,7 @@
 # sayistay_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import Optional, List, Union, Dict, Any, Literal
+from typing import Optional, List, Dict, Any, Literal
 from enum import Enum
 from .enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum
 

saidsurucu-yargi-mcp-f5fa007/sayistay_mcp_module/unified_client.py

@@ -2,8 +2,6 @@
 # Unified client for all three Sayıştay decision types
 
 import logging
-from typing import Optional, Dict, Any
-from urllib.parse import urlparse
 
 from .models import (
     SayistayUnifiedSearchRequest,

saidsurucu-yargi-mcp-f5fa007/starlette_app.py

@@ -13,7 +13,7 @@ import os
 from starlette.applications import Starlette
 from starlette.routing import Mount, Route
 from starlette.requests import Request
-from starlette.responses import JSONResponse, PlainTextResponse, RedirectResponse
+from starlette.responses import JSONResponse
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
 from starlette.middleware.authentication import AuthenticationMiddleware

saidsurucu-yargi-mcp-f5fa007/stripe_webhook.py

@@ -1,4 +1,5 @@
-import os, stripe
+import os
+import stripe
 from clerk_backend_api import Clerk                         # Clerk backend SDK
 from fastapi import APIRouter, Request, HTTPException
 

saidsurucu-yargi-mcp-f5fa007/uyusmazlik_mcp_module/client.py

@@ -3,7 +3,7 @@
 import httpx 
 import aiohttp 
 from bs4 import BeautifulSoup
-from typing import Dict, Any, List, Optional, Union, Tuple 
+from typing import List, Optional, Tuple 
 import logging
 import html
 import re

saidsurucu-yargi-mcp-f5fa007/yargitay_mcp_module/client.py

@@ -1,20 +1,16 @@
 # yargitay_mcp_module/client.py
 
 import httpx
-from bs4 import BeautifulSoup # Still needed for pre-processing HTML before markitdown
-from typing import Dict, Any, List, Optional
+from typing import Optional
 import logging
 import html
-import re
 import io
 from markitdown import MarkItDown
 
 from .models import (
     YargitayDetailedSearchRequest,
     YargitayApiSearchResponse,      
-    YargitayApiDecisionEntry,
-    YargitayDocumentMarkdown,     
-    CompactYargitaySearchResult 
+    YargitayDocumentMarkdown 
 )
 
 logger = logging.getLogger(__name__)

saidsurucu-yargi-mcp-f5fa007/yargitay_mcp_module/models.py

@@ -1,7 +1,7 @@
 # yargitay_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl, ConfigDict
-from typing import List, Optional, Dict, Any, Literal
+from typing import List, Optional, Literal
 
 # Yargıtay Chamber/Board Options
 YargitayBirimEnum = Literal[

sayistay_mcp_module/client.py

@@ -1,13 +1,11 @@
 # sayistay_mcp_module/client.py
 
 import httpx
-import re
 from bs4 import BeautifulSoup
-from typing import Dict, Any, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 import logging
-import html
 import io
-from urllib.parse import urlencode, urljoin
+from urllib.parse import urlencode
 from markitdown import MarkItDown
 
 from .models import (
@@ -16,7 +14,7 @@ from .models import (
     DaireSearchRequest, DaireSearchResponse, DaireDecision,
     SayistayDocumentMarkdown
 )
-from .enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum, WEB_KARAR_KONUSU_MAPPING
+from .enums import WEB_KARAR_KONUSU_MAPPING
 
 logger = logging.getLogger(__name__)
 if not logger.hasHandlers():

sayistay_mcp_module/models.py

@@ -1,7 +1,7 @@
 # sayistay_mcp_module/models.py
 
 from pydantic import BaseModel, Field
-from typing import Optional, List, Union, Dict, Any, Literal
+from typing import Optional, List, Dict, Any, Literal
 from enum import Enum
 from .enums import DaireEnum, KamuIdaresiTuruEnum, WebKararKonusuEnum
 

sayistay_mcp_module/unified_client.py

@@ -2,8 +2,6 @@
 # Unified client for all three Sayıştay decision types
 
 import logging
-from typing import Optional, Dict, Any
-from urllib.parse import urlparse
 
