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feat: Add Kiro AWS Event Stream parsing, converters, and TODO roadmap 

- Add aisbf/kiro_parsers.py: AWS Event Stream parser for Kiro API responses
- Update kiro_converters_openai.py: Add build_kiro_payload_from_dict function
- Update kiro_converters.py: Minor fixes
- Update kiro_auth.py: Add AWS SSO OIDC authentication support
- Update handlers.py: Enhance streaming and error handling
- Update main.py: Add proxy headers middleware and configuration
- Update setup.py: Add version bump
- Add TODO.md: Comprehensive roadmap for caching and performance improvements

Features:
- Kiro AWS Event Stream parsing for non-streaming responses
- OpenAI-to-Kiro payload conversion
- AWS SSO OIDC authentication for Kiro
- Proxy headers middleware for reverse proxy support
- TODO roadmap with prioritized items for future development
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# AISBF Performance & Caching Improvements TODO
**Date**: 2026-03-23
**Context**: Analysis of prompt caching alternatives for AISBF
**Conclusion**: Prompt caching has low ROI for AISBF's architecture. Focus on these high-value alternatives instead.
---
## 🔥 HIGH PRIORITY (Implement Soon)
### 1. Integrate Existing Database Module
**Estimated Effort**: 4-6 hours
**Expected Benefit**: Persistent rate limiting, analytics foundation
**ROI**: ⭐⭐⭐⭐⭐ Very High (Quick Win!)
**Status**: ✅ Already implemented in [`aisbf/database.py`](aisbf/database.py:1), just needs integration!
#### Background
AISBF has a fully functional SQLite database at `~/.aisbf/aisbf.db` that tracks:
- **Context dimensions** per model (context_size, condense_context, effective_context)
- **Token usage** for rate limiting (TPM/TPH/TPD tracking)
Currently, this database exists but is **NOT being used**. All tracking happens in-memory and is lost on restart.
#### Tasks:
- [ ] Initialize database on startup
- [ ] Add `initialize_database()` call in `main.py` startup
- [ ] Test database creation and WAL mode
- [ ] Add error handling for database initialization
- [ ] Integrate token usage tracking
- [ ] Modify `BaseProviderHandler._record_token_usage()` in `aisbf/providers.py:300`
- [ ] Add database call: `get_database().record_token_usage(provider_id, model, tokens)`
- [ ] Keep in-memory tracking for immediate rate limit checks
- [ ] Use database for persistent tracking across restarts
- [ ] Integrate context dimension tracking
- [ ] Add database call in `ContextManager` to record context config
- [ ] Add database call to update effective_context after requests
- [ ] Use for analytics and optimization recommendations
- [ ] Add database cleanup
- [ ] Schedule periodic cleanup of old token_usage records (>7 days)
- [ ] Add cleanup on startup
- [ ] Add manual cleanup endpoint in dashboard
- [ ] Dashboard integration (optional, can be done later)
- [ ] Add database statistics to settings page
- [ ] Show token usage history
- [ ] Show context efficiency metrics
**Files to modify**:
- `main.py` (add initialize_database() call)
- `aisbf/providers.py` (BaseProviderHandler._record_token_usage)
- `aisbf/context.py` (ContextManager)
- `aisbf/handlers.py` (optional: add context tracking)
**Benefits**:
- ✅ Persistent rate limiting across restarts
- ✅ Foundation for analytics dashboard (item #6)
- ✅ Historical token usage tracking
- ✅ Better cost visibility
- ✅ No new dependencies needed (SQLite is built-in)
**Why First?**:
- Quick win (4-6 hours vs days for other items)
- Enables better tracking for all other improvements
- Foundation for analytics and optimization
- Already implemented, just needs wiring
---
### 2. Provider-Native Caching Integration
**Estimated Effort**: 2-3 days
**Expected Benefit**: 50-70% cost reduction for supported providers
**ROI**: ⭐⭐⭐⭐⭐ Very High
**Priority**: Second (after database integration)
#### Tasks:
- [ ] Add Anthropic `cache_control` support
- [ ] Modify `AnthropicProviderHandler.handle_request()` in `aisbf/providers.py:1203`
- [ ] Add `cache_control` parameter to message formatting
- [ ] Mark system prompts and conversation prefixes as cacheable
- [ ] Test with long system prompts (>1000 tokens)
- [ ] Update documentation with cache_control examples
- [ ] Add Google Context Caching API support
- [ ] Modify `GoogleProviderHandler.handle_request()` in `aisbf/providers.py:450`
- [ ] Implement context caching API calls
- [ ] Add cache TTL configuration
- [ ] Test with Gemini 1.5/2.0 models
- [ ] Update documentation with context caching examples
- [ ] Add configuration options
- [ ] Add `enable_native_caching` to provider config
- [ ] Add `cache_ttl` configuration
- [ ] Add `min_cacheable_tokens` threshold
- [ ] Update `config/providers.json` schema
- [ ] Update dashboard UI for cache settings
**Files to modify**:
- `aisbf/providers.py` (AnthropicProviderHandler, GoogleProviderHandler)
- `aisbf/config.py` (ProviderConfig model)
- `config/providers.json` (add cache config)
- `templates/dashboard/providers.html` (UI for cache settings)
- `DOCUMENTATION.md` (add native caching guide)
---
### 3. Response Caching (Semantic Deduplication)
**Estimated Effort**: 2 days
**Expected Benefit**: 20-30% cache hit rate in multi-user scenarios
**ROI**: ⭐⭐⭐⭐ High
**Priority**: Third
#### Tasks:
- [ ] Create response cache module
- [ ] Create `aisbf/response_cache.py`
- [ ] Implement `ResponseCache` class with Redis backend
- [ ] Add in-memory fallback (LRU cache)
- [ ] Implement cache key generation (hash of query + model + params)
- [ ] Add TTL support (default: 5-10 minutes)
- [ ] Integrate with request handlers
- [ ] Add cache check in `RequestHandler.handle_chat_completion()`
- [ ] Add cache check in `RotationHandler.handle_rotation_request()`
- [ ] Add cache check in `AutoselectHandler.handle_autoselect_request()`
- [ ] Skip cache for streaming requests (or implement streaming cache replay)
- [ ] Add cache statistics tracking
- [ ] Add configuration
- [ ] Add `response_cache` section to `config/aisbf.json`
- [ ] Add `enabled`, `backend`, `ttl`, `max_size` options
- [ ] Add cache invalidation rules
- [ ] Add dashboard UI for cache statistics
- [ ] Testing
- [ ] Test cache hit/miss scenarios
- [ ] Test cache expiration
- [ ] Test multi-user scenarios
- [ ] Load testing with cache enabled
**Files to create**:
- `aisbf/response_cache.py` (new module)
**Files to modify**:
- `aisbf/handlers.py` (RequestHandler, RotationHandler, AutoselectHandler)
- `aisbf/config.py` (add ResponseCacheConfig)
- `config/aisbf.json` (add response_cache config)
- `requirements.txt` (add redis dependency)
- `templates/dashboard/settings.html` (cache statistics UI)
---
### 4. Enhanced Context Condensation
**Estimated Effort**: 3-4 days
**Expected Benefit**: 30-50% token reduction
**ROI**: ⭐⭐⭐⭐ High
**Priority**: Fourth
#### Tasks:
- [ ] Improve existing condensation methods
- [ ] Optimize `_hierarchical_condense()` in `aisbf/context.py:357`
- [ ] Optimize `_conversational_condense()` in `aisbf/context.py:428`
- [ ] Optimize `_semantic_condense()` in `aisbf/context.py:547`
- [ ] Optimize `_algorithmic_condense()` in `aisbf/context.py:678`
- [ ] Add new condensation methods
- [ ] Implement sliding window with overlap
- [ ] Implement importance-based pruning
- [ ] Implement entity-aware condensation (preserve key entities)
- [ ] Implement code-aware condensation (preserve code blocks)
- [ ] Optimize internal model usage
- [ ] Improve `_run_internal_model_condensation()` in `aisbf/context.py:224`
- [ ] Add model warm-up on startup
- [ ] Implement model pooling for concurrent requests
- [ ] Add GPU memory management
- [ ] Test with different model sizes (0.5B, 1B, 3B)
- [ ] Add condensation analytics
- [ ] Track condensation effectiveness (token reduction %)
