Merged in feature/context-caching (pull request #909)

Feature/context caching * Initial commit - context caching for DYNAMIC_PRIMARY * implement context caching for all relevant prompts * Remove option to not context cache * IndentationError fixed * Merge branch 'DEV' into feature/context-caching * Merge and format * Move documentation * Merged DEV into feature/context-caching * Update unit tests * Merged DEV into feature/context-caching * Update signatures * Fix test coverage gap Approved-by: Praneel Panchigar Approved-by: Karan Desai
2026-03-13 18:49:40 +00:00
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+# Context Caching Implementation - Complete
+
+## Summary
+
+Successfully implemented context caching for **6 high-value prompts** across all 3 client pipelines. This enables Anthropic's prompt caching at the exhibit/context level, where the same context is cached and reused across multiple field extractions, reducing token costs by **~84%** for repeated context processing.
+
+### Prompts with Context Caching
+1. **DYNAMIC_PRIMARY** (pilot) - Primary term field extraction
+2. **EXHIBIT_LEVEL** - Exhibit-level metadata extraction
+3. **DYNAMIC_ASSIGNMENT** - Dynamic term assignment to exhibit rows
+4. **REIMB_DATES_ASSIGNMENT** - Reimbursement date assignment (specialized)
+5. **LESSER_OF_DISTRIBUTION** - Lesser-of logic distribution across codes
+6. **LESSER_OF_CHECK** - Lesser-of presence validation
+
+### Pipelines Updated
+- ✅ **bcbs_promise** - All 5 applicable functions updated
+- ✅ **clover** - All 5 applicable functions updated  
+- ✅ **saas** - All 5 applicable functions updated
+
+## What Changed
+
+### 1. Extended LLM API (`llm_utils.py`)
+
+Added `context_for_caching` parameter throughout the call chain:
+- `invoke_claude()` - New optional parameter
+- `_build_claude_3_request_body()` - Structures multi-block messages with cache control
+- `get_cache_key()` - Includes context in cache key generation
+- `local_claude_3_and_up()` - Passes parameter through
+- `ec2_claude_3_and_up()` - Passes parameter through
+
+**Key Innovation**: Messages now support multiple content blocks where specific blocks can be marked for caching:
+
+```python
+"messages": [{
+  "role": "user",
+  "content": [
+    {
+      "type": "text",
+      "text": "Large exhibit text (40k tokens)",
+      "cache_control": {"type": "ephemeral"}  # CACHED
+    },
+    {
+      "type": "text",
+      "text": "Field-specific question (200 tokens)"  # NOT CACHED
+    }
+  ]
+}]
+```
+
+### 2. Updated Existing Prompt Templates (`prompt_templates.py`)
+
+Updated 6 existing functions to always split prompts into cacheable and fresh components:
+
+1. **DYNAMIC_PRIMARY()** - Caches exhibit text, field question stays fresh
+2. **EXHIBIT_LEVEL()** - Caches exhibit text, field questions stay fresh
+3. **DYNAMIC_ASSIGNMENT()** - Caches exhibit simplified text, term questions stay fresh
+4. **REIMB_DATES_ASSIGNMENT()** - Specialized for REIMB_DATES assignment
+5. **LESSER_OF_DISTRIBUTION()** - Caches exhibit text and cross-exhibit context
+6. **LESSER_OF_CHECK()** - Caches exhibit title context
+
+Each returns `(context_text, prompt, parser)` instead of `(prompt, parser)`.
+
+These functions now always return `(context_text, prompt, parser)` for context caching.
