> For the complete documentation index, see [llms.txt](https://solutions-explorer.gitbook.io/resources/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://solutions-explorer.gitbook.io/resources/additional-resources/architecture.md).

# 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.

***

### Key Components

<details>

<summary>🚀 Solution Database</summary>

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`](https://openai.com/index/new-embedding-models-and-api-updates/) from [OpenAI](https://openai.com/)
* **Vector Database:** Powered by [Typesense](https://typesense.org/), optimized for hybrid search capabilities.

</details>

<details>

<summary>🔍 Search Engine</summary>

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.

</details>

<details>

<summary>🤖 AI-Powered Agent</summary>

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`**](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) 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**](https://www.langchain.com/)**/**[**Langraph**](https://www.langchain.com/langgraph) for dynamic workflow execution.
* Deployment on the [**LangSmith Platform Cloud**](https://www.langchain.com/langsmith) for scalability and reliability.

</details>

<details>

<summary>📝 Summarization and Token Control</summary>

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.

</details>

<details>

<summary>🔀 Agent Workflow</summary>

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.

</details>

***

### System Workflow

<figure><img src="https://1292220741-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FKGAN4ko2qLFQDMJgRgce%2Fuploads%2FzMEmzLvSx0328leOWD4q%2FScreenshot%202024-11-29%20at%2010.27.15.png?alt=media&amp;token=b01de888-7f62-4597-9d87-dd313b5b1c71" alt=""><figcaption><p>Langraph architecture as displayed in Langraph Studio</p></figcaption></figure>

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.

***

### 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:

{% content-ref url="/pages/HJHIRJpZ4u740WJe8dYn" %}
[Smart Search](/resources/key-features/smart-search.md)
{% endcontent-ref %}

{% content-ref url="/pages/25yFlYDATVIgJ3uKKKAZ" %}
[AI Explorer](/resources/key-features/ai-explorer.md)
{% endcontent-ref %}