dockerfile

DOCKERFILE

@@ -0,0 +1,37 @@
+# Use an official Python runtime as a parent image
+FROM python:3.9-slim-buster
+
+# Set environment variables
+ENV EVAL_PORT=8000 \
+    MODEL_NAME=gpt-4 \
+    CELERY_BROKER_URL=redis://localhost:6379 \
+    SERVER=http://localhost:${EVAL_PORT} \
+    USE_GPU=False \
+    PLAYGROUND_DIR=playground \
+    LOG_LEVEL=INFO \
+    BOT_NAME=Orca \
+    # You will need to set these environment variables to your actual keys in production
+    OPENAI_API_KEY=your_openai_api_key \
+    WINEDB_HOST=your_winedb_host \
+    WINEDB_PASSWORD=your_winedb_password \
+    BING_SEARCH_URL=your_bing_search_url \
+    BING_SUBSCRIPTION_KEY=your_bing_subscription_key \
+    SERPAPI_API_KEY=your_serpapi_api_key
+
+# Set work directory
+WORKDIR /usr/src/app
+
+# Add requirements file
+COPY requirements.txt ./
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Bundle app source
+COPY . .
+
+# Expose port
+EXPOSE ${EVAL_PORT}
+
+# Run example.py when the container launches
+CMD ["python", "example.py"]

swarms/agents/swarms.py

@@ -39,7 +39,7 @@ vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {
 
 
 
-# General  ------------------------- WORKER NODE
+#-------------------------------------------------------------------------- WORKER NODE
 import pandas as pd
 from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT
 from langchain.chat_models import ChatOpenAI
@@ -47,23 +47,22 @@ from langchain.chat_models import ChatOpenAI
 from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
 from langchain.docstore.document import Document
 import asyncio
-import nest_asyncio
-
 
-llm = ChatOpenAI(model_name="gpt-4", temperature=1.0, openai_api_key="")
+import nest_asyncio
 
 # Tools
 import os
 from contextlib import contextmanager
 from typing import Optional
+
 from langchain.tools.file_management.read import ReadFileTool
 from langchain.tools.file_management.write import WriteFileTool
-
 ROOT_DIR = "./data/"
 
 from langchain.tools import BaseTool, DuckDuckGoSearchRun
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain, BaseCombineDocumentsChain
+
 from langchain.tools.human.tool import HumanInputRun
 from swarms.agents.workers.auto_agent import MultiModalVisualAgent
 from swarms.tools.main import Terminal, CodeWriter, CodeEditor, process_csv, WebpageQATool
@@ -84,7 +83,7 @@ class MultiModalVisualAgentTool(BaseTool):
 
 
 
-
+llm = ChatOpenAI(model_name="gpt-4", temperature=1.0, openai_api_key="")
 
 
 
@@ -159,49 +158,6 @@ class WorkerNode:
         self.agent.run([prompt])
 
 
-#worker node example
-worker_node = WorkerNode(llm, tools, vectorstore)
-worker_node.create_agent(
-    ai_name="Worker",
-    ai_role="Assistant",
-    human_in_the_loop=True,
-    search_kwargs={"k": 8}
-)
-
-
-
-tree_of_thoughts_prompt = """
-
-Imagine three different experts are answering this question. All experts will write down each chain of thought of each step of their thinking, then share it with the group. Then all experts will go on to the next step, etc. If any expert realises they're wrong at any point then they leave. The question is...
-
-
-"""
-
-
-#Input problem
-input_problem = """
-
-
-Input: 2 8 8 14
-Possible next steps:
-2 + 8 = 10 (left: 8 10 14)
-8 / 2 = 4 (left: 4 8 14)
-14 + 2 = 16 (left: 8 8 16)
-2 * 8 = 16 (left: 8 14 16)
-8 - 2 = 6 (left: 6 8 14)
-14 - 8 = 6 (left: 2 6 8)
-14 /  2 = 7 (left: 7 8 8)
-14 - 2 = 12 (left: 8 8 12)
-Input: use 4 numbers and basic arithmetic operations (+-*/) to obtain 24 in 1 equation
-Possible next steps:
-
-
-"""
-
-worker_node.run_agent([f"{tree_of_thoughts_prompt} {input_problem}"])
-
-
-
 
 
 
@@ -284,15 +240,6 @@ class BossNode:
         self.baby_agi(task)
 
 
-# Initialize boss node with given parameters
-boss_node = BossNode()
-
-# Create and execute a task
-task = boss_node.create_task("Write a weather report for SF today")
-boss_node.execute_task(task)
-
-
-
 
 
 
@@ -336,3 +283,66 @@ class MultiAgentDebate(Swarms):
     def execute(self, task):
         pass
 
+
+
+
+
+#worker node example
+worker_node = WorkerNode(llm, tools, vectorstore)
+worker_node.create_agent(
+    ai_name="Worker",
+    ai_role="Assistant",
+    human_in_the_loop=True,
+    search_kwargs={"k": 8}
+)
+
+
+
+tree_of_thoughts_prompt = """
+
+Imagine three different experts are answering this question. All experts will write down each chain of thought of each step of their thinking, then share it with the group. Then all experts will go on to the next step, etc. If any expert realises they're wrong at any point then they leave. The question is...
+
+
+"""
+
+
+#Input problem
+input_problem = """
+
+
+Input: 2 8 8 14
+Possible next steps:
+2 + 8 = 10 (left: 8 10 14)
+8 / 2 = 4 (left: 4 8 14)
+14 + 2 = 16 (left: 8 8 16)
+2 * 8 = 16 (left: 8 14 16)
+8 - 2 = 6 (left: 6 8 14)
+14 - 8 = 6 (left: 2 6 8)
+14 /  2 = 7 (left: 7 8 8)
+14 - 2 = 12 (left: 8 8 12)
+Input: use 4 numbers and basic arithmetic operations (+-*/) to obtain 24 in 1 equation
+Possible next steps:
+
+
+"""
+
+worker_node.run_agent([f"{tree_of_thoughts_prompt} {input_problem}"])
+
+
+
+
+
+
+
+
+###########################
+
+# Initialize boss node with given parameters
+boss_node = BossNode()
+
+# Create and execute a task
+task = boss_node.create_task("Write a weather report for SF today")
+boss_node.execute_task(task)
+
+
+