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OCI CIS AI Agent

Oracle Cloud Infrastructure — CIS Foundations Benchmark 3.0 — AI-Powered Compliance Platform

Overview

OCI CIS AI Agent is a self-hosted web application that automates CIS Oracle Cloud Infrastructure Foundations Benchmark 3.0 compliance checks, powered by OCI Generative AI for intelligent analysis and an MCP (Model Context Protocol) server architecture for extensible task execution.

The platform combines security compliance scanning, AI-powered chat with RAG (Retrieval-Augmented Generation), infrastructure exploration, and vector-based knowledge storage into a single, containerized solution with Oracle Cloud's official light theme.

Features

🤖 AI Chat Agent with RAG + MCP Tool Use

OCI Generative AI integration via official SDK (oci.generative_ai_inference)
RAG (Retrieval-Augmented Generation): automatically queries ADB vector store for relevant context before generating responses
MCP Tool Use (Function Calling): GenAI models can call tools from registered MCP servers during chat — supports both Cohere and Generic (OpenAI-style) function calling formats with automatic tool execution loop (max 5 iterations)
69 chat models + 11 embedding models across 6 providers: Cohere, Meta, Google, OpenAI (GPT-5.3/5.2/5.1/5/4.1/4o, Codex, Image, Audio, o1/o3/o4-mini, GPT-oss), xAI (Grok 4.1/4/3), ProtectAI
OCID-based model resolution: catalog maps model IDs to OCI resource IDs per region for reliable API calls
16 OCI regions supported with auto-generated endpoints
Full parameter control: temperature, max_tokens, top_p, top_k, frequency/presence penalty
Toggle MCP tools on/off per chat session
Conversation history with session management
On-Demand and Dedicated serving modes

🔍 OCI Account Explorer

Browse tenancy resources directly from the UI
Explore: Compartments, Regions, VCNs, Compute Instances, Autonomous Databases, Object Storage Buckets
Select which OCI connection to explore
Real-time API calls via OCI Python SDK

📊 CIS Compliance Reports

Automated CIS OCI Foundations Benchmark 3.0 execution
54 security controls across 8 domains (IAM, Networking, Compute, Logging, Storage, etc.)
HTML and JSON report output
Optional MCP server selection per report execution
Region filtering

🔌 MCP Server Registry + Tool Discovery

Register multiple MCP servers (stdio, SSE, Python module)
Automatic tool discovery: connect to MCP servers and discover available tools with names, descriptions, and input schemas
Manual tool definition: add/edit tools with JSON Schema parameter definitions
Chat Agent integration: discovered tools are automatically available as GenAI function calls during chat
Upload .py scripts directly to servers
Link MCP servers to ADB Vector databases as tools
Activate/deactivate servers
Select which MCP server to use per report execution

🗄️ Autonomous Database Vector Storage

Oracle Autonomous Database connection with mTLS Wallet authentication
python-oracledb Thin mode (no Oracle Client needed)
Wallet ZIP upload and automatic extraction
Connection testing
Multiple vector tables per ADB: register, edit, toggle active/inactive for each table
Multi-table RAG search: queries all active tables across all ADB configs, merges results by cosine distance
Link to GenAI config for embedding generation via OCI GenAI

🧬 Embeddings Management

Dedicated tab for managing vector embeddings
Preview chunks before embedding: review generated sections before creating embeddings
Embed CIS Reports: automatically chunk reports by section with tenancy name, regions, and compartments enrichment
Upload text files: upload .txt files, automatically chunked by paragraphs
Table selector: choose which ADB vector table to store embeddings in
OCI GenAI Embeddings: uses Cohere Embed models (v3.0/v4.0, multilingual, light) via OCI GenAI embed_text API
Browse, inspect and delete individual embeddings from the ADB vector store
11 embedding models supported including Cohere Embed v4.0 and OpenAI Text Embedding 3

