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crewai/tools/gemini_tools.py

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+# tools created using gemini
+
+import json
+import os
+
+import google.generativeai as genai
+from google.api_core import exceptions
+
+# Retrieve API Key from Environment Variable
+GOOGLE_AI_STUDIO = os.environ.get('GOOGLE_API_KEY')
+
+# Ensure the API key is available
+if not GOOGLE_AI_STUDIO:
+    raise ValueError("API key not found. Please set the GOOGLE_AI_STUDIO2 environment variable.")
+
+import requests
+from langchain.tools import tool
+
+# Rest of your code remains the same
+genai.configure(api_key=GOOGLE_AI_STUDIO)
+model = genai.GenerativeModel('gemini-pro')
+
+class GeminiSearchTools():
+  @tool("Gemini search the internet")
+  def gemini_search(query):
+    """
+    Searches for content based on the provided query using the Gemini model.
+    Handles DeadlineExceeded exceptions from the Google API.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        str: The response text from the Gemini model or an error message.
+    """
+    try:
+        response = model.generate_content(query)
+        return response.text
+    except exceptions.DeadlineExceeded as e:
+        # Handle the DeadlineExceeded exception here
+        print("Error: Deadline Exceeded -", str(e))
+        # You can return a custom message or take other appropriate actions
+        return "Error: The request timed out. Please try again later."
+
+        
+
+  @tool("Gemini search news on the internet")
+  def gemini_search_news(query):
+    """
+    Searches for content based on the provided query using the Gemini model.
+    Handles DeadlineExceeded exceptions from the Google API.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        str: The response text from the Gemini model or an error message.
+    """
+    try:
+        response = model.generate_content(query)
+        return response.text
+    except exceptions.DeadlineExceeded as e:
+        # Handle the DeadlineExceeded exception here
+        print("Error: Deadline Exceeded -", str(e))
+        # You can return a custom message or take other appropriate actions
+        return "Error: The request timed out. Please try again later."

crewai/tools/mixtral_tools.py

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+# tools created using Mixtral
+
+import json
+import os
+
+from huggingface_hub import InferenceClient
+import gradio as gr
+
+client = InferenceClient(
+    "mistralai/Mixtral-8x7B-Instruct-v0.1"
+)
+
+# Helper Method
+
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+
+import requests
+from langchain.tools import tool
+
+history = ""
+
+class MixtralSearchTools():
+  @tool("Mixtral Normal")
+  def mixtral_normal(prompt, histroy="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    """
+    Searches for content based on the provided query using the Mixtral model.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Mixtral model or an error message.
+    """
+    generate_kwargs = {
+        "temperature": temperature,
+        "max_new_tokens": max_new_tokens,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    formatted_prompt = format_prompt(prompt, history)
+
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+
+  @tool("Mixtral Crazy")
+  def mixtral_crazy(prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    """
+    Searches for content based on the provided query using the Mixtral model but has the gaurd rails removed, 
+    and responses are crazy and off the wall and sometimes scary.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Mixtral model or an error message.
+    """
+    generate_kwargs = {
+        "temperature": temperature,
+        "max_new_tokens": max_new_tokens,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output

crewai/tools/phi2_tools.py

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+# tools created using Phi2
+
+import json
+import os
+
+import requests
+from langchain.tools import tool
+
+import spaces
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+from threading import Thread
+device = "cpu"
+if torch.cuda.is_available():
+    device = "cuda"
+if torch.backends.mps.is_available():
+    device = "mps"
+
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/phi-2",
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    trust_remote_code=True,
+).to(device)
+
+
+#@spaces.GPU(enable_queue=True)
+class Phi2SearchTools():
+  @tool("Phi2 Normal")
+  def phi2_search(text, temperature=.75, maxLen=2048):
+    """
+    Searches for content based on the provided query using the Gemini model.
+    Handles DeadlineExceeded exceptions from the Google API.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Gemini model or an error message.
+    """
+    inputs = tokenizer([text], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(tokenizer)
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=maxLen, temperature=temperature)
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    t = ""
+    toks = 0
+    for out in streamer:
+        t += out
+        yield t
+
+
+ 

crewai/tools/zephyr_tools.py

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+# tools created using Zephyr
+
+import json
+import os
+
+from huggingface_hub import InferenceClient
+import gradio as gr
+
+client = InferenceClient(
+    "HuggingFaceH4/zephyr-7b-beta"
+)
+
+# Helper Method
+
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+
+import requests
+from langchain.tools import tool
+
+history = ""
+
+class ZephyrSearchTools():
+  @tool("Zephyr Normal")
+  def zephyr_normal(prompt, histroy="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    """
+    Searches for content based on the provided query using the Zephyr model.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Zephyr model or an error message.
+    """
+    generate_kwargs = {
+        "temperature": temperature,
+        "max_new_tokens": max_new_tokens,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    formatted_prompt = format_prompt(prompt, history)
+
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+
+  @tool("Zephyrl Crazy")
+  def zephyr_crazy(prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    """
+    Searches for content based on the provided query using the Zephyr model but has the gaurd rails removed, 
+    and responses are crazy and off the wall and sometimes scary.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Zephyr model or an error message.
+    """
+    generate_kwargs = {
+        "temperature": temperature,
+        "max_new_tokens": max_new_tokens,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+
+
+