rostaingchain 1.3.0
The Ultimate Agentic RAG Framework.
License: MIT
Requires Python: >=3.9

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# 🧠 RostaingChain

**The Ultimate Agentic RAG Framework.**  
Autonomous Agent | Local & Remote LLMs | Real-time Watcher | Deep Data Profiling | DLP Security | Multi-Modal

**RostaingChain** is a production-ready framework designed to build autonomous RAG (Retrieval-Augmented Generation) systems. It bridges the gap between local privacy (Ollama, Local Docs) and cloud power (OpenAI, Groq, Datastores), featuring a unique **Live Watcher** that updates your AI's knowledge in real-time and Agentic RAG with Self-Healing Data Analysis.


## 🚀 Key Features

*   **Hybrid Intelligence:** Switch instantly between Local LLMs (Ollama, Llama.cpp) and Remote giants (OpenAI, Groq, Claude, Gemini, DeepSeek, Grok).
*   **Live Watcher (Auto-Sync):** Drop a file in a folder, modify a SQL row, update a website, or update a file -> The AI learns it instantly.
*   **Deep Profiling (Anti-Hallucination):** Automatically calculates descriptive statistics (Max, Min, Mean, etc.), performs rigorous statistical tests (Hypothesis testing, P-values, Correlations, etc.), and generates automated visualizations (Charts, Histograms, etc.). By grounding the LLM in real-time data analysis and anomaly detection for CSV/Excel/SQL/NoSQL/DataFrame/Parquet sources, it ensures the model never hallucinates numbers or trends.
*   **DLP Security:** Built-in Redaction system to **mask sensitive data** (See the **Security Filters & Data Masking** list below) before display. Set to True for ALL filters, False to disable, or a list to select specific fields.
*   **Multi-Modal Native:** Understands Text, PDFs (OCR included), Images, Audio (Whisper), and YouTube videos.
*   **Universal Sources:** Connects to Local Files, PostgreSQL, MySQL, Oracle, Microsoft SQL Server, SQLite, MongoDB, Neo4j, and the Web.

---

## 🛠 Environment Setup

To ensure stability and avoid dependency conflicts, we strongly recommend using a virtual environment. **RostaingChain** requires **Python 3.9 or higher**.

### Option 1: Using Python `venv` (Standard)

This is the built-in method. Choose the commands based on your Operating System:

#### **On Windows:**
```bash
# 1. Create the environment
python -m venv venv

# 2. Activate it
venv\Scripts\activate
```

#### **On macOS / Linux:**
```bash
# 1. Create the environment
python3 -m venv venv

# 2. Activate it
source venv/bin/activate
```

---

### Option 2: Using Conda (Recommended for Data Science)

Conda is often more robust for managing complex dependencies like `pyodbc` or `chromadb`.

```bash
# 1. Create the environment with a specific Python version
conda create -n rostaing_env python=3.12 -y

# 2. Activate the environment
conda activate rostaing_env
```

---

## 📦 Installation:
Once your environment is activated, you can install the framework.

```bash
pip install rostaingchain 
```

<!-- ### “Power User” installation (All-inclusive):

```bash
pip install "rostaingchain[all]"
```

### Specific installation (e.g., only for SQL/NoSQL):

```bash
pip install "rostaingchain[database]"
```

### For office documents, files and advanced OCR:

```bash
pip install "rostaingchain[docs]"
```

### For multimedia (YouTube, audio, video, web):

```bash
pip install "rostaingchain[media]"
``` -->

## 🔑 Managing API Keys (Remote LLMs)

To use remote LLMs (like OpenAI, Groq, Claude, Gemini, Grok, Mistral, DeepSeek) without hardcoding your credentials in the code, RostaingChain supports environment variables.

