RAG, CVE and Scanner changes

.github/workflows/ci.yml

@@ -122,11 +122,15 @@ jobs:
         id: cred_scan
         run: |
           echo "=== Scanning for hardcoded credentials ==="
+          # Each credential-name pattern requires a quoted string literal on the
+          # right-hand side. This flags real hardcoded values (api_key = "sk-...")
+          # while ignoring safe assignments to function calls or expressions
+          # (api_key = str(data.get(...)) , _SECRET = secrets.token_urlsafe(32)).
           PATTERNS=(
-            "password\s*="
-            "secret\s*="
-            "api_key\s*="
-            "client_secret\s*="
+            "password\s*=\s*['\"]"
+            "secret\s*=\s*['\"]"
+            "api_key\s*=\s*['\"]"
+            "client_secret\s*=\s*['\"]"
             "AZURE_CLIENT_SECRET\s*=\s*['\"][^'\"]\+"
             "-----BEGIN.*PRIVATE KEY-----"
             "AccountKey="
@@ -145,7 +149,10 @@ jobs:
               grep -v "os\.getenv" | \
               grep -vE '^\s*#' | \
               grep -v "example" | \
-              grep -v "placeholder" || true)
+              grep -v "placeholder" | \
+              grep -v "\.get(" | \
+              grep -v "request\." | \
+              grep -v "config\." || true)
 
             if [ -n "$matches" ]; then
               echo "POTENTIAL CREDENTIAL LEAK — pattern '$pattern':"

.gitignore

@@ -216,3 +216,4 @@ __marimo__/
 
 # Streamlit
 .streamlit/secrets.toml
+ai/vectorstore/

ai/__init__.py

@@ -0,0 +1 @@
+

ai/embed.py

@@ -0,0 +1,138 @@
+"""Build the OpenShield knowledge base vector store for RAG AI insights"""
+
+
+import importlib.util
+import json
+import logging
+from pathlib import Path
+
+import chromadb
+
+logger = logging.getLogger(__name__)
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+RULES_DIR = REPO_ROOT / "scanner" / "rules"
+FRAMEWORKS_DIR = REPO_ROOT / "compliance" / "frameworks"
+SKILLS_DIR = REPO_ROOT / "ai" / "knowledge" / "skills"
+VECTORSTORE_DIR = REPO_ROOT / "ai" / "vectorstore"
+COLLECTION_NAME = "openshield"
+
+
+def _load_rule_module(path):
+    spec = importlib.util.spec_from_file_location(path.stem, path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _collect_skill_documents():
+    documents = []
+    if not SKILLS_DIR.exists():
+        logger.warning("Skills directory not found, skipping: %s", SKILLS_DIR)
+        return documents
+    for path in sorted(SKILLS_DIR.rglob("SKILL.md")):
+        try:
+            text = path.read_text(encoding="utf-8")
+        except Exception as exc:
+            logger.warning("Skipping %s: %s", path.name, exc)
+            continue
+        if not text.strip():
+            continue
+        skill_name = path.parent.name
+        documents.append({
+            "id": f"skill-{skill_name}",
+            "text": text,
+            "source": skill_name,
+            "type": "skill",
+        })
+    return documents
+
+
+def _collect_rule_documents():
+    documents = []
+    for path in sorted(RULES_DIR.glob("az_*.py")):
+        try:
+            module = _load_rule_module(path)
+        except Exception as exc:
+            logger.warning("Skipping %s: %s", path.name, exc)
+            continue
+        rule_id = getattr(module, "RULE_ID", None)
+        if not rule_id:
+            continue
+        text = (
+            f"OpenShield rule {rule_id}: {getattr(module, 'RULE_NAME', '')}\n"
+            f"Category: {getattr(module, 'CATEGORY', '')}\n"
+            f"Severity: {getattr(module, 'SEVERITY', '')}\n"
+            f"Description: {getattr(module, 'DESCRIPTION', '')}\n"
+            f"Remediation: {getattr(module, 'REMEDIATION', '')}"
+        )
+        documents.append({
+            "id": f"rule-{rule_id}",
+            "text": text,
+            "source": rule_id,
+            "type": "rule",
+        })
+    return documents
+
+
+def _collect_compliance_documents():
+    documents = []
+    for path in sorted(FRAMEWORKS_DIR.glob("*.json")):
+        framework = path.stem
+        try:
+            data = json.loads(path.read_text(encoding="utf-8"))
+        except Exception as exc:
+            logger.warning("Skipping %s: %s", path.name, exc)
+            continue
+        for control_id, control in data.get("controls", {}).items():
+            description = control.get("description", "")
+            if not description:
+                continue
+            text = (
+                f"{framework} control {control_id}: "
+                f"{control.get('control_name', '')}\n{description}"
+            )
+            documents.append({
+                "id": f"{framework}-{control_id}",
+                "text": text,
+                "source": f"{framework} {control_id}",
+                "type": "control",
+            })
+    return documents
+
+
+def build_vectorstore():
+    VECTORSTORE_DIR.mkdir(parents=True, exist_ok=True)
+    client = chromadb.PersistentClient(path=str(VECTORSTORE_DIR))
+
+    try:
+        client.delete_collection(COLLECTION_NAME)
+    except Exception as exc:
+        logger.info("Could not delete collection '%s' before rebuild: %s", COLLECTION_NAME, exc)
+    collection = client.create_collection(COLLECTION_NAME)
+
+    documents = (
+        _collect_skill_documents()
+        + _collect_rule_documents()
+        + _collect_compliance_documents()
+    )
+    if not documents:
+        raise RuntimeError("No documents found to embed. Check repo paths.")
+
+    collection.add(
+        ids=[d["id"] for d in documents],
+        documents=[d["text"] for d in documents],
+        metadatas=[
+            {"source": d["source"], "type": d["type"]} for d in documents
+        ],
+    )
+    logger.info(
+        "Embedded %d documents into '%s'.", len(documents), COLLECTION_NAME
+    )
+    return len(documents)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    count = build_vectorstore()
+    print(f"Done. Vector store built with {count} documents at {VECTORSTORE_DIR}")

ai/knowledge/LICENSE

@@ -0,0 +1,201 @@
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