Knowledge Base - BioAgents

Overview

The Knowledge Base system enables semantic search over custom documents using vector embeddings. Upload your own research papers, documentation, or datasets to create a private knowledge base that agents can query during literature search.

Use Cases

Search internal research documentation
Query proprietary datasets and protocols
Retrieve information from uploaded papers
Cross-reference findings with custom knowledge

Architecture

Components

Document Processor - Extracts text from PDF, DOCX, Markdown
Text Chunker - Splits documents into searchable chunks
Embedding Provider - Generates vector embeddings (Voyage AI, OpenAI, Cohere)
pgvector - PostgreSQL extension for vector similarity search
Cohere Reranker - Two-stage retrieval for precision

Configuration

Environment Variables

.env

# Knowledge Base Path
KNOWLEDGE_DOCS_PATH=/path/to/your/documents

# Embedding Provider (voyage, openai, cohere)
EMBEDDING_PROVIDER=voyage
VOYAGE_API_KEY=your-voyage-api-key

# Embedding Model
TEXT_EMBEDDING_MODEL=voyage-3
EMBEDDING_DIMENSIONS=1024

# Search Configuration
SIMILARITY_THRESHOLD=0.5
VECTOR_SEARCH_LIMIT=20
RERANK_FINAL_LIMIT=5
RERANKER_SCORE_THRESHOLD=0.3

# Reranking
USE_RERANKING=true
COHERE_API_KEY=your-cohere-api-key

Embedding Providers

Voyage AI (Recommended)
OpenAI
Cohere

EMBEDDING_PROVIDER=voyage
VOYAGE_API_KEY=your-api-key
TEXT_EMBEDDING_MODEL=voyage-3
EMBEDDING_DIMENSIONS=1024

Why Voyage?

Highest quality embeddings for scientific text
Superior retrieval performance
Optimized for long documents

EMBEDDING_PROVIDER=openai
OPENAI_API_KEY=your-api-key
TEXT_EMBEDDING_MODEL=text-embedding-3-large
EMBEDDING_DIMENSIONS=3072

When to use OpenAI:

Already using OpenAI for LLM
Prefer single provider for simplicity
Good general-purpose embeddings

EMBEDDING_PROVIDER=cohere
COHERE_API_KEY=your-api-key
TEXT_EMBEDDING_MODEL=embed-english-v3.0
EMBEDDING_DIMENSIONS=1024

When to use Cohere:

Using Cohere Reranker (same API key)
Cost-effective option
Strong multilingual support

Database Setup

Enable pgvector extension in PostgreSQL:

src/embeddings/setup.sql

-- Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Create documents table
CREATE TABLE IF NOT EXISTS documents (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  title TEXT NOT NULL,
  content TEXT NOT NULL,
  metadata JSONB DEFAULT '{}',
  embedding VECTOR(1024), -- Adjust dimensions based on model
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Create index for vector similarity search
CREATE INDEX IF NOT EXISTS documents_embedding_idx
  ON documents
  USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);

-- Create full-text search index
CREATE INDEX IF NOT EXISTS documents_content_idx
  ON documents
  USING gin(to_tsvector('english', content));

-- Vector similarity search function
CREATE OR REPLACE FUNCTION match_documents(
  query_embedding VECTOR(1024),
  match_threshold FLOAT DEFAULT 0.5,
  match_count INT DEFAULT 10
)
RETURNS TABLE (
  id UUID,
  title TEXT,
  content TEXT,
  metadata JSONB,
  similarity FLOAT
)
LANGUAGE sql
STABLE
AS $$
  SELECT
    id,
    title,
    content,
    metadata,
    1 - (embedding <=> query_embedding) AS similarity
  FROM documents
  WHERE 1 - (embedding <=> query_embedding) > match_threshold
  ORDER BY embedding <=> query_embedding
  LIMIT match_count;
$$;

Document Processing

Supported File Types

Format	Extensions	Notes
Markdown	`.md`	Front-matter support
PDF	`.pdf`	Text extraction only
Word	`.docx`	Requires mammoth