📜 Configuration Logs

Persistent activity log per configuration tab (OCI, GenAI, ADB, MCP)
Logs all test, save, upload, report, and ingest operations with severity (success/error/info)
Inline log panel at the bottom of each config tab with severity filter
Auto-cleanup of logs older than 30 days
Admin can view all logs; users see only their own
API endpoints for listing and clearing logs

🔐 Security

JWT authentication with configurable expiry
TOTP MFA (Google Authenticator / Authy compatible)
RBAC with 3 roles: Admin, User, Viewer
Audit logging for all operations
Encrypted credential storage

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Docker Compose                            │
│                                                              │
│  ┌──────────────┐       ┌──────────────────────────────┐    │
│  │  Nginx        │       │  FastAPI Backend              │    │
│  │  (Frontend)   │──────▶│  (Python 3.12)               │    │
│  │  :8080        │       │  :8000                        │    │
│  └──────────────┘       │                                │    │
│                          │  ┌─────────────┐              │    │
│                          │  │ OCI SDK      │──▶ OCI APIs │    │
│                          │  ├─────────────┤              │    │
│                          │  │ GenAI Client │──▶ LLM      │    │
│                          │  ├─────────────┤   (tools)   │    │
│                          │  │ MCP Client   │──▶ MCP Svrs │    │
│                          │  ├─────────────┤   (discover │    │
│                          │  │              │   +execute) │    │
│                          │  ├─────────────┤              │    │
│                          │  │ oracledb     │──▶ ADB      │    │
│                          │  ├─────────────┤              │    │
│                          │  │ RAG Pipeline │──▶ Embed +  │    │
│                          │  │              │   Search    │    │
│                          │  └─────────────┘              │    │
│                          │                                │    │
│                          │  SQLite (agent.db)             │    │
│                          └──────────────────────────────┘    │
│                                     │                        │
│                              agent-data volume               │
└─────────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

Docker and Docker Compose
OCI API Key pair (private .pem key + fingerprint)
OCI Tenancy OCID, User OCID, Compartment OCID

1. Clone & Configure

git clone https://github.com/nogueiragustavo/oci-cis-agent.git
cd oci-cis-agent
cp .env.example .env

Edit .env:

APP_SECRET=your-very-long-random-secret-string-here-at-least-64-chars
JWT_EXPIRY_HOURS=12
PORT=8080

2. Build & Run

docker compose up -d --build

3. Access

Open http://localhost:8080

Default credentials:

Username: admin
Password: admin123

⚠️ Change the default password immediately after first login.

Configuration Guide

Step 1 — OCI Credentials

Navigate to OCI Credentials tab and add:

Field	Description
Tenancy Name	Friendly name (e.g., `my-company`)
OCID Tenancy	`ocid1.tenancy.oc1..xxxxx`
OCID User	`ocid1.user.oc1..xxxxx`
Fingerprint	`aa:bb:cc:dd:ee:ff:...`
Region	`sa-saopaulo-1`, `us-ashburn-1`, etc.
Compartment OCID	`ocid1.compartment.oc1..xxxxx`
Private Key	`.pem` file
Key Passphrase	(optional) Only required if the private key is encrypted

Click Testar to validate the connection.

Step 2 — GenAI Model

Navigate to GenAI Config tab:

Select the OCI Credential created in Step 1 — Region and Compartment OCID are auto-filled from the selected credential
Choose a model from the catalog
Adjust the GenAI region if needed (auto-populated, must have Generative AI service available)
Adjust parameters (temperature, max_tokens, etc.)
The endpoint is auto-generated: https://inference.generativeai.{region}.oci.oraclecloud.com

For dedicated endpoints, switch Serving Type to DEDICATED and provide the endpoint ID.