+
+### 3. Updated All Client Prompt Calls
+
+Updated functions across all 3 pipelines:
+
+**bcbs_promise/prompts/prompt_calls.py**:
+- `prompt_exhibit_level()` 
+- `prompt_dynamic_primary()`
+- `prompt_dynamic_assignment()`
+- `prompt_lesser_of_distribution()`
+- `prompt_lesser_of_check()`
+
+**clover/prompts/prompt_calls.py**:
+- `prompt_exhibit_level()`
+- `prompt_dynamic_primary()`
+- `prompt_dynamic_assignment()`
+- `prompt_lesser_of_distribution()`
+- `prompt_lesser_of_check()`
+
+**saas/prompts/prompt_calls.py**:
+- `prompt_exhibit_level()`
+- `prompt_dynamic_primary()`
+- `prompt_dynamic_assignment()`
+- `prompt_lesser_of_distribution()`
+- `prompt_lesser_of_check()`
+
+Each function now:
+1. Uses the original template function (always split for caching)
+2. Receives `(context_text, prompt, parser)` tuple
+3. Passes `context_for_caching=context_text` to `invoke_claude()`
+4. Logs context length for monitoring
+
+## Cost Impact Analysis
+
+### Current Structure (Before)
+1. **System message** (cached): Field extraction instruction (~2k tokens)
+2. **User message** (NOT cached): Combined exhibit + field question (~40k tokens)
+
+For 20 fields on same exhibit:
+- Instruction: 2k × 1 creation = cached once ✓
+- Content: 40k × 20 calls = 800k tokens at $0.003/1k = **$2.40**
+
+### New Structure (After)
+1. **System message** (cached): Field extraction instruction (~2k tokens)
+2. **User message block 1** (cached): Exhibit context (~40k tokens)
+3. **User message block 2** (not cached): Field question (~200 tokens)
+
+For 20 fields on same exhibit:
+- Instruction: 2k × 1 creation = cached once ✓
+- Context: 40k × 1 creation at $0.00375/1k = $0.15
+- Context: 40k × 19 reads at $0.0003/1k = $0.228
+- Field questions: 20 × 200 tokens at $0.003/1k = $0.012
+- **Total: $0.39 (84% cost reduction)**
+
+### Break-Even Analysis
+- **1st field**: Pay 25% premium for cache creation
+- **2nd field**: Start saving with 90% cheaper cache reads
+- **3+ fields**: Massive savings accumulate
+
+## How It Works
+
+### Caching Layers (Claude API)
+```
+Layer 1: System Instruction (cached) ← Already implemented
+    ↓
+Layer 2: Exhibit Context (cached) ← NEW - This implementation
+    ↓
+Layer 3: Field Question (fresh) ← Changes per call
+```
+
+### Flow Example
+```python
+# Processing LOB field for exhibit
+context_text, prompt, parser = DYNAMIC_PRIMARY(
+    exhibit_text="[40k token exhibit]",
+    field_name="LOB",
+  field_prompt="Line of Business definition",
+)
+
+llm_utils.invoke_claude(
+    prompt=prompt,  # Just the field question
+    context_for_caching=context_text,  # Exhibit text (cached)
+    instruction=DYNAMIC_PRIMARY_INSTRUCTION(),  # Rules (already cached)
+    cache=True
+)
+# First call: Cache creation for exhibit
+# Cost: (2k instruction + 40k context) × cache multiplier + 200 tokens fresh
+
+# Processing PROGRAM field for SAME exhibit
+context_text, prompt, parser = DYNAMIC_PRIMARY(
+    exhibit_text="[SAME 40k token exhibit]",  # Same content
+    field_name="PROGRAM", 
+  field_prompt="Program definition",
+)
+
+llm_utils.invoke_claude(
+    prompt=prompt,  # Different field question
+    context_for_caching=context_text,  # SAME exhibit (cache hit!)
+    instruction=DYNAMIC_PRIMARY_INSTRUCTION(),
+    cache=True
+)
+# Second call: Cache read for exhibit
+# Cost: (2k + 40k) × cache read rate (90% cheaper) + 200 tokens fresh
+```
+
+## Testing
+
+Created comprehensive test suite in `src/tests/test_context_caching.py`:
+
+✅ `test_dynamic_primary_returns_three_values()` - Validates always-split signature  
+✅ `test_dynamic_primary_original_still_works()` - Backward compatibility  
+✅ `test_build_request_body_with_context_caching()` - Message structure verification  
+✅ `test_build_request_body_without_context_caching()` - Fallback behavior  
+✅ `test_cache_key_includes_context()` - Cache key uniqueness  
+
+## Monitoring & Validation
+
+To verify the implementation is working:
+
+1. **Check usage logs** for cache metrics:
+   ```python
+   # In usage_tracking.py logs, look for:
+   cache_creation_tokens: 40000  # First call
+   cache_read_tokens: 40000      # Subsequent calls
+   ```
+
+2. **Monitor cost per file** in usage reports:
+   - Should see dramatic cost reduction for files with many dynamic fields
+   - Exhibits with 10+ fields should show 80%+ savings on exhibit processing
+
+3. **Log analysis**:
+   ```
+   DEBUG: Context length for caching: 42567 chars
+   ```
+   This confirms context is being passed to caching layer.
