feat: Setup vllm class

.gitignore

@@ -75,6 +75,7 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 cover/
+cookies.json
 
 # Translations
 *.mo

bingchat.py

@@ -0,0 +1,7 @@
+from swarms.models.bing_chat import BingChat
+# Initialize the EdgeGPTModel
+bing = BingChat(cookies_path="./cookies.json")
+task = "generate topics for PositiveMed.com,:  1. Monitor Health Trends: Scan Google Alerts, authoritative health websites, and social media for emerging health, wellness, and medical discussions. 2. Keyword Research: Utilize tools like SEMrush to identify keywords with moderate to high search volume and low competition. Focus on long-tail, conversational keywords. 3. Analyze Site Data: Review PositiveMed's analytics to pinpoint popular articles and areas lacking recent content. 4. Crowdsourcing: Gather topic suggestions from the brand's audience and internal team, ensuring alignment with PositiveMed's mission. 5. Topic Evaluation: Assess topics for audience relevance, uniqueness, brand fit, current relevance, and SEO potential. 6. Tone and Style: Ensure topics can be approached with an educational, empowering, and ethical tone, in line with the brand's voice.  Use this framework to generate a list of potential topics that cater to PositiveMed's audience while staying true to its brand ethos.  Find trending topics for slowing and reversing aging think step by step and o into as much detail as possible"
+response = bing(task)
+
+print(response)

example.py

@@ -1,22 +1,29 @@
+from tabnanny import verbose
+from click import prompt
+from langchain import LLMChain
 from swarms.models import OpenAIChat
 from swarms import Worker
 from swarms.prompts import PRODUCT_AGENT_PROMPT
+from swarms.models.bing_chat import BingChat
 
-api_key = ""
+# api_key = ""
 
-llm = OpenAIChat(
+# llm = OpenAIChat(
-    openai_api_key=api_key,
+#     openai_api_key=api_key,
-    temperature=0.5,
+#     temperature=0.5,
-)
+# )
+
+llm = BingChat(cookies_path="./cookies.json")
+# llm = LLMChain(llm=bing.to_dict(), prompt=prompt, verbose=verbose)
 
 node = Worker(
     llm=llm,
     ai_name="Optimus Prime",
-    openai_api_key=api_key,
     ai_role=PRODUCT_AGENT_PROMPT,
     external_tools=None,
     human_in_the_loop=False,
     temperature=0.5,
+    use_openai=False
 )
 
 task = "Create an entirely new board game around riddles for physics"

playground/agents/bingchat.py

@@ -4,16 +4,12 @@ from swarms.tools.autogpt import EdgeGPTTool, tool
 from swarms.models import OpenAIChat
 import os
 
-api_key = os.getenv("OPENAI_API_KEY")
+load_dotenv("../.env")
+auth_cookie = os.environ.get("AUTH_COOKIE")
+auth_cookie_SRCHHPGUSR = os.environ.get("AUTH_COOKIE_SRCHHPGUSR")
 
 # Initialize the EdgeGPTModel
-edgegpt = BingChat()
+bing = BingChat(cookies_path="./cookies.json", auth_cookie_SRCHHPGUSR)
+task = "generate topics for PositiveMed.com,:  1. Monitor Health Trends: Scan Google Alerts, authoritative health websites, and social media for emerging health, wellness, and medical discussions. 2. Keyword Research: Utilize tools like SEMrush to identify keywords with moderate to high search volume and low competition. Focus on long-tail, conversational keywords. 3. Analyze Site Data: Review PositiveMed's analytics to pinpoint popular articles and areas lacking recent content. 4. Crowdsourcing: Gather topic suggestions from the brand's audience and internal team, ensuring alignment with PositiveMed's mission. 5. Topic Evaluation: Assess topics for audience relevance, uniqueness, brand fit, current relevance, and SEO potential. 6. Tone and Style: Ensure topics can be approached with an educational, empowering, and ethical tone, in line with the brand's voice.  Use this framework to generate a list of potential topics that cater to PositiveMed's audience while staying true to its brand ethos.  Find trending topics for slowing and reversing aging think step by step and o into as much detail as possible"
 