The GenAI connection follows Oracle's official SDK pattern:

# Auth via stored OCI config
config = oci.config.from_file(config_path, "DEFAULT")

# Client with endpoint, retry, and timeout
client = oci.generative_ai_inference.GenerativeAiInferenceClient(
    config=config,
    service_endpoint=endpoint,
    retry_strategy=oci.retry.NoneRetryStrategy(),
    timeout=(10, 240)
)

# Chat with TextContent + Message objects
chat_detail = oci.generative_ai_inference.models.ChatDetails()
chat_detail.serving_mode = OnDemandServingMode(model_id="...")
chat_detail.chat_request = GenericChatRequest(messages=[...])
chat_detail.compartment_id = compartment_id

Step 3 — MCP Servers (Optional)

Type	Use Case
`stdio`	Local Python scripts (e.g., CIS check runner)
`SSE`	Remote HTTP servers
`module`	Upload `.py` files directly

MCP servers can be linked to ADB Vector databases, enabling them to use the vector store as a tool during report execution.

Tool Discovery: After registering a server, click "Descobrir Tools" to automatically discover available tools via MCP protocol. You can also add tools manually with name, description, and JSON Schema parameters. Discovered tools are automatically available as function calls in the Chat Agent.

Step 4 — ADB Vector + RAG (Optional)

For persistent vector storage and RAG-powered chat:

Add DSN (TNS name from tnsnames.ora, e.g., myatp_high)
Set credentials (username/password)
Select a GenAI Config (for embedding generation via OCI GenAI)
Select an Embedding Model (Cohere Embed v4.0 recommended)
Upload Wallet ZIP (for mTLS)
Test the connection
Register vector tables: add the names of existing tables in your ADB that contain vectorized data (e.g., CIS_REPORT, CIS_RECOMMENDATIONS). Toggle tables active/inactive to control which are queried during RAG.

Step 5 — Embeddings (Optional)

Navigate to the Embeddings tab to populate the vector store:

From CIS Reports: Select a completed report, preview chunks (with tenancy/regions/compartments context), then generate embeddings
From text files: Upload .txt files for automatic chunking and embedding
Select target table: choose which ADB vector table to store embeddings in
Browse and manage existing embeddings per table

Once embeddings exist, the chat automatically uses RAG — it queries all active vector tables across all ADB configs for relevant context before generating responses with the selected GenAI model.

OCI IAM Policies

The following policies are required in your tenancy:

Allow group <group-name> to use generative-ai-family in compartment <compartment-name>
Allow group <group-name> to read all-resources in tenancy
Allow group <group-name> to inspect compartments in tenancy
Allow group <group-name> to inspect autonomous-databases in compartment <compartment-name>
Allow group <group-name> to read virtual-network-family in compartment <compartment-name>
Allow group <group-name> to read instance-family in compartment <compartment-name>
Allow group <group-name> to read objectstorage-namespaces in tenancy
Allow group <group-name> to read buckets in compartment <compartment-name>

Project Structure

oci-cis-agent/
├── backend/
│   ├── app.py              # FastAPI application (~2200 lines)
│   ├── cis_runner.py        # CIS Benchmark check executor
│   ├── Dockerfile           # Python 3.12 + OCI CLI
│   └── requirements.txt     # Dependencies
├── frontend/
│   └── index.html           # SPA with Oracle Cloud theme (~900 lines)
├── nginx/
│   └── default.conf         # Reverse proxy config
├── docker-compose.yml       # Orchestration
├── logo.svg                 # Project logo (Oracle AI Robot)
├── .env.example             # Environment template
├── .gitignore
└── README.md

API Reference

Authentication

Method	Endpoint	Description
POST	`/api/auth/login`	Login (username + password + optional TOTP)
POST	`/api/auth/logout`	Logout and invalidate session
POST	`/api/auth/register`	Create user (admin only)
POST	`/api/auth/change-password`	Change password

OCI Management

Method	Endpoint	Description
POST	`/api/oci/config`	Save OCI credentials (multipart)
GET	`/api/oci/configs`	List OCI credentials
PUT	`/api/oci/configs/{id}`	Update OCI credential (multipart)
POST	`/api/oci/test/{id}`	Test OCI connection
DELETE	`/api/oci/configs/{id}`	Delete OCI credential

