Sample Configurations

The examples on this page demonstrate using configuration options in a file to specify behavior preferences for the RAG server.

Minimal Configuration

pipelines:
  - name: "docs"
    database:
      host: "localhost"
      database: "mydb"
    tables:
      - table: "documents"
        text_column: "content"
        vector_column: "embedding"
    embedding_llm:
      provider: "openai"
      model: "text-embedding-3-small"
    rag_llm:
      provider: "openai"
      model: "gpt-4o-mini"

Production Configuration with TLS

server:
  listen_address: "0.0.0.0"
  port: 443
  tls:
    enabled: true
    cert_file: "/etc/ssl/certs/server.pem"
    key_file: "/etc/ssl/private/server.key"

pipelines:
  - name: "knowledge-base"
    description: "Corporate knowledge base search"
    database:
      host: "db.example.com"
      port: 5432
      database: "knowledge"
      username: "rag_user"
      ssl_mode: "require"
    tables:
      - table: "articles"
        text_column: "body"
        vector_column: "embedding"
      - table: "faqs"
        text_column: "answer"
        vector_column: "answer_embedding"
    embedding_llm:
      provider: "voyage"
      model: "voyage-3"
    rag_llm:
      provider: "anthropic"
      model: "claude-sonnet-4-20250514"
    token_budget: 8000
    top_n: 15

Local Development with Ollama

pipelines:
  - name: "local-docs"
    description: "Local document search"
    database:
      host: "localhost"
      database: "devdb"
    tables:
      - table: "docs"
        text_column: "content"
        vector_column: "embedding"
    embedding_llm:
      provider: "ollama"
      model: "nomic-embed-text"
    rag_llm:
      provider: "ollama"
      model: "llama3.2"
    token_budget: 2000
    top_n: 5

Using Defaults for Multiple Pipelines

This configuration uses defaults to avoid repeating LLM settings across multiple pipelines. Individual pipelines can override specific settings:

defaults:
  token_budget: 4000
  top_n: 10
  embedding_llm:
    provider: "openai"
    model: "text-embedding-3-small"
  rag_llm:
    provider: "anthropic"
    model: "claude-sonnet-4-20250514"

pipelines:
  # This pipeline uses all defaults
  - name: "docs"
    description: "Documentation search"
    database:
      host: "localhost"
      database: "docs_db"
    tables:
      - table: "documents"
        text_column: "content"
        vector_column: "embedding"

  # This pipeline overrides the completion model
  - name: "support"
    description: "Support knowledge base"
    database:
      host: "localhost"
      database: "support_db"
    tables:
      - table: "tickets"
        text_column: "resolution"
        vector_column: "embedding"
    rag_llm:
      provider: "anthropic"
      model: "claude-haiku-3-5-20241022"
    token_budget: 2000

  # This pipeline uses a different embedding provider
  - name: "research"
    description: "Research papers"
    database:
      host: "localhost"
      database: "research_db"
    tables:
      - table: "papers"
        text_column: "abstract"
        vector_column: "embedding"
    embedding_llm:
      provider: "voyage"
      model: "voyage-3"

Voyage Embeddings with Anthropic Completion

This configuration uses Voyage for high-quality embeddings and Anthropic Claude for completions, with API keys stored in external files:

api_keys:
  voyage: "/etc/pgedge/keys/voyage.key"
  anthropic: "/etc/pgedge/keys/anthropic.key"

pipelines:
  - name: "enterprise-search"
    description: "Enterprise document search with Voyage and Claude"
    database:
      host: "db.internal"
      port: 5432
      database: "documents"
      username: "rag_service"
      ssl_mode: "require"
    tables:
      - table: "knowledge_base"
        text_column: "content"
        vector_column: "embedding"
    embedding_llm:
      provider: "voyage"
      model: "voyage-3"
    rag_llm:
      provider: "anthropic"
      model: "claude-sonnet-4-20250514"
    token_budget: 8000
    top_n: 10