feat: Separate embedding configuration from chat model configuration

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