@ -44,7 +44,7 @@ import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import OpenAIChat, Agent
from swarms import Agent, OpenAIChat
# Load the environment variables
load_dotenv()
@ -54,10 +54,7 @@ api_key = os.environ.get("OPENAI_API_KEY")
# Initialize the language model
llm = OpenAIChat(
temperature=0.5,
model_name="gpt-4",
openai_api_key=api_key,
max_tokens=4000
temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000
)
@ -66,9 +63,6 @@ agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
# Run the workflow on a task
agent.run("Generate a 10,000 word blog on health and wellness.")
```
@ -79,6 +73,7 @@ ToolAgent is an agent that outputs JSON using any model from huggingface. It tak
```python
# Import necessary libraries
from transformers import AutoModelForCausalLM, AutoTokenizer
from swarms import ToolAgent
# Load the pre-trained model and tokenizer
@ -107,8 +102,6 @@ generated_data = agent.run(task)
# Print the generated data
print(generated_data)
```
@ -124,8 +117,10 @@ The `Worker` is a simple all-in-one agent equipped with an LLM, tools, and RAG f
```python
# Importing necessary modules
import os
from dotenv import load_dotenv
from swarms import Worker, OpenAIChat, tool
from swarms import OpenAIChat, Worker, tool
# Loading environment variables from .env file
load_dotenv()
@ -151,14 +146,10 @@ worker = Worker(
)
# Running the worker with a prompt
out = worker.run(
"Hello, how are you? Create an image of how your are doing!"
)
out = worker.run("Hello, how are you? Create an image of how your are doing!")
# Printing the output
print(out)
```
------
@ -175,9 +166,11 @@ Sequential Workflow enables you to sequentially execute tasks with `Agent` and t
```python
import os
from swarms import OpenAIChat, Agent, SequentialWorkflow
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, SequentialWorkflow
load_dotenv()
# Load the environment variables
@ -186,10 +179,7 @@ api_key = os.getenv("OPENAI_API_KEY")
# Initialize the language agent
llm = OpenAIChat(
temperature=0.5,
model_name="gpt-4",
openai_api_key=api_key,
max_tokens=4000
temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000
)
@ -207,12 +197,14 @@ workflow = SequentialWorkflow(max_loops=1)
# Add tasks to the workflow
workflow.add(
agent1, "Generate a 10,000 word blog on health and wellness.",
agent1,
"Generate a 10,000 word blog on health and wellness.",
)
# Suppose the next task takes the output of the first task as input
workflow.add(
agent2, "Summarize the generated blog",
agent2,
"Summarize the generated blog",
)
# Run the workflow
@ -231,8 +223,10 @@ for task in workflow.tasks:
```python
import os
from dotenv import load_dotenv
from swarms import OpenAIChat, Task, ConcurrentWorkflow, Agent
from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task
# Load environment variables from .env file
load_dotenv()
@ -254,7 +248,6 @@ workflow.add(tasks=[task1, task2, task3])
# Run the workflow
workflow.run()
```
### `RecursiveWorkflow`
@ -262,8 +255,10 @@ workflow.run()
```python
import os
from dotenv import load_dotenv
from swarms import OpenAIChat, Task, RecursiveWorkflow, Agent
from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task
# Load environment variables from .env file
load_dotenv()
@ -287,8 +282,6 @@ workflow.add(task3)
# Run the workflow
workflow.run()
```
@ -304,7 +297,7 @@ import os
from dotenv import load_dotenv
from swarms import Anthropic, Gemini, Mixtral, OpenAIChat, ModelParallelizer
from swarms import Anthropic, Gemini, Mixtral, ModelParallelizer, OpenAIChat
load_dotenv()
@ -346,10 +339,7 @@ import os
from dotenv import load_dotenv
from swarms import (
OpenAIChat,
Conversation,
)
from swarms import Conversation, OpenAIChat
conv = Conversation(
time_enabled=True,
@ -364,6 +354,7 @@ api_key = os.environ.get("OPENAI_API_KEY")
# Initialize the language model
llm = OpenAIChat(openai_api_key=api_key, model_name="gpt-4")
# Run the language model in a loop
def interactive_conversation(llm):
conv = Conversation()
@ -372,9 +363,7 @@ def interactive_conversation(llm):
conv.add("user", user_input)
if user_input.lower() == "quit":
break
task = (
conv.return_history_as_string()
) # Get the conversation history
task = conv.return_history_as_string() # Get the conversation history
out = llm(task)
conv.add("assistant", out)
print(
@ -386,7 +375,6 @@ def interactive_conversation(llm):
# Replace with your LLM instance
interactive_conversation(llm)
```
@ -405,7 +393,7 @@ import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import OpenAIChat, Agen t, SwarmNetwork
from swarms import Agent, OpenAIChat, SwarmNetwork
# Load the environment variables
load_dotenv()
@ -442,11 +430,8 @@ print(out)
# Run all the agents in the swarm network on a task
out = swarmnet.run_many_agents(
"Generate a 10,000 word blog on health and wellness."
