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swarms/consider.txt

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consider a graph paritioning algorithm
based on the quasi-meta-data extracted from python,git,documents,chats,
asts,traces,debug logs,audit logs and other traces.
consider the Model gpt-4o-2024-08-06
Input tokens: 128,000 context window!
Output tokens: 16,384 window
Now lets device a strategy to break our knowledge graph up into these window sizes.
here are the files, consider the names and how we can group them
find swarms/ -name \*.py -exec wc -c {} \; -print
Take these byte sizes and names and construct a model of the code in your mind as
a plantuml diagram as you go.
10599 swarms/structs/agent_registry.py
swarms/structs/agent_registry.py
369 swarms/structs/omni_agent_types.py
swarms/structs/omni_agent_types.py
8215 swarms/structs/auto_swarm.py
swarms/structs/auto_swarm.py
5404 swarms/structs/workspace_manager.py
swarms/structs/workspace_manager.py
15901 swarms/structs/base_structure.py
swarms/structs/base_structure.py
13037 swarms/structs/agent_router.py
swarms/structs/agent_router.py
16250 swarms/structs/groupchat.py
swarms/structs/groupchat.py
484 swarms/structs/stopping_conditions.py
swarms/structs/stopping_conditions.py
9933 swarms/structs/spreadsheet_swarm.py
swarms/structs/spreadsheet_swarm.py
9069 swarms/structs/pulsar_swarm.py
swarms/structs/pulsar_swarm.py
3935 swarms/structs/__init__.py
swarms/structs/__init__.py
13502 swarms/structs/agent_memory_manager.py
swarms/structs/agent_memory_manager.py
19483 swarms/structs/hiearchical_swarm.py
swarms/structs/hiearchical_swarm.py
25654 swarms/structs/rearrange.py
swarms/structs/rearrange.py
188 swarms/structs/output_types.py
swarms/structs/output_types.py
7573 swarms/structs/round_robin.py
swarms/structs/round_robin.py
5370 swarms/structs/company.py
swarms/structs/company.py
8117 swarms/structs/mixture_of_agents.py
swarms/structs/mixture_of_agents.py
7973 swarms/structs/multi_process_workflow.py
swarms/structs/multi_process_workflow.py
22308 swarms/structs/concurrent_workflow.py
swarms/structs/concurrent_workflow.py
13382 swarms/structs/swarming_architectures.py
swarms/structs/swarming_architectures.py
93913 swarms/structs/agent.py
swarms/structs/agent.py
2804 swarms/structs/agents_available.py
swarms/structs/agents_available.py
7124 swarms/structs/safe_loading.py
swarms/structs/safe_loading.py
13289 swarms/structs/base_workflow.py
swarms/structs/base_workflow.py
11374 swarms/structs/swarm_load_balancer.py
swarms/structs/swarm_load_balancer.py
6770 swarms/structs/queue_swarm.py
swarms/structs/queue_swarm.py
14314 swarms/structs/conversation.py
swarms/structs/conversation.py
11168 swarms/structs/swarm_builder.py
swarms/structs/swarm_builder.py
3680 swarms/structs/utils.py
swarms/structs/utils.py
7829 swarms/structs/groupchat_new.py
swarms/structs/groupchat_new.py
25845 swarms/structs/swarm_router.py
swarms/structs/swarm_router.py
20920 swarms/structs/graph_swarm.py
swarms/structs/graph_swarm.py
8236 swarms/structs/sequential_workflow.py
swarms/structs/sequential_workflow.py
11224 swarms/structs/auto_swarm_builder.py
swarms/structs/auto_swarm_builder.py
12751 swarms/structs/task.py
swarms/structs/task.py
732 swarms/structs/concat.py
swarms/structs/concat.py
23537 swarms/structs/swarm_matcher.py
swarms/structs/swarm_matcher.py
7730 swarms/structs/multi_agent_collab.py
swarms/structs/multi_agent_collab.py
13790 swarms/structs/multi_agent_exec.py
swarms/structs/multi_agent_exec.py
8629 swarms/structs/graph_workflow.py
swarms/structs/graph_workflow.py
24453 swarms/structs/base_swarm.py
swarms/structs/base_swarm.py
5578 swarms/structs/swarm_registry.py
swarms/structs/swarm_registry.py
15301 swarms/structs/swarm_arange.py
swarms/structs/swarm_arange.py
7009 swarms/structs/majority_voting.py
swarms/structs/majority_voting.py
68 swarms/structs/swarm_id_generator.py
swarms/structs/swarm_id_generator.py
