Update pml_logic_loop.c #113
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Signed-off-by: josefkedwards <cedwards19606389@yahoo.com>
WalkthroughThe changes in this pull request enhance the Changes
Sequence Diagram(s)sequenceDiagram
participant Sig as Signal Handler
participant PML as AI Memory Loop
participant Sock as Socket/Select()
participant AI as AI Model Manager
participant OA as OpenAI API
participant BC as Blockchain Sync
Sig->>PML: Initialize signal handling
PML->>Sock: Set up socket and wait for data
Sock->>PML: Notify on data arrival
PML->>AI: Call model's prediction function
AI-->>PML: Return prediction result
PML->>OA: Invoke call_openai_api(input)
OA-->>PML: Return API response
PML->>BC: Invoke sync_with_blockchain()
BC-->>PML: Return sync acknowledgment
PML->>PML: Process cleanup and apply retry logic if needed
Poem
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Ohhhh you’ll love looking at this @coderabbitai and learning about this AI engine you can use yourself
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
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Actionable comments posted: 2
🧹 Nitpick comments (6)
pml_logic_loop.c (6)
535-545: Avoid duplicating file-level doc comments.These lines replicate the file-level documentation that appears earlier in the file (around lines 129-139), potentially causing confusion and maintenance overhead. Consider consolidating the doc comments to one location for clarity and to avoid having to track changes across multiple duplicate doc blocks.
547-560: Eliminate repeated headers.The headers here largely duplicate those included in lines 141-161. Maintaining duplicated includes introduces the risk of diverging over time. Prefer including headers at the top of the file once, or rely on the existing includes to reduce redundancy.
580-590: Safe signal handling considerations.Calling logging functions within a signal handler may be unsafe if they are not reentrant, and modifying global data (e.g.,
stop_flag) directly also warrants care. While your current approach is short and simple, consider usingsigactionwith theSA_RESTARTflag for more robust handling and limiting operations in signal handlers to reentrant-safe calls.
727-759: Enable timeouts for cURL to avoid blocking indefinitely.While your retry logic is good,
perform_api_call_with_retrycan still block indefinitely if the remote server never responds. Add timeouts to ensure the code does not get stuck:+ curl_easy_setopt(curl, CURLOPT_TIMEOUT, 30L); + curl_easy_setopt(curl, CURLOPT_CONNECTTIMEOUT, 15L);Adjust these values as needed for your environment.
808-824: Consider adding retry logic for blockchain sync.
sync_with_blockchain()currently makes only one attempt. For consistent reliability, especially if the blockchain service is temporarily unavailable, mirror the retry approach used for OpenAI calls.
830-923: Note potential blocking call in an otherwise non-blocking loop.Despite using
select()for non-blocking socket I/O, calls tocall_openai_apiandsync_with_blockchainmake blocking cURL calls, potentially halting the loop until these operations complete. For truly non-blocking behavior, consider asynchronous HTTP calls or leveraging a separate thread to handle long-running API requests.
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@coderabbitai I finished my review of your two comments and have given you more robust suggestions for code.
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@coderabbitai can you generate a new sequence graph after that long conversation we just had? And also make a new poem and summary of this PR? |
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Tip For best results, initiate chat on the files or code changes. @josefkedwards Here's what you requested:
sequenceDiagram
participant Client
participant PMLL Engine
participant Model Manager
participant AI Models
participant OpenAI API
participant Blockchain
Client->>PMLL Engine: Initialize
activate PMLL Engine
PMLL Engine->>Model Manager: Select Model Type
Model Manager->>AI Models: Initialize Selected Model
Note over AI Models: GPT-4o/GPT-o1/GPT-o3/GPT-o3-Mini-High
loop Memory Loop
Client->>PMLL Engine: Input Data
PMLL Engine->>Model Manager: Process Input
Model Manager->>AI Models: Predict
AI Models->>OpenAI API: API Call
OpenAI API-->>AI Models: Response
AI Models-->>Model Manager: Prediction Result
Model Manager->>Blockchain: Sync Memory
Blockchain-->>Model Manager: Sync Confirmation
