Lola Send uses Markdown-based retrieval-augmented generation (RAG) to ground agent responses in bank-approved knowledge bases. Before generating a response, agents retrieve relevant passages from the knowledge base, reducing hallucination and ensuring answers align with approved documentation.
How it works
Lola Send’s RAG implementation loads Markdown documents from a designated directory and splits them for granular retrieval. When an agent processes a message, it retrieves the most relevant passages from the knowledge base and includes them as context for the AI model — alongside the conversation history and system prompt.
This dual approach — tool functions for real-time data (quotes, recipients, operations) and RAG for static knowledge (country lists, fee schedules, payer information) — ensures agents reference verified content for both dynamic and reference data.
Implementation
RAG is implemented as a singleton MarkdownRAG instance shared across all agents:
class MarkdownRAGSingleton:
_instance = None
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self, *args, **kwargs):
if self._initialized:
return
self.mdm = MarkdownRAG(*args, **kwargs)
self.mdm.load()
self._initialized = True
ast.set_rag_retrieval(rag).
Document loading
Documents are loaded from docs/{PROJECT_NAME}.md, where PROJECT_NAME is set via environment variable. The split_table_rows=True option splits table-heavy documents by row, enabling precise retrieval of individual entries (e.g., a specific country’s fee schedule or a single payer’s details).
def get_rag():
project_name = os.getenv("PROJECT_NAME")
singleton = MarkdownRAGSingleton(
"docs",
file_path=f"{project_name}.md",
split_table_rows=True
)
return singleton.mdm
Per-agent activation
Each agent opts into RAG retrieval explicitly:
ast.set_rag_retrieval(rag)
Knowledge base configuration
| Setting | Source | Purpose |
|---|
PROJECT_NAME | Environment variable | Determines which knowledge base file is loaded |
| Document path | docs/{PROJECT_NAME}.md | Markdown file containing the knowledge base |
split_table_rows | True | Splits tables by row for granular retrieval |
PROJECT_NAME environment variable. This enables white-label deployments where each bank has its own approved content.
RAG grounds agent responses in verified content. Combined with tool functions for real-time data, this dual approach minimizes hallucination risk — agents reference approved documentation for static knowledge and live backend services for transactional data.
Security considerations
- Bank-maintained content: The bank provides and maintains the knowledge base documents. Lola Send retrieves from them but does not modify them.
- Read-only access: The RAG system loads documents at startup and serves them as retrieval context. Agents cannot write to or alter the knowledge base at runtime.
- Content isolation: Each deployment’s knowledge base is scoped by
PROJECT_NAME. One deployment’s content is not accessible to another.
- No external retrieval: RAG retrieves from local Markdown files only — it does not fetch content from the internet or external sources.
Configuration and control
| Control | Description |
|---|
| Knowledge base content | The bank writes and maintains the Markdown documents |
PROJECT_NAME | Selects the knowledge base per deployment or brand |
split_table_rows | Controls retrieval granularity for table-heavy documents |
| Per-agent activation | Each agent can be configured with or without RAG retrieval |
