mistral caller, openai verison 2.8, llama function caller, tests for flow

Author: Kye

.gitignore

@@ -24,6 +24,7 @@ stderr_log.txt
 __pycache__/
 *.py[cod]
 *$py.class
+.grit
 error.txt
 
 # C extensions

README.md

@@ -74,10 +74,7 @@ flow = Flow(
 # out = flow.load_state("flow_state.json")
 # temp = flow.dynamic_temperature()
 # filter = flow.add_response_filter("Trump")
-
-# Run the flow
 out = flow.run("Generate a 10,000 word blog on health and wellness.")
-
 # out = flow.validate_response(out)
 # out = flow.analyze_feedback(out)
 # out = flow.print_history_and_memory()
@@ -138,6 +135,77 @@ for task in workflow.tasks:
 
 ---
 
+# Features 🤖 
+The Swarms framework is designed with a strong emphasis on reliability, performance, and production-grade readiness. 
+Below are the key features that make Swarms an ideal choice for enterprise-level AI deployments.
+
+## 🚀 Production-Grade Readiness
+- **Scalable Architecture**: Built to scale effortlessly with your growing business needs.
+- **Enterprise-Level Security**: Incorporates top-notch security features to safeguard your data and operations.
+- **Containerization and Microservices**: Easily deployable in containerized environments, supporting microservices architecture.
+
+## ⚙️ Reliability and Robustness
+- **Fault Tolerance**: Designed to handle failures gracefully, ensuring uninterrupted operations.
+- **Consistent Performance**: Maintains high performance even under heavy loads or complex computational demands.
+- **Automated Backup and Recovery**: Features automatic backup and recovery processes, reducing the risk of data loss.
+
+## 💡 Advanced AI Capabilities
+
+The Swarms framework is equipped with a suite of advanced AI capabilities designed to cater to a wide range of applications and scenarios, ensuring versatility and cutting-edge performance.
+
+### Multi-Modal Autonomous Agents
+- **Versatile Model Support**: Seamlessly works with various AI models, including NLP, computer vision, and more, for comprehensive multi-modal capabilities.
+- **Context-Aware Processing**: Employs context-aware processing techniques to ensure relevant and accurate responses from agents.
+
+### Function Calling Models for API Execution
+- **Automated API Interactions**: Function calling models that can autonomously execute API calls, enabling seamless integration with external services and data sources.
+- **Dynamic Response Handling**: Capable of processing and adapting to responses from APIs for real-time decision making.
+
+### Varied Architectures of Swarms
+- **Flexible Configuration**: Supports multiple swarm architectures, from centralized to decentralized, for diverse application needs.
+- **Customizable Agent Roles**: Allows customization of agent roles and behaviors within the swarm to optimize performance and efficiency.
+
+### Generative Models
+- **Advanced Generative Capabilities**: Incorporates state-of-the-art generative models to create content, simulate scenarios, or predict outcomes.
+- **Creative Problem Solving**: Utilizes generative AI for innovative problem-solving approaches and idea generation.
+
+### Enhanced Decision-Making
+- **AI-Powered Decision Algorithms**: Employs advanced algorithms for swift and effective decision-making in complex scenarios.
+- **Risk Assessment and Management**: Capable of assessing risks and managing uncertain situations with AI-driven insights.
+
+### Real-Time Adaptation and Learning
+- **Continuous Learning**: Agents can continuously learn and adapt from new data, improving their performance and accuracy over time.
+- **Environment Adaptability**: Designed to adapt to different operational environments, enhancing robustness and reliability.
+
+
+## 🔄 Efficient Workflow Automation
+- **Streamlined Task Management**: Simplifies complex tasks with automated workflows, reducing manual intervention.
+- **Customizable Workflows**: Offers customizable workflow options to fit specific business needs and requirements.
+- **Real-Time Analytics and Reporting**: Provides real-time insights into agent performance and system health.
+
+## 🌐 Wide-Ranging Integration
+- **API-First Design**: Easily integrates with existing systems and third-party applications via robust APIs.
+- **Cloud Compatibility**: Fully compatible with major cloud platforms for flexible deployment options.
+- **Continuous Integration/Continuous Deployment (CI/CD)**: Supports CI/CD practices for seamless updates and deployment.
+
+## 📊 Performance Optimization
+- **Resource Management**: Efficiently manages computational resources for optimal performance.
+- **Load Balancing**: Automatically balances workloads to maintain system stability and responsiveness.
+- **Performance Monitoring Tools**: Includes comprehensive monitoring tools for tracking and optimizing performance.
+
+## 🛡️ Security and Compliance
+- **Data Encryption**: Implements end-to-end encryption for data at rest and in transit.
+- **Compliance Standards Adherence**: Adheres to major compliance standards ensuring legal and ethical usage.
+- **Regular Security Updates**: Regular updates to address emerging security threats and vulnerabilities.
+
+## 💬 Community and Support
+- **Extensive Documentation**: Detailed documentation for easy implementation and troubleshooting.
+- **Active Developer Community**: A vibrant community for sharing ideas, solutions, and best practices.
+- **Professional Support**: Access to professional support for enterprise-level assistance and guidance.
+
+Swarms framework is not just a tool but a robust, scalable, and secure partner in your AI journey, ready to tackle the challenges of modern AI applications in a business environment.
+
+
 ## Documentation
 - For documentation, go here, [swarms.apac.ai](https://swarms.apac.ai)
 

example.py

@@ -1,7 +1,7 @@
 from swarms.models import OpenAIChat
 from swarms.structs import Flow
 
