Design Document:Vector Database

Vector Database

• Technology: Pinecone, Weaviate, or Chroma
• Purpose: Store embeddings for RAG system
• Content Types:
  • Process documentation embeddings
  • Historical failure analysis embeddings
  • Equipment manual embeddings
  • Best practice document embeddings
• Indexing: HNSW or IVF indexing for fast similarity search

Vector Database Design and Implementation

This section provides a detailed technical design for the vector database, a critical component of the Retrieval-Augmented Generation (RAG) system. It will store and index vector embeddings to enable fast and semantically relevant information retrieval for the LLM.

1. Technology Selection

For an on-premises or private cloud deployment, Weaviate or Chroma are excellent choices. Weaviate is a powerful, open-source vector search engine that can be self-hosted on a Kubernetes cluster. It supports multiple indexing algorithms and integrates well with various ML frameworks. ChromaDB is a simpler, lightweight open-source option that is easy to deploy and ideal for smaller-scale projects or proofs of concept. Pinecone is a fully managed cloud service and may not meet your private cloud requirements.

2. Purpose and Core Functionality

The vector database serves one primary purpose: to store high-dimensional vector embeddings of your company's proprietary knowledge base and enable lightning-fast similarity search. When a user asks a question, the system converts the query into a vector and uses the database to find the most relevant document chunks based on their vector similarity.

• Vector Embeddings: These are numerical representations of text. A sentence like "Etch recipe optimization" is converted into a list of numbers (a vector) that captures its semantic meaning.
• Similarity Search: This is the core operation. The database calculates the distance (e.g., cosine similarity) between the query vector and all the stored document vectors to find the most "similar" ones.

3. Content and Ingestion Pipeline

The data ingestion pipeline for the vector database is crucial for ensuring the RAG system has fresh and relevant knowledge.

• Documents: Your unstructured data (SOPs, BKMs, failure analysis reports, and equipment manuals) will be ingested using a scheduled job, as described in the ETL pipeline section.
• Chunking: Large documents are split into smaller, semantically coherent "chunks" (e.g., paragraphs or bullet points). This is a critical step, as feeding a large, unrelated document to the LLM can dilute the context.
• Embedding Model: A pre-trained, sentence-level transformer model (e.g., Sentence-Transformers) will be used to generate the vector embeddings for each text chunk. This model is chosen for its efficiency and effectiveness in capturing the semantic meaning of sentences.
• Metadata: In addition to the vector, each chunk will have associated metadata, such as the document_id, page_number, source_type (e.g., SOP, BKM), and timestamp. This metadata is essential for filtering and providing context in the final response.

4. Indexing for Fast Similarity Search

The choice of indexing algorithm is a key technical decision that impacts search speed and accuracy.

• HNSW (Hierarchical Navigable Small World): This is the state-of-the-art indexing algorithm for approximate nearest neighbor (ANN) search. It builds a graph data structure where each point (vector) is a node, and edges connect it to its "neighbors." Queries traverse this graph, moving from a random starting point to its nearest neighbors until the most similar vector is found. HNSW is highly performant and the recommended choice for a production system.
• IVF (Inverted File Index): This algorithm first partitions the data into clusters and creates an "inverted file" index. During a search, it only looks within a few relevant clusters, which is much faster than a brute-force search. It is less performant than HNSW but can be a good starting point for a large dataset.
• Implementation: Weaviate natively supports HNSW indexing. You will configure your schema to use HNSW and tune parameters like efConstruction and ef to balance indexing speed, search speed, and accuracy.

// Weaviate schema definition for a document
{
  "class": "SemiconductorDocument",
  "vectorIndexConfig": {
    "ef": 128,
    "efConstruction": 256,
    "maxConnections": 32,
    "distance": "cosine"
  },
  "properties": [
    {
      "name": "content",
      "dataType": ["text"]
    },
    {
      "name": "sourceDocumentId",
      "dataType": ["text"]
    },
    {
      "name": "pageNumber",
      "dataType": ["int"]
    }
  ]
}

This design ensures that your RAG system can efficiently retrieve the most relevant information from your vast knowledge base, providing accurate and context-aware responses to your users.