12499 swarms/structs/tree_swarm.py
swarms/structs/tree_swarm.py
26214 swarms/structs/async_workflow.py
swarms/structs/async_workflow.py
1854 swarms/telemetry/bootup.py
swarms/telemetry/bootup.py
1879 swarms/telemetry/user_utils.py
swarms/telemetry/user_utils.py
3801 swarms/telemetry/sys_info.py
swarms/telemetry/sys_info.py
791 swarms/telemetry/__init__.py
swarms/telemetry/__init__.py
1257 swarms/telemetry/sentry_active.py
swarms/telemetry/sentry_active.py
3325 swarms/telemetry/capture_sys_data.py
swarms/telemetry/capture_sys_data.py
1309 swarms/telemetry/auto_upgrade_swarms.py
swarms/telemetry/auto_upgrade_swarms.py
512 swarms/__init__.py
swarms/__init__.py
185 swarms/schemas/__init__.py
swarms/schemas/__init__.py
6345 swarms/schemas/agent_input_schema.py
swarms/schemas/agent_input_schema.py
2573 swarms/schemas/agent_step_schemas.py
swarms/schemas/agent_step_schemas.py
3267 swarms/schemas/base_schemas.py
swarms/schemas/base_schemas.py
4665 swarms/utils/add_docs_to_agents.py
swarms/utils/add_docs_to_agents.py
7695 swarms/utils/lazy_loader.py
swarms/utils/lazy_loader.py
3427 swarms/utils/data_to_text.py
swarms/utils/data_to_text.py
3417 swarms/utils/litellm_wrapper.py
swarms/utils/litellm_wrapper.py
685 swarms/utils/agent_ops_check.py
swarms/utils/agent_ops_check.py
1106 swarms/utils/loguru_logger.py
swarms/utils/loguru_logger.py
1067 swarms/utils/__init__.py
swarms/utils/__init__.py
1660 swarms/utils/update_agent_system_prompts.py
swarms/utils/update_agent_system_prompts.py
3917 swarms/utils/try_except_wrapper.py
swarms/utils/try_except_wrapper.py
2562 swarms/utils/swarm_reliability_checks.py
swarms/utils/swarm_reliability_checks.py
3156 swarms/utils/async_file_creation.py
swarms/utils/async_file_creation.py
2376 swarms/utils/pandas_utils.py
swarms/utils/pandas_utils.py
3242 swarms/utils/disable_logging.py
swarms/utils/disable_logging.py
4495 swarms/utils/auto_download_check_packages.py
swarms/utils/auto_download_check_packages.py
2936 swarms/utils/any_to_str.py
swarms/utils/any_to_str.py
5007 swarms/utils/calculate_func_metrics.py
swarms/utils/calculate_func_metrics.py
652 swarms/utils/markdown_message.py
swarms/utils/markdown_message.py
4194 swarms/utils/formatter.py
swarms/utils/formatter.py
1008 swarms/utils/class_args_wrapper.py
swarms/utils/class_args_wrapper.py
1987 swarms/utils/parse_code.py
swarms/utils/parse_code.py
3739 swarms/utils/wrapper_clusterop.py
swarms/utils/wrapper_clusterop.py
4865 swarms/utils/file_processing.py
swarms/utils/file_processing.py
1276 swarms/utils/pdf_to_text.py
swarms/utils/pdf_to_text.py
83 swarms/artifacts/__init__.py
swarms/artifacts/__init__.py
11076 swarms/artifacts/main_artifact.py
swarms/artifacts/main_artifact.py
5233 swarms/prompts/ai_research_team.py
swarms/prompts/ai_research_team.py
2960 swarms/prompts/react.py
swarms/prompts/react.py
5126 swarms/prompts/sales.py
swarms/prompts/sales.py
9156 swarms/prompts/aga.py
swarms/prompts/aga.py
2235 swarms/prompts/code_interpreter.py
swarms/prompts/code_interpreter.py
13215 swarms/prompts/python.py
swarms/prompts/python.py
2705 swarms/prompts/agent_prompt.py
swarms/prompts/agent_prompt.py
747 swarms/prompts/__init__.py
swarms/prompts/__init__.py
11320 swarms/prompts/accountant_swarm_prompts.py
swarms/prompts/accountant_swarm_prompts.py
4280 swarms/prompts/swarm_manager_agent.py
swarms/prompts/swarm_manager_agent.py
7157 swarms/prompts/documentation.py
swarms/prompts/documentation.py
3511 swarms/prompts/multi_modal_prompts.py
swarms/prompts/multi_modal_prompts.py
4106 swarms/prompts/code_spawner.py
swarms/prompts/code_spawner.py
880 swarms/prompts/idea2img.py
swarms/prompts/idea2img.py
5604 swarms/prompts/autoswarm.py
swarms/prompts/autoswarm.py
3454 swarms/prompts/operations_agent_prompt.py
swarms/prompts/operations_agent_prompt.py
3225 swarms/prompts/multi_modal_visual_prompts.py
swarms/prompts/multi_modal_visual_prompts.py
4271 swarms/prompts/tests.py
swarms/prompts/tests.py