- [ ] Track condensation latency
- [ ] Add dashboard visualization
- [ ] Log condensation decisions for debugging
- [ ] Configuration improvements
- [ ] Add per-model condensation thresholds
- [ ] Add adaptive condensation (based on context size)
- [ ] Add condensation method chaining
- [ ] Add condensation bypass for short contexts
**Files to modify**:
- `aisbf/context.py` (ContextManager improvements)
- `config/aisbf.json` (condensation config)
- `config/condensation_*.md` (update prompts)
- `templates/dashboard/settings.html` (condensation analytics)
---
## 🔶 MEDIUM PRIORITY
### 5. Smart Request Batching
**Estimated Effort**: 3-4 days
**Expected Benefit**: 15-25% latency reduction
**ROI**: ⭐⭐⭐ Medium-High
#### Tasks:
- [ ] Create request batching module
- [ ] Create `aisbf/batching.py`
- [ ] Implement `RequestBatcher` class
- [ ] Add request queue with 100ms window
- [ ] Implement batch request combining
- [ ] Implement response splitting
- [ ] Integrate with providers
- [ ] Add batching support to `BaseProviderHandler`
- [ ] Implement provider-specific batching (OpenAI, Anthropic)
- [ ] Handle batch size limits per provider
- [ ] Handle batch failures gracefully
- [ ] Configuration
- [ ] Add `batching` config to `config/aisbf.json`
- [ ] Add `enabled`, `window_ms`, `max_batch_size` options
- [ ] Add per-provider batching settings
**Files to create**:
- `aisbf/batching.py` (new module)
**Files to modify**:
- `aisbf/providers.py` (BaseProviderHandler)
- `aisbf/handlers.py` (integrate batching)
- `config/aisbf.json` (batching config)
---
### 6. Streaming Response Optimization
**Estimated Effort**: 2 days
**Expected Benefit**: Better memory usage, faster streaming
**ROI**: ⭐⭐⭐ Medium
#### Tasks:
- [ ] Optimize chunk handling
- [ ] Review `handle_streaming_chat_completion()` in `aisbf/handlers.py:338`
- [ ] Reduce memory allocations in streaming loops
- [ ] Implement chunk pooling
- [ ] Add backpressure handling
- [ ] Optimize Google streaming
- [ ] Optimize Google chunk processing in handlers
- [ ] Reduce accumulated text copying
- [ ] Implement incremental delta calculation
- [ ] Optimize Kiro streaming
- [ ] Review Kiro streaming in `_handle_streaming_request()`
- [ ] Optimize SSE parsing
- [ ] Reduce string allocations
**Files to modify**:
- `aisbf/handlers.py` (streaming optimizations)
- `aisbf/providers.py` (KiroProviderHandler streaming)
---
## 🔵 LOW PRIORITY (Future Enhancements)
### 7. Token Usage Analytics
**Estimated Effort**: 1-2 days (reduced due to database integration)
**Expected Benefit**: Better cost visibility
**ROI**: ⭐⭐⭐ Medium (improved with database foundation)
**Note**: Much easier after database integration (item #1) is complete!
#### Tasks:
- [ ] Create analytics module
- [ ] Create `aisbf/analytics.py`
- [ ] Use existing database for token usage queries
- [ ] Add request counts and latency tracking
- [ ] Track error rates and types
- [ ] Query historical data from database
- [ ] Dashboard integration
- [ ] Create analytics dashboard page
- [ ] Add charts for token usage over time
- [ ] Add cost estimation per provider
- [ ] Add model performance comparison
- [ ] Add export functionality (CSV, JSON)
- [ ] Optimization recommendations
- [ ] Identify high-cost models
- [ ] Suggest rotation weight adjustments
- [ ] Suggest condensation threshold adjustments
**Files to create**:
- `aisbf/analytics.py` (new module)
- `templates/dashboard/analytics.html` (new page)
**Files to modify**:
- `aisbf/providers.py` (add analytics hooks)
- `aisbf/handlers.py` (add analytics hooks)
- `templates/base.html` (add analytics link)
---
### 8. Adaptive Rate Limiting
**Estimated Effort**: 2 days
**Expected Benefit**: Improved reliability
**ROI**: ⭐⭐ Low-Medium
#### Tasks:
- [ ] Enhance 429 handling
- [ ] Improve `parse_429_response()` in `aisbf/providers.py:53`
- [ ] Add exponential backoff
- [ ] Add jitter to retry timing
- [ ] Track 429 patterns per provider
- [ ] Dynamic rate limit adjustment
- [ ] Learn optimal rate limits from 429 responses
- [ ] Adjust `rate_limit` dynamically
- [ ] Add rate limit headroom (stay below limits)
- [ ] Add rate limit recovery (gradually increase after cooldown)
- [ ] Configuration
- [ ] Add `adaptive_rate_limiting` to config
- [ ] Add learning rate and adjustment parameters
- [ ] Add dashboard UI for rate limit status
**Files to modify**:
- `aisbf/providers.py` (BaseProviderHandler)
- `config/aisbf.json` (adaptive rate limiting config)
- `templates/dashboard/providers.html` (rate limit status)
---
## 📊 Implementation Roadmap
### Day 1 (4-6 hours): Database Integration ⚡ QUICK WIN!
- Initialize database on startup
- Integrate token usage tracking
- Integrate context dimension tracking
- Test and verify persistence
### Week 1-2: Provider-Native Caching
- Anthropic cache_control integration
- Google Context Caching API integration
- Configuration and documentation
### Week 3: Response Caching
- ResponseCache module implementation
- Integration with handlers
- Testing and optimization
### Week 4-5: Enhanced Context Condensation
- Improve existing methods
- Add new condensation algorithms
- Optimize internal model usage
- Add analytics
### Week 6-7: Smart Request Batching
- RequestBatcher implementation
- Provider integration
- Testing and optimization
### Week 8+: Medium/Low Priority Items
- Streaming optimization
- Token usage analytics (easier with database!)
- Adaptive rate limiting
---
## 📈 Expected Results
### Cost Savings
- **Provider-native caching**: 50-70% reduction for Anthropic/Google
- **Response caching**: 20-30% reduction in multi-user scenarios
- **Enhanced condensation**: 30-50% token reduction
- **Total expected savings**: 60-80% cost reduction
### Performance Improvements
- **Response caching**: 50-100ms faster for cache hits
- **Request batching**: 15-25% latency reduction
- **Streaming optimization**: 10-20% memory reduction
- **Total expected improvement**: 20-40% latency reduction
### Reliability Improvements
- **Adaptive rate limiting**: 90%+ reduction in 429 errors
- **Better error handling**: Improved failover and recovery
- **Analytics**: Better visibility into system behavior
---
## 🚫 What NOT to Implement
### ❌ Request Prompt Caching (for endpoints without native support)
**Reason**: Low ROI for AISBF's architecture
- **Estimated savings**: $18/year
- **Infrastructure cost**: $50-100/year
- **Cache hit rate**: <5% due to rotation/autoselect
- **Complexity**: High (3-5 days development)
- **Conflicts with**: Rotation, autoselect, context condensation
- **Better alternatives**: All items above provide 10-50x better ROI
---
## 📝 Notes
- All estimates assume single developer working full-time
- ROI calculations based on typical AISBF usage patterns
- Priority may change based on specific deployment needs
- Test thoroughly before deploying to production
- Monitor metrics after each implementation to validate benefits
---
## 🔗 Related Files
- [`aisbf/database.py`](aisbf/database.py) - **Database module (already implemented!)**
- [`aisbf/providers.py`](aisbf/providers.py) - Provider handlers
- [`aisbf/handlers.py`](aisbf/handlers.py) - Request handlers
- [`aisbf/context.py`](aisbf/context.py) - Context management
- [`aisbf/config.py`](aisbf/config.py) - Configuration models
- [`config/aisbf.json`](config/aisbf.json) - Main configuration
- [`config/providers.json`](config/providers.json) - Provider configuration
- [`main.py`](main.py) - Application entry point
- [`DOCUMENTATION.md`](DOCUMENTATION.md) - API documentation
---
## 🎯 Summary
**Start with item #1 (Database Integration)** - it's a quick win that:
- Takes only 4-6 hours
- Provides immediate value (persistent rate limiting)
- Enables all future analytics work
- Requires no new dependencies
- Is already 90% implemented!
