feat: memory system fixes and Redis config cleanup (v1.3.0)

- Fix Qdrant client to use query_points API (qdrant-client >= 1.10)
- Rename REDIS_DB to REDIS_BENCHMARK_DB for clarity
- Update Redis defaults to match stack allocation (benchmark=6, memory=1)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

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

.env.example

@@ -21,7 +21,8 @@ SEARXNG_TIMEOUT=30
 # Redis Configuration
 REDIS_HOST=redis-shared
 REDIS_PORT=6379
-REDIS_DB=1
+REDIS_MEMORY_DB=1
+REDIS_BENCHMARK_DB=6
 REDIS_TIMEOUT=5
 
 # Logging
@@ -30,4 +31,4 @@ ENABLE_BENCHMARKS=true
 # Note: Log format is auto-selected based on ENVIRONMENT (console for dev, json for production)
 
 # CORS (comma-separated list)
-CORS_ORIGINS=*
+CORS_ORIGINS=["*"]

.gitea/workflows/build.yml

@@ -13,15 +13,23 @@ jobs:
       - name: Login to Gitea Registry
         uses: docker/login-action@v3
         with:
-          registry: git.schweitz.net
+          registry: git.schweitz.internal
           username: ${{ secrets.REGISTRY_USER }}
           password: ${{ secrets.REGISTRY_PASSWORD }}
 
       - name: Build and push
-        uses: docker/build-push-action@v5
+        uses: docker/build-push-action@v6
         with:
           context: .
           push: true
+          provenance: false
+          sbom: false
           tags: |
-            git.schweitz.net/jpmschweitzer/tatlock:latest
-            git.schweitz.net/jpmschweitzer/tatlock:${{ github.ref_name }}
+            git.schweitz.internal/jpmschweitzer/tatlock:latest
+            git.schweitz.internal/jpmschweitzer/tatlock:${{ github.ref_name }}
+
+      - name: Trigger Watchtower update
+        if: success()
+        run: |
+          curl -sf -H "Authorization: Bearer ${{ secrets.WATCHTOWER_TOKEN }}" \
+            http://watchtower:8080/v1/update

CHANGELOG.md

@@ -7,6 +7,52 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [1.3.0] - 2025-12-14
+
+### Fixed
+
+- **Memory**: Update Qdrant client to use `query_points` API (qdrant-client >= 1.10)
+
+### Changed
+
+- **Config**: Rename `REDIS_DB` to `REDIS_BENCHMARK_DB` for clarity
+- **Config**: Update Redis defaults to match stack allocation (benchmark=6, memory=1)
+
+## [1.2.5] - 2025-12-14
+
+### Fixed
+
+- **Dependencies**: Add missing `pydantic-settings` (not included in pydantic-ai-slim)
+
+## [1.2.4] - 2025-12-14
+
+### Added
+
+- **CI**: Trigger Watchtower update after successful image push
+
+## [1.2.3] - 2025-12-14
+
+### Fixed
+
+- **CI**: Upgrade to build-push-action@v6, disable provenance and sbom for Gitea registry
+
+## [1.2.2] - 2025-12-13
+
+### Fixed
+
+- **CI**: Add `provenance: false` to docker/build-push-action to fix Gitea registry push
+
+## [1.2.1] - 2025-12-13
+
+### Changed
+
+- **Dependency slimming**: Switched from `pydantic-ai` to `pydantic-ai-slim[openai]`
+  - Removes unused LLM provider SDKs (anthropic, boto3, cohere, google-genai, groq, huggingface)
+  - Production packages: 53 (down from ~158)
+  - Production footprint: 178MB
+  - Tatlock uses Ollama via OpenAI-compatible API, so only `openai` extra is needed
+  - See `DEPENDENCY_SLIM.md` for rollback instructions
+
 ## [1.2.0] - 2025-12-13
 
 ### Added

README.md

@@ -432,7 +432,7 @@ For LLM agent development guidelines and architectural decisions, see [AGENTS.md
 
 ## Version
 
-Current version: **1.2.0** - Phase F: Memory System (The Biographer)
+Current version: **1.3.0** - Memory system fixes and Redis config cleanup
 
 ---
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tatlock"
-version = "1.2.0"
+version = "1.3.0"
 description = "OpenAI-compatible API with Ollama backend"
 requires-python = ">=3.12"
 dependencies = []

requirements.txt

@@ -14,11 +14,17 @@ uvicorn[standard]>=0.38,<0.39
 # Latest: 2.12.4 (Nov 5, 2025) - No known CVEs
 pydantic>=2.11,<2.13
 
+# Pydantic settings for configuration management
+# Required explicitly since pydantic-ai-slim doesn't include it
+# Latest: 2.12.0 (Dec 2025) - No known CVEs
+pydantic-settings>=2.12,<2.13
+
 # AI/LLM integration
 # PydanticAI: Agent framework for using Pydantic with LLMs
-# Latest: 1.27.0 (Dec 5, 2025) - No known CVEs
-# Supports Ollama backend out of the box
-pydantic-ai>=1.27,<1.28
+# Using slim version with only openai extra (Ollama uses OpenAI-compatible API)
+# This avoids installing SDKs for anthropic, cohere, google, groq, huggingface, etc.
+# See DEPENDENCY_SLIM.md for rollback instructions if this breaks
+pydantic-ai-slim[openai]>=1.27,<1.28
 
 # HTTP client for Ollama communication
 # Latest: 0.28.1 - No known CVEs

src/core/config.py

@@ -101,9 +101,9 @@ class Config(BaseSettings):
         default=6379,
         description="Redis server port"
     )
-    REDIS_DB: int = Field(
-        default=1,
-        description="Redis database number"
+    REDIS_BENCHMARK_DB: int = Field(
+        default=6,
+        description="Redis database number for benchmarks"
     )
     REDIS_TIMEOUT: int = Field(
         default=5,
@@ -146,7 +146,7 @@ class Config(BaseSettings):
 
     # Redis Memory Database (separate from benchmarks)
     REDIS_MEMORY_DB: int = Field(
-        default=2,
+        default=1,
         description="Redis database number for memory cache"
     )
     REDIS_MEMORY_TTL_HOURS: int = Field(
@@ -170,7 +170,7 @@ class Config(BaseSettings):
     @property
     def redis_url(self) -> str:
         """Construct Redis connection URL for benchmarks."""
-        return f"redis://{self.REDIS_HOST}:{self.REDIS_PORT}/{self.REDIS_DB}"
+        return f"redis://{self.REDIS_HOST}:{self.REDIS_PORT}/{self.REDIS_BENCHMARK_DB}"
 
     @property
     def redis_memory_url(self) -> str:

src/core/qdrant.py

@@ -232,14 +232,14 @@ class MemoryQdrantClient:
                     ]
                 )
 
-            # Search
-            results = self._client.search(
+            # Search using new Query API (qdrant-client >= 1.10)
+            results = self._client.query_points(
                 collection_name=collection_name,
-                query_vector=query_vector,
+                query=query_vector,
                 limit=limit,
                 query_filter=query_filter,
                 score_threshold=score_threshold,
-            )
+            ).points
 
             # Format results
             memories = []