[CODE QUALITY]

playground/models/kosmos.py

@@ -7,4 +7,4 @@ model = Kosmos()
 out = model.run("Analyze the reciepts in this image", "docs.jpg")
 
 # Print the output
-print(out)
+print(out)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "3.8.2"
+version = "3.8.5"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]

swarms/memory/base_db.py

@@ -156,4 +156,4 @@ class AbstractDatabase(ABC):
 
         """
 
-        pass
+        pass

swarms/memory/base_vectordatabase.py

@@ -91,7 +91,6 @@ class AbstractVectorDatabase(ABC):
 
         pass
 
-
     @abstractmethod
     def get(self, query: str):
         """
@@ -139,4 +138,4 @@ class AbstractVectorDatabase(ABC):
 
         """
 
-        pass
+        pass

swarms/models/__init__.py

@@ -48,7 +48,7 @@ from swarms.models.vip_llava import VipLlavaMultiModal  # noqa: E402
 from swarms.models.llava import LavaMultiModal  # noqa: E402
 from swarms.models.qwen import QwenVLMultiModal  # noqa: E402
 from swarms.models.clipq import CLIPQ  # noqa: E402
-from swarms.models.kosmos_two import Kosmos  # noqa: E402   
+from swarms.models.kosmos_two import Kosmos  # noqa: E402
 from swarms.models.fuyu import Fuyu  # noqa: E402
 
 # from swarms.models.dalle3 import Dalle3

swarms/models/openai_models.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import asyncio
+import functools
 import logging
 import sys
 from typing import (
@@ -16,6 +18,7 @@ from typing import (
     Optional,
     Set,
     Tuple,
+    Type,
     Union,
 )
 
@@ -23,7 +26,7 @@ from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
-from langchain.llms.base import BaseLLM, create_base_retry_decorator
+from langchain.llms.base import BaseLLM
 from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import Generation, LLMResult
 from langchain.schema.output import GenerationChunk
@@ -32,7 +35,17 @@ from langchain.utils import (
     get_pydantic_field_names,
 )
 from langchain.utils.utils import build_extra_kwargs
+from tenacity import (
+    RetryCallState,
+    before_sleep_log,
+    retry,
+    retry_base,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
 
+logger = logging.getLogger(__name__)
 
 from importlib.metadata import version
 
@@ -41,6 +54,62 @@ from packaging.version import parse
 logger = logging.getLogger(__name__)
 
 
+@functools.lru_cache
+def _log_error_once(msg: str) -> None:
+    """Log an error once."""
+    logger.error(msg)
+
+
+def create_base_retry_decorator(
+    error_types: List[Type[BaseException]],
+    max_retries: int = 1,
+    run_manager: Optional[
+        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
+) -> Callable[[Any], Any]:
+    """Create a retry decorator for a given LLM and provided list of error types."""
+
+    _logging = before_sleep_log(logger, logging.WARNING)
+
+    def _before_sleep(retry_state: RetryCallState) -> None:
+        _logging(retry_state)
+        if run_manager:
+            if isinstance(run_manager, AsyncCallbackManagerForLLMRun):
+                coro = run_manager.on_retry(retry_state)
+                try:
+                    loop = asyncio.get_event_loop()
+                    if loop.is_running():
+                        loop.create_task(coro)
+                    else:
+                        asyncio.run(coro)
+                except Exception as e:
+                    _log_error_once(f"Error in on_retry: {e}")
+            else:
+                run_manager.on_retry(retry_state)
+        return None
+
+    min_seconds = 4
+    max_seconds = 10
+    # Wait 2^x * 1 second between each retry starting with
+    # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
+    retry_instance: "retry_base" = retry_if_exception_type(
+        error_types[0]
+    )
+    for error in error_types[1:]:
+        retry_instance = retry_instance | retry_if_exception_type(
+            error
+        )
+    return retry(
+        reraise=True,
+        stop=stop_after_attempt(max_retries),
+        wait=wait_exponential(
+            multiplier=1, min=min_seconds, max=max_seconds
+        ),
+        retry=retry_instance,
+        before_sleep=_before_sleep,
+    )
+
+
 def is_openai_v1() -> bool:
     _version = parse(version("openai"))
     return _version.major >= 1
@@ -833,7 +902,7 @@ class OpenAIChat(BaseLLM):
     """
 
     client: Any  #: :meta private:
-    model_name: str = "gpt-3.5-turbo-1106"
+    model_name: str = "gpt-4-1106-preview"
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     openai_api_key: Optional[str] = None
     openai_api_base: Optional[str] = None

swarms/structs/conversation.py

@@ -75,7 +75,7 @@ class Conversation(BaseStructure):
         self.autosave = autosave
         self.save_filepath = save_filepath
         self.conversation_history = []
-        
+
         # If system prompt is not None, add it to the conversation history
         if self.system_prompt:
             self.add("system", self.system_prompt)

swarms/structs/multi_agent_rag.py

@@ -9,24 +9,24 @@ from swarms.structs.agent import Agent
 class MultiAgentRag:
     """
     Represents a multi-agent RAG (Relational Agent Graph) structure.
-    
+
     Attributes:
         agents (List[Agent]): List of agents in the multi-agent RAG.
         db (AbstractVectorDatabase): Database used for querying.
         verbose (bool): Flag indicating whether to print verbose output.
     """
+
     agents: List[Agent]
     db: AbstractVectorDatabase
     verbose: bool = False
-    
-    
+
     def query_database(self, query: str):
         """
         Queries the database using the given query string.
-        
+
         Args:
             query (str): The query string.
-        
+
         Returns:
             List: The list of results from the database.
         """
@@ -35,15 +35,14 @@ class MultiAgentRag:
             agent_results = agent.long_term_memory_prompt(query)
             results.extend(agent_results)
         return results
-    
-    
+
     def get_agent_by_id(self, agent_id) -> Optional[Agent]:
         """
         Retrieves an agent from the multi-agent RAG by its ID.
-        
+
         Args:
             agent_id: The ID of the agent to retrieve.
-        
+
         Returns:
             Agent or None: The agent with the specified ID, or None if not found.
         """
@@ -51,47 +50,36 @@ class MultiAgentRag:
             if agent.agent_id == agent_id:
                 return agent
         return None
-    
+
     def add_message(
-        self,
-        sender: Agent,
-        message: str,
-        *args,
-        **kwargs
+        self, sender: Agent, message: str, *args, **kwargs
     ):
         """
         Adds a message to the database.
-        
+
         Args:
             sender (Agent): The agent sending the message.
             message (str): The message to add.
             *args: Additional positional arguments.
             **kwargs: Additional keyword arguments.
-        
+
         Returns:
             int: The ID of the added message.
         """
         doc = f"{sender.ai_name}: {message}"
-        
+
         return self.db.add(doc)
-    
-    def query(
-        self,
-        message: str,
-        *args,
-        **kwargs
-    ):
+
+    def query(self, message: str, *args, **kwargs):
         """
         Queries the database using the given message.
-        
+
         Args:
             message (str): The message to query.
             *args: Additional positional arguments.
             **kwargs: Additional keyword arguments.
-        
+
         Returns:
             List: The list of results from the database.
         """
         return self.db.query(message)
-    
-