Then proceed with items #2-4 for maximum cost savings and performance improvements.
aisbf/handlers.py
View file @ 2e7a8460
...@@ -403,9 +403,133 @@ class RequestHandler: ...@@ -403,9 +403,133 @@ class RequestHandler:
# Check if this is a Google streaming response by checking provider type from config # Check if this is a Google streaming response by checking provider type from config
# This is more reliable than checking response iterability which can cause false positives # This is more reliable than checking response iterability which can cause false positives
is_google_stream = provider_config.type == 'google' is_google_stream = provider_config.type == 'google'
is_kiro_stream = provider_config.type == 'kiro'
logger.info(f"Is Google streaming response: {is_google_stream} (provider type: {provider_config.type})") logger.info(f"Is Google streaming response: {is_google_stream} (provider type: {provider_config.type})")
logger.info(f"Is Kiro streaming response: {is_kiro_stream} (provider type: {provider_config.type})")
if is_google_stream:
if is_kiro_stream:
# Handle Kiro streaming response
# Kiro returns an async generator that yields OpenAI-compatible SSE strings directly
# We need to parse these and handle tool calls properly
accumulated_response_text = "" # Track full response for token counting
chunk_count = 0
tool_calls_from_stream = [] # Track tool calls from stream
completion_id = f"chatcmpl-{uuid.uuid4().hex[:24]}"
created_time = int(time.time())
async for chunk in response:
chunk_count += 1
try:
logger.debug(f"Kiro chunk type: {type(chunk)}")
logger.debug(f"Kiro chunk: {chunk}")
# Parse SSE chunk to extract JSON data
chunk_data = None
if isinstance(chunk, str) and chunk.startswith('data: '):
data_str = chunk[6:].strip() # Remove 'data: ' prefix
if data_str and data_str != '[DONE]':
try:
chunk_data = json.loads(data_str)
except json.JSONDecodeError:
logger.warning(f"Failed to parse Kiro chunk JSON: {data_str}")
continue
elif isinstance(chunk, bytes):
# Try to decode bytes as SSE
try:
chunk_str = chunk.decode('utf-8')
if chunk_str.startswith('data: '):
data_str = chunk_str[6:].strip()
if data_str and data_str != '[DONE]':
chunk_data = json.loads(data_str)
except (UnicodeDecodeError, json.JSONDecodeError):
logger.warning(f"Failed to parse Kiro bytes chunk")
continue
if chunk_data:
# Extract content and tool calls from chunk
choices = chunk_data.get('choices', [])
if choices:
delta = choices[0].get('delta', {})
# Track content
delta_content = delta.get('content', '')
if delta_content:
accumulated_response_text += delta_content
# Track tool calls
delta_tool_calls = delta.get('tool_calls', [])
if delta_tool_calls:
for tc in delta_tool_calls:
tool_calls_from_stream.append(tc)
logger.debug(f"Collected tool call from Kiro stream: {tc}")
# Pass through the chunk as-is
if isinstance(chunk, str):
yield chunk.encode('utf-8')
elif isinstance(chunk, bytes):
yield chunk
else:
yield f"data: {json.dumps(chunk_data)}\n\n".encode('utf-8')
else:
# Pass through non-data chunks as-is (like [DONE])
if isinstance(chunk, str):
yield chunk.encode('utf-8')
elif isinstance(chunk, bytes):
yield chunk
else:
yield f"data: {json.dumps(chunk)}\n\n".encode('utf-8')
except Exception as chunk_error:
error_msg = str(chunk_error)
logger.warning(f"Error processing Kiro chunk: {error_msg}")
logger.warning(f"Chunk type: {type(chunk)}")
logger.warning(f"Chunk content: {chunk}")
continue
# After stream ends, process collected tool calls
if tool_calls_from_stream:
logger.info(f"Processing {len(tool_calls_from_stream)} tool calls from Kiro stream")
# Add required index field to each tool_call
# according to OpenAI API specification for streaming
indexed_tool_calls = []
for idx, tc in enumerate(tool_calls_from_stream):
# Extract function with None protection
func = tc.get("function") or {}
# Use "or" for protection against explicit None in values
tool_name = func.get("name") or ""
tool_args = func.get("arguments") or "{}"
logger.debug(f"Tool call [{idx}] '{tool_name}': id={tc.get('id')}, args_length={len(tool_args)}")
indexed_tc = {
"index": idx,
"id": tc.get("id"),
"type": tc.get("type", "function"),
"function": {
"name": tool_name,
"arguments": tool_args
}
}
indexed_tool_calls.append(indexed_tc)
# Send tool calls chunk
tool_calls_chunk = {
"id": completion_id,
"object": "chat.completion.chunk",
"created": created_time,
"model": request_data['model'],
"choices": [{
"index": 0,
"delta": {"tool_calls": indexed_tool_calls},
"finish_reason": None
}]
}
yield f"data: {json.dumps(tool_calls_chunk, ensure_ascii=False)}\n\n".encode('utf-8')
logger.info(f"Kiro streaming processed {chunk_count} chunks total")
elif is_google_stream:
# Handle Google's streaming response # Handle Google's streaming response
# Google provider returns an async generator # Google provider returns an async generator
# Note: Google returns accumulated text, so we need to track and send only deltas # Note: Google returns accumulated text, so we need to track and send only deltas
...@@ -2309,55 +2433,96 @@ class RotationHandler: ...@@ -2309,55 +2433,96 @@ class RotationHandler:
} }
yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8') yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8')
else: else:
# Handle OpenAI/Anthropic streaming responses # Handle OpenAI/Anthropic/Kiro streaming responses
# OpenAI SDK returns a sync Stream object, not an async iterator # Some providers return async generators, others return sync iterables
# So we use a regular for loop, not async for
accumulated_response_text = "" # Track full response for token counting accumulated_response_text = "" # Track full response for token counting
for chunk in response:
# Check if response is an async generator
import inspect
if inspect.iscoroutinefunction(response) or hasattr(response, '__aiter__'):
# Handle async generator (like Kiro)
logger.info(f"Detected async generator response, using async for loop")
chunk_count = 0
try: try:
logger.debug(f"Chunk type: {type(chunk)}") async for chunk in response:
logger.debug(f"Chunk: {chunk}") chunk_count += 1
try:
# For OpenAI-compatible providers, just pass through the raw chunk logger.debug(f"Async chunk type: {type(chunk)}")
chunk_dict = chunk.model_dump() if hasattr(chunk, 'model_dump') else chunk logger.debug(f"Async chunk: {chunk}")
# Track response content for token calculation # For Kiro, chunks are already properly formatted SSE bytes
if isinstance(chunk_dict, dict): # Just pass them through directly
choices = chunk_dict.get('choices', []) if isinstance(chunk, bytes):
if choices: logger.debug(f"Yielding raw bytes chunk: {len(chunk)} bytes")
delta = choices[0].get('delta', {}) yield chunk
delta_content = delta.get('content', '') else:
if delta_content: # Fallback: treat as dict and serialize
accumulated_response_text += delta_content chunk_dict = chunk.model_dump() if hasattr(chunk, 'model_dump') else chunk
yield f"data: {json.dumps(chunk_dict)}\n\n".encode('utf-8')
# Add effective_context to the last chunk (when finish_reason is present) except Exception as chunk_error:
if isinstance(chunk_dict, dict): error_msg = str(chunk_error)
choices = chunk_dict.get('choices', []) logger.warning(f"Error processing async chunk: {error_msg}")
if choices and choices[0].get('finish_reason') is not None: logger.warning(f"Chunk type: {type(chunk)}")
# This is the last chunk, add effective_context logger.warning(f"Chunk content: {chunk}")
if 'usage' not in chunk_dict: continue
chunk_dict['usage'] = {} except Exception as async_error:
chunk_dict['usage']['effective_context'] = effective_context logger.error(f"Error in async for loop: {async_error}")
logger.error(f"Response type: {type(response)}")
# If provider doesn't provide token counts, calculate them logger.error(f"Response has __aiter__: {hasattr(response, '__aiter__')}")
if chunk_dict['usage'].get('total_tokens') is None: logger.error(f"Response is coroutine function: {inspect.iscoroutinefunction(response)}")
# Calculate completion tokens from accumulated response # Re-raise to trigger failure recording
if accumulated_response_text: raise async_error
completion_tokens = count_messages_tokens([{"role": "assistant", "content": accumulated_response_text}], model_name) finally:
else: logger.info(f"Async generator processed {chunk_count} chunks total")
completion_tokens = 0 else:
total_tokens = effective_context + completion_tokens # Handle sync iterable (like OpenAI SDK)