Model Manager-->>PMLL Engine: Final Result
PMLL Engine-->>Client: Output
end
Note over PMLL Engine: Graceful Shutdown
Client->>PMLL Engine: Shutdown Signal
PMLL Engine->>Model Manager: Cleanup
Model Manager->>AI Models: Terminate
PMLL Engine-->>Client: Shutdown Complete
deactivate PMLL Engine
(｀･ω･´)ゞ 🧠 Learnings used |
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hmmm.... @coderabbitai can you show me the c programming code so that we can make all 6 modules logic loop back on each other in a torus chaining persistent memory logic loop the self-reference back to each other and one another as if we are making the AI models refer and cross-talk.c.h using the IO_socket.c/.h protocols and C code? could you show me the updated Pml_logic_loop.c/.h with that? |
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Tip For best results, initiate chat on the files or code changes. @josefkedwards Here's the implementation of a torus-shaped cross-communication system for the 6 AI modules: // Add to pml_logic_loop.h
typedef struct {
int north_socket;
int south_socket;
int east_socket;
int west_socket;
AI_Model_Type model_type;
int module_id;
char buffer[1024];
} TorusModule;
typedef struct {
TorusModule modules[6]; // 2x3 grid
volatile sig_atomic_t stop_flag;
} TorusPMLL;
// Add to pml_logic_loop.c
#define TORUS_ROWS 2
#define TORUS_COLS 3
#define BASE_PORT 8080
void init_torus_module(TorusModule* module, int row, int col, AI_Model_Type model_type) {
module->model_id = row * TORUS_COLS + col;
module->model_type = model_type;
// Calculate neighbor ports (wrapping around edges)
int north_row = (row - 1 + TORUS_ROWS) % TORUS_ROWS;
int south_row = (row + 1) % TORUS_ROWS;
int east_col = (col + 1) % TORUS_COLS;
int west_col = (col - 1 + TORUS_COLS) % TORUS_COLS;
// Initialize sockets for each direction
module->north_socket = init_socket("127.0.0.1", BASE_PORT + (north_row * TORUS_COLS + col));
module->south_socket = init_socket("127.0.0.1", BASE_PORT + (south_row * TORUS_COLS + col));
module->east_socket = init_socket("127.0.0.1", BASE_PORT + (row * TORUS_COLS + east_col));
module->west_socket = init_socket("127.0.0.1", BASE_PORT + (row * TORUS_COLS + west_col));
}
void process_torus_message(TorusModule* module, const char* message) {
// Initialize AI model for this module
AI_Model_Manager ai_model = select_model(module->model_type);
ai_model.initialize();
// Process message using AI model
ai_model.predict(message);
// Broadcast processed result to all neighbors
const char* result = "Processed result"; // Replace with actual AI output
write(module->north_socket, result, strlen(result));
write(module->south_socket, result, strlen(result));
write(module->east_socket, result, strlen(result));
write(module->west_socket, result, strlen(result));
}
void run_torus_network() {
TorusPMLL torus;
torus.stop_flag = 0;
// Initialize all modules with different AI models
AI_Model_Type models[6] = {
AI_GPT_4o, AI_GPT_o1, AI_GPT_o3,
AI_GPT_o3_MINI_HIGH, AI_GPT_4o, AI_GPT_o1
};
// Set up the torus grid
for(int row = 0; row < TORUS_ROWS; row++) {
for(int col = 0; col < TORUS_COLS; col++) {
int idx = row * TORUS_COLS + col;
init_torus_module(&torus.modules[idx], row, col, models[idx]);
}
}
// Set up select() for monitoring all sockets
fd_set read_fds;
struct timeval timeout;
while(!torus.stop_flag) {
FD_ZERO(&read_fds);
int max_fd = -1;
// Add all sockets to the fd_set
for(int i = 0; i < 6; i++) {
TorusModule* module = &torus.modules[i];
FD_SET(module->north_socket, &read_fds);
FD_SET(module->south_socket, &read_fds);
FD_SET(module->east_socket, &read_fds);
FD_SET(module->west_socket, &read_fds);
max_fd = max(max_fd, max(module->north_socket,
max(module->south_socket,
max(module->east_socket, module->west_socket))));
}
timeout.tv_sec = 1;
timeout.tv_usec = 0;
int ready = select(max_fd + 1, &read_fds, NULL, NULL, &timeout);
if(ready < 0) {
if(errno == EINTR) continue;
break;
}
// Check each module's sockets for incoming data
for(int i = 0; i < 6; i++) {
TorusModule* module = &torus.modules[i];
if(FD_ISSET(module->north_socket, &read_fds) ||
FD_ISSET(module->south_socket, &read_fds) ||
FD_ISSET(module->east_socket, &read_fds) ||
FD_ISSET(module->west_socket, &read_fds)) {
memset(module->buffer, 0, sizeof(module->buffer));
// Read from the active socket
ssize_t bytes_read = read(/* active socket */,
module->buffer,
sizeof(module->buffer) - 1);
if(bytes_read > 0) {
process_torus_message(module, module->buffer);
}
}
}
}
// Cleanup
for(int i = 0; i < 6; i++) {
