Configuration Reference

Before invoking the pgedge-rag-server executable, you need to create a .YAML file that contains the deployment details for the RAG server. These details include:

• connection information.
• AI provider properties.
• API key location.
• embedding information for your Postgres database.

The default name of the file is pgedge-rag-server.yaml.

When you invoke pgedge-rag-server you can optionally include the -config option to specify the complete path to a custom location for the configuration file. If you do not specify a location on the command line, the server searches for configuration files in:

1. /etc/pgedge/pgedge-rag-server.yaml
2. the directory that contains the pgedge-rag-server binary.

Configuration File Structure

The configuration file includes the following top-level sections:

• server - HTTP/HTTPS server settings
• defaults - Default values for pipelines (LLM providers, token budget, etc.)
• pipelines - RAG pipeline definitions

You can optionally set the API key value in the configuration file, on the command line, or in an environment variable.

Specifying Properties in the Server Section

Use the properties shown below to specify connection properties for your RAG server:

server:
  listen_address: "0.0.0.0"
  port: 8080
  tls:
    enabled: true
    cert_file: "/path/to/cert.pem"
    key_file: "/path/to/key.pem"

Specifying Properties in the Defaults Section

The defaults section allows you to set default values for LLM providers, API keys, and other settings that can be overridden per-pipeline. This is useful when most pipelines share the same configuration.

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

The token budget prevents sending too much context to the LLM; this ensures predictable LLM costs while maximizing relevant context. The orchestrator:

1. Estimates tokens for each document (approximately 4 characters per token).
2. Includes documents until the budget is reached.
3. Truncates the final document at a sentence boundary if it exceeds the remaining budget.

When you set default values, your individual pipelines definitions can omit the corresponding fields and will inherit the default values. A Pipeline can also override specific fields while inheriting others.

Specifying Properties in the Pipeline Section

Each pipeline defines a RAG search configuration with its own database, embedding provider, and completion provider. Use the properties in the sections that follow to provide information in the pipelines section:

pipelines:
  - name: "my-docs"
    description: "Search my documentation"
    database:
      host: "localhost"
      port: 5432
      database: "mydb"
      username: "postgres"
      password: ""
      ssl_mode: "prefer"
    tables:
      - table: "documents"
        text_column: "content"
        vector_column: "embedding"
    embedding_llm:
      provider: "openai"
      model: "text-embedding-3-small"
    rag_llm:
      provider: "anthropic"
      model: "claude-sonnet-4-20250514"
    token_budget: 4000
    top_n: 10

Pipeline Properties

Database Properties

Table Properties

Each table entry specifies a table with text content and its corresponding vector embeddings. Each table used in a pipeline must have:

• A text column containing the document content
• A vector column containing the embedding (using pgvector)

The filter field allows you to specify a filter that will be applied to all queries for this table. It can be specified in two formats:

Raw SQL (for complex queries like subqueries):

tables:
  - table: "documents"
    text_column: "content"
    vector_column: "embedding"
    filter: "source_id IN (SELECT id FROM sources WHERE product='pgEdge')"

Structured filter (using conditions):

tables:
  - table: "documents"
    text_column: "content"
    vector_column: "embedding"
    filter:
      conditions:
        - column: "product"
          operator: "="
          value: "pgAdmin"
        - column: "status"
          operator: "="
          value: "published"
      logic: "AND"

Raw SQL filters are useful when you need complex expressions like subqueries, JOINs, or functions that cannot be expressed with the structured format. Since config files are controlled by administrators, raw SQL is safe to use here.

Filters can also be specified per-request via the API's filter parameter. API filters must use the structured format (for security) and will be combined with any configured filter using AND.

Supported operators (for structured filters): =, !=, <, >, <=, >=, LIKE, ILIKE, IN, NOT IN, IS NULL, IS NOT NULL

LLM Provider Properties

The embedding_llm and rag_llm properties use the same configuration structure:

The RAG server supports the following providers:

Anthropic does not provide embedding models; use OpenAI or Voyage for embeddings with Anthropic for completions.