[DOCS]

.gitignore

@@ -17,6 +17,7 @@ chroma
 Accounting Assistant_state.json
 Unit Testing Agent_state.json
 Devin_state.json
+hire_researchers
 json_logs
 Medical Image Diagnostic Agent_state.json
 flight agent_state.json

swarms/schemas/agent_input_schema.py

@@ -0,0 +1,143 @@
+from typing import Any, Callable, Dict, List, Optional
+from pydantic import BaseModel, Field
+from pydantic.v1 import validator
+
+
+class AgentSchema(BaseModel):
+    llm: Any = Field(..., description="The language model to use")
+    max_tokens: int = Field(
+        ..., description="The maximum number of tokens", ge=1
+    )
+    context_window: int = Field(
+        ..., description="The context window size", ge=1
+    )
+    user_name: str = Field(..., description="The user name")
+    agent_name: str = Field(..., description="The name of the agent")
+    system_prompt: str = Field(..., description="The system prompt")
+    template: Optional[str] = Field(default=None)
+    max_loops: Optional[int] = Field(default=1, ge=1)
+    stopping_condition: Optional[Callable[[str], bool]] = Field(
+        default=None
+    )
+    loop_interval: Optional[int] = Field(default=0, ge=0)
+    retry_attempts: Optional[int] = Field(default=3, ge=0)
+    retry_interval: Optional[int] = Field(default=1, ge=0)
+    return_history: Optional[bool] = Field(default=False)
+    stopping_token: Optional[str] = Field(default=None)
+    dynamic_loops: Optional[bool] = Field(default=False)
+    interactive: Optional[bool] = Field(default=False)
+    dashboard: Optional[bool] = Field(default=False)
+    agent_description: Optional[str] = Field(default=None)
+    tools: Optional[List[Callable]] = Field(default=None)
+    dynamic_temperature_enabled: Optional[bool] = Field(default=False)
+    sop: Optional[str] = Field(default=None)
+    sop_list: Optional[List[str]] = Field(default=None)
+    saved_state_path: Optional[str] = Field(default=None)
+    autosave: Optional[bool] = Field(default=False)
+    self_healing_enabled: Optional[bool] = Field(default=False)
+    code_interpreter: Optional[bool] = Field(default=False)
+    multi_modal: Optional[bool] = Field(default=False)
+    pdf_path: Optional[str] = Field(default=None)
+    list_of_pdf: Optional[str] = Field(default=None)
+    tokenizer: Optional[Any] = Field(default=None)
+    long_term_memory: Optional[Any] = Field(default=None)
+    preset_stopping_token: Optional[bool] = Field(default=False)
+    traceback: Optional[Any] = Field(default=None)
+    traceback_handlers: Optional[Any] = Field(default=None)
+    streaming_on: Optional[bool] = Field(default=False)
+    docs: Optional[List[str]] = Field(default=None)
+    docs_folder: Optional[str] = Field(default=None)
+    verbose: Optional[bool] = Field(default=False)
+    parser: Optional[Callable] = Field(default=None)
+    best_of_n: Optional[int] = Field(default=None)
+    callback: Optional[Callable] = Field(default=None)
+    metadata: Optional[Dict[str, Any]] = Field(default=None)
+    callbacks: Optional[List[Callable]] = Field(default=None)
+    logger_handler: Optional[Any] = Field(default=None)
+    search_algorithm: Optional[Callable] = Field(default=None)
+    logs_to_filename: Optional[str] = Field(default=None)
+    evaluator: Optional[Callable] = Field(default=None)
+    output_json: Optional[bool] = Field(default=False)
+    stopping_func: Optional[Callable] = Field(default=None)
+    custom_loop_condition: Optional[Callable] = Field(default=None)
+    sentiment_threshold: Optional[float] = Field(default=None)
+    custom_exit_command: Optional[str] = Field(default="exit")
+    sentiment_analyzer: Optional[Callable] = Field(default=None)
+    limit_tokens_from_string: Optional[Callable] = Field(default=None)
+    custom_tools_prompt: Optional[Callable] = Field(default=None)
+    tool_schema: Optional[Any] = Field(default=None)
+    output_type: Optional[Any] = Field(default=None)
+    function_calling_type: Optional[str] = Field(default="json")
+    output_cleaner: Optional[Callable] = Field(default=None)
+    function_calling_format_type: Optional[str] = Field(default="OpenAI")
+    list_base_models: Optional[List[Any]] = Field(default=None)
+    metadata_output_type: Optional[str] = Field(default="json")
+    state_save_file_type: Optional[str] = Field(default="json")
+    chain_of_thoughts: Optional[bool] = Field(default=False)
+    algorithm_of_thoughts: Optional[bool] = Field(default=False)
+    tree_of_thoughts: Optional[bool] = Field(default=False)
+    tool_choice: Optional[str] = Field(default="auto")
+    execute_tool: Optional[bool] = Field(default=False)
+    rules: Optional[str] = Field(default=None)
+    planning: Optional[bool] = Field(default=False)
+    planning_prompt: Optional[str] = Field(default=None)
+    device: Optional[str] = Field(default=None)
+    custom_planning_prompt: Optional[str] = Field(default=None)
+    memory_chunk_size: Optional[int] = Field(default=2000, ge=0)
+    agent_ops_on: Optional[bool] = Field(default=False)
+    log_directory: Optional[str] = Field(default=None)
+    project_path: Optional[str] = Field(default=None)
+    tool_system_prompt: Optional[str] = Field(default="tool_sop_prompt()")
+    top_p: Optional[float] = Field(default=0.9, ge=0, le=1)
+    top_k: Optional[int] = Field(default=None)
+    frequency_penalty: Optional[float] = Field(default=0.0, ge=0, le=1)
+    presence_penalty: Optional[float] = Field(default=0.0, ge=0, le=1)
+    temperature: Optional[float] = Field(default=0.1, ge=0, le=1)
+
+    @validator(
+        "tools",
+        "docs",
+        "sop_list",
+        "callbacks",
+        "list_base_models",
+        each_item=True,
+    )
+    def check_list_items_not_none(cls, v):
+        if v is None:
+            raise ValueError("List items must not be None")
+        return v
+
+    @validator(
+        "tokenizer",
+        "memory",
+        "traceback",
+        "traceback_handlers",
+        "parser",
+        "callback",
+        "search_algorithm",
+        "evaluator",
+        "stopping_func",
+        "custom_loop_condition",
+        "sentiment_analyzer",
+        "limit_tokens_from_string",
+        "custom_tools_prompt",
+        "output_cleaner",
+    )
+    def check_optional_callable_not_none(cls, v):
+        if v is not None and not callable(v):
+            raise ValueError(f"{v} must be a callable")
+        return v
+
+
+# # Example of how to use the schema
+# agent_data = {
+#     "llm": "OpenAIChat",
+#     "max_tokens": 4096,
+#     "context_window": 8192,
+#     "user_name": "Human",
+#     "agent_name": "test-agent",
+#     "system_prompt": "Custom system prompt",
+# }
+
+# agent = AgentSchema(**agent_data)
+# print(agent)