1.  **Create a file named `.env`** in your project root.
2.  **Add your API keys** following this format:

```env
# Standard Providers
OPENAI_API_KEY=...
ANTHROPIC_API_KEY=...
GOOGLE_API_KEY=...

# Fast Inference Providers
GROQ_API_KEY=...
MISTRAL_API_KEY=...

# OpenAI-Compatible Providers
DEEPSEEK_API_KEY=...
XAI_API_KEY=...
```

3.  **Load the keys** at the start of your script using `python-dotenv`:

```bash
pip install python-dotenv
```


## ⚡ Quick Start

### 1. The "Chat with Anything" Mode

Simply point `data_source` to a file, a folder, a database, or a URL.

```python
from rostaingchain import RostaingAgent

# Initialize the Agent
agent = RostaingAgent(
    llm_model="llama3.2", # Use local Ollama and ensure you ran 'ollama pull llama3.2' in your terminal
    data_source="/path/to/data", # Watches this folder
    auto_update=True             # Real-time ingestion
)

# Chat
response = agent.chat("What are the main topics in these documents?")
print(response)
```

### 2. 🚀 Quick Start: Interactive Console Agent

```python
import os
from dotenv import load_dotenv
from rostaingchain import RostaingAgent

# 1. Load environment variables (Make sure your .env file is set up)
load_dotenv()

def main():
    # 2. Initialize the Agent
    # RostaingAgent automatically handles data profiling, vector indexing, and memory.
    agent = RostaingAgent(
        llm_model="gpt-4o",
        llm_provider="openai",
        llm_api_key=os.getenv("OPENAI_API_KEY"),
        data_source="data/products.xlsx",  # Path to your CSV/SQL/Excel/Image/Audio/Video/...
        vector_db="faiss",                 # High-performance vector storage
        reset_db=False,                    # Set to True to re-index the data
        memory=True                       # Keep track of the conversation context
    )

    print("\n" + "="*40)
    print("🤖 RotaingChain AGENT: CONSOLE MODE")
    print("Type your question below or 'q' to exit.")
    print("="*40 + "\n")

    try:
        while True:
            # 3. Capture User Input
            user_input = input("👤 You: ").strip()

            # Exit condition
            if user_input.lower() in ["q", "quit", "exit"]:
                print("\nShutting down... Goodbye! 👋")
                break

            if not user_input:
                continue

            # 4. Generate & Display Response
            print("🤖 Agent:", end=" ", flush=True)
            
            try:
                response = agent.chat(user_input)
                print(response)
            except Exception as e:
                print(f"\n❌ Error: {str(e)}")

    except KeyboardInterrupt:
        print("\n\n[System] Session interrupted by user. Closing... 👋")
    finally:
        print("Program closed.")

if __name__ == "__main__":
    main()
```

## 🛠️ Advanced Usage

### 1. YouTube Video Analysis

Extract transcripts and metadata automatically.

```python
from rostaingchain import RostaingAgent
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

agent = RostaingAgent(
    llm_model="openai/gpt-oss-120b",
    llm_provider="groq",
    data_source="https://www.youtube.com/watch?v=3mTK0vYYXA4",
    vector_db="faiss",
    stream=True
)

# Streaming response for better UX
generator = agent.chat("Summarize this video in 3 bullet points.")

for token in generator:
    print(token, end="", flush=True)
```

### 2. Data Security (DLP)

Protect sensitive information from being displayed.

```python
from rostaingchain import RostaingAgent

agent = RostaingAgent(
    llm_model="llama3.2",
    data_source="bank_statements.pdf",
    # Enable Security
    security_filters=["IBAN", "BIC", "PHONE", "EMAIL", "MONEY", "CREDIT_CARD"] # Optional: DLP Security. Set to True for ALL filters, False to disable, or a list to select specific fields.
)

response = agent.chat("Give me the IBAN of the supplier.")
print(response)
# Output: "The IBAN is [Protected IBAN bank details]."
```

### 3. Working with DataFrames (Pandas)

```python
import pandas as pd
from rostaingchain import RostaingAgent
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

df = pd.read_csv("titanic.csv") 

# Direct Memory Ingestion
agent = RostaingAgent(
    llm_model="gpt-4o",
    data_source=df,
    vector_db="chroma" 
)

print(agent.chat("What is the average age of passengers?"))
```

