AI Architecture Overview

Welcome to the architecture documentation for the Smart Search / AI Explorer. This section provides a high-level overview of how the system is structured and the technologies powering its intelligent search and retrieval capabilities.

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Key Components

🚀 Solution Database

Every solution in our database is vectorized, enabling semantic understanding and advanced search functionalities. This preprocessing ensures that solutions can be effectively retrieved based on context, relevance, and similarity.

• Vectorization Model: text-embedding-3-large from OpenAI

• Vector Database: Powered by Typesense, optimized for hybrid search capabilities.

🔍 Search Engine

The Smart Search utilizes a hybrid approach, combining keyword-based search with semantic search to maximize retrieval effectiveness:

• Keyword Search: Traditional exact or partial matching for direct queries.

• Semantic Search: Contextual matching using vector embeddings.

• Ranking Factors:

  • Length of query

  • Weights assigned to keyword and semantic search results

This approach ensures accurate and relevant results for both simple and complex queries.

🤖 AI-Powered Agent

At the heart of the system is an adaptive RAG (Retrieval-Augmented Generation) agent that enhances interaction and search capabilities. The agent architecture includes:

LLM Layer

The agent leverages the GPT-4o-mini model for natural language understanding and response generation.

Vector Store Integration

The agent connects to the vector store to retrieve relevant context and solutions, ensuring precise and informative responses.

Adaptive Toolset

Unlike standard RAG implementations, the agent includes:

• A layer of intelligence to determine the best tools to use for each request.

• Integration with LangChain/Langraph for dynamic workflow execution.

• Deployment on the LangSmith Platform Cloud for scalability and reliability.

📝 Summarization and Token Control

To minimize costs and reduce environmental impact, the agent incorporates:

• Summarization: Condenses retrieved information to optimize token usage before processing by the LLM.

• Token Control: Limits token usage intelligently by summarizing conversations and deleting irrelevant messages to ensure efficient operation.

This approach not only saves resources but also aligns with sustainability goals.

🔀 Agent Workflow

The agent's workflow follows the architecture below:

1. Start: The user input is analyzed.

2. Agent Decision: The agent determines the best action using tools or interacting with the vector store.

3. Summarization: If required, retrieved data is summarized to reduce token consumption.

4. Token Filtering: Irrelevant or redundant data is filtered and removed.

5. Error Handling: If issues arise (e.g., recursion limits), the agent invokes an error handler.

6. Final Response: A concise and informative response is generated.

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System Workflow

Langraph architecture as displayed in Langraph Studio

1. Query Processing:

   • User inputs are processed to identify query type, length, and context.

   • Query is sent to the hybrid search engine.

2. Retrieval:

   • The vector database provides semantic matches.

   • Keyword matches are ranked and combined with semantic results.

3. Agent Interaction:

   • The RAG agent processes retrieved data.

   • Summarization reduces token usage for efficient LLM processing.

   • Adaptive tools may be invoked as needed.

4. Response Generation:

   • GPT-4o-mini generates natural language responses enriched by retrieved solutions.

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Technical Stack

Component	Technology
Vectorization Model	OpenAI text-embedding-3-large
Vector Database	Typesense
Search Engine	Hybrid (Keyword + Semantic)
AI Agent Framework	LangChain / LangGraph
Deployment Platform	LangSmith Platform Cloud
LLM	GPT-4o-mini
Summarization	LangGraph Summarization Tools
Reranking	Cohere Rerank-v3.5

For detailed implementation instructions, refer to the Technical key feature sections:

Smart Search

AI Explorer