swarms/schemas/schemas.py

@@ -16,35 +16,6 @@ class TaskInput(BaseModel):
     )
 
 
-class Artifact(BaseModel):
-    """
-    Represents an artifact.
-
-    Attributes:
-        artifact_id (str): Id of the artifact.
-        file_name (str): Filename of the artifact.
-        relative_path (str, optional): Relative path of the artifact in the agent's workspace.
-    """
-
-    artifact_id: str = Field(
-        ...,
-        description="Id of the artifact",
-        examples=["b225e278-8b4c-4f99-a696-8facf19f0e56"],
-    )
-    file_name: str = Field(
-        ...,
-        description="Filename of the artifact",
-        examples=["main.py"],
-    )
-    relative_path: str | None = Field(
-        None,
-        description=(
-            "Relative path of the artifact in the agent's workspace"
-        ),
-        examples=["python/code/"],
-    )
-
-
 class ArtifactUpload(BaseModel):
     file: bytes = Field(..., description="File to upload")
     relative_path: str | None = Field(
@@ -86,22 +57,22 @@ class TaskRequestBody(BaseModel):
     additional_input: TaskInput | None = None
 
 
-class Task(TaskRequestBody):
+# class Task(TaskRequestBody):
-    task_id: str = Field(
+#     task_id: str = Field(
-        ...,
+#         ...,
-        description="The ID of the task.",
+#         description="The ID of the task.",
-        examples=["50da533e-3904-4401-8a07-c49adf88b5eb"],
+#         examples=["50da533e-3904-4401-8a07-c49adf88b5eb"],
-    )
+#     )
-    artifacts: list[Artifact] = Field(
+#     artifacts: list[Artifact] = Field(
-        [],
+#         [],
-        description="A list of artifacts that the task has produced.",
+#         description="A list of artifacts that the task has produced.",
-        examples=[
+#         examples=[
-            [
+#             [
-                "7a49f31c-f9c6-4346-a22c-e32bc5af4d8e",
+#                 "7a49f31c-f9c6-4346-a22c-e32bc5af4d8e",
-                "ab7b4091-2560-4692-a4fe-d831ea3ca7d6",
+#                 "ab7b4091-2560-4692-a4fe-d831ea3ca7d6",
-            ]
+#             ]
-        ],
+#         ],
-    )
+#     )
 
 
 class StepRequestBody(BaseModel):
@@ -144,7 +115,7 @@ class Step(BaseModel):
             " <write_to_file('output.txt', 'Washington')"
         ],
     )
-    artifacts: list[Artifact] = Field(
+    artifacts: list[Any] = Field(
         [],
         description="A list of artifacts that the step has produced.",
     )

swarms/structs/agent.py

@@ -266,6 +266,7 @@ class Agent(BaseStructure):
         top_k: int = None,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
+        temperature: float = 0.1,
         *args,
         **kwargs,
     ):
@@ -356,6 +357,7 @@ class Agent(BaseStructure):
         self.top_k = top_k
         self.frequency_penalty = frequency_penalty
         self.presence_penalty = presence_penalty
+        self.temperature
 
