How to Build a Company‑Specific Chatbot with LLMs and Vector Databases

Why LLM + Vector Database for a Chatbot?

The breakthrough of ChatGPT demonstrated that large language models (LLMs) can generate human‑like text, reviving interest in generative AI and creating demand for domain‑specific, up‑to‑date conversational agents. Enterprises need to supplement LLMs with their own knowledge bases to achieve depth and timeliness.

What Is a Vector Database?

Most real‑world data is unstructured (images, audio, video, text). AI techniques extract feature vectors from this data, which are then stored, analyzed, and retrieved in a vector database. Approximate Nearest Neighbor Search (ANNS) enables fast similarity search by sacrificing a small amount of precision for speed, distinguishing vector databases from traditional relational databases.

Vector Database Implementations

Two main approaches exist: (1) a dedicated vector‑index service that provides creation and retrieval capabilities, forming a proprietary vector database; (2) embedding ANNS indexes into a traditional DBMS, yielding a hybrid system that combines vector search with full SQL functionality. The latter inherits ACID transactions, high availability, and extensibility.

AnalyticDB PostgreSQL (ADB‑PG) Overview

ADB‑PG is a cloud‑native data warehouse that supports both vector and full‑text search. It offers distributed transactions, mixed‑load processing, and can store and query both structured and semi‑structured data. Its vector search engine supports dot‑product, Hamming, and Euclidean distances, and is optimized with SIMD instructions.

Building an Enterprise Chatbot with LLM + ADB‑PG

The workflow consists of two main pipelines:

Backend data processing and storage : extract text from source documents, split into semantic chunks, generate embeddings, and store both the raw chunks and their vectors in ADB‑PG.

Frontend query flow : (1) optional question refinement to produce an independent query, (2) retrieve the most relevant chunks via vector similarity search, and (3) let the LLM reason over the retrieved context to produce the final answer.

Backend Processing Steps

Extract raw text and split into meaningful chunks.

Generate embeddings for each chunk using an embedding model.

Insert the chunk, its metadata, and the embedding into the chunks table.

CREATE TABLE chunks(
  id serial primary key,
  chunk varchar(1024),
  intime timestamp,
  url varchar(1024),
  feature real[]
);

CREATE INDEX ON chunks USING ann(feature) WITH (dim=1536);
CREATE INDEX ON chunks(intime);

Query Examples

Vector similarity search:

SELECT id, chunk, intime, url FROM chunks
ORDER BY feature <-> ARRAY[10, 2.0, ..., 1536.0]
LIMIT 100;

Fusion search combining time filter and vector similarity:

SELECT id, chunk, intime, url FROM chunks
WHERE intime > '2023-04-01' AND intime <= '2023-05-01'
ORDER BY feature <-> ARRAY[10, 2.0, ..., 1536.0]
LIMIT 100;

Practical Use Cases

By storing enterprise‑specific documents, PDFs, emails, or real‑time information as embeddings, the chatbot can answer questions that are outside the LLM’s pre‑training data, such as legal statutes, travel recommendations, sports statistics, educational topics, or financial analysis.

Conclusion

Combining an LLM with a vector‑enabled database like ADB‑PG provides a one‑stop solution for building knowledgeable, up‑to‑date enterprise chatbots. The LLM supplies reasoning ability, while the vector database supplies fast, semantic retrieval of proprietary knowledge, ensuring accurate and timely responses.