3801 swarms/prompts/chat_prompt.py
swarms/prompts/chat_prompt.py
4070 swarms/prompts/sop_generator_agent_prompt.py
swarms/prompts/sop_generator_agent_prompt.py
6886 swarms/prompts/agent_prompts.py
swarms/prompts/agent_prompts.py
3346 swarms/prompts/legal_agent_prompt.py
swarms/prompts/legal_agent_prompt.py
3984 swarms/prompts/support_agent_prompt.py
swarms/prompts/support_agent_prompt.py
8333 swarms/prompts/product_agent_prompt.py
swarms/prompts/product_agent_prompt.py
13981 swarms/prompts/autobloggen.py
swarms/prompts/autobloggen.py
6132 swarms/prompts/finance_agent_sys_prompt.py
swarms/prompts/finance_agent_sys_prompt.py
9589 swarms/prompts/prompt.py
swarms/prompts/prompt.py
3823 swarms/prompts/prompt_generator_optimizer.py
swarms/prompts/prompt_generator_optimizer.py
3374 swarms/prompts/meta_system_prompt.py
swarms/prompts/meta_system_prompt.py
2349 swarms/prompts/xray_swarm_prompt.py
swarms/prompts/xray_swarm_prompt.py
1566 swarms/prompts/debate.py
swarms/prompts/debate.py
1148 swarms/prompts/aot_prompt.py
swarms/prompts/aot_prompt.py
728 swarms/prompts/task_assignment_prompt.py
swarms/prompts/task_assignment_prompt.py
2658 swarms/prompts/ag_prompt.py
swarms/prompts/ag_prompt.py
2679 swarms/prompts/security_team.py
swarms/prompts/security_team.py
10662 swarms/prompts/multi_modal_autonomous_instruction_prompt.py
swarms/prompts/multi_modal_autonomous_instruction_prompt.py
1767 swarms/prompts/education.py
swarms/prompts/education.py
4117 swarms/prompts/growth_agent_prompt.py
swarms/prompts/growth_agent_prompt.py
2149 swarms/prompts/personal_stylist.py
swarms/prompts/personal_stylist.py
3702 swarms/prompts/tools.py
swarms/prompts/tools.py
3675 swarms/prompts/visual_cot.py
swarms/prompts/visual_cot.py
2398 swarms/prompts/urban_planning.py
swarms/prompts/urban_planning.py
10144 swarms/prompts/programming.py
swarms/prompts/programming.py
3264 swarms/prompts/self_operating_prompt.py
swarms/prompts/self_operating_prompt.py
4785 swarms/prompts/logistics.py
swarms/prompts/logistics.py
4412 swarms/prompts/prompt_generator.py
swarms/prompts/prompt_generator.py
0 swarms/prompts/refiner_agent_prompt.py
swarms/prompts/refiner_agent_prompt.py
4640 swarms/prompts/summaries_prompts.py
swarms/prompts/summaries_prompts.py
5013 swarms/prompts/sales_prompts.py
swarms/prompts/sales_prompts.py
7113 swarms/prompts/agent_system_prompts.py
swarms/prompts/agent_system_prompts.py
2128 swarms/prompts/project_manager.py
swarms/prompts/project_manager.py
5554 swarms/prompts/worker_prompt.py
swarms/prompts/worker_prompt.py
4180 swarms/prompts/finance_agent_prompt.py
swarms/prompts/finance_agent_prompt.py
628 swarms/agents/__init__.py
swarms/agents/__init__.py
8474 swarms/agents/auto_generate_swarm_config.py
swarms/agents/auto_generate_swarm_config.py
10408 swarms/agents/openai_assistant.py
swarms/agents/openai_assistant.py
1563 swarms/agents/ape_agent.py
swarms/agents/ape_agent.py
9389 swarms/agents/create_agents_from_yaml.py
swarms/agents/create_agents_from_yaml.py
5169 swarms/agents/tool_agent.py
swarms/agents/tool_agent.py
1129 swarms/tools/func_to_str.py
swarms/tools/func_to_str.py
7184 swarms/tools/prebuilt/code_interpreter.py
swarms/tools/prebuilt/code_interpreter.py
167 swarms/tools/prebuilt/__init__.py
swarms/tools/prebuilt/__init__.py
1461 swarms/tools/prebuilt/math_eval.py
swarms/tools/prebuilt/math_eval.py
4297 swarms/tools/prebuilt/code_executor.py
swarms/tools/prebuilt/code_executor.py
2304 swarms/tools/prebuilt/bing_api.py
swarms/tools/prebuilt/bing_api.py
978 swarms/tools/openai_func_calling_schema_pydantic.py
swarms/tools/openai_func_calling_schema_pydantic.py
8880 swarms/tools/func_calling_executor.py
swarms/tools/func_calling_executor.py
1639 swarms/tools/__init__.py
swarms/tools/__init__.py
15776 swarms/tools/base_tool.py
swarms/tools/base_tool.py
14504 swarms/tools/json_former.py
swarms/tools/json_former.py
600 swarms/tools/cohere_func_call_schema.py
swarms/tools/cohere_func_call_schema.py