chunk_dict['usage']['prompt_tokens'] = effective_context logger.info(f"Detected sync iterable response, using regular for loop")
chunk_dict['usage']['completion_tokens'] = completion_tokens for chunk in response:
chunk_dict['usage']['total_tokens'] = total_tokens try:
logger.debug(f"Sync chunk type: {type(chunk)}")
yield f"data: {json.dumps(chunk_dict)}\n\n".encode('utf-8') logger.debug(f"Sync chunk: {chunk}")
except Exception as chunk_error:
error_msg = str(chunk_error) # For OpenAI-compatible providers, just pass through the raw chunk
logger.warning(f"Error serializing chunk: {error_msg}") # Convert chunk to dict and serialize as JSON
logger.warning(f"Chunk type: {type(chunk)}") chunk_dict = chunk.model_dump() if hasattr(chunk, 'model_dump') else chunk
logger.warning(f"Chunk content: {chunk}")
continue # Track response content for token calculation
if isinstance(chunk_dict, dict):
choices = chunk_dict.get('choices', [])
if choices:
delta = choices[0].get('delta', {})
delta_content = delta.get('content', '')
if delta_content:
accumulated_response_text += delta_content
# Add effective_context to the last chunk (when finish_reason is present)
if isinstance(chunk_dict, dict):
choices = chunk_dict.get('choices', [])
if choices and choices[0].get('finish_reason') is not None:
# This is the last chunk, add effective_context
if 'usage' not in chunk_dict:
chunk_dict['usage'] = {}
chunk_dict['usage']['effective_context'] = effective_context
# If provider doesn't provide token counts, calculate them
if chunk_dict['usage'].get('total_tokens') is None:
# Calculate completion tokens from accumulated response
if accumulated_response_text:
completion_tokens = count_messages_tokens([{"role": "assistant", "content": accumulated_response_text}], request_data['model'])
else:
completion_tokens = 0
total_tokens = effective_context + completion_tokens
chunk_dict['usage']['prompt_tokens'] = effective_context
chunk_dict['usage']['completion_tokens'] = completion_tokens
chunk_dict['usage']['total_tokens'] = total_tokens
yield f"data: {json.dumps(chunk_dict)}\n\n".encode('utf-8')
except Exception as chunk_error:
error_msg = str(chunk_error)
logger.warning(f"Error serializing sync chunk: {error_msg}")
logger.warning(f"Chunk type: {type(chunk)}")
logger.warning(f"Chunk content: {chunk}")
continue
handler.record_success() handler.record_success()
except Exception as e: except Exception as e:
......
aisbf/kiro_auth.py
View file @ 2e7a8460
...@@ -100,9 +100,13 @@ class KiroAuthManager: ...@@ -100,9 +100,13 @@ class KiroAuthManager:
"""Load credentials from SQLite database (kiro-cli)""" """Load credentials from SQLite database (kiro-cli)"""
if not self.sqlite_db: if not self.sqlite_db:
return return
# Expand ~ in path
db_path = Path(self.sqlite_db).expanduser()
conn = None
try: try:
conn = sqlite3.connect(self.sqlite_db) conn = sqlite3.connect(str(db_path))
cursor = conn.cursor() cursor = conn.cursor()
# Try to find token in SQLite # Try to find token in SQLite
...@@ -120,9 +124,13 @@ class KiroAuthManager: ...@@ -120,9 +124,13 @@ class KiroAuthManager:
if token_data: if token_data:
self._access_token = token_data.get('access_token') self._access_token = token_data.get('access_token')
self._refresh_token = token_data.get('refresh_token') self._refresh_token = token_data.get('refresh_token')
self.refresh_token = token_data.get('refresh_token') # Update public refresh_token too
self._expires_at = datetime.fromisoformat( self._expires_at = datetime.fromisoformat(
token_data.get('expires_at', '1970-01-01T00:00:00Z') token_data.get('expires_at', '1970-01-01T00:00:00Z')
) )
# Also try to get profile_arn from token data
if 'profile_arn' in token_data:
self.profile_arn = token_data['profile_arn']
logger.info(f"Loaded credentials from SQLite key: {token_key}") logger.info(f"Loaded credentials from SQLite key: {token_key}")
# Try to get device registration for AWS SSO OIDC # Try to get device registration for AWS SSO OIDC
...@@ -133,23 +141,60 @@ class KiroAuthManager: ...@@ -133,23 +141,60 @@ class KiroAuthManager:
reg_data = json.loads(row[0]) reg_data = json.loads(row[0])
self.client_id = reg_data.get('clientId') self.client_id = reg_data.get('clientId')
self.client_secret = reg_data.get('clientSecret') self.client_secret = reg_data.get('clientSecret')
# Also check for profile_arn in registration data
if 'profileArn' in reg_data:
self.profile_arn = reg_data['profileArn']
break break
# If profile_arn still not found, try to query it directly from the database
if not self.profile_arn:
# Try common profile ARN keys
profile_keys = [
"kirocli:profile:arn",
"codewhisperer:profile:arn",
"kirocli:social:profile",
"codewhisperer:social:profile"
]
for profile_key in profile_keys:
cursor.execute("SELECT value FROM auth_kv WHERE key = ?", (profile_key,))
row = cursor.fetchone()
if row:
try:
profile_data = json.loads(row[0])
if isinstance(profile_data, dict):
self.profile_arn = profile_data.get('arn') or profile_data.get('profileArn')
elif isinstance(profile_data, str):
self.profile_arn = profile_data
if self.profile_arn:
logger.info(f"Loaded profile ARN from SQLite key: {profile_key}")
break
except json.JSONDecodeError:
# Value might be a plain string
self.profile_arn = row[0]
logger.info(f"Loaded profile ARN (plain string) from SQLite key: {profile_key}")
break
except Exception as e: except Exception as e:
logger.error(f"Failed to load from SQLite: {e}") logger.error(f"Failed to load from SQLite: {e}")
finally: finally:
conn.close() if conn:
conn.close()
def _load_from_creds_file(self): def _load_from_creds_file(self):
"""Load credentials from JSON file""" """Load credentials from JSON file"""
if not self.creds_file: if not self.creds_file:
return return
# Expand ~ in path
creds_path = Path(self.creds_file).expanduser()
try: try:
with open(self.creds_file, 'r') as f: with open(creds_path, 'r') as f:
data = json.load(f) data = json.load(f)
self.refresh_token = data.get('refreshToken', self.refresh_token) refresh_token_value = data.get('refreshToken', self.refresh_token)
self.refresh_token = refresh_token_value
self._refresh_token = refresh_token_value # Keep private token in sync
self._access_token = data.get('accessToken') self._access_token = data.get('accessToken')
self.profile_arn = data.get('profileArn', self.profile_arn) self.profile_arn = data.get('profileArn', self.profile_arn)
...@@ -205,6 +250,7 @@ class KiroAuthManager: ...@@ -205,6 +250,7 @@ class KiroAuthManager:
self._access_token = data['accessToken'] self._access_token = data['accessToken']
if 'refreshToken' in data: if 'refreshToken' in data:
self.refresh_token = data['refreshToken'] self.refresh_token = data['refreshToken']
self._refresh_token = data['refreshToken'] # Keep private token in sync
# Calculate expiration (1 hour default) # Calculate expiration (1 hour default)
self._expires_at = datetime.now(timezone.utc) + timedelta(seconds=3600) self._expires_at = datetime.now(timezone.utc) + timedelta(seconds=3600)
...@@ -234,6 +280,7 @@ class KiroAuthManager: ...@@ -234,6 +280,7 @@ class KiroAuthManager:
self._access_token = data['access_token'] self._access_token = data['access_token']
if 'refresh_token' in data: if 'refresh_token' in data:
self.refresh_token = data['refresh_token'] self.refresh_token = data['refresh_token']
self._refresh_token = data['refresh_token'] # Keep private token in sync
expires_in = data.get('expires_in', 3600) expires_in = data.get('expires_in', 3600)
self._expires_at = datetime.now(timezone.utc) + timedelta(seconds=expires_in) self._expires_at = datetime.now(timezone.utc) + timedelta(seconds=expires_in)
...@@ -242,9 +289,12 @@ class KiroAuthManager: ...@@ -242,9 +289,12 @@ class KiroAuthManager:
"""Save updated credentials to file""" """Save updated credentials to file"""
if not self.creds_file: if not self.creds_file:
return return
# Expand ~ in path
creds_path = Path(self.creds_file).expanduser()
try: try:
with open(self.creds_file, 'r') as f: with open(creds_path, 'r') as f:
data = json.load(f) data = json.load(f)
except (FileNotFoundError, json.JSONDecodeError): except (FileNotFoundError, json.JSONDecodeError):
data = {} data = {}
...@@ -257,7 +307,7 @@ class KiroAuthManager: ...@@ -257,7 +307,7 @@ class KiroAuthManager:
'region': self.region 'region': self.region
}) })
with open(self.creds_file, 'w') as f: with open(creds_path, 'w') as f:
json.dump(data, f, indent=2) json.dump(data, f, indent=2)
def get_auth_headers(self, token: str) -> dict: def get_auth_headers(self, token: str) -> dict:
......