OCI Account Explorer

Method	Endpoint	Description
GET	`/api/oci/explore/{id}/compartments`	List compartments
GET	`/api/oci/explore/{id}/regions`	List subscribed regions
GET	`/api/oci/explore/{id}/vcns`	List VCNs
GET	`/api/oci/explore/{id}/instances`	List compute instances
GET	`/api/oci/explore/{id}/databases`	List Autonomous Databases
GET	`/api/oci/explore/{id}/buckets`	List Object Storage buckets

Generative AI

Method	Endpoint	Description
GET	`/api/genai/models`	List available models and regions
POST	`/api/genai/config`	Save GenAI configuration
GET	`/api/genai/configs`	List GenAI configurations
PUT	`/api/genai/configs/{id}`	Update GenAI configuration
POST	`/api/genai/test/{id}`	Test GenAI connection
DELETE	`/api/genai/configs/{id}`	Delete GenAI config

MCP Servers

Method	Endpoint	Description
POST	`/api/mcp/servers`	Register MCP server
GET	`/api/mcp/servers`	List MCP servers
PUT	`/api/mcp/servers/{id}`	Update MCP server
PUT	`/api/mcp/servers/{id}/toggle`	Activate/deactivate
POST	`/api/mcp/servers/{id}/upload`	Upload script file
PUT	`/api/mcp/servers/{id}/link-adb`	Link to ADB Vector
POST	`/api/mcp/servers/{id}/discover-tools`	Auto-discover tools from MCP server
PUT	`/api/mcp/servers/{id}/tools`	Manually update tool definitions
DELETE	`/api/mcp/servers/{id}`	Delete MCP server

ADB Vector

Method	Endpoint	Description
POST	`/api/adb/config`	Save ADB connection (with GenAI config + embedding model)
GET	`/api/adb/configs`	List ADB connections (includes vector tables)
PUT	`/api/adb/configs/{id}`	Update ADB connection (multipart)
POST	`/api/adb/parse-wallet`	Parse wallet ZIP and extract DSN names
POST	`/api/adb/{id}/upload-wallet`	Upload wallet ZIP
POST	`/api/adb/test/{id}`	Test ADB connection
GET	`/api/adb/{id}/tables`	List vector tables for ADB config
POST	`/api/adb/{id}/tables`	Add vector table
PUT	`/api/adb/{id}/tables/{tid}`	Update vector table (name, description, active)
DELETE	`/api/adb/{id}/tables/{tid}`	Remove vector table
DELETE	`/api/adb/configs/{id}`	Delete ADB config

Embeddings

Method	Endpoint	Description
GET	`/api/embeddings/preview/{rid}`	Preview report chunks before embedding (with tenancy/regions/compartments)
POST	`/api/embeddings/report/{rid}`	Generate embeddings from CIS report (chunked by section, accepts `table_name`)
POST	`/api/embeddings/upload`	Upload .txt file and generate embeddings (accepts `table_name`)
GET	`/api/embeddings/{vid}/list`	List embeddings in ADB (paginated, accepts `table_name` query param)
DELETE	`/api/embeddings/{vid}/{doc_id}`	Delete individual embedding (accepts `table_name` query param)

Chat & Reports

Method	Endpoint	Description
POST	`/api/chat`	Send message (with RAG + MCP tool use, accepts `use_tools` flag)
POST	`/api/reports/run`	Execute CIS report
GET	`/api/reports`	List reports
GET	`/api/reports/{id}/html`	View HTML report
GET	`/api/reports/{id}/download`	Download report

Config Logs

Method	Endpoint	Description
GET	`/api/config-logs`	List config logs (filterable by `config_type`, `severity`, `config_id`)
DELETE	`/api/config-logs`	Clear config logs (admin, filterable by `config_type`, `config_id`)

Admin

Method	Endpoint	Description
GET	`/api/users`	List users (admin)
PUT	`/api/users/{id}`	Update user role/status
POST	`/api/mfa/setup`	Generate MFA secret
POST	`/api/mfa/verify`	Activate MFA
GET	`/api/audit-log`	View audit log (admin)
GET	`/api/health`	Health check

Supported GenAI Models (69 Chat + 11 Embedding)

All models include OCID mapping for us-ashburn-1. For other regions, use the "Personalizado (usar OCID)" option.