 from .models import (
     SayistayUnifiedSearchRequest,

semantic_search/__init__.py

@@ -0,0 +1,7 @@
+# semantic_search/__init__.py
+
+from .embedder import EmbeddingGemma
+from .vector_store import VectorStore
+from .processor import DocumentProcessor
+
+__all__ = ['EmbeddingGemma', 'VectorStore', 'DocumentProcessor']

semantic_search/embedder.py

@@ -0,0 +1,173 @@
+# semantic_search/embedder.py
+
+import logging
+from typing import List, Optional
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import torch
+
+logger = logging.getLogger(__name__)
+
+class EmbeddingGemma:
+    """
+    Wrapper for Google's EmbeddingGemma model.
+    Handles query and document encoding with proper prompt templates.
+    """
+    
+    def __init__(self, model_name: str = "google/embeddinggemma-300m", device: Optional[str] = None):
+        """
+        Initialize EmbeddingGemma model.
+        
+        Args:
+            model_name: HuggingFace model name
+            device: Device to run model on ('cuda', 'cpu', or None for auto)
+        """
+        self.model_name = model_name
+        
+        # Auto-detect device if not specified
+        if device is None:
+            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        else:
+            self.device = device
+            
+        logger.info(f"Initializing EmbeddingGemma on device: {self.device}")
+        
+        try:
+            # Load model with float32 precision (EmbeddingGemma doesn't support float16)
+            self.model = SentenceTransformer(model_name, device=self.device)
+            self.model.eval()  # Set to evaluation mode
+            
+            # Set precision to float32 or bfloat16
+            if self.device == 'cuda' and torch.cuda.is_bf16_supported():
+                logger.info("Using bfloat16 precision for CUDA")
+                self.dtype = torch.bfloat16
+            else:
+                logger.info("Using float32 precision")
+                self.dtype = torch.float32
+                
+            logger.info(f"Successfully loaded model: {model_name}")
+            
+        except Exception as e:
+            logger.error(f"Failed to load EmbeddingGemma model: {e}")
+            raise
+    
+    def encode_query(self, query: str, task: str = "search result") -> np.ndarray:
+        """
+        Encode a search query with appropriate prompt template.
+        
+        Args:
+            query: The search query text
+            task: Task type for prompt template (search result, question answering, etc.)
+            
+        Returns:
+            Numpy array of embeddings (768 dimensions)
+        """
+        # Apply query prompt template
+        prompted_query = f"task: {task} | query: {query}"
+        
+        try:
+            with torch.no_grad():
+                # Encode with model
+                embeddings = self.model.encode(
+                    prompted_query,
+                    convert_to_numpy=True,
+                    normalize_embeddings=True,  # L2 normalization for cosine similarity
+                    show_progress_bar=False
+                )
+            
+            logger.debug(f"Encoded query: {query[:50]}... -> shape: {embeddings.shape}")
+            return embeddings
+            
+        except Exception as e:
+            logger.error(f"Failed to encode query: {e}")
+            raise
+    
+    def encode_documents(self, documents: List[str], titles: Optional[List[str]] = None) -> np.ndarray:
+        """
+        Encode multiple documents with appropriate prompt template.
+        
+        Args:
+            documents: List of document texts
+            titles: Optional list of document titles
+            
+        Returns:
+            Numpy array of embeddings (N x 768 dimensions)
+        """
+        if not documents:
+            return np.array([])
+        
+        # Apply document prompt template
+        prompted_docs = []
+        for i, doc in enumerate(documents):
+            title = titles[i] if titles and i < len(titles) else "none"
+            prompted_doc = f"title: {title} | text: {doc}"
+            prompted_docs.append(prompted_doc)
+        
+        try:
+            with torch.no_grad():
+                # Batch encode documents
+                embeddings = self.model.encode(
+                    prompted_docs,
+                    convert_to_numpy=True,
+                    normalize_embeddings=True,
+                    show_progress_bar=len(documents) > 10,
+                    batch_size=8  # Adjust based on memory
+                )
+            
+            logger.info(f"Encoded {len(documents)} documents -> shape: {embeddings.shape}")
+            return embeddings
+            
+        except Exception as e:
+            logger.error(f"Failed to encode documents: {e}")
+            raise
+    
+    def reduce_dimensions(self, embeddings: np.ndarray, target_dim: int = 512) -> np.ndarray:
+        """
+        Reduce embedding dimensions using Matryoshka Representation Learning.
+        
+        Args:
+            embeddings: Original embeddings (N x 768)
+            target_dim: Target dimension (512, 256, or 128)
+            
+        Returns:
+            Reduced embeddings (N x target_dim)
+        """
+        if target_dim not in [512, 256, 128]:
+            raise ValueError(f"Target dimension must be 512, 256, or 128, got {target_dim}")
+        
+        if len(embeddings.shape) == 1:
+            # Single embedding
+            reduced = embeddings[:target_dim]
+            # Re-normalize after truncation
+            norm = np.linalg.norm(reduced)
+            if norm > 0:
+                reduced = reduced / norm
+        else:
+            # Multiple embeddings
+            reduced = embeddings[:, :target_dim]
+            # Re-normalize each embedding
+            norms = np.linalg.norm(reduced, axis=1, keepdims=True)
+            reduced = reduced / (norms + 1e-8)  # Avoid division by zero
+        
+        logger.debug(f"Reduced dimensions: {embeddings.shape} -> {reduced.shape}")
+        return reduced
+    
+    def compute_similarity(self, query_embedding: np.ndarray, document_embeddings: np.ndarray) -> np.ndarray:
+        """
+        Compute cosine similarity between query and documents.
+        
+        Args:
+            query_embedding: Query embedding (768,)
+            document_embeddings: Document embeddings (N x 768)
+            
+        Returns:
+            Similarity scores (N,)
+        """
+        # Ensure query is 2D for matrix multiplication
+        if len(query_embedding.shape) == 1:
+            query_embedding = query_embedding.reshape(1, -1)
+        
+        # Compute cosine similarity (embeddings are already normalized)
+        similarities = np.dot(document_embeddings, query_embedding.T).squeeze()
+        
+        return similarities

