[DOCKERFILE]

.gitignore

@@ -14,6 +14,7 @@ static/generated
 runs
 Financial-Analysis-Agent_state.json
 experimental
+ffn_alternatives
 artifacts_five
 encryption
 errors

Dockerfile

@@ -20,6 +20,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 
 # Install swarms packages
 RUN pip install --no-cache-dir swarm-models swarms
+RUN pip install transformers torch litellm tiktoken openai pandas numpy pypdf
+
 
 # Production stage
 FROM python:3.11-slim-bullseye

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "7.0.1"
+version = "7.0.3"
 description = "Swarms - TGSC"
 license = "MIT"
 authors = ["Kye Gomez <[email protected]>"]

simple_example.py

@@ -2,8 +2,8 @@
 
 Agent(
     agent_name="Stock-Analysis-Agent",
-    model_name="gpt-4o-mini",
+    model_name="groq/deepseek-r1-distill-llama-70b",
     max_loops="auto",
     interactive=True,
-    streaming_on=True,
-).run("What are 5 hft algorithms")
+    streaming_on=False,
+).run("What are the best ways to analyze macroeconomic data?")

swarms/agents/tool_agent.py

@@ -1,12 +1,10 @@
 from typing import Any, Optional, Callable
 from swarms.tools.json_former import Jsonformer
 from swarms.utils.loguru_logger import initialize_logger
-from swarms.utils.lazy_loader import lazy_import_decorator
 
 logger = initialize_logger(log_folder="tool_agent")
 
 
-@lazy_import_decorator
 class ToolAgent:
     """
     Represents a tool agent that performs a specific task using a model and tokenizer.

swarms/structs/agent.py

@@ -563,7 +563,7 @@ def __init__(
             max_loops=self.max_loops,
             steps=self.short_memory.to_dict(),
             full_history=self.short_memory.get_str(),
-            total_tokens=count_tokens(self.short_memory.get_str()),
+            total_tokens=count_tokens(text=self.short_memory.get_str()),
             stopping_token=self.stopping_token,
             interactive=self.interactive,
             dynamic_temperature_enabled=self.dynamic_temperature_enabled,

swarms/structs/swarm_matcher.py

@@ -8,7 +8,6 @@
 from swarms.utils.auto_download_check_packages import (
     auto_check_and_download_package,
 )
-from swarms.utils.lazy_loader import lazy_import_decorator
 from swarms.utils.loguru_logger import initialize_logger
 
 logger = initialize_logger(log_folder="swarm_matcher")
@@ -29,7 +28,6 @@ class SwarmMatcherConfig(BaseModel):
     )
 
 
-@lazy_import_decorator
 class SwarmMatcher:
     """
     A class for matching tasks to swarm types based on their descriptions.
@@ -271,7 +269,6 @@ def initialize_swarm_types(matcher: SwarmMatcher):
     logger.debug("Swarm types initialized")
 
 
-@lazy_import_decorator
 def swarm_matcher(task: str, *args, **kwargs):
     """
     Runs the SwarmMatcher example with predefined tasks and swarm types.

swarms/tools/json_former.py

@@ -1,7 +1,6 @@
 import json
 from typing import Any, Dict, List, Union
 
-from swarms.utils.lazy_loader import lazy_import_decorator
 from pydantic import BaseModel
 from swarms.tools.logits_processor import (
     NumberStoppingCriteria,
@@ -24,7 +23,6 @@
 GENERATION_MARKER = "|GENERATION|"
 
 
-@lazy_import_decorator
 class Jsonformer:
     """
     Initializes the FormatTools class.

swarms/utils/litellm_tokenizer.py

@@ -6,7 +6,8 @@ def count_tokens(text: str, model: str = "gpt-4o") -> int:
     try:
         from litellm import encode
     except ImportError:
-        subprocess.run(["pip", "install", "litellm"])
+        import sys
+        subprocess.run([sys.executable, "-m", "pip", "install", "litellm"])
         from litellm import encode
 
     return len(encode(model=model, text=text))

swarms/utils/pdf_to_text.py

@@ -4,8 +4,9 @@
     import pypdf
 except ImportError:
     import subprocess
+    import sys
 
-    subprocess.check_call(["python", "-m", "pip", "install", "pypdf"])
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "pypdf"])
     import pypdf