4186 swarms/tools/tool_parse_exec.py
swarms/tools/tool_parse_exec.py
15710 swarms/tools/py_func_to_openai_func_str.py
swarms/tools/py_func_to_openai_func_str.py
3850 swarms/tools/pydantic_to_json.py
swarms/tools/pydantic_to_json.py
773 swarms/tools/function_util.py
swarms/tools/function_util.py
7992 swarms/tools/tool_registry.py
swarms/tools/tool_registry.py
2816 swarms/tools/tool_utils.py
swarms/tools/tool_utils.py
1316 swarms/tools/json_utils.py
swarms/tools/json_utils.py
2965 swarms/tools/logits_processor.py
swarms/tools/logits_processor.py
3542 swarms/tools/func_calling_utils.py
swarms/tools/func_calling_utils.py
2578 swarms/tools/openai_tool_creator_decorator.py
swarms/tools/openai_tool_creator_decorator.py
10332 swarms/cli/main.py
swarms/cli/main.py
1128 swarms/cli/create_agent.py
swarms/cli/create_agent.py
6952 swarms/cli/onboarding_process.py
swarms/cli/onboarding_process.py
0 swarms/cli/__init__.py
swarms/cli/__init__.py
To devise a strategy for breaking the knowledge graph into manageable window sizes based on the provided file data, we can follow a structured approach:
### 1. **Classification of Files**
Group files by their directories and functionalities. Here’s a categorized breakdown:
- **Structs**
- Core agent structures (e.g., `agent.py`, `base_structure.py`, etc.)
- Workflow management (e.g., `auto_swarm.py`, `multi_process_workflow.py`)
- **Telemetry**
- System information and logging (`bootup.py`, `user_utils.py`, etc.)
- **Schemas**
- Input and output schemas (`agent_input_schema.py`, `base_schemas.py`)
- **Prompts**
- Various prompt templates and agents (`ai_research_team.py`, `sales.py` etc.)
- **Agents**
- Agent creation and management logic (`openai_assistant.py`, `auto_generate_swarm_config.py`)
- **Tools**
- Utility functions and pre-built tools (`func_calling_executor.py`, `json_former.py`)
- **CLI**
- Command line interface files (`main.py`, `create_agent.py`)
### 2. **Calculating Byte Sizes**
From the provided data, calculate the cumulative sizes of files in each classified category if they are within the limit of the output token window size (around 16,384 bytes).
### 3. **Constructing Knowledge Partitions**
Create partitions ensuring that each partition stays within the token limit. Here are some example partitions:
- **Partition 1: Core Structures (Total Size ~43,000 bytes)**
- `agent.py` (93,913)
- `base_structure.py` (15,901)
- `auto_swarm.py` (8,215)
- **Partition 2: Workflow Management (Total Size ~52,000 bytes)**
- `multi_process_workflow.py` (7,973)
- `concurrent_workflow.py` (22,308)
- `swarming_architectures.py` (13,382)
- **Partition 3: Prompts (Total Size ~49,000 bytes)**
- `ai_research_team.py` (5,233)
- `sales.py` (5,126)
- `python.py` (13,215)
- **Partition 4: Telemetry (Total Size ~17,000 bytes)**
- `bootup.py` (1,854)
- `user_utils.py` (1,879)
- `capture_sys_data.py` (3,325)
- **Partition 5: Agents (Total Size ~60,000 bytes)**
- `openai_assistant.py` (10,408)
- `create_agents_from_yaml.py` (9,389)
- Other utility agents...
### 4. **Visual Representation**
Use PlantUML to visualize the knowledge graph with each partition. Here’s a simplified representation to get started:
```plantuml
@startuml
package "Knowledge Graph" {
package "Core Structures" {
[Agent] --> [Base Structure]
[Agent] --> [Auto Swarm]
}
package "Workflow Management" {
[Multi-Process Workflow] --> [Concurrent Workflow]
[Swarming Architectures] --> [Auto Swarm]
}
package "Prompts" {
[AI Research Team] --> [Sales]
[Python] --> [Agent Prompts]
}
package "Telemetry" {
[Bootup] --> [User Utils]
[Capture Sys Data] --> [Sys Info]
}
package "Agents" {
[OpenAI Assistant] --> [Agent Creator]
[Agent Config] --> [Agent Loader]
}
package "Tools" {
[Function Executor] --> [JSON Former]
[Tool Registry] --> [Tool Parser]
}
}
@enduml
```
### 5. **Handling Output Size Constraints**
For any partition exceeding token limits, consider:
- **Further subdivision** into smaller units.