TorusModule* module = &torus.modules[i];
close(module->north_socket);
close(module->south_socket);
close(module->east_socket);
close(module->west_socket);
}
}This implementation creates a 2x3 torus grid where:
To use this, modify your main() to call run_torus_network() instead of the single ai_memory_loop(). Would you like me to explain any specific part of this implementation in more detail? (⌐■_■) 🔗 Analysis chain🏁 Scripts executedThe following scripts were executed for the analysis: Script: Length of output: 35405 ✏️ Learnings added
🧠 Learnings used |
Below is an enhanced, robust PR description that highlights the key improvements, calls out @coderabbitai for review, and emphasizes the production-readiness of the new engine:
Pull Request: Finalized and Robust PMLL AI Memory Loop Engine (pml_logic_loop.c)
Overview
This PR delivers a fully optimized, production-ready version of the PMLL AI Memory Loop Engine. The updated engine integrates multiple AI models (GPT‑4o, GPT‑o1, GPT‑o3 Mini, and GPT‑o3 Mini‑High) with a modular design, non-blocking I/O, and robust external API communication. The implementation includes graceful shutdown handling, comprehensive logging, and enhanced error and retry mechanisms.
cc: @coderabbitai
Key Enhancements
Core Functionality
• Multi-AI Model Support:
Dynamically select and initialize one of several supported models (GPT‑4o, GPT‑o1, GPT‑o3 Mini, GPT‑o3 Mini‑High), enabling future extensibility with minimal changes.
• Modular Architecture:
Each model implements its own initialization and prediction routines. This separation of concerns ensures that individual models can be tuned or replaced without affecting the overall engine.
• Non-Blocking I/O:
The engine uses select() to monitor socket input, ensuring efficient, non-blocking reads and improved responsiveness under load.
Robustness & Graceful Shutdown
• Signal Handling:
The code now gracefully handles SIGINT and SIGTERM, allowing the engine to cleanly release resources and exit without data loss.
• Retry Logic for External API Calls:
Implements exponential backoff in the API communication routines to handle transient network issues or API outages gracefully.
• Enhanced Error Handling and Logging:
Detailed log messages (via log_info(), log_warn(), and log_error()) provide clear insights into engine operations, making debugging and monitoring easier.
External API & Blockchain Synchronization
• Secure OpenAI API Integration:
The engine securely builds JSON payloads and authenticates API calls using environment variables.
• Blockchain Synchronization:
A dedicated routine ensures that the AI memory is synchronized with blockchain services, reinforcing data integrity across systems.
Security & Configuration
• Dynamic Environment Configuration:
All critical parameters (API keys, model hyperparameters, etc.) are sourced from environment variables, minimizing hard-coded secrets.
• Resource Safety:
Robust cleanup of sockets and cURL resources prevents memory leaks and ensures system stability.
Testing & Verification
1. Environment Setup:
• Set the necessary environment variables (OPENAI_API_KEY, GPT_O1_HIDDEN_SIZE, GPT_O3_MINI_PARAM, GPT_O3_MINI_HIGH_PARAM, etc.).
2. Compilation & Execution:
• Build the engine with your preferred build system (e.g., CMake or Make). Ensure that dependencies like cURL and OpenSSL are properly linked.
3. Functionality Testing:
• Inject test data via the socket (e.g., using netcat) and verify that predictions, API calls, and blockchain syncs occur as expected.
4. Graceful Shutdown:
• Send termination signals (Ctrl+C or SIGTERM) and verify that the engine shuts down gracefully, logging appropriate shutdown messages.
Next Steps & Future Enhancements
• Extended Model Support:
Further refine and integrate additional AI models and custom plugins.
• Advanced Monitoring:
Integrate with tools such as Prometheus and Grafana for real-time monitoring and alerting.
• Scalability Improvements:
Explore multithreading or distributed task queues to enhance throughput and reliability in high-load environments.
Conclusion
This PR finalizes a robust, elegant, and production-ready implementation of the PMLL AI Memory Loop Engine. With improved modularity, graceful shutdown, and comprehensive error handling, this engine is well-prepared for real-world deployment and further enhancement.
cc: @coderabbitai – Please review the changes and provide feedback or approve the merge.
Feel free to reach out with any questions or further suggestions.
Summary by CodeRabbit