-api_key = ""
+api_key = "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"
 
 # Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
 llm = OpenAIChat(

playground/models/dall3.py

@@ -1,8 +1,6 @@
 from swarms.models import Dalle3
 
-dalle3 = Dalle3(
-    openai_api_key=""
-)
+dalle3 = Dalle3(openai_api_key="")
 task = "A painting of a dog"
 image_url = dalle3(task)
-print(image_url)
+print(image_url)

pyproject.toml

@@ -45,6 +45,7 @@ wget = "*"
 griptape = "*"
 httpx = "*"
 tiktoken = "*"
+safetensors = "*"
 attrs = "*"
 ggl = "*"
 ratelimit = "*"

requirements.txt

@@ -48,6 +48,7 @@ opencv-python-headless
 imageio-ffmpeg
 invisible-watermark
 kornia
+safetensors
 numpy
 omegaconf
 open_clip_torch

swarms/artifacts/main.py

@@ -10,6 +10,20 @@ class Artifact(BaseModel):
     """
 
     Artifact that has the task has been produced
+
+    Attributes:
+    -----------
+
+    artifact_id: str
+        ID of the artifact
+
+    file_name: str
+        Filename of the artifact
+
+    relative_path: str
+        Relative path of the artifact
+
+
     """
 
     artifact_id: StrictStr = Field(..., description="ID of the artifact")

swarms/memory/pg.py

@@ -2,7 +2,7 @@
 from typing import Optional
 from attr import define, field, Factory
 from dataclasses import dataclass
-from swarms.memory.vector_stores.base import BaseVectorStore
+from swarms.memory.base import BaseVectorStore
 from sqlalchemy.engine import Engine
 from sqlalchemy import create_engine, Column, String, JSON
 from sqlalchemy.ext.declarative import declarative_base

swarms/models/__init__.py

@@ -6,20 +6,21 @@
 # LLMs
 from swarms.models.anthropic import Anthropic  # noqa: E402
 from swarms.models.petals import Petals  # noqa: E402
-from swarms.models.mistral import Mistral # noqa: E402
-from swarms.models.openai_models import OpenAI, AzureOpenAI, OpenAIChat # noqa: E402
-from swarms.models.zephyr import Zephyr # noqa: E402
-from swarms.models.biogpt import BioGPT # noqa: E402
-from swarms.models.huggingface import HuggingfaceLLM # noqa: E402
-from swarms.models.wizard_storytelling import WizardLLMStoryTeller # noqa: E402
-from swarms.models.mpt import MPT7B # noqa: E402
+from swarms.models.mistral import Mistral  # noqa: E402
+from swarms.models.openai_models import OpenAI, AzureOpenAI, OpenAIChat  # noqa: E402
+from swarms.models.zephyr import Zephyr  # noqa: E402
+from swarms.models.biogpt import BioGPT  # noqa: E402
+from swarms.models.huggingface import HuggingfaceLLM  # noqa: E402
+from swarms.models.wizard_storytelling import WizardLLMStoryTeller  # noqa: E402
+from swarms.models.mpt import MPT7B  # noqa: E402
 
 # MultiModal Models
-from swarms.models.idefics import Idefics # noqa: E402
-from swarms.models.kosmos_two import Kosmos # noqa: E402
-from swarms.models.vilt import Vilt # noqa: E402
-from swarms.models.nougat import Nougat # noqa: E402
-from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA # noqa: E402
+from swarms.models.idefics import Idefics  # noqa: E402
+from swarms.models.kosmos_two import Kosmos  # noqa: E402
+from swarms.models.vilt import Vilt  # noqa: E402
+from swarms.models.nougat import Nougat  # noqa: E402
+from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA  # noqa: E402
+
 # from swarms.models.gpt4v import GPT4Vision
 # from swarms.models.dalle3 import Dalle3
 # from swarms.models.distilled_whisperx import DistilWhisperModel # noqa: E402

swarms/models/dalle3.py

@@ -1,11 +1,11 @@
-
 import concurrent.futures
 import logging
 import os
 import uuid
 from dataclasses import dataclass
 from io import BytesIO
 from typing import List
+
 import backoff
 import openai
 import requests