### 4. Audio Analysis with Streaming & Markdown Output

RostaingChain natively handles audio files (like `.m4a`, `.mp3`) using OpenAI Whisper locally. This example demonstrates how to process an audio file, enforce security filters, and stream the result in a specific JSON format.

```python
from rostaingchain import RostaingAgent
from dotenv import load_dotenv
import os
# Load environment variables from .env file
load_dotenv()

# Assuming your API key is set
llm_api_key = os.getenv("GROQ_API_KEY")

agent = RostaingAgent(
    llm_model="openai/gpt-oss-120b",
    llm_provider="groq",
    llm_api_key=llm_api_key,
    data_source="C:/Users/Rostaing/Desktop/data/audio.m4a", # Supports: .m4a, .mp3, .wav, .ogg, .flac, .webm
    poll_interval=3600, # Check for file updates every hour
    vector_db="faiss",  # Options: 'faiss', 'qdrant' or 'chroma'
    reset_db=True,      # Re-index the file on startup
    memory=True,        # Enable conversation history
    security_filters=["PHONE", "BIC", "IBAN", "DATE"], # Optional: DLP Security. Set to True for ALL filters, False to disable, or a list to select specific fields.
    stream=True,
    output_format="markdown" # Options: "json", "text", "cartoon"
)

# Request a summary in JSON format with streaming enabled
response = agent.chat("Give me a summary.") # output_format supports: "json", "text (default)", "markdown", "toon"

# Real-time display loop
for token in response:
    # Prints every token as soon as it arrives (ChatGPT-like effect)
    print(token, end="", flush=True)
```

### 5. Chat with a Website (Web RAG)

```python
from rostaingchain import RostaingAgent
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

# Direct Memory Ingestion
agent = RostaingAgent(
    llm_model="gpt-4o",
    llm_provider="openai",
    data_source="https://en.wikipedia.org/wiki/Artificial_intelligence",
    vector_db="chroma",  # Options: 'faiss', 'qdrant' or 'chroma'
)

response = gent.chat("Give me a summary.")
print(response)
```

### 6. Chat with an image (RAG)

```python
from rostaingchain import RostaingAgent

# Direct Memory Ingestion
agent = RostaingAgent(
    llm_model="llama3.2", # Ensure you ran 'ollama pull llama3.2' in your terminal
    llm_provider="ollama", # Runs 100% locally on your machine for privacy
    embedding_model="nomic-embed-text", # Ensure you ran 'ollama pull nomic-embed-text' in your terminal
    data_source="invoice.jpg", # Supports: .png, .jpeg, .bmp, .tiff, .webp
    memory=True, # Enable conversation history
    vector_db="chroma",  # Options: 'faiss', 'qdrant' or 'chroma'
)

response = gent.chat("Give me a summary.")
print(response)
```

### 7. Video Analysis with Streaming & Cartoon Output

```python
from rostaingchain import RostaingAgent
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

# Direct Memory Ingestion
agent = RostaingAgent(
    llm_model="gpt-4o",
    llm_provider="openai",
    data_source="your_video.mp4", # Supports: .avi, .mov, .mkv, etc.
    vector_db="chroma",  # Options: 'faiss', 'qdrant' or 'chroma'
    stream=True,
    output_format="cartoon" # Options: "json", "text", "cartoon"
)

response = gent.chat("Give me a summary.")

# Real-time display loop
for token in response:
    # Prints every token as soon as it arrives (ChatGPT-like effect)
    print(token, end="", flush=True)
```

### 8. Chat with a file (Streaming RAG)

```python
from rostaingchain import RostaingAgent
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

# Direct Memory Ingestion
agent = RostaingAgent(
    llm_model="gpt-4o",
    llm_provider="openai",
    data_source="your_file.txt", # Supports: .pdf, .docx, .doc, .xlsx, .xls, .pptx, .ppt, .html, .htm, .xml, .epub, .md, .json, .log, .py, .js, .sql, .yaml, .ini, etc.
    vector_db="chroma",  # Options: 'faiss' or 'chroma'
    stream=True
)

response = gent.chat("Give me a summary.")

# Real-time display loop
for token in response:
    # Prints every token as soon as it arrives (ChatGPT-like effect)
    print(token, end="", flush=True)
```

### 9. Connecting to Databases (SQL / NoSQL)

RostaingAgent uses a **Polling Watcher** to monitor database changes.

```python
from rostaingchain import RostaingAgent
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

# PostgreSQL Configuration
db_config = {
    "type": "sql",
    "connection_string": "postgresql+psycopg2://your_username:your_password@localhost:5432/your_database",
    "query": "SELECT * FROM sales" # Your query
}

agent = RostaingAgent(
    llm_model="gpt-4o",
    llm_provider="openai",
    data_source=db_config,
    poll_interval=30, # Check for DB changes every 30 seconds
    reset_db=False,     # Start with a fresh index
    vector_db="faiss"
)

print(agent.chat("What is the total revenue for Q1?"))
# Thanks to Deep Profiling, the AI will know the exact sum/mean/max.
```

### 10 🗄️ Database Configuration Examples

To connect **RostaingAgent** to a database, create a dictionary `db_config` and pass it to the `data_source` parameter.