Document Processor

src/embeddings/documentProcessor.ts

import matter from "front-matter";
import mammoth from "mammoth";
import { PDFParse } from "pdf-parse";

export class DocumentProcessor {
  async processFile(filePath: string): Promise<ProcessedDocument | null> {
    const ext = path.extname(filePath).toLowerCase();
    let content: string;
    let frontMatterData: any = {};

    switch (ext) {
      case ".md":
        const rawContent = await fs.readFile(filePath, "utf-8");
        const parsed = matter(rawContent);
        frontMatterData = parsed.attributes;
        content = parsed.body;
        break;

      case ".docx":
        const buffer = await fs.readFile(filePath);
        const result = await mammoth.extractRawText({ buffer });
        content = result.value;
        break;

      case ".pdf":
        const parser = new PDFParse({ url: filePath });
        const pdfResult = await parser.getText();
        await parser.destroy();
        content = pdfResult.text;
        break;

      default:
        return null;
    }

    return {
      title: path.basename(filePath),
      content: content.trim(),
      metadata: {
        filePath,
        type: ext.slice(1),
        size: stats.size,
        lastModified: stats.mtime,
        ...frontMatterData,
      },
    };
  }

  async processDirectory(dirPath: string): Promise<ProcessedDocument[]> {
    const documents: ProcessedDocument[] = [];
    const entries = await fs.readdir(dirPath, { withFileTypes: true });

    for (const entry of entries) {
      const fullPath = path.join(dirPath, entry.name);

      if (entry.isDirectory()) {
        const subDocs = await this.processDirectory(fullPath);
        documents.push(...subDocs);
      } else if (entry.isFile()) {
        const doc = await this.processFile(fullPath);
        if (doc) documents.push(doc);
      }
    }

    return documents;
  }
}

Vector Search Pipeline

Two-Stage Retrieval

The system uses a two-stage approach for optimal precision:

Vector Search - Fast approximate nearest neighbor search (20 results)
Reranking - Precise relevance scoring using Cohere (top 5)

src/embeddings/vectorSearch.ts

export class VectorSearchWithReranker {
  async search(
    query: string,
    options: {
      vectorLimit?: number;      // Stage 1: Vector search limit (default: 20)
      finalLimit?: number;       // Stage 2: Final results after reranking (default: 5)
      useReranking?: boolean;    // Enable/disable reranking (default: true)
    } = {}
  ): Promise<Document[]> {
    const {
      vectorLimit = CONFIG.VECTOR_SEARCH_LIMIT,
      finalLimit = CONFIG.RERANK_FINAL_LIMIT,
      useReranking = CONFIG.USE_RERANKING,
    } = options;

    // Stage 1: Vector search
    const vectorResults = await this.vectorSearch(query, vectorLimit);

    if (vectorResults.length === 0) {
      return [];
    }

    // Stage 2: Rerank with Cohere
    if (useReranking && vectorResults.length > 1) {
      return await this.rerank(query, vectorResults, finalLimit);
    }

    return vectorResults.slice(0, finalLimit);
  }
}

Vector Search (Stage 1)

src/embeddings/vectorSearch.ts

async vectorSearch(query: string, limit = 20): Promise<Document[]> {
  // Generate query embedding
  const queryEmbedding = await this.embeddingProvider.generateEmbedding(query);

  // Cosine similarity search via pgvector
  const { data, error } = await supabase.rpc("match_documents", {
    query_embedding: queryEmbedding,
    match_threshold: CONFIG.SIMILARITY_THRESHOLD,  // Default: 0.5
    match_count: limit,
  });

  if (error) throw error;

  return data.map((doc: any) => ({
    id: doc.id,
    title: doc.title,
    content: doc.content,
    metadata: doc.metadata,
    similarity: doc.similarity,  // Cosine similarity score
  }));
}

Reranking (Stage 2)

src/embeddings/vectorSearch.ts

async rerank(
  query: string,
  documents: Document[],
  topN = 5
): Promise<Document[]> {
  const response = await cohere.rerank({
    model: "rerank-english-v3.0",
    query: query,
    documents: documents.map((doc) => ({
      text: `${doc.title}\n${doc.content}`,
    })),
    topN: Math.min(topN, documents.length),
    returnDocuments: true,
  });