         # Name
         self.name = agent_name
@@ -472,7 +474,8 @@ class Agent(BaseStructure):
             self.short_memory.add(role=self.user_name, content=self.sop)
 
         # If agent_ops is on => activate agentops
-        self.activate_agentops()
+        if agent_ops_on is True:
+            self.activate_agentops()
 
     def set_system_prompt(self, system_prompt: str):
         """Set the system prompt"""
@@ -483,19 +486,19 @@ class Agent(BaseStructure):
         self.feedback.append(feedback)
         logging.info(f"Feedback received: {feedback}")
 
-    def initialize_llm(self, llm: Any) -> None:
+    # TODO: Implement the function
-        return llm(
+    # def initialize_llm(self, llm: Any) -> None:
-            system_prompt=self.system_prompt,
+    #     return llm(
-            max_tokens=self.max_tokens,
+    #         system_prompt=self.system_prompt,
-            context_length=self.context_length,
+    #         max_tokens=self.max_tokens,
-            temperature=self.temperature,
+    #         context_length=self.context_length,
-            top_p=self.top_p,
+    #         temperature=self.temperature,
-            top_k=self.top_k,
+    #         top_p=self.top_p,
-            frequency_penalty=self.frequency_penalty,
+    #         top_k=self.top_k,
-            presence_penalty=self.presence_penalty,
+    #         frequency_penalty=self.frequency_penalty,
-            stop=self.stopping_token,
+    #         presence_penalty=self.presence_penalty,
-            engine=self.engine,
+    #         stop=self.stopping_token,
-        )
+    #     )
 
     def agent_initialization(self):
         try:
@@ -718,8 +721,8 @@ class Agent(BaseStructure):
             response = None
             all_responses = []
 
-            if self.tokenizer is not None:
+            # if self.tokenizer is not None:
-                self.check_available_tokens()
+            #     self.check_available_tokens()
 
             while self.max_loops == "auto" or loop_count < self.max_loops:
                 loop_count += 1
@@ -733,6 +736,7 @@ class Agent(BaseStructure):
                 # Task prompt
                 task_prompt = self.short_memory.return_history_as_string()
 
+                # Parameters
                 attempt = 0
                 success = False
                 while attempt < self.retry_attempts and not success:
@@ -743,6 +747,14 @@ class Agent(BaseStructure):
                                     task, *args, **kwargs
                                 )
                             )
+
+                            if exists(self.tokenizer):
+                                task_prompt = (
+                                    self.count_and_shorten_context_window(
+                                        memory_retrieval
+                                    )
+                                )
+
                             # Merge the task prompt with the memory retrieval
                             task_prompt = f"{task_prompt} Documents: Available {memory_retrieval}"
 
@@ -758,14 +770,6 @@ class Agent(BaseStructure):
                             all_responses.append(response)
 
                         else:
-
-                            if exists(self.tokenizer):
-                                task_prompt = (
-                                    self.count_and_shorten_context_window(
-                                        task_prompt
-                                    )
-                                )
-
                             response_args = (
                                 (task_prompt, *args)
                                 if img is None
@@ -1996,3 +2000,23 @@ class Agent(BaseStructure):
                 f"Error with the base models, check the base model types and make sure they are initialized {error}"
             )
             raise error
+
+    async def count_tokens_and_subtract_from_context_window(
+        self, response: str, *args, **kwargs
+    ):
+        """
+        Count the number of tokens in the response and subtract it from the context window.
+
+        Args:
+            response (str): The response to count the tokens from.
+
+        Returns:
+            str: The response after counting the tokens and subtracting it from the context window.
+        """
+        # Count the number of tokens in the response
+        tokens = self.tokenizer.count_tokens(response)
+
+        # Subtract the number of tokens from the context window
+        self.context_length -= len(tokens)
+
+        return response

test.py

@@ -0,0 +1,16 @@
+import requests
+
+url = "https://linkedin-api8.p.rapidapi.com/linkedin-to-email"
+
+querystring = {
+    "url": "https://www.linkedin.com/in/nicolas-nahas-3ba227170/"
+}
+
+headers = {
+    "x-rapidapi-key": "8c6cd073d2msh9fc7d37c26ce73bp1dea6ajsn81819935da85",
+    "x-rapidapi-host": "linkedin-api8.p.rapidapi.com",
+}
+
+response = requests.get(url, headers=headers, params=querystring)
+
+print(response.json())