Chat Models

Provider	Model	Model ID	API Format
Cohere	Command A Reasoning	`cohere.command-a-reasoning`	COHERE
Cohere	Command A Vision	`cohere.command-a-vision`	COHERE
Cohere	Command A	`cohere.command-a-03-2025`	COHERE
Cohere	Command R+ (08-2024)	`cohere.command-r-plus-08-2024`	COHERE
Cohere	Command R (08-2024)	`cohere.command-r-08-2024`	COHERE
Meta	Llama 4 Maverick	`meta.llama-4-maverick-17b-128e-instruct-fp8`	GENERIC
Meta	Llama 4 Scout	`meta.llama-4-scout-17b-16e-instruct`	GENERIC
Meta	Llama Guard 4 (12B)	`meta.llama-guard-4-12b`	GENERIC
Google	Gemini 2.5 Pro	`google.gemini-2.5-pro`	GENERIC
Google	Gemini 2.5 Flash	`google.gemini-2.5-flash`	GENERIC
Google	Gemini 2.5 Flash-Lite	`google.gemini-2.5-flash-lite`	GENERIC
OpenAI	GPT-5.3 Codex	`openai.gpt-5.3-codex`	GENERIC
OpenAI	GPT-5.2 Codex	`openai.gpt-5.2-codex`	GENERIC
OpenAI	GPT-5.2 Pro	`openai.gpt-5.2-pro`	GENERIC
OpenAI	GPT-5.2 Pro (2025-12-11)	`openai.gpt-5.2-pro-2025-12-11`	GENERIC
OpenAI	GPT-5.2	`openai.gpt-5.2`	GENERIC
OpenAI	GPT-5.2 (2025-12-11)	`openai.gpt-5.2-2025-12-11`	GENERIC
OpenAI	GPT-5.2 Chat Latest	`openai.gpt-5.2-chat-latest`	GENERIC
OpenAI	GPT-5.1 Codex Max	`openai.gpt-5.1-codex-max`	GENERIC
OpenAI	GPT-5.1 Codex	`openai.gpt-5.1-codex`	GENERIC
OpenAI	GPT-5.1 Codex Mini	`openai.gpt-5.1-codex-mini`	GENERIC
OpenAI	GPT-5.1	`openai.gpt-5.1`	GENERIC
OpenAI	GPT-5.1 (2025-11-13)	`openai.gpt-5.1-2025-11-13`	GENERIC
OpenAI	GPT-5.1 Chat Latest	`openai.gpt-5.1-chat-latest`	GENERIC
OpenAI	GPT-5 Codex	`openai.gpt-5-codex`	GENERIC
OpenAI	GPT-5	`openai.gpt-5`	GENERIC
OpenAI	GPT-5 (2025-08-07)	`openai.gpt-5-2025-08-07`	GENERIC
OpenAI	GPT-5 Mini	`openai.gpt-5-mini`	GENERIC
OpenAI	GPT-5 Mini (2025-08-07)	`openai.gpt-5-mini-2025-08-07`	GENERIC
OpenAI	GPT-5 Nano	`openai.gpt-5-nano`	GENERIC
OpenAI	GPT-5 Nano (2025-08-07)	`openai.gpt-5-nano-2025-08-07`	GENERIC
OpenAI	GPT-4.1	`openai.gpt-4.1`	GENERIC
OpenAI	GPT-4.1 (2025-04-14)	`openai.gpt-4.1-2025-04-14`	GENERIC
OpenAI	GPT-4.1 Mini	`openai.gpt-4.1-mini`	GENERIC