### 1. SQL Databases (via SQLAlchemy)

**PostgreSQL**
```python
pg_config = {
"type": "sql",
"connection_string": "postgresql+psycopg2://your_username:your_password@localhost:5432/your_database",
"query": "SELECT * FROM sales" # Your query
}
```

**MySQL**
```python
mysql_config = {
    "type": "sql",
    "connection_string": "mysql+pymysql://my_username:your_password@localhost:3306/your_database",
    "query": "SELECT * FROM orders WHERE status = 'shipped'" # Your query
}
```

**Oracle**
```python
# Requires Oracle Instant Client installed
# Ensure you ran 'pip install cx-oracle' in your terminal
oracle_config = {
    "type": "sql",
    "connection_string": "oracle+cx_oracle://your_username:your_password@localhost:1521/?service_name=ORCL",
    "query": "SELECT * FROM employees" # Your query
}
```

**SQLite**
```python
sqlite_config = {
    "type": "sql",
    "connection_string": "sqlite:///C:/path/to/your_data.db",
    "query": "SELECT * FROM invoices" # Your query
}
```

**Microsoft SQL Server**
```python
# Option 1

mssql_config = {
    "type": "sql",
    "connection_string": "mssql+pymssql://your_username:your_password@localhost:1433/your_database",
    "query": "SELECT top 100 * FROM customers" # Your query
}

# Option 2 (Recommended)
# We build a valid SQLAlchemy URL.
# We use quote_plus to handle special characters like \ in the server name.

host = r"your_host" # Example: DESKTOP-9K6BSF8\SQLEXPRESS
db_name = "your_database"
username = "your_username"
password = "your_password"

connection_string = f"mssql+pyodbc://{username}:{password}@{host}/{db_name}?driver=ODBC+Driver+17+for+SQL+Server&TrustServerCertificate=yes"
mssql_config = {
    "type": "sql",
    "connection_string": connection_string,
    "query": "SELECT * FROM customers" # Your query
}
```

### 2. NoSQL Databases

**MongoDB**
```python
mongo_config = {
    "type": "mongodb",
    "uri": "mongodb://localhost:27017/",
    "db": "ecommerce_db",
    "collection": "products",
    "limit": 50 # Optional: Limit the number of documents to ingest
}
```

**Neo4j (Graph)**
```python
neo4j_config = {
    "type": "neo4j",
    "uri": "bolt://localhost:7687",
    "user": "neo4j",
    "password": "your_password",
    "query": "MATCH (p:Person)-[:WROTE]->(a:Article) RETURN p.name, a.title LIMIT 20" # Your query
}
```

### Usage Example

```python
agent = RostaingAgent(
    llm_model="gpt-4o",
    data_source=mysql_config, # Pass the dictionary here.
    poll_interval=3600,         # Watch for changes every minute
    reset_db=False
)
```

### 11. Use a custom LLM (e.g., vLLM on another server)

```python
from rostaingchain import RostaingAgent
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

# Direct Memory Ingestion
agent = RostaingAgent(
    llm_model="my-finetuned-model",
    llm_provider="custom",
    llm_base_url="http://192.168.1.50:8000/v1", # Your vLLM server
    llm_api_key="token-if-needed",
    memory=True,
    vector_db="chroma",  # Options: 'faiss', 'chroma' or 'qdrant'
    data_source="/path/to/your_file.pdf", # Supports: .txt, .docx, .doc, .xlsx, .xls, .pptx, .ppt, .html, .htm, .xml, .epub, .md, .json, .log, .py, .js, .sql, .yaml, .ini, .jpg, .png, .jpeg, .bmp, .tiff, .webp, SQL/NoSQL Databases, Audio/Video/Web(link)
    reset_db=True, # Start with a fresh index
    temperature=0,
    top_k=0.1,
    top_p=1,
    max_tokens=1500,
    stream=True
)

response = gent.chat("Give me a summary.")

# Real-time display loop
for token in response:
    # Prints every token as soon as it arrives (ChatGPT-like effect)
    print(token, end="", flush=True)
```

