This commit implements a comprehensive solution for separating embedding
and chat model configurations, enabling flexible provider combinations
and graceful handling of embedding failures.
## Problem Statement
Previously, the TradingAgents memory system used the same backend_url for
both chat models and embeddings. This caused critical failures when:
- Using OpenRouter for chat (doesn't support OpenAI embedding endpoints)
- Using Anthropic/Google for chat (don't provide embeddings)
- The embedding endpoint returned HTML error pages instead of JSON
- Users wanted to mix providers (e.g., OpenRouter chat + OpenAI embeddings)
Error example:
AttributeError: 'str' object has no attribute 'data'
# Caused by: OpenRouter returned HTML page instead of embedding JSON
## Solution
Implemented three key features:
1. **Separate Embedding Client Configuration**
- New config parameters independent of chat LLM settings
- embedding_provider: "openai", "ollama", or "none"
- embedding_backend_url: Separate API endpoint
- embedding_model: Specific model to use
- enable_memory: Boolean flag to enable/disable memory
2. **Multiple Provider Support**
- OpenAI: Production-grade embeddings (recommended)
- Ollama: Local embeddings for offline/development
- None: Disable memory system entirely
3. **Graceful Fallback**
- System continues when embeddings fail
- Comprehensive error logging
- Memory operations return empty results instead of crashing
- Agents function without historical context when memory disabled
## Changes
### Core Framework
- tradingagents/default_config.py: Added 4 new embedding config params
- tradingagents/agents/utils/memory.py: Complete refactor with error handling
- tradingagents/graph/trading_graph.py: Separated embedding initialization
### CLI/User Interface
- cli/utils.py: Added select_embedding_provider() function
- cli/main.py: Added Step 7 for embedding provider selection
### Documentation (New Files)
- docs/EMBEDDING_CONFIGURATION.md: Complete usage guide (381 lines)
- docs/EMBEDDING_MIGRATION.md: Implementation details (374 lines)
- CHANGELOG_EMBEDDING.md: Release notes (225 lines)
- FEATURE_EMBEDDING_README.md: Branch overview (418 lines)
### Testing & Verification
- tests/test_embedding_config.py: Comprehensive test suite
- verify_config.py: Simple config verification script
## Example Usage
```python
# OpenRouter for chat, OpenAI for embeddings
config = {
"llm_provider": "openrouter",
"backend_url": "https://openrouter.ai/api/v1",
"deep_think_llm": "deepseek/deepseek-chat-v3-0324:free",
"embedding_provider": "openai",
"embedding_backend_url": "https://api.openai.com/v1",
"embedding_model": "text-embedding-3-small",
"enable_memory": True,
}
```
## Backward Compatibility
✅ 100% Backward Compatible - No breaking changes!
Existing configurations work without modification. Smart defaults
applied when embedding settings are omitted.
## Testing
- All core files pass diagnostics with no errors
- Configuration verification script passes all checks
- Supports scenarios: OpenRouter+OpenAI, All Ollama, Disabled Memory
- Graceful fallback tested for invalid URLs and missing API keys
## Benefits
- Enables using OpenRouter/other providers for chat
- Reduces costs (can use local embeddings or disable memory)
- Improves reliability (graceful degradation on failures)
- Maintains full backward compatibility
- Comprehensive documentation and examples
Fixes: OpenRouter compatibility issues
Closes: Embedding/chat provider coupling
Implements: Graceful fallback for memory operations
- Replace FinnHub with Alpha Vantage API in README documentation
- Implement comprehensive Alpha Vantage modules:
- Stock data (daily OHLCV with date filtering)
- Technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands, ATR)
- Fundamental data (overview, balance sheet, cashflow, income statement)
- News and sentiment data with insider transactions
- Update news analyst tools to use ticker-based news search
- Integrate Alpha Vantage vendor methods into interface routing
- Maintain backward compatibility with existing vendor system
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Added support for running CLI and Ollama server via Docker
- Introduced tests for local embeddings model and standalone Docker setup
- Enabled conditional Ollama server launch via LLM_PROVIDER
Surapong Kanoktipsatharporn
luohy15
mirza-samad-ahmed-baig
Max Wong
Yijia Xiao
Geeta Chauhan
Edward Sun
Atharv Sabde
maxer137
Yijia-Xiao