OpenAI	GPT-4.1 Mini (2025-04-14)	`openai.gpt-4.1-mini-2025-04-14`	GENERIC
OpenAI	GPT-4.1 Nano	`openai.gpt-4.1-nano`	GENERIC
OpenAI	GPT-4.1 Nano (2025-04-14)	`openai.gpt-4.1-nano-2025-04-14`	GENERIC
OpenAI	GPT-4o	`openai.gpt-4o`	GENERIC
OpenAI	GPT-4o (2024-08-06)	`openai.gpt-4o-2024-08-06`	GENERIC
OpenAI	GPT-4o (2024-11-20)	`openai.gpt-4o-2024-11-20`	GENERIC
OpenAI	GPT-4o Mini	`openai.gpt-4o-mini`	GENERIC
OpenAI	GPT-4o Mini Search Preview	`openai.gpt-4o-mini-search-preview`	GENERIC
OpenAI	GPT-4o Mini Search (2025-03-11)	`openai.gpt-4o-mini-search-preview-2025-03-11`	GENERIC
OpenAI	GPT-4o Search Preview	`openai.gpt-4o-search-preview`	GENERIC
OpenAI	GPT-4o Search (2025-03-11)	`openai.gpt-4o-search-preview-2025-03-11`	GENERIC
OpenAI	GPT Image 1.5	`openai.gpt-image-1.5`	GENERIC
OpenAI	GPT Image 1	`openai.gpt-image-1`	GENERIC
OpenAI	GPT Audio	`openai.gpt-audio`	GENERIC
OpenAI	o4-mini	`openai.o4-mini`	GENERIC
OpenAI	o4-mini (2025-04-16)	`openai.o4-mini-2025-04-16`	GENERIC
OpenAI	o3	`openai.o3`	GENERIC
OpenAI	o3 (2025-04-16)	`openai.o3-2025-04-16`	GENERIC
OpenAI	o3-mini	`openai.o3-mini`	GENERIC
OpenAI	o3-mini (2025-01-31)	`openai.o3-mini-2025-01-31`	GENERIC
OpenAI	o1	`openai.o1`	GENERIC
OpenAI	o1 (2024-12-17)	`openai.o1-2024-12-17`	GENERIC
OpenAI	GPT-oss (120B)	`openai.gpt-oss-120b`	GENERIC
OpenAI	GPT-oss (20B)	`openai.gpt-oss-20b`	GENERIC
xAI	Grok 4	`xai.grok-4`	GENERIC
xAI	Grok 4.1 Fast Reasoning	`xai.grok-4-1-fast-reasoning`	GENERIC
xAI	Grok 4.1 Fast Non-Reasoning	`xai.grok-4-1-fast-non-reasoning`	GENERIC
xAI	Grok 4 Fast Reasoning	`xai.grok-4-fast-reasoning`	GENERIC
xAI	Grok 4 Fast Non-Reasoning	`xai.grok-4-fast-non-reasoning`	GENERIC
xAI	Grok 3	`xai.grok-3`	GENERIC
xAI	Grok 3 Mini	`xai.grok-3-mini`	GENERIC
xAI	Grok 3 Fast	`xai.grok-3-fast`	GENERIC
xAI	Grok 3 Mini Fast	`xai.grok-3-mini-fast`	GENERIC
xAI	Grok Code Fast 1	`xai.grok-code-fast-1`	GENERIC
ProtectAI	DeBERTa Prompt Injection v2	`protectai.deberta-v3-base-prompt-injection-v2`	GENERIC

