# Layered of System Architecture

**Definition**

The proposed system leverages RAG with additional layers for enhanced data retrieval, contextual awareness, and ethical content filtering. This system:

Integrates structured and unstructured data using a local vector database for real-time knowledge updates.

Employs content-filtering LLM prompts to ensure safety and compliance.

Uses dynamic prompts and external API integrations for domain-specific data queries, including real-time cryptocurrency prices, historical OHLCV data, and market trend analysis.

1. **Content Filter Layer**

This layer is the first checkpoint for incoming user queries. It uses a large language model (LLM) with a specialized content-filter prompt to detect inappropriate content, including:

* Toxicity: Offensive or harmful language.
* NSFW: Not Safe for Work content.
* NSFL: Not Safe for Life content.

If the content is deemed inappropriate, the system respectfully declines the request and notifies the user. Otherwise, it passes the query forward for processing.

**2. Retrieval-Augmented Generation (RAG)**

The heart of the system, the RAG module combines retrieval capabilities with generative AI to produce contextually relevant and accurate responses. Key components include:

* Market Assessment Prompt:\
  Queries market-specific data (e.g., cryptocurrency trends, historical OHLCV data) from the local vector database or external knowledge bases.
* Context Awareness Prompt:\
  Uses conversational history to refine responses, ensuring continuity and personalization in interactions.
* Market Assessment Prompts:

Leverage external APIs and databases for real-time financial insights.

Support for cryptocurrency-specific use cases such as trending gainers/losers or market cap analysis.

**3. Data Retrieval and Knowledge Integration**

The RAG module interacts with both structured and unstructured data sources via:

* **Local Vector Database:**

Stores pre-indexed embeddings of structured and unstructured knowledge for fast retrieval.

Serves as a bridge between real-time queries and the external knowledge base.

* **External Knowledge Base:**

Houses historical and domain-specific data, enabling comprehensive responses.

Integrates with APIs to fetch real-time updates (e.g., cryptocurrency prices, trading volume).

* **JSON Analysis Layer**

\+ For data-heavy requests, the system employs an LLM-based JSON analyzer to parse structured inputs. The workflow includes:

\+ Using a JSON prompt for structured data (e.g., historical OHLCV data for cryptocurrencies).

\+ Producing machine-readable outputs for further processing, such as JSON files for technical analysis.

* **Call Function Module**

Handles external data fetching via real-time APIs and supports functions such as:

\+ Web search API

\+ Cryptocurrency data retrieval APIs (e.g. prices, chart data, and news).

\+ Analysis of domain-specific data.

* **Response Generation and Real-Time Websocket**

Once the RAG module synthesizes data, responses are sent to the user via a real-time websocket. This ensures:

\+ Low latency communication.

\+ Human-readable, concise, and actionable insights.

\+ Dynamic adjustments to context or follow-up questions.

By combining RAG with content filtering and hybrid data retrieval, the system ensures accurate, safe, and responsive interactions for users in high-stakes industries such as cryptocurrency and finance

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