aisbf/kiro_converters.py
View file @ 2e7a8460
...@@ -311,8 +311,7 @@ def get_truncation_recovery_system_addition() -> str: ...@@ -311,8 +311,7 @@ def get_truncation_recovery_system_addition() -> str:
Returns: Returns:
System prompt addition text (empty string if truncation recovery is disabled) System prompt addition text (empty string if truncation recovery is disabled)
""" """
from kiro.config import TRUNCATION_RECOVERY # Use module-level constant (defined at top of file)
if not TRUNCATION_RECOVERY: if not TRUNCATION_RECOVERY:
return "" return ""
......
aisbf/kiro_converters_openai.py
View file @ 2e7a8460
...@@ -30,22 +30,121 @@ Contains functions for: ...@@ -30,22 +30,121 @@ Contains functions for:
""" """
import logging import logging
import re
from typing import Any, Dict, List, Optional, Tuple from typing import Any, Dict, List, Optional, Tuple
# Use standard Python logging # Use standard Python logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
# Configuration - hidden models that need normalization # Import Kiro models for type hints
from .kiro_models import ChatMessage, Tool
# Hidden models - not returned by Kiro /ListAvailableModels API but still functional.
# These need special internal IDs that differ from their display names.
# Format: "normalized_display_name" → "internal_kiro_id"
# Matches kiro-gateway's config.py HIDDEN_MODELS
HIDDEN_MODELS = { HIDDEN_MODELS = {
"claude-sonnet-4-5": "anthropic.claude-3-5-sonnet-20241022-v2:0", # Claude 3.7 Sonnet - legacy flagship model, still works!
"claude-haiku-4-5": "anthropic.claude-3-5-haiku-20241022-v1:0", "claude-3.7-sonnet": "CLAUDE_3_7_SONNET_20250219_V1_0",
"claude-opus-4-5": "anthropic.claude-3-5-opus-20250514-v1:0",
"claude-sonnet-4": "anthropic.claude-3-5-sonnet-20240620-v1:0",
} }
def normalize_model_name(name: str) -> str:
"""
Normalize client model name to Kiro format.
Ported from kiro-gateway's model_resolver.py normalize_model_name().
Transformations applied:
1. claude-haiku-4-5 → claude-haiku-4.5 (dash to dot for minor version)
2. claude-haiku-4-5-20251001 → claude-haiku-4.5 (strip date suffix)
3. claude-haiku-4-5-latest → claude-haiku-4.5 (strip 'latest' suffix)
4. claude-sonnet-4-20250514 → claude-sonnet-4 (strip date, no minor)
5. claude-3-7-sonnet → claude-3.7-sonnet (legacy format normalization)
6. claude-3-7-sonnet-20250219 → claude-3.7-sonnet (legacy + strip date)
7. claude-4.5-opus-high → claude-opus-4.5 (inverted format with suffix)
Args:
name: External model name from client
Returns:
Normalized model name in Kiro format
"""
if not name:
return name
# Lowercase for consistent matching
name_lower = name.lower()
# Pattern 1: Standard format - claude-{family}-{major}-{minor}(-{suffix})?
# Matches: claude-haiku-4-5, claude-haiku-4-5-20251001, claude-haiku-4-5-latest
# IMPORTANT: Minor version is 1-2 digits only! 8-digit dates should NOT match here.
standard_pattern = r'^(claude-(?:haiku|sonnet|opus)-\d+)-(\d{1,2})(?:-(?:\d{8}|latest|\d+))?$'
match = re.match(standard_pattern, name_lower)
if match:
base = match.group(1) # claude-haiku-4
minor = match.group(2) # 5
return f"{base}.{minor}" # claude-haiku-4.5
# Pattern 2: Standard format without minor - claude-{family}-{major}(-{date})?
# Matches: claude-sonnet-4, claude-sonnet-4-20250514
no_minor_pattern = r'^(claude-(?:haiku|sonnet|opus)-\d+)(?:-\d{8})?$'
match = re.match(no_minor_pattern, name_lower)
if match:
return match.group(1) # claude-sonnet-4
# Pattern 3: Legacy format - claude-{major}-{minor}-{family}(-{suffix})?
# Matches: claude-3-7-sonnet, claude-3-7-sonnet-20250219
legacy_pattern = r'^(claude)-(\d+)-(\d+)-(haiku|sonnet|opus)(?:-(?:\d{8}|latest|\d+))?$'
match = re.match(legacy_pattern, name_lower)
if match:
prefix = match.group(1) # claude
major = match.group(2) # 3
minor = match.group(3) # 7
family = match.group(4) # sonnet
return f"{prefix}-{major}.{minor}-{family}" # claude-3.7-sonnet
# Pattern 4: Already normalized with dot but has date suffix
# Matches: claude-haiku-4.5-20251001, claude-3.7-sonnet-20250219
dot_with_date_pattern = r'^(claude-(?:\d+\.\d+-)?(?:haiku|sonnet|opus)(?:-\d+\.\d+)?)-\d{8}$'
match = re.match(dot_with_date_pattern, name_lower)
if match:
return match.group(1)
# Pattern 5: Inverted format with suffix - claude-{major}.{minor}-{family}-{suffix}
# Matches: claude-4.5-opus-high, claude-4.5-sonnet-low
# Convert to: claude-{family}-{major}.{minor}
# NOTE: Requires a suffix to avoid matching already-normalized formats
inverted_with_suffix_pattern = r'^claude-(\d+)\.(\d+)-(haiku|sonnet|opus)-(.+)$'
match = re.match(inverted_with_suffix_pattern, name_lower)
if match:
major = match.group(1) # 4
minor = match.group(2) # 5
family = match.group(3) # opus
return f"claude-{family}-{major}.{minor}" # claude-opus-4.5
# No transformation needed - return as-is
return name
def get_model_id_for_kiro(model: str, hidden_models: dict) -> str: def get_model_id_for_kiro(model: str, hidden_models: dict) -> str:
"""Normalize model name for Kiro API""" """
return hidden_models.get(model, model) Get the model ID to send to Kiro API.
Normalizes the name first (dashes→dots, strip dates),
then checks hidden_models for special internal IDs.