)
out = swarmnet.run_many_agents("Generate a 10,000 word blog on health and wellness.")
print(out)
```
@ -513,8 +498,6 @@ else:
# Output the result of the task
print(f"Task result: {task.result}")
```
---
@ -535,14 +518,7 @@ from dotenv import load_dotenv
from transformers import AutoModelForCausalLM, AutoTokenizer
# Import the models, structs, and telemetry modules
from swarms import (
Gemini,
GPT4VisionAPI,
Mixtral,
OpenAI,
ToolAgent,
BlocksList,
)
from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent
# Load the environment variables
load_dotenv()
@ -552,9 +528,7 @@ openai_api_key = os.getenv("OPENAI_API_KEY")
gemini_api_key = os.getenv("GEMINI_API_KEY")
# Tool Agent
model = AutoModelForCausalLM.from_pretrained(
"databricks/dolly-v2-12b"
)
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b")
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b")
json_schema = {
"type": "object",
@ -565,9 +539,7 @@ json_schema = {
"courses": {"type": "array", "items": {"type": "string"}},
},
}
toolagent = ToolAgent(
model=model, tokenizer=tokenizer, json_schema=json_schema
)
toolagent = ToolAgent(model=model, tokenizer=tokenizer, json_schema=json_schema)
# Blocks List which enables you to build custom swarms by adding classes or functions
swarm = BlocksList(
@ -619,9 +591,7 @@ blocks_by_parent_name = swarm.get_by_parent_name(swarm.name)
blocks_by_parent_type = swarm.get_by_parent_type(type(swarm).__name__)
# Get blocks by parent description
blocks_by_parent_description = swarm.get_by_parent_description(
swarm.description
)
blocks_by_parent_description = swarm.get_by_parent_description(swarm.description)
# Run the block in the swarm
inference = swarm.run_block(toolagent, "Hello World")
@ -636,19 +606,21 @@ Here's a production grade swarm ready for real-world deployment in a factory and
```python
from swarms.structs import Agent
import os
from dotenv import load_dotenv
from swarms.models import GPT4VisionAPI
from swarms.prompts.logistics import (
Efficiency_Agent_Prompt,
Health_Security_Agent_Prompt,
Quality_Control_Agent_Prompt,
Productivity_Agent_Prompt,
Quality_Control_Agent_Prompt,
Safety_Agent_Prompt,
Security_Agent_Prompt,
Sustainability_Agent_Prompt,
Efficiency_Agent_Prompt,
)
from swarms.structs import Agent
# Load ENV
load_dotenv()
@ -686,9 +658,7 @@ productivity_agent = Agent(
)
# Initiailize safety agent
safety_agent = Agent(
llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True
)
safety_agent = Agent(llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True)
# Init the security agent
security_agent = Agent(
@ -748,7 +718,9 @@ Run the agent with multiple modalities useful for various real-world tasks in ma
```python
# Description: This is an example of how to use the Agent class to run a multi-modal workflow
import os
from dotenv import load_dotenv
from swarms.models.gpt4_vision_api import GPT4VisionAPI
from swarms.structs import Agent
@ -775,17 +747,11 @@ img = "assembly_line.jpg"
## Initialize the workflow
agent = Agent(
llm=llm,
max_loops="auto",
autosave=True,
dashboard=True,
multi_modal=True
llm=llm, max_loops="auto", autosave=True, dashboard=True, multi_modal=True
)
# Run the workflow on a task
agent.run(task=task, img=img)
```
---
@ -857,14 +823,10 @@ model = QwenVLMultiModal(
)
# Run the model
response = model(
"Hello, how are you?", "https://example.com/image.jpg"
)
response = model("Hello, how are you?", "https://example.com/image.jpg")
# Print the response
print(response)
```
@ -882,7 +844,6 @@ out = model.run("Analyze the reciepts in this image", "docs.jpg")
# Print the output
print(out)
```
@ -923,8 +884,6 @@ model.set_max_length(200)
# Clear the chat history of the model
model.clear_chat_history()
```
## Radically Simple AI Model APIs
@ -941,9 +900,7 @@ We provide a vast array of language and multi-modal model APIs for you to genera
from swarms.models import Anthropic
# Initialize an instance of the Anthropic class
model = Anthropic(
anthropic_api_key=""
)
model = Anthropic(anthropic_api_key="")
# Using the run method
completion_1 = model.run("What is the capital of France?")
@ -952,7 +909,6 @@ print(completion_1)
# Using the __call__ method
completion_2 = model("How far is the moon from the earth?", stop=["miles", "km"])
print(completion_2)
```
@ -964,12 +920,16 @@ from swarms.models import HuggingfaceLLM
custom_config = {
"quantize": True,
"quantization_config": {"load_in_4bit": True},
"verbose": True
"verbose": True,
}
inference = HuggingfaceLLM(model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config)
inference = HuggingfaceLLM(
model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config
)
# Generate text based on a prompt
prompt_text = "Create a list of known biggest risks of structural collapse with references"
prompt_text = (
"Create a list of known biggest risks of structural collapse with references"
)
generated_text = inference(prompt_text)
print(generated_text)
```
@ -1027,7 +987,6 @@ task = "A person is walking on the street."
# Generate the video!
video_path = zeroscope(task)
print(video_path)
```
Kye
11 months ago