semantic_search/processor.py

@@ -0,0 +1,305 @@
+# semantic_search/processor.py
+
+import logging
+import re
+from typing import List, Dict, Any, Optional
+from dataclasses import dataclass
+import hashlib
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class DocumentChunk:
+    """Represents a chunk of a document."""
+    chunk_id: str
+    document_id: str
+    text: str
+    metadata: Dict[str, Any]
+    chunk_index: int
+    total_chunks: int
+
+class DocumentProcessor:
+    """
+    Processes legal documents for semantic search.
+    Handles chunking, cleaning, and metadata extraction.
+    """
+    
+    def __init__(self, 
+                 chunk_size: int = 1000,
+                 chunk_overlap: int = 200,
+                 min_chunk_size: int = 100):
+        """
+        Initialize document processor.
+        
+        Args:
+            chunk_size: Target size for each chunk in characters
+            chunk_overlap: Number of overlapping characters between chunks
+            min_chunk_size: Minimum chunk size to keep
+        """
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+        self.min_chunk_size = min_chunk_size
+        
+        logger.info(f"Initialized DocumentProcessor (chunk_size={chunk_size}, overlap={chunk_overlap})")
+    
+    def process_document(self, 
+                        document_id: str,
+                        text: str,
+                        metadata: Optional[Dict[str, Any]] = None) -> List[DocumentChunk]:
+        """
+        Process a single document into chunks.
+        
+        Args:
+            document_id: Unique document identifier
+            text: Document text content
+            metadata: Optional document metadata
+            
+        Returns:
+            List of document chunks
+        """
+        if not text or len(text.strip()) < self.min_chunk_size:
+            logger.warning(f"Document {document_id} too short to process")
+            return []
+        
+        # Clean text
+        cleaned_text = self._clean_text(text)
+        
+        # Extract metadata from text if not provided
+        if metadata is None:
+            metadata = {}
+        
+        # Add extracted metadata
+        extracted_metadata = self._extract_metadata(cleaned_text)
+        metadata.update(extracted_metadata)
+        
+        # Create chunks
+        chunks = self._create_chunks(cleaned_text)
+        
+        # Create DocumentChunk objects
+        document_chunks = []
+        for i, chunk_text in enumerate(chunks):
+            chunk_id = self._generate_chunk_id(document_id, i)
+            
+            chunk = DocumentChunk(
+                chunk_id=chunk_id,
+                document_id=document_id,
+                text=chunk_text,
+                metadata={
+                    **metadata,
+                    'chunk_index': i,
+                    'total_chunks': len(chunks)
+                },
+                chunk_index=i,
+                total_chunks=len(chunks)
+            )
+            document_chunks.append(chunk)
+        
+        logger.info(f"Processed document {document_id} into {len(chunks)} chunks")
+        return document_chunks
+    
+    def _clean_text(self, text: str) -> str:
+        """
+        Clean and normalize text for processing.
+        
+        Args:
+            text: Raw text
+            
+        Returns:
+            Cleaned text
+        """
+        # Remove excessive whitespace
+        text = re.sub(r'\s+', ' ', text)
+        
+        # Remove special characters but keep Turkish characters
+        # Keep: letters, numbers, spaces, and common punctuation
+        text = re.sub(r'[^\w\s\.\,\;\:\!\?\-\(\)\"\'ÇĞIİÖŞÜçğıiöşü]', ' ', text)
+        
+        # Remove multiple spaces
+        text = re.sub(r' +', ' ', text)
+        
+        # Trim
+        text = text.strip()
+        
+        return text
+    
+    def _extract_metadata(self, text: str) -> Dict[str, Any]:
+        """
+        Extract metadata from legal document text.
+        
+        Args:
+            text: Document text
+            
+        Returns:
+            Extracted metadata
+        """
+        metadata = {}
+        
+        # Extract case numbers (Esas/Karar)
+        esas_pattern = r'E(?:sas)?[\s\.\:]*(\d{4})[\/\-](\d+)'
+        karar_pattern = r'K(?:arar)?[\s\.\:]*(\d{4})[\/\-](\d+)'
+        
+        esas_match = re.search(esas_pattern, text[:500])  # Look in first 500 chars
+        if esas_match:
+            metadata['esas_no'] = f"E.{esas_match.group(1)}/{esas_match.group(2)}"
+        
+        karar_match = re.search(karar_pattern, text[:500])
+        if karar_match:
+            metadata['karar_no'] = f"K.{karar_match.group(1)}/{karar_match.group(2)}"
+        
+        # Extract dates (DD.MM.YYYY or DD/MM/YYYY format)
+        date_pattern = r'(\d{1,2})[\.\/](\d{1,2})[\.\/](\d{4})'
+        dates = re.findall(date_pattern, text[:1000])  # Look in first 1000 chars
+        if dates:
+            # Take the first date as decision date
+            day, month, year = dates[0]
+            metadata['karar_tarihi'] = f"{year}-{month.zfill(2)}-{day.zfill(2)}"
+        