-
+bing(task)
-# Initialize the Worker with the custom tool
-worker = Worker(llm=llm, ai_name="EdgeGPT Worker", external_tools=[edgegpt])
-
-# Use the worker to process a task
-task = "Hello, my name is ChatGPT"
-response = worker.run(task)
-print(response)

revgpt.py

@@ -0,0 +1,29 @@
+import os
+import sys
+from dotenv import load_dotenv
+from swarms.models.revgptV4 import RevChatGPTModelv4
+from swarms.models.revgptV1 import RevChatGPTModelv1
+
+root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+sys.path.append(root_dir)
+
+load_dotenv()
+
+config = {
+    "model": os.getenv("REVGPT_MODEL"),
+    "plugin_ids": [os.getenv("REVGPT_PLUGIN_IDS")],
+    "disable_history": os.getenv("REVGPT_DISABLE_HISTORY") == "True",
+    "PUID": os.getenv("REVGPT_PUID"),
+    "unverified_plugin_domains": [os.getenv("REVGPT_UNVERIFIED_PLUGIN_DOMAINS")],
+}
+
+# For v1 model
+model = RevChatGPTModelv1(access_token=os.getenv("ACCESS_TOKEN"), **config)
+# model = RevChatGPTModelv4(access_token=os.getenv("ACCESS_TOKEN"), **config)
+
+# For v3 model
+# model = RevChatGPTModel(access_token=os.getenv("OPENAI_API_KEY"), **config)
+
+task = "Write a cli snake game"
+response = model.run(task)
+print(response)

swarms/models/__init__.py

@@ -18,8 +18,8 @@ from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA
 # from swarms.models.fuyu import Fuyu # Not working, wait until they update
 import sys
 
-log_file = open("stderr_log.txt", "w")
+# log_file = open("stderr_log.txt", "w")
-sys.stderr = log_file
+# sys.stderr = log_file
 
 
 __all__ = [

swarms/models/bing_chat.py

@@ -1,4 +1,5 @@
 """Bing-Chat model by Micorsoft"""
+import os
 import asyncio
 import json
 from pathlib import Path
@@ -25,19 +26,9 @@ class BingChat:
 
     """
 
-
+    def __init__(self, cookies_path: str = None):
-    def __init__(self, cookies_path: str = None, auth_cookie: str = None, auth_cookie_SRCHHPGUSR: str = None):
+        self.cookies = json.loads(open(cookies_path, encoding="utf-8").read())
-        auth_cookie = os.environ("AUTH_COOKIE")
-        auth_cookie_SRCHHPGUSR = os.enviro("AUTH_COOKIE_SRCHHPGUSR")
-        if cookies_path:
-            self.cookies = json.loads(open(cookies_path, encoding="utf-8").read())
-        elif auth_cookie:
-            self.cookies = auth_cookie
-        else:
-            raise ValueError("Either cookies_path or auth_cookie must be provided.")
         self.bot = asyncio.run(Chatbot.create(cookies=self.cookies))
-        self.auth_cookie = auth_cookie
-        self.auth_cookie_SRCHHPGUSR = auth_cookie_SRCHHPGUSR
 
     def __call__(
         self, prompt: str, style: ConversationStyle = ConversationStyle.creative

swarms/models/bioclip.py

@@ -75,6 +75,7 @@ class BioClip:
             'adenocarcinoma histopathology',
             'brain MRI',
             'covid line chart',
+            'covid line chart',
             'squamous cell carcinoma histopathology',
             'immunohistochemistry histopathology',
             'bone X-ray',

swarms/models/revgptV1.py

@@ -35,13 +35,13 @@ from httpx import AsyncClient
 from OpenAIAuth import Auth0 as Authenticator
 from rich.live import Live
 from rich.markdown import Markdown
-import schemas.typings as t
+import swarms.schemas.typings as t
 from swarms.utils.revutils import create_completer
 from swarms.utils.revutils import create_session
 from swarms.utils.revutils import get_input
 
 # BASE_URL = environ.get("CHATGPT_BASE_URL", "http://192.168.250.249:9898/api/")
-# BASE_URL = os.environ.get("CHATGPT_BASE_URL", "https://ai.fakeopen.com/api/")
+BASE_URL = os.environ.get("CHATGPT_BASE_URL", "https://ai.fakeopen.com/api/")
 # BASE_URL = environ.get("CHATGPT_BASE_URL", "https://bypass.churchless.tech/")
 
 bcolors = t.Colors()

swarms/models/revgptV4.py

@@ -40,14 +40,14 @@ from rich.markdown import Markdown
 import argparse
 import re
 