  // Filter by relevance score threshold
  const rerankedResults = response.results
    .map((result) => ({
      ...documents[result.index],
      relevanceScore: result.relevanceScore,
    }))
    .filter((doc) => doc.relevanceScore >= CONFIG.RERANKER_SCORE_THRESHOLD);

  return rerankedResults;
}

Integration with Literature Agent

Knowledge Base is automatically queried during literature searches:

src/agents/literature/knowledge.ts

export async function initKnowledgeBase() {
  const docsPath = process.env.KNOWLEDGE_DOCS_PATH;

  if (!docsPath) {
    logger.info("KNOWLEDGE_DOCS_PATH not set, skipping knowledge base initialization");
    return;
  }

  const vectorSearch = new VectorSearchWithReranker();
  const processor = new DocumentProcessor();

  // Process all documents in directory
  const documents = await processor.processDirectory(docsPath);

  if (documents.length === 0) {
    logger.warn("No documents found in KNOWLEDGE_DOCS_PATH");
    return;
  }

  // Add to vector database
  await vectorSearch.addDocuments(documents);

  logger.info(`Knowledge base initialized with ${documents.length} documents`);
}

Usage in Chat/Deep Research:

src/routes/chat.ts

// Knowledge base is queried if KNOWLEDGE_DOCS_PATH is configured
if (process.env.KNOWLEDGE_DOCS_PATH) {
  const knowledgePromise = literatureAgent({
    objective: task.objective,
    type: "KNOWLEDGE",
  }).then((result) => {
    if (result.count && result.count > 0) {
      task.output += `${result.output}\n\n`;
    }
  });
  literaturePromises.push(knowledgePromise);
}

Adding Documents

Via File System

# Set documents path
export KNOWLEDGE_DOCS_PATH=/path/to/docs

# Add documents
mkdir -p /path/to/docs
cp research_paper.pdf /path/to/docs/
cp protocol.md /path/to/docs/

# Restart server to reindex
bun run dev

Via API

import { VectorSearchWithReranker } from "./embeddings/vectorSearch";

const vectorSearch = new VectorSearchWithReranker();

// Add single document
await vectorSearch.addDocument(
  "Research Paper Title",
  "Full text content...",
  { author: "Jane Doe", year: 2024 }
);

// Add batch
await vectorSearch.addDocuments([
  {
    title: "Paper 1",
    content: "Content 1...",
    metadata: { author: "Author 1" }
  },
  {
    title: "Paper 2",
    content: "Content 2...",
    metadata: { author: "Author 2" }
  }
]);

Search API

Direct Search

const vectorSearch = new VectorSearchWithReranker();

const results = await vectorSearch.search(
  "What are the mechanisms of autophagy?",
  {
    vectorLimit: 20,    // Stage 1: Vector search
    finalLimit: 5,      // Stage 2: Reranked results
    useReranking: true  // Enable reranking
  }
);

console.log(results);
// [
//   {
//     id: "550e8400-e29b-41d4-a716-446655440000",
//     title: "Autophagy in Aging",
//     content: "Full document text...",
//     metadata: { filePath: "/docs/autophagy.pdf" },
//     similarity: 0.87,       // Cosine similarity
//     relevanceScore: 0.95    // Reranker score
//   }
// ]

Get Statistics

const stats = await vectorSearch.getStats();

console.log(stats);
// {
//   totalDocuments: 42,
//   embeddingProvider: "voyage",
//   embeddingModel: "voyage-3",
//   embeddingDimensions: 1024
// }

Performance Tuning

Vector Index Configuration

-- IVFFlat index for approximate nearest neighbor search
CREATE INDEX documents_embedding_idx
  ON documents
  USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);  -- Increase for larger datasets

Index Guidelines:

lists = 100: Good for < 10,000 documents
lists = 200: Good for 10,000 - 100,000 documents
lists = 500: Good for > 100,000 documents

