The repository that contains the code for the backend of our Tyndale AI system

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Danny 4c084d7668 feat: add AskSage LLM provider integration Implement AskSageAdapter using the official asksageclient SDK to support AskSage as an LLM provider option. This enables users to leverage AskSage's API with configurable email, API key, and model settings. - Add AskSageAdapter class with async support via thread pool - Update Settings to include asksage_email, asksage_api_key, asksage_model - Extend llm_mode literal to include "asksage" option - Update dependency injection to instantiate AskSageAdapter when configured - Remove completed OPENAI_INTEGRATION_PLAN.md - Update requirements.txt with full dependency list including asksageclient		2026-01-16 12:16:34 -06:00
.claude/agents	feat: add OpenAI integration with dependency injection support	2026-01-13 15:17:44 -06:00
app	feat: add AskSage LLM provider integration	2026-01-16 12:16:34 -06:00
.env.example	feat: add AskSage LLM provider integration	2026-01-16 12:16:34 -06:00
Dockerfile	feat: add OpenAI integration with dependency injection support	2026-01-13 15:17:44 -06:00
README.md	feat: add FastAPI skeleton for LLM chat service	2026-01-07 19:32:57 -06:00
requirements.txt	feat: add AskSage LLM provider integration	2026-01-16 12:16:34 -06:00

README.md

Tyndale AI Service

LLM Chat Service for algorithmic trading support - codebase Q&A, P&L summarization, and strategy enhancement suggestions.

Quick Start

Local Development

# Install dependencies
pip install -r requirements.txt

# Run the server
uvicorn app.main:app --reload --port 8080

Docker

# Build
docker build -t tyndale-ai-service .

# Run
docker run -p 8080:8080 -e LLM_MODE=local tyndale-ai-service

API Endpoints

Health Check

curl http://localhost:8080/health

Response:

{"status": "ok"}

Chat

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello, how are you?"}'

Response:

{
  "conversation_id": "uuid-generated-if-not-provided",
  "response": "...",
  "mode": "local",
  "sources": []
}

With conversation ID:

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Follow up question", "conversation_id": "my-conversation-123"}'

Environment Variables

Variable	Description	Default
`LLM_MODE`	`local` or `remote`	`local`
`LLM_REMOTE_URL`	Remote LLM endpoint URL	(empty)
`LLM_REMOTE_TOKEN`	Bearer token for remote LLM	(empty)

Remote Mode Setup

export LLM_MODE=remote
export LLM_REMOTE_URL=https://your-llm-service.com/generate
export LLM_REMOTE_TOKEN=your-api-token

uvicorn app.main:app --reload --port 8080

The remote adapter expects the LLM service to accept:

{"conversation_id": "...", "message": "..."}

And return:

{"response": "..."}

Project Structure

tyndale-ai-service/
├── app/
│   ├── __init__.py
│   ├── main.py          # FastAPI app + routes
│   ├── schemas.py       # Pydantic models
│   ├── config.py        # Environment config
│   └── llm/
│       ├── __init__.py
│       └── adapter.py   # LLM adapter interface + implementations
├── requirements.txt
├── Dockerfile
├── .env.example
└── README.md

Features

Dual mode operation: Local stub or remote LLM
Conversation tracking: UUID generation for new conversations
Security: 10,000 character message limit, no content logging
Cloud Run ready: Port 8080, stateless design
Async: Full async/await support with httpx