How to build an agent for a corporate knowledge base (wiki) with minimal costs when dealing with a large volume of documents?

To build an agent for a corporate knowledge base with a large volume of documents and minimal costs, the following approach can be used:

1. Indexing and Search:

   • Use ready-made solutions for full-text search, such as Elasticsearch or OpenSearch. They scale well and allow quick searches over large data volumes.
   • To save resources, configure incremental indexing, updating the index only for changed documents.

2. Natural Language Processing:

   • For understanding user queries, lightweight NLP libraries or cloud APIs (e.g., OpenAI, Google NLP) can be used to extract keywords and intents.
   • If the budget is limited, simple parsing and synonyms can suffice.

3. Agent Architecture:

   • Create a microservice in Node.js that accepts requests, processes them, queries the search index, and returns results.
   • Cache popular queries to reduce load.

4. Storage Optimization:

   • Store documents in a format suitable for indexing (e.g., JSON or Markdown).
   • Use data compression and deduplication.

5. Example of a simple Elasticsearch query in Node.js:

const { Client } = require('@elastic/elasticsearch');
const client = new Client({ node: 'http://localhost:9200' });

async function search(query) {
  const { body } = await client.search({
    index: 'knowledge_base',
    body: {
      query: {
        match: { content: query }
      }
    }
  });
  return body.hits.hits;
}

search('how to set up VPN').then(results => {
  console.log(results);
});

This approach will allow you to quickly and cost-effectively build an efficient agent for a corporate knowledge base.