Caching

Search results are cached for 5 minutes:

src/embeddings/vectorSearch.ts

const cacheKey = `search_${query}_${vectorLimit}_${finalLimit}_${useReranking}`;
const cached = this.cache.get(cacheKey);
if (cached) return cached;

// ... perform search ...

this.cache.set(cacheKey, finalResults, 300000); // 5 min cache

Batch Processing

// Process documents in batches to avoid rate limits
const batchSize = 10;
for (let i = 0; i < documents.length; i += batchSize) {
  const batch = documents.slice(i, i + batchSize);
  await vectorSearch.addDocuments(batch);
  await new Promise(resolve => setTimeout(resolve, 1000)); // Rate limit
}

Best Practices

Choosing Embedding Models

Voyage AI: Best for scientific/technical content
OpenAI: Good general-purpose option
Cohere: Cost-effective, strong multilingual

Always match EMBEDDING_DIMENSIONS to your model!

Document Chunking

Keep chunks < 1000 tokens for optimal embedding quality
Preserve context: don’t split mid-sentence
Use overlapping chunks for continuity

Similarity Thresholds

SIMILARITY_THRESHOLD=0.5: Good default (cosine similarity)
RERANKER_SCORE_THRESHOLD=0.3: Filters low-quality reranked results
Adjust based on precision/recall needs

Reranking Strategy

Always use reranking for user-facing queries (better precision)
Disable reranking for high-throughput batch processing
Increase vectorLimit if top results are poor

Troubleshooting

No results returned

Possible causes:

Similarity threshold too high (lower SIMILARITY_THRESHOLD)
No documents indexed (check getStats())
Query too specific (broaden search terms)

Solution:

# Check document count
SELECT COUNT(*) FROM documents;

# Lower threshold
SIMILARITY_THRESHOLD=0.3

Poor search quality

Possible causes:

Weak embedding model
Reranking disabled
Insufficient vector search candidates

Solution:

# Use better embedding model
EMBEDDING_PROVIDER=voyage
TEXT_EMBEDDING_MODEL=voyage-3

# Enable reranking
USE_RERANKING=true

# Increase candidates for reranking
VECTOR_SEARCH_LIMIT=50
RERANK_FINAL_LIMIT=10

Slow queries

Possible causes:

Missing vector index
Index needs tuning
Cold cache

Solution:

-- Rebuild index with more lists
DROP INDEX documents_embedding_idx;
CREATE INDEX documents_embedding_idx
  ON documents
  USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 200);

-- Analyze table
ANALYZE documents;

Chat Mode

Query knowledge base via chat

Deep Research

Use knowledge base in research cycles

File Upload

Upload documents to knowledge base

pgvector Docs

PostgreSQL vector extension

Get Started

Core Concepts

Agents

Configuration

Features

Deployment

Advanced

​Overview

Use Cases

​Architecture

​Components

​Configuration

​Environment Variables

​Embedding Providers

​Database Setup

​Document Processing

​Supported File Types

​Document Processor

​Vector Search Pipeline

​Two-Stage Retrieval

​Vector Search (Stage 1)

​Reranking (Stage 2)

​Integration with Literature Agent

​Adding Documents

​Via File System

​Via API

​Search API

​Direct Search

​Get Statistics

​Performance Tuning

​Vector Index Configuration

​Caching

​Batch Processing

​Best Practices

​Troubleshooting

​Related Resources

Chat Mode

Deep Research

File Upload

pgvector Docs

Build docs developers (and LLMs) love

Overview

Architecture

Components

Configuration

Environment Variables

Embedding Providers

Database Setup

Document Processing

Supported File Types

Document Processor

Vector Search Pipeline

Two-Stage Retrieval

Vector Search (Stage 1)

Reranking (Stage 2)

Integration with Literature Agent

Adding Documents

Via File System

Via API

Search API

Direct Search

Get Statistics

Performance Tuning

Vector Index Configuration

Caching

Batch Processing

Best Practices

Troubleshooting

Related Resources