+        # Extract court/chamber name
+        chamber_patterns = [
+            r'(\d+)\.\s*Hukuk\s+Dairesi',
+            r'(\d+)\.\s*Ceza\s+Dairesi',
+            r'Hukuk\s+Genel\s+Kurulu',
+            r'Ceza\s+Genel\s+Kurulu',
+            r'(\d+)\.\s*Daire'
+        ]
+        
+        for pattern in chamber_patterns:
+            match = re.search(pattern, text[:500], re.IGNORECASE)
+            if match:
+                metadata['chamber'] = match.group(0)
+                break
+        
+        return metadata
+    
+    def _create_chunks(self, text: str) -> List[str]:
+        """
+        Create overlapping chunks from text.
+        
+        Args:
+            text: Cleaned document text
+            
+        Returns:
+            List of text chunks
+        """
+        chunks = []
+        
+        # Split by sentences for better semantic coherence
+        sentences = self._split_sentences(text)
+        
+        current_chunk = []
+        current_size = 0
+        
+        for sentence in sentences:
+            sentence_size = len(sentence)
+            
+            # If adding this sentence exceeds chunk size
+            if current_size + sentence_size > self.chunk_size and current_chunk:
+                # Save current chunk
+                chunk_text = ' '.join(current_chunk)
+                chunks.append(chunk_text)
+                
+                # Create overlap for next chunk
+                overlap_size = 0
+                overlap_sentences = []
+                
+                # Add sentences from the end until we reach overlap size
+                for sent in reversed(current_chunk):
+                    overlap_size += len(sent)
+                    overlap_sentences.insert(0, sent)
+                    if overlap_size >= self.chunk_overlap:
+                        break
+                
+                # Start new chunk with overlap
+                current_chunk = overlap_sentences
+                current_size = sum(len(s) for s in current_chunk)
+            
+            # Add sentence to current chunk
+            current_chunk.append(sentence)
+            current_size += sentence_size
+        
+        # Add final chunk if not empty
+        if current_chunk:
+            chunk_text = ' '.join(current_chunk)
+            if len(chunk_text) >= self.min_chunk_size:
+                chunks.append(chunk_text)
+        
+        return chunks
+    
+    def _split_sentences(self, text: str) -> List[str]:
+        """
+        Split text into sentences.
+        
+        Args:
+            text: Text to split
+            
+        Returns:
+            List of sentences
+        """
+        # Simple sentence splitting for Turkish text
+        # Split on period, question mark, exclamation, but not on abbreviations
+        
+        # Common Turkish abbreviations to preserve
+        abbreviations = ['Dr', 'Prof', 'Av', 'Md', 'Yrd', 'Doç', 'No', 'S', 'vs', 'vb', 'bkz']
+        
+        # Replace abbreviations temporarily
+        temp_text = text
+        replacements = {}
+        for i, abbr in enumerate(abbreviations):
+            placeholder = f"__ABBR{i}__"
+            temp_text = temp_text.replace(f"{abbr}.", placeholder)
+            replacements[placeholder] = f"{abbr}."
+        
+        # Split sentences
+        sentence_endings = re.compile(r'[.!?]+')
+        sentences = sentence_endings.split(temp_text)
+        
+        # Restore abbreviations and clean
+        cleaned_sentences = []
+        for sentence in sentences:
+            # Restore abbreviations
+            for placeholder, original in replacements.items():
+                sentence = sentence.replace(placeholder, original)
+            
+            # Clean and add if not empty
+            sentence = sentence.strip()
+            if sentence and len(sentence) > 10:  # Minimum sentence length
+                cleaned_sentences.append(sentence)
+        
+        return cleaned_sentences
+    
+    def _generate_chunk_id(self, document_id: str, chunk_index: int) -> str:
+        """
+        Generate unique chunk ID.
+        
+        Args:
+            document_id: Parent document ID
+            chunk_index: Index of chunk in document
+            
+        Returns:
+            Unique chunk ID
+        """
+        chunk_string = f"{document_id}_chunk_{chunk_index}"
+        chunk_hash = hashlib.md5(chunk_string.encode()).hexdigest()[:8]
+        return f"{document_id}_c{chunk_index}_{chunk_hash}"
+    
+    def combine_chunks(self, chunks: List[DocumentChunk]) -> str:
+        """
+        Combine chunks back into full document text.
+        
+        Args:
+            chunks: List of document chunks
+            
+        Returns:
+            Combined text
+        """
+        if not chunks:
+            return ""
+        
+        # Sort by chunk index
+        sorted_chunks = sorted(chunks, key=lambda x: x.chunk_index)
+        
+        # For overlapping chunks, we need to be careful about duplication
+        # Simple approach: just concatenate with space
+        combined = " ".join([chunk.text for chunk in sorted_chunks])
+        
+        return combined

