Skip to content

Commit

Permalink
[cleanup]
Browse files Browse the repository at this point in the history
  • Loading branch information
Your Name committed Oct 12, 2024
1 parent e84484a commit e5cc76a
Showing 1 changed file with 89 additions and 153 deletions.
242 changes: 89 additions & 153 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -130,7 +130,7 @@ Run example in Collab: <a target="_blank" href="https://colab.research.google.co
---
## `Agent` Class
The `Agent` class is a fundamental component of the Swarms framework, designed to execute tasks autonomously. It fuses llms, tools and long-term memory capabilities to perform complex operations. The `Agent` class is highly customizable, allowing for fine-grained control over its behavior and interactions.
The `Agent` class is a fundamental component of the Swarms framework, designed to execute tasks autonomously. It fuses llms, tools and long-term memory capabilities to create a full stack agent. The `Agent` class is highly customizable, allowing for fine-grained control over its behavior and interactions.
### `run` Method
The `run` method is the primary entry point for executing tasks with an `Agent` instance. It accepts a task string as the main input task and processes it according to the agent's configuration. And, it can also accept an `img` parameter such as `img="image_filepath.png` to process images if you have a VLM
Expand Down Expand Up @@ -201,190 +201,126 @@ out = agent.run(
print(out)
```
-----
### Agent with RAG
`Agent` equipped with quasi-infinite long term memory. Great for long document understanding, analysis, and retrieval.
### Integrating RAG with Swarms for Enhanced Long-Term Memory
`Agent` equipped with quasi-infinite long term memory using RAG (Relational Agent Graph) for advanced document understanding, analysis, and retrieval capabilities.
**Mermaid Diagram for RAG Integration**
```mermaid
graph TD
A[Initialize Agent with RAG] --> B[Receive Task]
B --> C[Query Long-Term Memory]
C --> D[Process Task with Context]
D --> E[Generate Response]
E --> F[Update Long-Term Memory]
F --> G[Return Output]
```
**Step 1: Initialize the ChromaDB Client**
```python
import os
from swarms_memory import ChromaDB
from swarms import Agent
# Initialize the ChromaDB client for long-term memory management
chromadb = ChromaDB(
metric="cosine", # Metric for similarity measurement
output_dir="finance_agent_rag", # Directory for storing RAG data
# docs_folder="artifacts", # Uncomment and specify the folder containing your documents
)
```
**Step 2: Define the Model**
```python
from swarm_models import Anthropic
from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
)
# Initilaize the chromadb client
chromadb = ChromaDB(
metric="cosine",
output_dir="fiance_agent_rag",
# docs_folder="artifacts", # Folder of your documents
)
# Model
# Define the Anthropic model for language processing
model = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
```
**Step 3: Initialize the Agent with RAG**
```python
from swarms import Agent
# Initialize the agent
# Initialize the agent with RAG capabilities
agent = Agent(
agent_name="Financial-Analysis-Agent",
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
agent_description="Agent creates ",
agent_description="Agent creates a comprehensive financial analysis",
llm=model,
max_loops="auto",
autosave=True,
dashboard=False,
verbose=True,
streaming_on=True,
dynamic_temperature_enabled=True,
saved_state_path="finance_agent.json",
user_name="swarms_corp",
retry_attempts=3,
context_length=200000,
long_term_memory=chromadb,
max_loops="auto", # Auto-adjusts loops based on task complexity
autosave=True, # Automatically saves agent state
dashboard=False, # Disables dashboard for this example
verbose=True, # Enables verbose mode for detailed output
streaming_on=True, # Enables streaming for real-time processing
dynamic_temperature_enabled=True, # Dynamically adjusts temperature for optimal performance
saved_state_path="finance_agent.json", # Path to save agent state
user_name="swarms_corp", # User name for the agent
retry_attempts=3, # Number of retry attempts for failed tasks
context_length=200000, # Maximum length of the context to consider
long_term_memory=chromadb, # Integrates ChromaDB for long-term memory management
)
# Run the agent with a sample task
agent.run(
"What are the components of a startups stock incentive equity plan"
)
```
-------
### `Agent` ++ Long Term Memory ++ Tools!
An LLM equipped with long term memory and tools, a full stack agent capable of automating all and any digital tasks given a good prompt.