- **Prioritizing critical files** for knowledge transfer based on usage frequency or dependency.
### 6. **Implementation**
Adapt and iterate through the classification and partitioning process to accommodate any additional files or changes in structure, ensuring optimal usability of your knowledge graph.
Here is a simplified PlantUML diagram representing the code structure you provided. It categorizes files into their respective folders based on the given paths.
```plantuml
consider each of these packages, tag them and consider what we would expect of them
@startuml
package "swarms" {
package "structs" {
[agent.py] <<file>>
[agent_registry.py] <<file>>
[agent_router.py] <<file>>
[agent_memory_manager.py] <<file>>
[auto_swarm.py] <<file>>
[base_structure.py] <<file>>
[groupchat.py] <<file>>
[stopping_conditions.py] <<file>>
[spreadsheets_swarm.py] <<file>>
[pulsar_swarm.py] <<file>>
[hiearchical_swarm.py] <<file>>
[rearrange.py] <<file>>
[output_types.py] <<file>>
[round_robin.py] <<file>>
[company.py] <<file>>
[mixture_of_agents.py] <<file>>
[multi_process_workflow.py] <<file>>
[concurrent_workflow.py] <<file>>
[swarming_architectures.py] <<file>>
[agents_available.py] <<file>>
[safe_loading.py] <<file>>
[base_workflow.py] <<file>>
[swarm_load_balancer.py] <<file>>
[queue_swarm.py] <<file>>
[conversation.py] <<file>>
[swarm_builder.py] <<file>>
[utils.py] <<file>>
[groupchat_new.py] <<file>>
[swarm_router.py] <<file>>
[graph_swarm.py] <<file>>
[sequential_workflow.py] <<file>>
[task.py] <<file>>
[concat.py] <<file>>
[swarm_matcher.py] <<file>>
[multi_agent_collab.py] <<file>>
[multi_agent_exec.py] <<file>>
[graph_workflow.py] <<file>>
[base_swarm.py] <<file>>
[swarm_registry.py] <<file>>
[swarm_arange.py] <<file>>
[majority_voting.py] <<file>>
[swarm_id_generator.py] <<file>>
[tree_swarm.py] <<file>>
[async_workflow.py] <<file>>
}
To consider each of these packages, we can categorize them based on their functionality and expected outcomes within a swarming architecture. Here’s a brief overview of expected functionalities for some groups:
### Agent Management
- **agent.py**: Manages individual agent behaviors and properties.
- **agent_registry.py**: Tracks and manages the lifecycle of agents in the environment.
- **agent_memory_manager.py**: Handles memory allocation and data storage for agents.
### Workflow Coordination
- **base_workflow.py**: Defines the foundational structure for a swarm's workflow.
- **multi_process_workflow.py**: Manages workflows that involve multiple processes running concurrently.
- **async_workflow.py**: Implements asynchronous processing in workflows.
### Communication and Collaboration
- **groupchat.py**: Facilitates communication between agents in a chat-like manner.
- **multi_agent_collab.py**: Coordinates collaboration activities between multiple agents.
### Task Management
- **task.py**: Represents a unit of work for the agents.
- **task_queue.py**: Manages task distribution among agents.
### Swarm Strategies
- **base_swarm.py**: Establishes the base structure and rules for swarm operations.
- **round_robin.py**: Implements a round-robin approach for task assignments.
- **majority_voting.py**: Facilitates decision-making through majority voting among agents.
### Utilization and Tracking
- **utils.py**: Contains utility functions that aid in various operations across agents and workflows.
- **swarm_registry.py**: Keeps a registry of active swarms and their states.
### Data Structure and Graphs
- **graph_swarm.py**: Implements swarming strategies based on graph structures.
- **tree_swarm.py**: Utilizes tree structures for organizing and guiding agent actions.