semantic_search/vector_store.py

@@ -0,0 +1,235 @@
+# semantic_search/vector_store.py
+
+import logging
+import numpy as np
+from typing import List, Dict, Any, Tuple, Optional
+from dataclasses import dataclass
+import json
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class Document:
+    """Represents a document with its embedding and metadata."""
+    id: str
+    text: str
+    embedding: np.ndarray
+    metadata: Dict[str, Any]
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary (excluding embedding for serialization)."""
+        return {
+            'id': self.id,
+            'text': self.text,
+            'metadata': self.metadata
+        }
+
+class VectorStore:
+    """
+    In-memory vector storage with similarity search capabilities.
+    Future versions can use Faiss, ChromaDB, or other vector databases.
+    """
+    
+    def __init__(self, dimension: int = 768):
+        """
+        Initialize vector store.
+        
+        Args:
+            dimension: Embedding dimension size
+        """
+        self.dimension = dimension
+        self.documents: List[Document] = []
+        self.embeddings: Optional[np.ndarray] = None
+        self.index_built = False
+        
+        logger.info(f"Initialized VectorStore with dimension: {dimension}")
+    
+    def add_documents(self, 
+                     ids: List[str],
+                     texts: List[str],
+                     embeddings: np.ndarray,
+                     metadata: Optional[List[Dict[str, Any]]] = None) -> int:
+        """
+        Add documents to the vector store.
+        
+        Args:
+            ids: Document IDs
+            texts: Document texts
+            embeddings: Document embeddings (N x dimension)
+            metadata: Optional metadata for each document
+            
+        Returns:
+            Number of documents added
+        """
+        if len(ids) != len(texts) or len(ids) != embeddings.shape[0]:
+            raise ValueError("Mismatched lengths for ids, texts, and embeddings")
+        
+        if metadata and len(metadata) != len(ids):
+            raise ValueError("Metadata length doesn't match document count")
+        
+        # Add documents
+        for i in range(len(ids)):
+            doc = Document(
+                id=ids[i],
+                text=texts[i],
+                embedding=embeddings[i],
+                metadata=metadata[i] if metadata else {}
+            )
+            self.documents.append(doc)
+        
+        # Rebuild index
+        self._build_index()
+        
+        logger.info(f"Added {len(ids)} documents to vector store. Total: {len(self.documents)}")
+        return len(ids)
+    
+    def _build_index(self):
+        """Build or rebuild the embedding index."""
+        if not self.documents:
+            self.embeddings = None
+            self.index_built = False
+            return
+        
+        # Stack all embeddings into a single array
+        self.embeddings = np.vstack([doc.embedding for doc in self.documents])
+        self.index_built = True
+        
+        logger.debug(f"Built index with shape: {self.embeddings.shape}")
+    
+    def search(self, 
+              query_embedding: np.ndarray,
+              top_k: int = 10,
+              threshold: Optional[float] = None) -> List[Tuple[Document, float]]:
+        """
+        Search for similar documents using cosine similarity.
+        
+        Args:
+            query_embedding: Query embedding vector
+            top_k: Number of results to return
+            threshold: Optional similarity threshold (0-1)
+            
+        Returns:
+            List of (Document, similarity_score) tuples
+        """
+        if not self.index_built or self.embeddings is None:
+            logger.warning("No documents in vector store")
+            return []
+        
+        # Ensure query is 2D
+        if len(query_embedding.shape) == 1:
+            query_embedding = query_embedding.reshape(1, -1)
+        
+        # Compute cosine similarities (assuming normalized embeddings)
+        similarities = np.dot(self.embeddings, query_embedding.T).squeeze()
+        
+        # Apply threshold if specified
+        if threshold is not None:
+            valid_indices = np.where(similarities >= threshold)[0]
+            if len(valid_indices) == 0:
+                logger.info(f"No documents above threshold {threshold}")
+                return []
+            similarities = similarities[valid_indices]
+            valid_docs = [self.documents[i] for i in valid_indices]
+        else:
+            valid_docs = self.documents
+        
+        # Get top-k indices
+        top_k = min(top_k, len(valid_docs))
+        if top_k == 0:
+            return []
+        
+        # Use argpartition for efficiency with large arrays
+        if len(similarities) > top_k:
+            top_indices = np.argpartition(similarities, -top_k)[-top_k:]
+            top_indices = top_indices[np.argsort(similarities[top_indices])[::-1]]
+        else:
+            top_indices = np.argsort(similarities)[::-1]
+        
+        # Create results
+        results = []
+        for idx in top_indices:
+            doc = valid_docs[idx] if threshold else self.documents[idx]
+            score = float(similarities[idx])
+            results.append((doc, score))
+        
+        logger.info(f"Search returned {len(results)} results (top_k={top_k})")
+        return results
+    
+    def hybrid_search(self,
+                     query_embedding: np.ndarray,
+                     keyword_scores: Dict[str, float],
+                     top_k: int = 10,
+                     alpha: float = 0.5) -> List[Tuple[Document, float]]:
+        """
+        Hybrid search combining vector similarity and keyword scores.
+        
+        Args:
+            query_embedding: Query embedding vector
+            keyword_scores: Document ID to keyword relevance score mapping
+            top_k: Number of results to return
+            alpha: Weight for vector similarity (1-alpha for keyword score)
+            
+        Returns:
+            List of (Document, combined_score) tuples
+        """
+        if not self.index_built:
+            logger.warning("No documents in vector store")
+            return []
+        
+        # Get vector similarities
+        vector_results = self.search(query_embedding, top_k=len(self.documents))
+        
+        # Combine scores
+        combined_scores = []
+        for doc, vector_score in vector_results:
+            keyword_score = keyword_scores.get(doc.id, 0.0)
+            # Normalize keyword score to 0-1 range if needed
+            if keyword_score > 1.0:
+                keyword_score = keyword_score / max(keyword_scores.values())
+            
+            combined_score = alpha * vector_score + (1 - alpha) * keyword_score
+            combined_scores.append((doc, combined_score))
+        
+        # Sort by combined score and return top-k
+        combined_scores.sort(key=lambda x: x[1], reverse=True)
+        results = combined_scores[:top_k]
+        
+        logger.info(f"Hybrid search returned {len(results)} results")
+        return results
+    
+    def clear(self):
+        """Clear all documents from the store."""
+        self.documents = []
+        self.embeddings = None
+        self.index_built = False
+        logger.info("Cleared vector store")
+    
+    def size(self) -> int:
+        """Get number of documents in store."""
+        return len(self.documents)
+    
+    def get_by_id(self, doc_id: str) -> Optional[Document]:
+        """Get document by ID."""
+        for doc in self.documents:
+            if doc.id == doc_id:
+                return doc
+        return None
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """Get statistics about the vector store."""
+        stats = {
+            'num_documents': len(self.documents),
+            'dimension': self.dimension,
+            'index_built': self.index_built,
+            'memory_usage_mb': 0
+        }
+        
+        if self.embeddings is not None:
+            # Estimate memory usage
+            memory_bytes = self.embeddings.nbytes
+            for doc in self.documents:
+                memory_bytes += len(doc.text.encode('utf-8'))
+                memory_bytes += len(json.dumps(doc.metadata).encode('utf-8'))
+            stats['memory_usage_mb'] = memory_bytes / (1024 * 1024)
+        
+        return stats