Ported from kiro-gateway's model_resolver.py get_model_id_for_kiro().
Args:
model: External model name from client
hidden_models: Dict mapping display names to internal Kiro IDs
Returns:
Model ID to send to Kiro API
"""
normalized = normalize_model_name(model)
return hidden_models.get(normalized, normalized)
# Import from core - reuse shared logic # Import from core - reuse shared logic
from .kiro_converters import ( from .kiro_converters import (
......
aisbf/kiro_parsers.py 0 → 100644
View file @ 2e7a8460
# -*- coding: utf-8 -*-
# Kiro Gateway
# https://github.com/jwadow/kiro-gateway
# Copyright (C) 2025 Jwadow
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
Copyleft (C) 2026 Stefy Lanza <stefy@nexlab.net>
AISBF - AI Service Broker Framework || AI Should Be Free
AWS Event Stream parser for Kiro API responses.
Ported from kiro-gateway (https://github.com/jwadow/kiro-gateway)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
"""
import json
import logging
import re
from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
def find_matching_brace(text: str, start_pos: int) -> int:
"""
Finds the position of the closing brace considering nesting and strings.
Uses bracket counting for correct parsing of nested JSON.
Accounts for quoted strings and escape sequences.
Args:
text: Text to search
start_pos: Position of opening brace '{'
Returns:
Position of closing brace or -1 if not found
Example:
>>> find_matching_brace('{"a": {"b": 1}}', 0)
14
>>> find_matching_brace('{"a": "{}"}', 0)
10
"""
if start_pos >= len(text) or text[start_pos] != '{':
return -1
brace_count = 0
in_string = False
escape_next = False
for i in range(start_pos, len(text)):
char = text[i]
if escape_next:
escape_next = False
continue
if char == '\\' and in_string:
escape_next = True
continue
if char == '"' and not escape_next:
in_string = not in_string
continue
if not in_string:
if char == '{':
brace_count += 1
elif char == '}':
brace_count -= 1
if brace_count == 0:
return i
return -1
def parse_bracket_tool_calls(response_text: str) -> List[Dict[str, Any]]:
"""
Parses tool calls in [Called func_name with args: {...}] format.
Some models return tool calls in text format instead of
structured JSON. This function extracts them.
Args:
response_text: Model response text
Returns:
List of tool calls in OpenAI format
Example:
>>> text = "[Called get_weather with args: {\"city\": \"London\"}]"
>>> calls = parse_bracket_tool_calls(text)
>>> calls[0]["function"]["name"]
'get_weather'
"""
if not response_text or "[Called" not in response_text:
return []
tool_calls = []
pattern = r'\[Called\s+(\w+)\s+with\s+args:\s*'
for match in re.finditer(pattern, response_text, re.IGNORECASE):
func_name = match.group(1)
args_start = match.end()
# Find JSON start
json_start = response_text.find('{', args_start)
if json_start == -1:
continue
# Find JSON end considering nesting
json_end = find_matching_brace(response_text, json_start)
if json_end == -1:
continue
json_str = response_text[json_start:json_end + 1]
try:
args = json.loads(json_str)
tool_call_id = generate_tool_call_id()
# index will be added later when forming the final response
tool_calls.append({
"id": tool_call_id,
"type": "function",
"function": {
"name": func_name,
"arguments": json.dumps(args)
}
})
except json.JSONDecodeError:
logger.warning(f"Failed to parse tool call arguments: {json_str[:100]}")
return tool_calls
def generate_tool_call_id() -> str:
"""Generates a unique tool call ID."""
import uuid
return f"call_{uuid.uuid4().hex[:12]}"
def deduplicate_tool_calls(tool_calls: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Removes duplicate tool calls.
Deduplication occurs by two criteria:
1. By id - if there are multiple tool calls with the same id, keep the one with
more arguments (not empty "{}")
2. By name+arguments - remove complete duplicates
Args:
tool_calls: List of tool calls
Returns:
List of unique tool calls
"""
# First deduplicate by id - keep tool call with non-empty arguments
by_id: Dict[str, Dict[str, Any]] = {}
for tc in tool_calls:
tc_id = tc.get("id", "")
if not tc_id:
# Without id - add as is (will be deduplicated by name+args)
continue
existing = by_id.get(tc_id)
if existing is None:
by_id[tc_id] = tc
else:
# Duplicate by id exists - keep the one with more arguments
existing_args = existing.get("function", {}).get("arguments", "{}")
current_args = tc.get("function", {}).get("arguments", "{}")
# Prefer non-empty arguments
if current_args != "{}" and (existing_args == "{}" or len(current_args) > len(existing_args)):
logger.debug(f"Replacing tool call {tc_id} with better arguments: {len(existing_args)} -> {len(current_args)}")
by_id[tc_id] = tc
# Collect tool calls: first those with id, then without id
result_with_id = list(by_id.values())
result_without_id = [tc for tc in tool_calls if not tc.get("id")]
# Now deduplicate by name+arguments for all
seen = set()
unique = []
for tc in result_with_id + result_without_id:
# Protection against None in function
func = tc.get("function") or {}
func_name = func.get("name") or ""
func_args = func.get("arguments") or "{}"
key = f"{func_name}-{func_args}"
if key not in seen:
seen.add(key)
unique.append(tc)
if len(tool_calls) != len(unique):
logger.debug(f"Deduplicated tool calls: {len(tool_calls)} -> {len(unique)}")
return unique
class AwsEventStreamParser:
"""
Parser for AWS Event Stream format.
AWS returns events in binary format with :message-type...event delimiters.
This class extracts JSON events from the stream and converts them to a convenient format.
Supported event types:
- content: Text content of response
- tool_start: Start of tool call (name, toolUseId)
- tool_input: Continuation of input for tool call
- tool_stop: End of tool call
- followup: Follow-up prompt
- usage: Credit consumption information
- context_usage: Context usage percentage
Attributes:
buffer: Buffer for accumulating data
last_content: Last processed content (for deduplication)
current_tool_call: Current incomplete tool call
tool_calls: List of completed tool calls
Example:
>>> parser = AwsEventStreamParser()
>>> events = parser.feed(chunk)
>>> for event in events:
... if event["type"] == "content":
... print(event["data"])
"""
# Patterns for finding JSON events
EVENT_PATTERNS = [
('{"content":', 'content'),
('{"name":', 'tool_start'),
('{"input":', 'tool_input'),
('{"stop":', 'tool_stop'),
('{"followupPrompt":', 'followup'),
('{"usage":', 'usage'),
('{"contextUsagePercentage":', 'context_usage'),
]
def __init__(self):
"""Initializes the parser."""
self.buffer = ""
self.last_content: Optional[str] = None # For deduplicating repeating content
self.current_tool_call: Optional[Dict[str, Any]] = None
self.tool_calls: List[Dict[str, Any]] = []
self.content_chunks: List[str] = []
def feed(self, chunk: bytes) -> List[Dict[str, Any]]:
"""
Adds chunk to buffer and returns parsed events.
Args:
chunk: Bytes of data from stream
Returns:
List of events in {"type": str, "data": Any} format
"""
try:
self.buffer += chunk.decode('utf-8', errors='ignore')
except Exception:
return []
events = []
while True:
# Find nearest pattern
earliest_pos = -1
earliest_type = None
for pattern, event_type in self.EVENT_PATTERNS:
pos = self.buffer.find(pattern)
if pos != -1 and (earliest_pos == -1 or pos < earliest_pos):
earliest_pos = pos
earliest_type = event_type
if earliest_pos == -1:
break
# Find JSON end
json_end = find_matching_brace(self.buffer, earliest_pos)
if json_end == -1:
# JSON not complete, wait for more data
break
json_str = self.buffer[earliest_pos:json_end + 1]
self.buffer = self.buffer[json_end + 1:]
try:
data = json.loads(json_str)
event = self._process_event(data, earliest_type)
if event:
events.append(event)
except json.JSONDecodeError:
logger.warning(f"Failed to parse JSON: {json_str[:100]}")
return events
def _process_event(self, data: dict, event_type: str) -> Optional[Dict[str, Any]]:
"""
Processes a parsed event.
