An AI Agent uses a vector database for similarity retrieval by leveraging embeddings—numerical vector representations of data (such as text, images, or audio)—to find semantically similar items. Here's how the process works:
Embedding Generation: First, the AI Agent converts input data (like a user query or a document) into a high-dimensional vector using an embedding model. For example, a natural language query like "Explain quantum computing" is transformed into a vector that captures its semantic meaning.
Vector Storage: These vectors are stored in a vector database, which is optimized for storing and searching high-dimensional vectors efficiently. Each vector is usually associated with metadata (e.g., document ID, text content, timestamp) to provide context after retrieval.
Similarity Search: When the AI Agent needs to find relevant information (e.g., answering a question or retrieving a document), it converts the new query into a vector and performs a similarity search within the vector database. The most common similarity metric is cosine similarity, though other metrics like Euclidean distance or inner product can also be used. The database returns the vectors (and their associated data) that are closest to the query vector in the vector space.
Result Utilization: The AI Agent uses the retrieved results—typically a list of top-k most similar vectors—to inform its next actions, such as generating a response, summarizing content, or making decisions.
Example: Suppose an AI-powered customer support assistant receives a user question: “How can I reset my password?” The agent converts this question into an embedding vector. It then queries the vector database where past support tickets, FAQs, and knowledge base articles are stored as vectors. The vector database retrieves the most similar entries, such as an FAQ titled “Steps to Reset Your Password,” which the agent then uses to craft a helpful response.
In cloud-based environments, services like Tencent Cloud’s Vector Database (Tencent Cloud VectorDB) offer scalable, high-performance solutions tailored for storing and retrieving vector embeddings. They support fast similarity search, integrate well with machine learning models, and are suitable for building intelligent AI Agents that rely on semantic understanding and retrieval.