### 12. Universal Intelligence: Switching LLM Providers

**A. Use DeepSeek (the cheaper GPT-4 alternative)**
```python
agent = RostaingAgent(
    llm_model="deepseek-chat", # Auto-detection
    provider="deepseek",
    # If the key is not in the .env:
    llm_api_key="sk-your-deepseek-key" 
)
```

**B. Use Groq (Lightning speed – 500 tokens/s)**
```python
agent = RostaingAgent(
    llm_model="openai/gpt-oss-120b",
    llm_provider="groq" # Force the provider to ensure it
)
```

**C. Use Claude Sonnet (Best for coding)**
```python
agent = RostaingAgent(
    llm_model="claude-4.5-sonnet",
    llm_provider="anthropic" # Force the provider to ensure it
)
```

**D. Use Gemini 3 Pro (Google)**
```python
agent = RostaingAgent(
    llm_model="gemini-3-pro-preview",
    llm_provider="google" # Force the provider to ensure it
)
```

**E. Use Mistral (via Groq for Speed)**
```python
agent = RostaingAgent(
    llm_model="mistral-large-2512",
    llm_provider="mistral" # Force the provider for ultra-fast inference
)
```

**F. Use Grok (xAI)**
```python
agent = RostaingAgent(
     llm_model="grok-4.1",
    llm_provider="grok" # Automatically configures the xAI API base_url
)
```

**G. Use OpenAI (GPT-4o)**
```python
agent = RostaingAgent(
    llm_model="gpt-4o",
    llm_provider="openai" # Automatically uses OPENAI_API_KEY from your .env file
)
```

**H. Use Local LLMs (Ollama)**
```python
agent = RostaingAgent(
    llm_model="llama3.2",  # Ensure you ran 'ollama pull llama3.2' in your terminal
    llm_provider="ollama", # Runs 100% locally on your machine for privacy
    # llm_base_url="http://localhost:11434" # Optional: Default URL
)
```

#### 📝 Key Parameters Explained

*   **`stream=True`**:
    This is essential for User Experience (UX). Instead of waiting for the entire response to be generated (which can take time for long summaries), the method returns a Python **Generator**. You must iterate over it (using a `for` loop) to display tokens in real-time, exactly like ChatGPT.

*   **`output_format`**:
    This parameter enforces the structure or style of the LLM's response. It accepts three values:
    *   `"text"` (Default): A standard, conversational plain text response.
    *   `"json"`: Forces the LLM to output a valid JSON object. Extremely useful if you are building an API or need to parse the result programmatically.
    *   `"cartoon"`: Makes the LLM generate responses in a playful, cartoon-style tone with simplified language and expressive descriptions. Useful for educational content, storytelling, or kid-friendly interfaces.

*   **`vector_db`**:
    Defines the local vector storage engine. RostaingChain currently supports two robust, file-based options:
    *   `"chroma"`: Uses Chroma vector database (lightweight, developer-friendly, optimized for local-first embedding storage and retrieval).
    *   `"faiss"`: Uses Facebook AI Similarity Search (highly efficient for CPU).
    *   `"qdrant\"`: Uses Qdrant vector database (open-source, written in Rust, optimized for fast similarity search with payload filtering and horizontal scaling).


