fix: Resolve deprecation warnings in Pydantic and asyncio

Fixed deprecation warnings to ensure forward compatibility:

1. Pydantic v2 Migration (embedding/models.py):
   - Migrated from class Config to model_config = ConfigDict()
   - Replaced deprecated class-based config pattern
   - Fixes PydanticDeprecatedSince20 warnings (3 occurrences)
   - Forward compatible with Pydantic v3.0

2. Asyncio Deprecation Fix (test_async_scraping.py):
   - Changed asyncio.iscoroutinefunction() to inspect.iscoroutinefunction()
   - Fixes Python 3.16 deprecation warning (2 occurrences)
   - Uses recommended inspect module API

3. Lock File Update (uv.lock):
   - Updated dependency lock file

Impact:
- Reduces test warnings from 141 to ~75
- Improves forward compatibility
- No functional changes

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

src/skill_seekers/embedding/models.py

@@ -3,12 +3,22 @@ Pydantic models for embedding API.
 """
 
 from typing import Any
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, ConfigDict
 
 
 class EmbeddingRequest(BaseModel):
     """Request model for single embedding generation."""
 
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "text": "This is a test document about Python programming.",
+                "model": "text-embedding-3-small",
+                "normalize": True
+            }
+        }
+    )
+
     text: str = Field(..., description="Text to generate embedding for")
     model: str = Field(
         default="text-embedding-3-small",
@@ -19,19 +29,25 @@ class EmbeddingRequest(BaseModel):
         description="Normalize embeddings to unit length"
     )
 
-    class Config:
-        json_schema_extra = {
-            "example": {
-                "text": "This is a test document about Python programming.",
-                "model": "text-embedding-3-small",
-                "normalize": True
-            }
-        }
-
 
 class BatchEmbeddingRequest(BaseModel):
     """Request model for batch embedding generation."""
 
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "texts": [
+                    "First document about Python",
+                    "Second document about JavaScript",
+                    "Third document about Rust"
+                ],
+                "model": "text-embedding-3-small",
+                "normalize": True,
+                "batch_size": 32
+            }
+        }
+    )
+
     texts: list[str] = Field(..., description="List of texts to embed")
     model: str = Field(
         default="text-embedding-3-small",
@@ -46,20 +62,6 @@ class BatchEmbeddingRequest(BaseModel):
         description="Batch size for processing (default: 32)"
     )
 
-    class Config:
-        json_schema_extra = {
-            "example": {
-                "texts": [
-                    "First document about Python",
-                    "Second document about JavaScript",
-                    "Third document about Rust"
-                ],
-                "model": "text-embedding-3-small",
-                "normalize": True,
-                "batch_size": 32
-            }
-        }
-
 
 class EmbeddingResponse(BaseModel):
     """Response model for embedding generation."""
@@ -89,6 +91,17 @@ class BatchEmbeddingResponse(BaseModel):
 class SkillEmbeddingRequest(BaseModel):
     """Request model for skill content embedding."""
 
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "skill_path": "/path/to/skill/react",
+                "model": "text-embedding-3-small",
+                "chunk_size": 512,
+                "overlap": 50
+            }
+        }
+    )
+
     skill_path: str = Field(..., description="Path to skill directory")
     model: str = Field(
         default="text-embedding-3-small",
@@ -103,16 +116,6 @@ class SkillEmbeddingRequest(BaseModel):
         description="Overlap between chunks (tokens)"
     )
 
-    class Config:
-        json_schema_extra = {
-            "example": {
-                "skill_path": "/path/to/skill/react",
-                "model": "text-embedding-3-small",
-                "chunk_size": 512,
-                "overlap": 50
-            }
-        }
-
 
 class SkillEmbeddingResponse(BaseModel):
     """Response model for skill content embedding."""