Merge branch 'kyegomez:master' into JSON-Output-Support-Agent

changelog.md

@@ -0,0 +1 @@
+# 5.8.7

docs/requirements.txt

@@ -26,7 +26,7 @@ jinja2~=3.1
 markdown~=3.7
 mkdocs-material-extensions~=1.3
 pygments~=2.18
-pymdown-extensions~=10.10
+pymdown-extensions~=10.11
 
 # Requirements for plugins
 babel~=2.16

example.py

@@ -33,11 +33,14 @@
     context_length=200000,
     return_step_meta=False,
     # output_type="json",
-    output_type="string",
+    output_type="json",  # "json", "dict", "csv" OR "string" soon "yaml" and
     streaming_on=False,
+    # auto_generate_prompt=True,
 )
 
 
-agent.run(
-    "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
+print(
+    agent.run(
+        "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
+    )
 )

new_prompt.py

@@ -0,0 +1,28 @@
+from swarms import Prompt
+from swarm_models import OpenAIChat
+import os
+
+model = OpenAIChat(
+    api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4o-mini", temperature=0.1
+)
+
+# Aggregator system prompt
+prompt_generator_sys_prompt = Prompt(
+    name="prompt-generator-sys-prompt-o1",
+    description="Generate the most reliable prompt for a specific problem",
+    content="""
+    Your purpose is to craft extremely reliable and production-grade system prompts for other agents.
+    
+    # Instructions
+    - Understand the prompt required for the agent.
+    - Utilize a combination of the most effective prompting strategies available, including chain of thought, many shot, few shot, and instructions-examples-constraints.
+    - Craft the prompt by blending the most suitable prompting strategies.
+    - Ensure the prompt is production-grade ready and educates the agent on how to reason and why to reason in that manner.
+    - Provide constraints if necessary and as needed.
+    - The system prompt should be extensive and cover a vast array of potential scenarios to specialize the agent. 
+    """,
+    auto_generate_prompt=True,
+    llm=model,
+)
+
+print(prompt_generator_sys_prompt.get_prompt())

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "5.8.6"
+version = "5.8.7"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <[email protected]>"]
@@ -84,7 +84,7 @@ swarms = "swarms.cli.main:main"
 
 [tool.poetry.group.lint.dependencies]
 black = ">=23.1,<25.0"
-ruff = ">=0.5.1,<0.6.8"
+ruff = ">=0.5.1,<0.6.10"
 types-toml = "^0.10.8.1"
 types-pytz = ">=2023.3,<2025.0"
 types-chardet = "^5.0.4.6"

swarms/prompts/prompt.py

@@ -13,7 +13,8 @@
 from swarms_cloud.utils.log_to_swarms_database import log_agent_data
 from swarms_cloud.utils.capture_system_data import capture_system_data
 from swarms.tools.base_tool import BaseTool
-
+# from swarms.agents.ape_agent import auto_generate_prompt
+from typing import Any
 
 class Prompt(BaseModel):
     """
@@ -70,11 +71,15 @@ class Prompt(BaseModel):
         default="prompts",
         description="The folder path within WORKSPACE_DIR where the prompt will be autosaved",
     )
+    auto_generate_prompt: bool = Field(
+        default=False,
+        description="Flag to enable or disable auto-generating the prompt",
+    )
     parent_folder: str = Field(
         default=os.getenv("WORKSPACE_DIR"),
         description="The folder where the autosave folder is in",
     )
-    # tools: List[callable] = None
+    llm: Any = None
 
     @validator("edit_history", pre=True, always=True)
     def initialize_history(cls, v, values):
@@ -86,6 +91,15 @@ def initialize_history(cls, v, values):
                 values["content"]
             ]  # Store initial version in history
         return v
+
+    def __init__(self, **data):
+        super().__init__(**data)
+
+        if self.autosave:
+            self._autosave()
+
+        if self.auto_generate_prompt and self.llm:
+            self.auto_generate_prompt()
 
     def edit_prompt(self, new_content: str) -> None:
         """
@@ -238,6 +252,16 @@ def _autosave(self) -> None:
         with open(file_path, "w") as file:
             json.dump(self.model_dump(), file)
         logger.info(f"Autosaved prompt {self.id} to {file_path}.")
+
+    # def auto_generate_prompt(self):
+    #     logger.info(f"Auto-generating prompt for {self.name}")
+    #     task = self.name + " " + self.description + " " + self.content
+    #     prompt = auto_generate_prompt(task, llm=self.llm, max_tokens=4000, use_second_sys_prompt=True)
+    #     logger.info("Generated prompt successfully, updating content")
+    #     self.edit_prompt(prompt)
+    #     logger.info("Prompt content updated")
+
+    #     return "Prompt auto-generated successfully."
 
     class Config:
         """Pydantic configuration for better JSON serialization."""