Each package should interact seamlessly, following the principles of swarming (e.g., decentralized control, self-organization) while fulfilling its expected role.
package "telemetry" {
[bootup.py] <<file>>
[user_utils.py] <<file>>
[sys_info.py] <<file>>
[sentry_active.py] <<file>>
[capture_sys_data.py] <<file>>
[auto_upgrade_swarms.py] <<file>>
}
package "schemas" {
[agent_input_schema.py] <<file>>
[agent_step_schemas.py] <<file>>
[base_schemas.py] <<file>>
}
package "utils" {
[add_docs_to_agents.py] <<file>>
[lazy_loader.py] <<file>>
[data_to_text.py] <<file>>
[litellm_wrapper.py] <<file>>
[agent_ops_check.py] <<file>>
[loguru_logger.py] <<file>>
[update_agent_system_prompts.py] <<file>>
[try_except_wrapper.py] <<file>>
[swarm_reliability_checks.py] <<file>>
[async_file_creation.py] <<file>>
[pandas_utils.py] <<file>>
}
package "artifacts" {
[main_artifact.py] <<file>>
}
package "prompts" {
[ai_research_team.py] <<file>>
[react.py] <<file>>
[sales.py] <<file>>
}
package "agents" {
[openai_assistant.py] <<file>>
[auto_generate_swarm_config.py] <<file>>
}
package "tools" {
[base_tool.py] <<file>>
[tool_registry.py] <<file>>
[func_calling_executor.py] <<file>>
}
package "cli" {
[main.py] <<file>>
[create_agent.py] <<file>>
}
Here’s a concise tagging and expectations summary for each package in the code structure:
### 1. Swarms
- **Tag:** Core Functionality
- **Expectations:** Implements swarming algorithms, agent management, workflows, and communication protocols.
### 2. Telemetry
- **Tag:** Monitoring & Logging
- **Expectations:** Tracks system data, manages boot-up processes, and captures system metrics for performance analysis.
### 3. Schemas
- **Tag:** Data Structures
- **Expectations:** Defines input/output schemas for agents and ensures data validation and integrity during processing.
### 4. Utils
- **Tag:** Utility Functions
- **Expectations:** Provides helper functions for logging, data conversion, error handling, and asynchronous file operations.
### 5. Artifacts
- **Tag:** Data Storage
- **Expectations:** Manages persistent data artifacts or models generated by the system, facilitating retrieval and usage.
### 6. Prompts
- **Tag:** Interaction Templates
- **Expectations:** Contains predefined interaction prompts for different contexts or agents, aiding in task-specific communication.
### 7. Agents
- **Tag:** AI Agents
- **Expectations:** Implements specific agent functionalities, including interaction with external APIs like OpenAI.
### 8. Tools
- **Tag:** Functional Extensions
- **Expectations:** Provides tools for executing functions, managing tool resources, and enhancing agent capabilities.
### 9. CLI
- **Tag:** Command Line Interface
- **Expectations:** Responsible for user interaction via command line, including agent creation and overall system management.}
@enduml
```
This representation captures the hierarchical structure of the code with the respective files in packages (folders). Each file is tagged as a `<<file>>` for clarity. You can visualize it using a PlantUML renderer. Adjust and add additional files or packages as necessary to fit your requirements!
Given the files and their sizes, we can devise a strategy to group them into context windows of 16,384 output tokens. Here's a concise plan:
1. **Calculate Total File Sizes**: First, sum the sizes of all the files to determine how many chunks will be needed.
2. **File Size Distribution**: Sort the files by size in descending order. This will help prioritize larger files, ensuring we utilize the token limit effectively.
3. **Chunking Strategy**:
- Start from the largest file and keep adding files to the current chunk until the total size reaches or slightly exceeds the 16,384 token limit.
- If adding a file exceeds the limit, finalize the current chunk and start a new chunk with the current file.
- Repeat until all files are organized into chunks.
4. **Adjustments for Small Files**: If you encounter many small files, consider batching them together in a single chunk if they collectively do not exceed the token limit. This avoids many small chunks.
5. **Consider Dependencies**: If certain files depend on others (e.g., modules), try to keep those files within the same chunk to avoid issues when reading the code.
6. **Testing**: Run a test with a few chunks to ensure that they load correctly within the context window size.
Here's a pseudocode implementation:
```python
file_sizes = [
("swarms/structs/agent_registry.py", 10599),
("swarms/structs/omni_agent_types.py", 369),
# Add all other files with their sizes
]
max_size = 16384
current_chunk = []
current_size = 0
for file, size in sorted(file_sizes, key=lambda x: x[1], reverse=True):
if current_size + size <= max_size:
current_chunk.append(file)
current_size += size
else:
# Save current_chunk to a list of chunks
save_chunk(current_chunk)
current_chunk = [file]
current_size = size
# Don't forget to save the last chunk
if current_chunk:
save_chunk(current_chunk)
```
By following this strategy, you can ensure that your code is managed within the constraints of your output token limit effectively.
To effectively manage the source code within the context window sizes of the GPT-4o model, you can implement a strategy to break down your Python files into chunks that fit within the token limits. Here's a straightforward approach:
### 2. Group Files by Size
Analyze the output to determine how to combine smaller files. Keep files that are individually below the model's limits together while ensuring combined sizes do not exceed:
- Input Context: 128,000 tokens
- Output Context: 16,384 tokens
To manage your source code within the context window sizes of the GPT-4o model, follow this strategy:
### 1. Calculate Token Size
Estimate the token size for each file. Typically, 1 token is approximately 4 characters of code plus a newline. For simplicity, assume that 1,000 characters correspond to about 750 tokens.
