docs: Vectax Integration (#1471)

* docs: Vectax integration Signed-off-by: Anush008 <anushshetty90@gmail.com> * docs: Mirror sec Signed-off-by: Anush008 <anushshetty90@gmail.com> * docs: Mirror sec Signed-off-by: Anush008 <anushshetty90@gmail.com> * docs: Vectax cover Signed-off-by: Anush008 <anushshetty90@gmail.com> * docs: Updated RBAC Signed-off-by: Anush008 <anushshetty90@gmail.com> --------- Signed-off-by: Anush008 <anushshetty90@gmail.com>
2026-09-28 07:28:30 +02:00 · 2025-02-21 09:20:59 +05:30
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 | [LangGraph](/documentation/frameworks/langgraph/)             | Python, Javascript libraries for building stateful, multi-actor applications.                                        |
 | [LlamaIndex](/documentation/frameworks/llama-index/)          | A data framework for building LLM applications with modular integrations.                                            |
 | [Mastra](/documentation/frameworks/mastra/)                   | Typescript framework to build AI applications and features quickly.                                                  |
+| [Mirror Security](/documentation/frameworks/mirror-security/) | Python framework for vector encryption and access control.                                                           |
 | [Mem0](/documentation/frameworks/mem0/)                       | Self-improving memory layer for LLM applications, enabling personalized AI experiences.                              |
 | [MemGPT](/documentation/frameworks/memgpt/)                   | System to build LLM agents with long term memory & custom tools                                                      |
 | [Neo4j GraphRAG](/documentation/frameworks/neo4j-graphrag/)   | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python.                |
@@ -0,0 +1,185 @@
+---
+title: VectaX - Mirror Security
+---
+
+![VectaX Logo](/documentation/frameworks/mirror-security/vectax-logo.png)
+
+[VectaX](https://mirrorsecurity.io/vectax) by Mirror Security is an AI-centric access control and encryption system  designed for managing and protecting vector embeddings. It combines similarity-preserving encryption with fine-grained RBAC to enable secure storage, retrieval, and operations on vector data.
+
+It can be integrated with Qdrant to secure vector searches.
+
+We'll see how to do so using basic VectaX vector encryption and the sophisticated RBAC mechanism. You can obtain an API key and the Mirror SDK from the [Mirror Security Platform](https://platform.mirrorsecurity.io/en/login).
+
+Let's set up both the VectaX and Qdrant clients.
+
+```python
+from mirror_sdk.core.mirror_core import MirrorSDK, MirrorConfig
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, VectorParams
+
+# Get your API key from
+# https://platform.mirrorsecurity.io
+config = MirrorConfig(
+    api_key="<your_api_key>",
+    server_url="https://mirrorapi.azure-api.net/v1",
+    secret="<your_encrypt_secret>",
+)
+mirror_sdk = MirrorSDK(config)
+
+# Connects to http://localhost:6333/ by default
+qdrant = QdrantClient()
+```
+
+## Vector Encryption
+
+Now, let's secure vector embeddings using VectaX encryption.
+
+```python
+from qdrant_client.models import PointStruct
+from mirror_sdk.core.models import VectorData
+
+# Generate or retrieve vector embeddings
+# embedding = generate_document_embedding()
+
+vector_data = VectorData(vector=embedding, id="doc1")
+encrypted = mirror_sdk.vectax.encrypt(vector_data)
+
+point = PointStruct(
+    id=0,
+    vector=encrypted.ciphertext,
+    payload={
+        "content": "Document content",
+        "iv": encrypted.iv,
+        "auth_hash": encrypted.auth_hash
+    }
+)
+qdrant.upsert(collection_name="vectax", points=[point])
+
+# Encrypt a query vector for secure search
+# query_embedding = generate_query_embedding(...)
+
+encrypted_query = mirror_sdk.vectax.encrypt(
+    VectorData(vector=query_embedding, id="query")
+)
+
+results = qdrant.query_points(
+    collection_name="vectax",
+    query=encrypted_query.ciphertext,
+    limit=5
+).points
+```
+
+## Vector Search with RBAC
+
+RBAC allows fine-grained access control over encrypted vector data based on roles, groups, and departments.
+
+### Defining Access Policies
+
+```python
+app_policy = {
+    "roles": ["admin", "analyst", "user"],
+    "groups": ["team_a", "team_b"],
+    "departments": ["research", "engineering"],
+}
+mirror_sdk.set_policy(app_policy)
+```
+
+### Generating Access Keys
+
+```python
+# Generate a secret key for use by the 'admin' role holders.
+admin_key = mirror_sdk.rbac.generate_user_secret_key(
+    {"roles": ["admin"], "groups": ["team_a"], "departments": ["research"]}
+)
+```
+
+### Storing Encrypted Data with RBAC Policies
+
+We can now store data that is only accessible to users with the "admin" role.
+
+```python
+from mirror_sdk.core.models import RBACVectorData
+from mirror_sdk.utils import encode_binary_data
+
+policy = {
+    "roles": ["admin"],
+    "groups": ["team_a"],
+    "departments": ["research"],
+}
+# vector_embedding = generate_vector_embedding(...)
+vector_data = RBACVectorData(
+    # Generate or retrieve vector embeddings
+    vector=vector_embedding,
+    id=1,
+    access_policy=policy,
+)
+encrypted = mirror_sdk.rbac.encrypt(vector_data)
+
+qdrant.upsert(
+    collection_name="vectax",
+    points=[
+        models.PointStruct(
+            id=1,
+            vector=encrypted.crypto.ciphertext,
+            payload={
+                "encrypted_header": encrypted.encrypted_header,
+                "encrypted_vector_metadata": encode_binary_data(
+                    encrypted.crypto.serialize()
+                ),
+                "content": "My content",
+            },
+        )
+    ],
+)
+```
+
+### Querying with Role-Based Decryption
+
+Using the admin key, only accessible data will be decrypted.
+
+```python
+from mirror_sdk.core import MirrorError
+from mirror_sdk.core.models import MirrorCrypto
+from mirror_sdk.utils import decode_binary_data
+
+# Encrypt a query vector for secure search
+# query_embedding = generate_query_embedding(...)
+
+query_data = RBACVectorData(vector=query_embedding, id="query", access_policy=policy)
+encrypted_query = mirror_sdk.rbac.encrypt(query_data)
+
+results = qdrant.query_points(
+    collection_name="vectax", query=encrypted_query.crypto.ciphertext, limit=10
+)
+
+accessible_results = []
+for point in results.points:
+    try:
+        encrypted_vector_metadata = decode_binary_data(
+            point.payload["encrypted_vector_metadata"]
+        )
+        mirror_data = MirrorCrypto.deserialize(encrypted_vector_metadata)
+        admin_decrypted = mirror_sdk.rbac.decrypt(
+            mirror_data,
+            point.payload["encrypted_header"],
+            admin_key,
+        )
+        accessible_results.append(
+            {
+                "id": point.id,
+                "content": point.payload["content"],
+                "score": point.score,
+                "accessible": True,
+            }
+        )
+
+    except MirrorError as e:
+        print(f"Access denied for point {point.id}: {e}")
+
+# Proceed to only use results within `accessible_results`.
+```
+
+## Further Reading
+
+- [Mirror Security Docs](https://docs.mirrorsecurity.io/introduction)
+- [Mirror Security Blog](https://mirrorsecurity.io/blog)