+
+## Implementation Status
+
+### ✅ Completed
+All high-value prompts have been migrated to context caching across all 3 client pipelines:
+
+1. **DYNAMIC_PRIMARY** ✅ - Primary term field extraction (pilot implementation)
+2. **EXHIBIT_LEVEL** ✅ - Exhibit-level metadata extraction
+3. **DYNAMIC_ASSIGNMENT** ✅ - Dynamic term assignment to exhibit rows
+4. **REIMB_DATES_ASSIGNMENT** ✅ - Reimbursement date assignment (specialized)
+5. **LESSER_OF_DISTRIBUTION** ✅ - Lesser-of logic distribution across codes
+6. **LESSER_OF_CHECK** ✅ - Lesser-of presence validation
+
+**Cost Savings**: Estimated 80-85% reduction in token costs for repeated exhibit/context processing across these 6 prompts.
+
+### Future Considerations
+
+**Lower Priority Candidates** (evaluate after monitoring current implementation):
+- **METHODOLOGY_BREAKOUT** - Could cache reimbursement terms for multiple breakout operations
+- **Other exhibit-level prompts** - If processing changes to single-field-at-a-time pattern
+
+**Monitoring Required**:
+- Track cache hit rates and actual cost savings in production
+- Validate that 5-minute cache TTL aligns with typical processing patterns
+- Identify any additional prompts with repeated context usage patterns
+
+### Implementation Pattern (For Future Extensions)
+For any new prompt to extend:
+1. Update `[PROMPT_NAME]()` to return `(context, prompt, parser)`
+2. Update corresponding `prompt_[name]()` function to use caching version
+3. Pass context via `context_for_caching` parameter
+4. Monitor cache metrics to validate savings
+
+## Backward Compatibility
+
+✅ Original `DYNAMIC_PRIMARY()` function remains unchanged  
+✅ Other templates continue to work without modification  
+✅ `context_for_caching` parameter is optional (defaults to None)  
+✅ When None, behavior is identical to previous implementation  
+✅ All tests should pass without modification  
+
+## Files Modified
+
+**Core Infrastructure:**
+- [src/utils/llm_utils.py](src/utils/llm_utils.py) - Extended API with `context_for_caching` parameter
+- [src/prompts/prompt_templates.py](src/prompts/prompt_templates.py) - Added 6 context-caching template variants
+
+**Pipeline Updates (All 3 Clients):**
+- [src/pipelines/clients/bcbs_promise/prompts/prompt_calls.py](src/pipelines/clients/bcbs_promise/prompts/prompt_calls.py) - Updated 5 functions
+- [src/pipelines/clients/clover/prompts/prompt_calls.py](src/pipelines/clients/clover/prompts/prompt_calls.py) - Updated 5 functions
+- [src/pipelines/saas/prompts/prompt_calls.py](src/pipelines/saas/prompts/prompt_calls.py) - Updated 5 functions
+
+**Testing & Documentation:**
+- [src/tests/test_context_caching.py](src/tests/test_context_caching.py) - Comprehensive test suite
+- [CONTEXT_CACHING_IMPLEMENTATION.md](CONTEXT_CACHING_IMPLEMENTATION.md) - This documentation
+
+## Technical Notes
+
+- Anthropic prompt caching requires minimum 1024 tokens for cache block
+- Cache TTL is 5 minutes for `ephemeral` type
+- Only works with Claude 3.5+ Sonnet v2 models (checked via `_supports_prompt_cache()`)
+- Cache keys include both instruction and context to ensure uniqueness
+- Multiple content blocks in user messages is supported by Bedrock Messages API