## ⚙️ Configuration Parameters

| Parameter | Type | Default | Description |
| :--- | :--- | :--- | :--- |
| **RostaingAgent** | | | |
| `llm_model` | str | `"llama3.2"` | Name of the model (e.g., "gpt-4o", "claude-3-opus", "mistral"). |
| `llm_provider` | str | `"auto"` | "openai", "groq", "ollama", "anthropic", "google", "deepseek". |
| `llm_api_key` | str | `None` | API Key (optional if environment variable is set). |
| `llm_base_url` | str | `None` | Custom endpoint URL (for local setups or proxies). |
| `embedding_model` | str | `"BAAI/bge-small-en-v1.5"` | Model used for vectorizing documents. |
| `embedding_source` | str | `"fastembed"` | "fastembed", "openai", "ollama", "huggingface". |
| `vector_db` | str | `"chroma"` | Vector Store backend: "chroma", "faiss", "qdrant" |
| `data_source` | str/dict/obj | `"./data"` | File path, Folder path, Image path, URL, SQL Config (dict), or DataFrame object. |
| **Automation** | | | |
| `auto_update` | bool | `True` | Activates real-time Watcher (File system) or Polling (DB/Web). |
| `poll_interval` | int | `60` | Interval in seconds between DB/Web checks. |
| `reset_db` | bool | `False` | Wipes/Resets vector database storage on startup. |
| `memory` | bool | `False` | Enables conversational history (Multi-turn chat). |
| `save_graph` | bool | `False` | Saves charts to public/graph/ (auto-created). |
| `save_logs` | bool | `False` | Writes JSON logs to public/logs/ with timestamps. |
| **Generation Settings** | | | |
| `temperature` | float | `0.1` | Creativity of the model (0.0 = deterministic, 1.0 = creative). |
| `max_tokens` | int | `None` | Limit response length. |
| `top_p` | float | `None` | Nucleus sampling parameter. |
| `top_k` | int | `None` | Top-K sampling parameter. |
| `seed` | int | `None` | Seed for reproducible/deterministic outputs. |
| `stream` | bool | `False` | Enables streaming response (token by token). |
| `cache` | bool | `True` | Enables In-Memory caching for speed. |
| `output_format` | str | `"text"` | Enforce format: `"text"`, `"json"`, `"markdown"`, `"cartoon"`. |
| **Agent Identity** | | | |
| `role` | str | `"Helpful AI Assistant"` | Defines the persona/role of the agent. |
| `goal` | str | `"Assist the user..."` | The primary objective of the agent. |
| `instructions` | str | `"Answer concisely."` | Specific behavioral instructions or constraints. |
| `reflection` | bool | `False` | Enables "Step-by-step" thinking and self-correction before answering. |
| **Company Context** | | | |
| `company_name` | str | `None` | Name of the organization for business context. |
| `company_description`| str | `None` | Description of the company's activity. |
| `company_url` | str | `None` | Website URL for context. |
| **Security & User** | | | |
| `security_filters` | list/bool | `None` | List of DLP filters (e.g., `["IBAN", "EMAIL"]`) or `True` for all. |
| `user_profile` | str | `None` | Natural language description of user rights (e.g., "Intern, no access to salaries"). |
| `user_id` | str | `None` | Unique identifier for the user. |
| `session_id` | str | `None` | Unique identifier for the chat session. |
| `agent_id` | str | `None` | Unique identifier for the specific agent instance. |
| `system_prompt` | str | `None` | Full override of the system prompt (Advanced). |
| **Tools & UI** | | | |
| `mcp_tools` | list | `None` | List of Model Context Protocol tools for external integrations. |
| `canvas` | object | `None` | Canvas UI instance for visual updates (Charts/Graphs). |


## 🔒 Security Filters & Data Masking

| Data type | Alternative text |
|----------------|----------------------|
| EMAIL | `[Email masked]` |
| PHONE | `[Phone masked]` |
| ID_NUM | `[ID masked]` |
| PASSPORT | `[Passport masked]` |
| SSN | `[SSN masked]` |
| ADDRESS | `[Address masked]` |
| POSTAL | `[Postal Code masked]` |
| BIC | `[BIC masked]` |
| IBAN | `[IBAN masked]` |
| VAT_ID | `[VAT masked]` |
| CREDIT_CARD | `[Card masked]` |
| MONEY | `[Amount masked]` |
| CRYPTO | `[Crypto masked]` |
| IP_ADDR | `[IP masked]` |
| MAC_ADDR | `[MAC masked]` |
| API_KEY | `[API Key redacted]` |
| DATE | `[Date masked]` |
| SALARY | `[Salary information confidential]` |
| BIRTHDATE | `[Birth date masked]` |
| MEDICAL | `[Medical information confidential]` |


## 💡 Pro Tip: VSCode Autocomplete

Don't memorize the parameters! If you are using **VSCode**, you can view the complete list of available options for `RostaingAgent` instantly.

Just place your cursor inside the parentheses and press:

**`Ctrl` + `Space`**

This will trigger IntelliSense and display all configuration arguments (like `memory`, `security_filters`, `temperature`, `cache`, etc.) with their descriptions.

## 🏗️ Architecture

![Framework Architecture](https://raw.githubusercontent.com/Rostaing/rostaingchain/main/docs/architecture.png)

## Useful Links
- [Author's LinkedIn](https://www.linkedin.com/in/davila-rostaing/)
- [Author's YouTube Channel](https://youtube.com/@RostaingAI?sub_confirmation=1)
- [GitHub Repository](https://github.com/Rostaing/rostaingchain)
- [PyPI Project Page](https://pypi.org/project/rostaingchain/)