-import schemas.typings as t
+import swarms.schemas.typings as t
 from prompt_toolkit import prompt
 from prompt_toolkit import PromptSession
 from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
 from prompt_toolkit.completion import WordCompleter
 from prompt_toolkit.history import InMemoryHistory
 from prompt_toolkit.key_binding import KeyBindings
-from schemas.typings import Colors
+from swarms.schemas.typings import Colors
 
 bindings = KeyBindings()
 

swarms/models/vllm.py

@@ -0,0 +1,55 @@
+from vllm import LLM, SamplingParams
+import openai
+import ray
+import uvicorn
+from vllm.entrypoints import api_server as vllm_api_server
+from vllm.entrypoints.openai import api_server as openai_api_server
+from skypilot import SkyPilot
+
+class VLLMModel:
+    def __init__(self, model_name="facebook/opt-125m", tensor_parallel_size=1):
+        self.model_name = model_name
+        self.tensor_parallel_size = tensor_parallel_size
+        self.model = LLM(model_name, tensor_parallel_size=tensor_parallel_size)
+        self.temperature = 1.0
+        self.max_tokens = None
+        self.sampling_params = SamplingParams(temperature=self.temperature)
+
+    def generate_text(self, prompt: str) -> str:
+        output = self.model.generate([prompt], self.sampling_params)
+        return output[0].outputs[0].text
+
+    def set_temperature(self, value: float):
+        self.temperature = value
+        self.sampling_params = SamplingParams(temperature=self.temperature)
+
+    def set_max_tokens(self, value: int):
+        self.max_tokens = value
+        self.sampling_params = SamplingParams(temperature=self.temperature, max_tokens=self.max_tokens)
+
+    def offline_batched_inference(self, prompts: list) -> list:
+        outputs = self.model.generate(prompts, self.sampling_params)
+        return [output.outputs[0].text for output in outputs]
+
+    def start_api_server(self):
+        uvicorn.run(vllm_api_server.app, host="0.0.0.0", port=8000)
+
+    def start_openai_compatible_server(self):
+        uvicorn.run(openai_api_server.app, host="0.0.0.0", port=8000)
+
+    def query_openai_compatible_server(self, prompt: str):
+        openai.api_key = "EMPTY"
+        openai.api_base = "http://localhost:8000/v1"
+        completion = openai.Completion.create(model=self.model_name, prompt=prompt)
+        return completion
+
+    def distributed_inference(self, prompt: str):
+        ray.init()
+        self.model = LLM(self.model_name, tensor_parallel_size=self.tensor_parallel_size)
+        output = self.model.generate(prompt, self.sampling_params)
+        ray.shutdown()
+        return output[0].outputs[0].text
+
+    def run_on_cloud_with_skypilot(self, yaml_file):
+        sky = SkyPilot()
+        sky.launch(yaml_file)

swarms/workers/worker.py

@@ -68,11 +68,13 @@ class Worker:
         temperature: float = 0.5,
         llm=None,
         openai_api_key: str = None,
+        use_openai: bool = True,
     ):
         self.temperature = temperature
         self.human_in_the_loop = human_in_the_loop
         self.llm = llm
         self.openai_api_key = openai_api_key
+        self.use_openai = use_openai
         self.ai_name = ai_name
         self.ai_role = ai_role
         self.coordinates = (
@@ -149,24 +151,25 @@ class Worker:
             self.tools.extend(external_tools)
 
     def setup_memory(self):
-        """
+            """
-        Set up memory for the worker.
+            Set up memory for the worker.
-        """
+            """
-        openai_api_key = os.getenv("OPENAI_API_KEY") or self.openai_api_key
+            if self.use_openai:  # Only use OpenAI if use_openai is True
-        try:
+                openai_api_key = os.getenv("OPENAI_API_KEY") or self.openai_api_key
-            embeddings_model = OpenAIEmbeddings(openai_api_key=openai_api_key)
+                try:
-            embedding_size = 1536
+                    embeddings_model = OpenAIEmbeddings(openai_api_key=openai_api_key)
-            index = faiss.IndexFlatL2(embedding_size)
+                    embedding_size = 1536
-
+                    index = faiss.IndexFlatL2(embedding_size)
-            self.vectorstore = FAISS(
+
-                embeddings_model.embed_query, index, InMemoryDocstore({}), {}
+                    self.vectorstore = FAISS(
-            )
+                        embeddings_model.embed_query, index, InMemoryDocstore({}), {}
-
+                    )
-        except Exception as error:
+
-            raise RuntimeError(
+                except Exception as error:
-                f"Error setting up memory perhaps try try tuning the embedding size: {error}"
+                    raise RuntimeError(
-            )
+                        f"Error setting up memory perhaps try try tuning the embedding size: {error}"
-
+                    )
+                    
     def setup_agent(self):
         """
         Set up the autonomous agent.