Custom OCID: You can also use any model available in your region by selecting "Personalizado (usar OCID)" and providing the full model OCID.

Embedding Models

Provider	Model	Model ID	Dimensions
Cohere	Embed v4.0 (Multimodal)	`cohere.embed-v4.0`	1536
OpenAI	Text Embedding 3 Large	`openai.text-embedding-3-large`	3072
OpenAI	Text Embedding 3 Small	`openai.text-embedding-3-small`	1536
Cohere	Embed English v3.0	`cohere.embed-english-v3.0`	1024
Cohere	Embed Multilingual v3.0	`cohere.embed-multilingual-v3.0`	1024
Cohere	Embed English Light v3.0	`cohere.embed-english-light-v3.0`	384
Cohere	Embed Multilingual Light v3.0	`cohere.embed-multilingual-light-v3.0`	384
Cohere	Embed English Image v3.0	`cohere.embed-english-image-v3.0`	1024
Cohere	Embed Multilingual Image v3.0	`cohere.embed-multilingual-image-v3.0`	1024
Cohere	Embed English Light Image v3.0	`cohere.embed-english-light-image-v3.0`	384
Cohere	Embed Multilingual Light Image v3.0	`cohere.embed-multilingual-light-image-v3.0`	384

GenAI Regions

us-chicago-1 · us-ashburn-1 · us-phoenix-1 · uk-london-1 · eu-frankfurt-1 · ap-tokyo-1 · ap-osaka-1 · sa-saopaulo-1 · ca-toronto-1 · ap-melbourne-1 · ap-mumbai-1 · eu-amsterdam-1 · me-jeddah-1 · ap-singapore-1 · ap-seoul-1 · sa-vinhedo-1

Tech Stack

Component	Technology
Backend	Python 3.12, FastAPI 0.115, Uvicorn
Frontend	Vanilla JS SPA, Oracle Cloud UI theme
Auth	JWT + TOTP MFA + RBAC
Database	SQLite (WAL mode)
OCI SDK	`oci` 2.133.0, `oci-cli`
ADB	`python-oracledb` 2.4.1 (Thin mode)
GenAI	`oci.generative_ai_inference`
Container	Docker Compose, Nginx reverse proxy
MCP	Model Context Protocol SDK (stdio/SSE) with tool discovery + execution

Versioning

Version	Date	Changes
v1.5	2026-03	MCP Tool Use in Chat (GenAI function calling with auto tool discovery + execution via MCP SDK), multi-table ADB vector search, preview chunks before embedding, enriched embeddings with tenancy/regions/compartments, searchable dropdowns, editable vector tables, orphaned report cleanup on restart
v1.4	2026-03	In-place config editing, 69 chat + 11 embedding models with OCID resolution (OpenAI GPT-5.3/5.2/5.1/5/4.1/4o/Codex/Image/Audio, xAI, Google, Meta, ProtectAI), OpenAI Text Embedding 3, custom OCID support, wallet auto-parse with DSN extraction
v1.3	2026-03	Persistent config logs per tab, GenAI auto-fill from OCI credentials, inline UX feedback with loading spinners, MCP type-switch fix, encrypted key passphrase support, Docker stdin hang fix
v1.2	2026-03	RAG pipeline (OCI GenAI embeddings + ADB vector search), dedicated Embeddings tab, CIS report chunking, file upload embedding
v1.1	2025-02	OCI SDK GenAI (exact pattern), OCI Account Explorer, MCP↔ADB linking, full chat parameters
v1.0	2025-02	Initial release: OCI theme, GenAI integration, MCP servers, ADB vector, JWT+MFA+RBAC

Development

Run Backend Locally

cd backend
pip install -r requirements.txt
DATA_DIR=./data uvicorn app:app --reload --port 8000

Run with Docker

docker compose up -d --build
docker compose logs -f backend

Rebuild After Changes

docker compose down
docker compose up -d --build

Troubleshooting

OCI CLI test fails with timeout: Check that your API key is correctly configured and the tenancy OCID is valid. Ensure outbound HTTPS (443) is allowed.

GenAI returns 401/403: Verify the IAM policy Allow group ... to use generative-ai-family in compartment ... exists. Check that the compartment OCID in the GenAI config matches the policy.

OCI CLI test hangs with passphrase prompt (Docker): If your private key is encrypted (ENCRYPTED in the PEM header), provide the passphrase in the Key Passphrase field when saving the credential. Unencrypted keys work without a passphrase. The app automatically detects encrypted keys and blocks upload if no passphrase is provided.

ADB connection fails: Ensure the wallet ZIP contains tnsnames.ora and ewallet.pem. The DSN must match a service name in tnsnames.ora (e.g., myatp_high).

Container won't start:

docker compose logs backend

License

MIT

_{Built with ❤️ for Oracle Cloud Infrastructure security compliance}

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