starlette_app.py

@@ -13,7 +13,7 @@ import os
 from starlette.applications import Starlette
 from starlette.routing import Mount, Route
 from starlette.requests import Request
-from starlette.responses import JSONResponse, PlainTextResponse, RedirectResponse
+from starlette.responses import JSONResponse
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
 from starlette.middleware.authentication import AuthenticationMiddleware

stripe_webhook.py

@@ -1,4 +1,5 @@
-import os, stripe
+import os
+import stripe
 from clerk_backend_api import Clerk                         # Clerk backend SDK
 from fastapi import APIRouter, Request, HTTPException
 

uyusmazlik_mcp_module/client.py

@@ -2,7 +2,7 @@
 
 import httpx 
 from bs4 import BeautifulSoup
-from typing import Dict, Any, List, Optional, Union, Tuple 
+from typing import List, Optional, Tuple 
 import logging
 import html
 import re

yargitay_mcp_module/client.py

@@ -1,20 +1,16 @@
 # yargitay_mcp_module/client.py
 
 import httpx
-from bs4 import BeautifulSoup # Still needed for pre-processing HTML before markitdown
-from typing import Dict, Any, List, Optional
+from typing import Optional
 import logging
 import html
-import re
 import io
 from markitdown import MarkItDown
 
 from .models import (
     YargitayDetailedSearchRequest,
     YargitayApiSearchResponse,      
-    YargitayApiDecisionEntry,
-    YargitayDocumentMarkdown,     
-    CompactYargitaySearchResult 
+    YargitayDocumentMarkdown 
 )
 
 logger = logging.getLogger(__name__)

yargitay_mcp_module/models.py

@@ -1,7 +1,7 @@
 # yargitay_mcp_module/models.py
 
 from pydantic import BaseModel, Field, HttpUrl, ConfigDict
-from typing import List, Optional, Dict, Any, Literal
+from typing import List, Optional, Literal
 
 # Yargıtay Chamber/Board Options
 YargitayBirimEnum = Literal[