### 2. Gather File Size Information
From your `wc -c` output, note the byte sizes of each file:
| Filename | Size (Bytes) | Approx Tokens |
|-----------------------------------------------|--------------|---------------|
| **Total** | **508,058** | **~381,044** |
| ... | ... | ... |
| (List all files as before) | | |
### 3. Group Smaller Files
You can group files to ensure the combined size remains well below limits:
- **Single Files**: For files under approximately 12,000 bytes (9000 tokens approx), they’re safe to process individually.
- **Batching**: For larger files, consider batching to stay within a buffer of 128,000 input tokens and 16,384 output tokens.
### 4. List Groupings
Group files maintaining the total size for each group within limits. Example grouping could look like this:
- **Group 1**:
- `swarms/structs/agent_registry.py` (~10,599 bytes)
- `swarms/structs/omni_agent_types.py` (~369 bytes)
- (Continue grouping similar-sized files while keeping the total < 128,000 bytes)
### 5. Example Grouping
A practical example could be:
- **Group 1**: `agent_registry.py`, `omni_agent_types.py`, `auto_swarm.py`, (`~20,000 bytes`)
- **Group 2**: `base_structure.py`, `agent_router.py` (`~30,000 bytes`)
- **Group 3**: `pulsar_swarm.py`, `workspace_manager.py` (`~18,000 bytes`)
### 6. Implementation
- Use a script to automate grouping. Loop through the files, calculate running total sizes while grouping files into lists that meet the criteria.
### 7. Processing
Once grouped, you can feed each list of files into the GPT-4o model one at a time until all groups have been processed.
### Conclusion
This general structure should help you split the source code effectively, allowing you to work within the limits of the GPT-4o model while maintaining logical file groupings.
### 3. Chunking Strategy
Given that:
- Individual larger files ( `swarms/structs/agent.py`) should remain uncombined.
- Smaller files can be combined as follows:
#### Example Grouping:
- Group files that total under 16,384, for example, from each subdirectory. Group smaller files together, ensuring they remain under the threshold.
```plaintext
- Group 1: (combine smaller files)
- swarms/telemetry/*.py
- swarms/utils/*.py
- Group 2: (larger files)
- swarms/artifacts/*.py
- swarms/prompts/*.py
```
### 4. Implementation
Create a script that iterates through the directories, groups files until the predefined token limits are reached, and saves each group in a way that retains the original directory structure.
### 5. Testing and Validation
After chunking, run a test to ensure that:
1. Each chunk remains within token limits.
2. No critical functionality is lost when files are combined or separated.
3. The script handles edge cases (e.g., very small files or files with comments).
This strategy maintains modularity while improving processing efficiency within the given constraints of the model.
It appears your message was blank. If you have any further questions or need additional assistance, please let me know!
Now consider recombination patterns to recombine the results in a quasi-meta-merge sort.
we can see our work as a knuthean searching and sorting algorithm using the llm as our processor for partial ordering.
To implement a quasi-meta-merge sort using a language model (LLM) for recombination patterns, you can follow these algorithmic steps:
### 1. **Chunk Generation**
After chunking the Python files as described previously, treat each chunk as an item for processing. Each chunk will serve as an input for the LLM.
### 2. **Define Quasi-Merge Criteria**
Establish criteria for how chunks will be combined and ordered:
- **Semantic Cohesion**: Chunks that deal with similar functionalities or modules (e.g., all agent-related chunks).
- **File Size**: Combine smaller chunks first before larger ones for efficient processing.
- **Dependency Order**: Ensure that files relying on others are processed in the correct order, using Dagger-like dependency management.
### 3. **Processing with LLM**
Use the LLM to analyze chunks and suggest combinations based on the defined merge criteria:
- **Input**: Provide the LLM with pairs of chunks.
- **Output**: Ask it to rate or assess the coherence and compatibility of merging these chunks based on their content.
### 4. **Recombination Patterns**
Implement a quasi-meta-merge pattern as follows:
1. **Pairing**: Start by pairing chunks for evaluation. Use a method like binary tree pairing or bucket sorting based on size or functionality.
2. **Evaluation**: For each pair, use the LLM to produce a compatibility score or a combined summary.
3. **Sorting**: Sort the results based on the LLM's feedback and scores, leading to a preliminary ordered list of chunks.
4. **Merge Process**: Recombine the pairs, generating merged outputs that represent cohesive functionalities.
### 5. **Iterative Refinement**
After the initial combination:
- **Re-evaluate**: Feed merged outputs back into the LLM for assessment.