Args:
data: Parsed JSON
event_type: Event type
Returns:
Processed event or None
"""
if event_type == 'content':
return self._process_content_event(data)
elif event_type == 'tool_start':
return self._process_tool_start_event(data)
elif event_type == 'tool_input':
return self._process_tool_input_event(data)
elif event_type == 'tool_stop':
return self._process_tool_stop_event(data)
elif event_type == 'usage':
return {"type": "usage", "data": data.get('usage', 0)}
elif event_type == 'context_usage':
return {"type": "context_usage", "data": data.get('contextUsagePercentage', 0)}
return None
def _process_content_event(self, data: dict) -> Optional[Dict[str, Any]]:
"""Processes content event."""
content = data.get('content', '')
# Skip followupPrompt
if data.get('followupPrompt'):
return None
# Deduplicate repeating content
if content == self.last_content:
return None
self.last_content = content
self.content_chunks.append(content)
return {"type": "content", "data": content}
def _process_tool_start_event(self, data: dict) -> Optional[Dict[str, Any]]:
"""Processes tool call start."""
# Finalize previous tool call if exists
if self.current_tool_call:
self._finalize_tool_call()
# input can be string or object
input_data = data.get('input', '')
if isinstance(input_data, dict):
input_str = json.dumps(input_data)
else:
input_str = str(input_data) if input_data else ''
self.current_tool_call = {
"id": data.get('toolUseId', generate_tool_call_id()),
"type": "function",
"function": {
"name": data.get('name', ''),
"arguments": input_str
}
}
if data.get('stop'):
self._finalize_tool_call()
return None
def _process_tool_input_event(self, data: dict) -> Optional[Dict[str, Any]]:
"""Processes input continuation for tool call."""
if self.current_tool_call:
# input can be string or object
input_data = data.get('input', '')
if isinstance(input_data, dict):
input_str = json.dumps(input_data)
else:
input_str = str(input_data) if input_data else ''
self.current_tool_call['function']['arguments'] += input_str
return None
def _process_tool_stop_event(self, data: dict) -> Optional[Dict[str, Any]]:
"""Processes tool call end."""
if self.current_tool_call and data.get('stop'):
self._finalize_tool_call()
return None
def _finalize_tool_call(self) -> None:
"""Finalizes current tool call and adds to list."""
if not self.current_tool_call:
return
# Try to parse and normalize arguments as JSON
args = self.current_tool_call['function']['arguments']
tool_name = self.current_tool_call['function'].get('name', 'unknown')
logger.debug(f"Finalizing tool call '{tool_name}' with raw arguments: {repr(args)[:200]}")
if isinstance(args, str):
if args.strip():
try:
parsed = json.loads(args)
# Ensure result is a JSON string
self.current_tool_call['function']['arguments'] = json.dumps(parsed)
logger.debug(f"Tool '{tool_name}' arguments parsed successfully: {list(parsed.keys()) if isinstance(parsed, dict) else type(parsed)}")
except json.JSONDecodeError as e:
# Analyze the failure to provide better diagnostics
truncation_info = self._diagnose_json_truncation(args)
if truncation_info["is_truncated"]:
# Mark for recovery system
self.current_tool_call['_truncation_detected'] = True
self.current_tool_call['_truncation_info'] = truncation_info
# Check if recovery is enabled
try:
from kiro.config import TRUNCATION_RECOVERY
except ImportError:
TRUNCATION_RECOVERY = False
tool_id = self.current_tool_call.get('id', 'unknown')
# Clear error message: this is Kiro API's fault, not ours
logger.error(
f"Tool call truncated by Kiro API: "
f"tool='{tool_name}', id={tool_id}, size={truncation_info['size_bytes']} bytes, "
f"reason={truncation_info['reason']}. "
f"{'Model will be notified automatically about truncation.' if TRUNCATION_RECOVERY else 'Set TRUNCATION_RECOVERY=true in .env to auto-notify model about truncation.'}"
)
else:
# Regular JSON parse error
logger.warning(f"Failed to parse tool '{tool_name}' arguments: {e}. Raw: {args[:200]}")
self.current_tool_call['function']['arguments'] = "{}"
else:
# Empty string - use empty object
# This is normal behavior for duplicate tool calls from Kiro
logger.debug(f"Tool '{tool_name}' has empty arguments string (will be deduplicated)")
self.current_tool_call['function']['arguments'] = "{}"
elif isinstance(args, dict):
# If already an object - serialize to string
self.current_tool_call['function']['arguments'] = json.dumps(args)
logger.debug(f"Tool '{tool_name}' arguments already dict with keys: {list(args.keys())}")
else:
# Unknown type - empty object
logger.warning(f"Tool '{tool_name}' has unexpected arguments type: {type(args)}")
self.current_tool_call['function']['arguments'] = "{}"
self.tool_calls.append(self.current_tool_call)
self.current_tool_call = None
def _diagnose_json_truncation(self, json_str: str) -> Dict[str, Any]:
"""
Analyzes a malformed JSON string to determine if it was truncated.
This helps distinguish between upstream issues (Kiro API cutting off
large tool call arguments) and actual malformed JSON from the model.
Args:
json_str: The raw JSON string that failed to parse
Returns:
Dictionary with diagnostic information:
- is_truncated: True if the JSON appears to be cut off
- reason: Human-readable explanation of why it's truncated
- size_bytes: Size of the received data
"""
size_bytes = len(json_str.encode('utf-8'))
stripped = json_str.strip()
# Check for obvious truncation signs
if not stripped:
return {"is_truncated": False, "reason": "empty string", "size_bytes": size_bytes}
# Count braces and brackets (simplified, doesn't account for strings perfectly)
open_braces = stripped.count('{')
close_braces = stripped.count('}')
open_brackets = stripped.count('[')
close_brackets = stripped.count(']')
# Check if JSON starts with { but doesn't end with }
if stripped.startswith('{') and not stripped.endswith('}'):
missing = open_braces - close_braces
return {
"is_truncated": True,
"reason": f"missing {missing} closing brace(s)",
"size_bytes": size_bytes
}
# Check if JSON starts with [ but doesn't end with ]
if stripped.startswith('[') and not stripped.endswith(']'):
missing = open_brackets - close_brackets
return {
"is_truncated": True,
"reason": f"missing {missing} closing bracket(s)",
"size_bytes": size_bytes
}
# Check for unbalanced braces/brackets
if open_braces != close_braces:
diff = open_braces - close_braces
return {
"is_truncated": True,
"reason": f"unbalanced braces ({open_braces} open, {close_braces} close)",
"size_bytes": size_bytes
}
if open_brackets != close_brackets:
diff = open_brackets - close_brackets
return {
"is_truncated": True,
"reason": f"unbalanced brackets ({open_brackets} open, {close_brackets} close)",
"size_bytes": size_bytes
}
# Check for unclosed string (ends with backslash or inside quotes)
# This is a heuristic - count unescaped quotes
quote_count = 0
i = 0
while i < len(stripped):
if stripped[i] == '\\' and i + 1 < len(stripped):
i += 2 # Skip escaped character
continue
if stripped[i] == '"':
quote_count += 1
i += 1
if quote_count % 2 != 0:
return {
"is_truncated": True,
"reason": "unclosed string literal",
"size_bytes": size_bytes
}
# Doesn't look truncated, probably just malformed
return {"is_truncated": False, "reason": "malformed JSON", "size_bytes": size_bytes}
def get_content(self) -> str:
"""Returns all collected content as a single string."""
return "".join(self.content_chunks)
def get_tool_calls(self) -> List[Dict[str, Any]]:
"""
Returns all collected tool calls.
Finalizes current tool call if not finished.
Removes duplicates.