```python
from swarms import Agent
from swarm_models import OpenAIChat
from swarms_memory import ChromaDB
import subprocess
import os
# Making an instance of the ChromaDB class
memory = ChromaDB(
metric="cosine",
n_results=3,
output_dir="results",
docs_folder="docs",
)
# Model
model = OpenAIChat(
openai_api_key=os.getenv("OPENAI_API_KEY"),
model_name="gpt-4o-mini",
temperature=0.1,
)
# Tools in swarms are simple python functions and docstrings
def terminal(
code: str,
):
"""
Run code in the terminal.
Args:
code (str): The code to run in the terminal.
Returns:
str: The output of the code.
"""
out = subprocess.run(
code, shell=True, capture_output=True, text=True
).stdout
return str(out)
def browser(query: str):
"""
Search the query in the browser with the `browser` tool.
Args:
query (str): The query to search in the browser.
Returns:
str: The search results.
"""
import webbrowser
url = f"https://www.google.com/search?q={query}"
webbrowser.open(url)
return f"Searching for {query} in the browser."
def create_file(file_path: str, content: str):
"""
Create a file using the file editor tool.
Args:
file_path (str): The path to the file.
content (str): The content to write to the file.
Returns:
str: The result of the file creation operation.
"""
with open(file_path, "w") as file:
file.write(content)
return f"File {file_path} created successfully."
def file_editor(file_path: str, mode: str, content: str):
"""
Edit a file using the file editor tool.
Args:
file_path (str): The path to the file.
mode (str): The mode to open the file in.
content (str): The content to write to the file.
Returns:
str: The result of the file editing operation.
"""
with open(file_path, mode) as file:
file.write(content)
return f"File {file_path} edited successfully."
-------
# Agent
agent = Agent(
agent_name="Devin",
system_prompt=(
"Autonomous agent that can interact with humans and other"
" agents. Be Helpful and Kind. Use the tools provided to"
" assist the user. Return all code in markdown format."
),
llm=model,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
streaming=True,
long_term_memory=memory,
)
### Misc Agent Settings
We provide vast array of features to save agent states using json, yaml, toml, upload pdfs, batched jobs, and much more!
# Run the agent
out = agent(
"Create a CSV file with the latest tax rates for C corporations in the following ten states and the District of Columbia: Alabama, California, Florida, Georgia, Illinois, New York, North Carolina, Ohio, Texas, and Washington."
)
print(out)
**Method Table**
| Method | Description |
| --- | --- |
| `to_dict()` | Converts the agent object to a dictionary. |
| `to_toml()` | Converts the agent object to a TOML string. |
| `model_dump_json()` | Dumps the model to a JSON file. |
| `model_dump_yaml()` | Dumps the model to a YAML file. |
| `ingest_docs()` | Ingests documents into the agent's knowledge base. |
| `receive_message()` | Receives a message from a user and processes it. |
| `send_agent_message()` | Sends a message from the agent to a user. |
| `filtered_run()` | Runs the agent with a filtered system prompt. |
| `bulk_run()` | Runs the agent with multiple system prompts. |
| `add_memory()` | Adds a memory to the agent. |
| `check_available_tokens()` | Checks the number of available tokens for the agent. |
| `tokens_checks()` | Performs token checks for the agent. |
| `print_dashboard()` | Prints the dashboard of the agent. |
| `get_docs_from_doc_folders()` | Fetches all the documents from the doc folders. |
| `activate_agentops()` | Activates agent operations. |
| `check_end_session_agentops()` | Checks the end of the session for agent operations. |
**Mermaid Diagram**
```mermaid
graph LR
A[Agent] -->|to_dict()|> B[Dictionary]
A -->|to_toml()|> C[TOML String]
A -->|model_dump_json()|> D[JSON File]
A -->|model_dump_yaml()|> E[YAML File]
A -->|ingest_docs()|> F[Knowledge Base]
A -->|receive_message()|> G[Processed Message]
A -->|send_agent_message()|> H[Sent Message]
A -->|filtered_run()|> I[Filtered Run]
A -->|bulk_run()|> J[Bulk Run]
A -->|add_memory()|> K[Memory]
A -->|check_available_tokens()|> L[Token Check]
A -->|tokens_checks()|> M[Token Checks]
A -->|print_dashboard()|> N[Dashboard]
A -->|get_docs_from_doc_folders()|> O[Documents]
A -->|activate_agentops()|> P[Agent Ops]
A -->|check_end_session_agentops()|> Q[Session Check]
```
-------
### Misc Agent Settings
We provide vast array of features to save agent states using json, yaml, toml, upload pdfs, batched jobs, and much more!
```python
# # Convert the agent object to a dictionary
Expand Down

0 comments on commit e5cc76a

Please sign in to comment.