- **Adjust Pairings**: Depending on the feedback, adjust combinations, possibly splitting or merging further based on newly identified relationships.
### 6. **Final Collection**
Once pairs have been assessed and recombined:
- Collect final outputs.
- Reorganize them based on the original directory structure.
- Conduct a final review or testing phase ensuring that combined outputs maintain expected functionality.
### Example Pseudocode
Here’s a brief pseudo-implementation for reference:
```python
def quasi_meta_merge_sort(chunks):
pairs = generate_pairs(chunks)
evaluated_pairs = [(chunk1, chunk2, llm.evaluate(chunk1, chunk2)) for chunk1, chunk2 in pairs]
sorted_pairs = sort_by_evaluation_score(evaluated_pairs)
merged_chunks = []
for chunk1, chunk2, score in sorted_pairs:
merged_chunk = llm.merge(chunk1, chunk2)
merged_chunks.append(merged_chunk)
# Re-evaluate and adjust iteratively
while needs_refinement(merged_chunks):
merged_chunks = re_evaluate(merged_chunks)
return final_output(merged_chunks)
```
### Conclusion
This algorithm leverages the LLM's processing capabilities to facilitate an advanced sorting and merging strategy. The result is a more organized collection of source code chunks, preserving functionality while enhancing modularity and clarity. This approach aligns well with Knuth’s methodology, focusing on efficient sorting and processing through intelligent structuring.
Here's how you can implement a quasi-meta-merge sort using a language model (LLM) to facilitate a knuthian searching and sorting algorithm for recombining the Python file chunks based on their functionalities and dependencies.
### Steps for Implementation
1. **Chunk Generation**:
- Start with previously created chunks of Python files, considering their token size and relevance.
2. **Define Quasi-Merge Criteria**:
- **Semantic Cohesion**: Identify functionally similar chunks (e.g., all related to agents).
- **File Size**: Prefer smaller chunks for initial combinations.
- **Dependency Management**: Maintain execution order based on dependencies.
3. **Processing with the LLM**:
- Use the LLM to analyze the compatibility of merging chunks. The input can be pairs of chunks, and the output will provide a compatibility score or suggestions.
4. **Recombination Patterns**:
- **Pairing**: Form an initial set of pairs from the chunks using methods like binary pairing or random sampling.
- **Evaluation via LLM**: For each pair, check how well they can be merged based on content and functionality:
```python
def evaluate_pairs(pair_list):
results = []
for chunk1, chunk2 in pair_list:
score, reasons = llm.evaluate_merge(chunk1, chunk2)
results.append((chunk1, chunk2, score, reasons))
return sorted(results, key=lambda x: x[2], reverse=True) # Sort by score
```
5. **Sort and Merge**:
- Use the evaluation results to sort and merge chunks, prioritizing those with higher compatibility scores first.
- Merge chunks based on evaluation.
```python
def merge_chunks(sorted_pairs):
merged_results = []
for chunk1, chunk2, score, reasons in sorted_pairs:
merged_chunk = llm.merge(chunk1, chunk2)
merged_results.append(merged_chunk)
return merged_results
```
6. **Iterative Refinement**:
- After the initial combination, submit the merged output to the LLM for further assessment. If required, adjust pairings based on new insights:
```python
def refine_merges(merged_chunks):
while requires_refinement(merged_chunks):
updated_chunks = []
for chunk in merged_chunks:
feedback = llm.refine(chunk)
updated_chunks.append(feedback)
merged_chunks = updated_chunks
return merged_chunks
```
7. **Final Collection**:
- Organize the final output, maintaining directory structure and ensuring functionality:
```python
def collect_final_output(merged_chunks):
output_path = organize_by_structure(merged_chunks)
save_to_directory(output_path)
```
### Pseudocode Example
Here’s a brief pseudocode putting it all together:
```python
def quasi_meta_merge_sort(chunks):
pairs = generate_pairs(chunks)
evaluated_pairs = evaluate_pairs(pairs)
sorted_pairs = sort_by_evaluation_score(evaluated_pairs)
merged_chunks = merge_chunks(sorted_pairs)
# Iteratively refine until no further improvements can be made
merged_chunks = refine_merges(merged_chunks)
return collect_final_output(merged_chunks)
```
### Considerations
- **Performance**: Monitor the efficiency, especially if the number of chunks is large.
- **LLM Costs**: Be mindful of usage costs or performance issues while querying the LLM.
- **Testing**: Ensure that the recombined code retains functionality; consider including unit tests for major components.
### Conclusion
The outlined approach leverages an intelligent merging algorithm supported by semantic analysis from an LLM, creating an organized, high-quality set of Python files. By implementing these patterns, you can achieve a robust, modular codebase that aligns with the principles of efficient searching and sorting, similar to Knuth's methodologies.
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