How Vector Embeddings and Knowledge Graphs Enhance RAG Applications

When a user query comes in, it undergoes a meticulous process to ensure accurate and contextually relevant responses. Here's a detailed breakdown of how this system functions:

User Query Conversion: Every user query is initially converted into a vector embedding using an embedding model. Vector embeddings transform textual data into numerical representations that can be processed by machine learning models.
Vector and Semantic Search: Once the user query is converted into a vector embedding, it is passed through a vector database. The vector database employs vector search and semantic search techniques to retrieve similar chunks of information. Semantic search utilizes the meaning and context of the query rather than just matching keywords.
Compiling the Response: After the relevant chunks are retrieved, these chunks – along with the original user query – form the context for generating the final response. This compiled dataset is then fed into a large language model (LLM), which processes it to generate a coherent and contextually appropriate answer for the user.

Keywords

User Query
Vector Embedding
Embedding Model
Vector Database
Vector Search
Semantic Search
Large Language Model (LLM)
Relevant Chunks
Contextual Response

FAQ

Q1: What happens initially when a user query is received?

A1: The user query is converted into a vector embedding using an embedding model.

Q2: How are similar chunks of information retrieved?

A2: The converted vector embedding is passed through a vector database that employs vector search and semantic search techniques to retrieve similar chunks of information.

Q3: What role does the large language model (LLM) play in this process?

A3: The LLM takes the compiled dataset comprising the user query and the retrieved chunks to generate a coherent and contextually appropriate answer for the user.

Q4: What is the advantage of using semantic search in this system?

A4: Semantic search utilizes the meaning and context of the query, enabling more accurate and relevant information retrieval compared to simple keyword matching.