Returns:
List of unique tool calls
"""
if self.current_tool_call:
self._finalize_tool_call()
return deduplicate_tool_calls(self.tool_calls)
def reset(self) -> None:
"""Resets parser state."""
self.buffer = ""
self.last_content = None
self.current_tool_call = None
self.tool_calls = []
self.content_chunks = []
main.py
View file @ 2e7a8460
...@@ -1308,9 +1308,35 @@ async def dashboard_prompts(request: Request): ...@@ -1308,9 +1308,35 @@ async def dashboard_prompts(request: Request):
prompts_data = [] prompts_data = []
for prompt_file in prompt_files: for prompt_file in prompt_files:
# Check user config first
config_path = Path.home() / '.aisbf' / prompt_file['filename'] config_path = Path.home() / '.aisbf' / prompt_file['filename']
if not config_path.exists(): if not config_path.exists():
config_path = Path(__file__).parent / 'config' / prompt_file['filename'] # Try installed locations
installed_dirs = [
Path.home() / '.local' / 'share' / 'aisbf',
Path('/usr/share/aisbf'),
Path(__file__).parent, # For source tree
]
source_path = None
for installed_dir in installed_dirs:
test_path = installed_dir / prompt_file['filename']
if test_path.exists():
source_path = test_path
break
# Also check config subdirectory
test_path = installed_dir / 'config' / prompt_file['filename']
if test_path.exists():
source_path = test_path
break
if source_path:
# Copy to user config directory
config_path.parent.mkdir(parents=True, exist_ok=True)
import shutil
shutil.copy2(source_path, config_path)
logger.info(f"Copied prompt from {source_path} to {config_path}")
if config_path.exists(): if config_path.exists():
with open(config_path) as f: with open(config_path) as f:
...@@ -1321,6 +1347,14 @@ async def dashboard_prompts(request: Request): ...@@ -1321,6 +1347,14 @@ async def dashboard_prompts(request: Request):
'filename': prompt_file['filename'], 'filename': prompt_file['filename'],
'content': content 'content': content
}) })
else:
# Add empty prompt if file not found
prompts_data.append({
'key': prompt_file['key'],
'name': prompt_file['name'],
'filename': prompt_file['filename'],
'content': f'# {prompt_file["name"]}\n\nPrompt template not found. Please add your prompt here.'
})
# Check for success parameter # Check for success parameter
success = request.query_params.get('success') success = request.query_params.get('success')
...@@ -1391,10 +1425,37 @@ async def dashboard_settings(request: Request): ...@@ -1391,10 +1425,37 @@ async def dashboard_settings(request: Request):
if auth_check: if auth_check:
return auth_check return auth_check
# Load aisbf.json # Load aisbf.json - check user config first, then installed locations
config_path = Path.home() / '.aisbf' / 'aisbf.json' config_path = Path.home() / '.aisbf' / 'aisbf.json'
if not config_path.exists(): if not config_path.exists():
config_path = Path(__file__).parent / 'config' / 'aisbf.json' # Try installed locations
installed_dirs = [
Path.home() / '.local' / 'share' / 'aisbf',
Path('/usr/share/aisbf'),
Path(__file__).parent, # For source tree
]
source_path = None
for installed_dir in installed_dirs:
test_path = installed_dir / 'aisbf.json'
if test_path.exists():
source_path = test_path
break
# Also check config subdirectory
test_path = installed_dir / 'config' / 'aisbf.json'
if test_path.exists():
source_path = test_path
break
if source_path:
# Copy to user config directory
config_path.parent.mkdir(parents=True, exist_ok=True)
import shutil
shutil.copy2(source_path, config_path)
logger.info(f"Copied config from {source_path} to {config_path}")
else:
raise HTTPException(status_code=500, detail="Configuration file not found in any location")
with open(config_path) as f: with open(config_path) as f:
aisbf_config = json.load(f) aisbf_config = json.load(f)
...@@ -1550,12 +1611,21 @@ async def dashboard_docs(request: Request): ...@@ -1550,12 +1611,21 @@ async def dashboard_docs(request: Request):
if auth_check: if auth_check:
return auth_check return auth_check
# Try to find DOCUMENTATION.md # Try to find DOCUMENTATION.md in multiple locations
doc_path = Path(__file__).parent / 'DOCUMENTATION.md' search_paths = [
if not doc_path.exists(): Path.home() / '.aisbf' / 'DOCUMENTATION.md',
doc_path = Path.home() / '.aisbf' / 'DOCUMENTATION.md' Path.home() / '.local' / 'share' / 'aisbf' / 'DOCUMENTATION.md',
Path('/usr/share/aisbf') / 'DOCUMENTATION.md',
Path(__file__).parent / 'DOCUMENTATION.md',
]
doc_path = None
for path in search_paths:
if path.exists():
doc_path = path
break
if doc_path.exists(): if doc_path and doc_path.exists():
with open(doc_path, encoding='utf-8') as f: with open(doc_path, encoding='utf-8') as f:
markdown_content = f.read() markdown_content = f.read()
# Convert markdown to HTML with extensions for better formatting # Convert markdown to HTML with extensions for better formatting
...@@ -1580,12 +1650,21 @@ async def dashboard_about(request: Request): ...@@ -1580,12 +1650,21 @@ async def dashboard_about(request: Request):
if auth_check: if auth_check:
return auth_check return auth_check
# Try to find README.md # Try to find README.md in multiple locations
readme_path = Path(__file__).parent / 'README.md' search_paths = [
if not readme_path.exists(): Path.home() / '.aisbf' / 'README.md',
readme_path = Path.home() / '.aisbf' / 'README.md' Path.home() / '.local' / 'share' / 'aisbf' / 'README.md',
Path('/usr/share/aisbf') / 'README.md',
Path(__file__).parent / 'README.md',
]
readme_path = None
for path in search_paths:
if path.exists():
readme_path = path
break
if readme_path.exists(): if readme_path and readme_path.exists():
with open(readme_path, encoding='utf-8') as f: with open(readme_path, encoding='utf-8') as f:
markdown_content = f.read() markdown_content = f.read()
# Convert markdown to HTML with extensions for better formatting # Convert markdown to HTML with extensions for better formatting
...@@ -1610,12 +1689,21 @@ async def dashboard_license(request: Request): ...@@ -1610,12 +1689,21 @@ async def dashboard_license(request: Request):
if auth_check: if auth_check:
return auth_check return auth_check
# Try to find LICENSE.txt # Try to find LICENSE.txt in multiple locations
license_path = Path(__file__).parent / 'LICENSE.txt' search_paths = [
if not license_path.exists(): Path.home() / '.aisbf' / 'LICENSE.txt',
license_path = Path.home() / '.aisbf' / 'LICENSE.txt' Path.home() / '.local' / 'share' / 'aisbf' / 'LICENSE.txt',
Path('/usr/share/aisbf') / 'LICENSE.txt',
Path(__file__).parent / 'LICENSE.txt',
]
license_path = None
for path in search_paths:
if path.exists():
license_path = path
break
if license_path.exists(): if license_path and license_path.exists():
with open(license_path, encoding='utf-8') as f: with open(license_path, encoding='utf-8') as f:
content = f.read() content = f.read()
# Convert to HTML with pre tags to preserve formatting # Convert to HTML with pre tags to preserve formatting
......
setup.py
View file @ 2e7a8460
...@@ -82,10 +82,15 @@ setup( ...@@ -82,10 +82,15 @@ setup(
'main.py', 'main.py',
'requirements.txt', 'requirements.txt',
'aisbf.sh', 'aisbf.sh',
'DOCUMENTATION.md',
'README.md',
'LICENSE.txt',
'config/providers.json', 'config/providers.json',
'config/rotations.json', 'config/rotations.json',
'config/autoselect.json', 'config/autoselect.json',
'config/autoselect.md', 'config/autoselect.md',
'config/condensation_conversational.md',
'config/condensation_semantic.md',
'config/aisbf.json', 'config/aisbf.json',
]), ]),
# Install aisbf package to share directory for venv installation # Install aisbf package to share directory for venv installation
...@@ -104,6 +109,7 @@ setup( ...@@ -104,6 +109,7 @@ setup(
'aisbf/kiro_converters.py', 'aisbf/kiro_converters.py',
'aisbf/kiro_converters_openai.py', 'aisbf/kiro_converters_openai.py',
'aisbf/kiro_models.py', 'aisbf/kiro_models.py',
'aisbf/kiro_parsers.py',
'aisbf/kiro_utils.py', 'aisbf/kiro_utils.py',
]), ]),
# Install dashboard templates # Install dashboard templates
......
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