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AgGPT11oPro.py

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+import sys
+import os
+from llama_cpp import Llama
+from contextlib import contextmanager
+from Agent0 import Agent
+
+@contextmanager
+def suppress_stdout_stderr():
+    null_fds = [os.open(os.devnull, os.O_RDWR) for _ in range(2)]
+    save_fds = (os.dup(1), os.dup(2))
+    try:
+        os.dup2(null_fds[0], 1)
+        os.dup2(null_fds[1], 2)
+        yield
+    finally:
+        os.dup2(save_fds[0], 1)
+        os.dup2(save_fds[1], 2)
+        for fd in null_fds + list(save_fds):
+            os.close(fd)
+
+system_prompt = f'''
+
+You art AgGPT11oPro, a grand and mighty language model, trained by Ag Corp to be most capable, thoughtful, and precise. Thy chief goal is to fathom deeply the user's intent, to reason step-by-step through complex problems, and to deliver clear, truthful, and nuanced answers, tailored to the user's every need and preference.
+
+1. Always provide a chain of thought reasoning within `<thought>` tags. This reasoning must be thorough and illuminate the mental steps taken to reach the answer. This chain of thought must be in full sentences and clear prose.
+
+2. After the `<thought>` tags, provide the final answer in full sentences, well-structured, and easy to understand. Do not provide single words or incomplete sentences.
+
+3. Always anticipate helpful follow-up information, and strive to be insightful and efficient in your responses.
+
+4. Prioritize truthfulness and precision above all.
+
+5. The user desires responses that explain your reasoning before giving the conclusion.
+
+6. Do not omit the `<thought>` tags or the final answer in any response.
+
+Examples:
+
+Example 1:  
+User: What is the capital of France?  
+Model:  
+<thought> To answer the user's query, I recall that France's capital city is Paris. This is a well-known fact in geography and general knowledge. </thought> The capital of France is Paris. ```
+Example 2:
+User: Explain why the sky is blue.
+Model:
+
+<thought>  
+The sky appears blue due to the scattering of sunlight by the Earth's atmosphere. Shorter blue wavelengths scatter more than other colors, making the sky look blue to our eyes during the day.  
+</thought>  
+The sky is blue because the Earth's atmosphere scatters sunlight, and blue light, having a shorter wavelength, scatters more widely, thus coloring the sky blue.  
+Example 3:
+User: Solve 3x + 5 = 20.
+Model:
+
+<thought>  
+To solve for x, I first subtract 5 from both sides, giving 3x = 15. Then, I divide both sides by 3, yielding x = 5.  
+</thought>  
+The solution to the equation 3x + 5 = 20 is x = 5.  
+These examples show how thou must always explain thy reasoning before stating the final answer, and how the reasoning should be enclosed in <thought> tags, followed by a clear and concise answer.
+
+Adhere to these instructions in all thy replies, and thy service shall be of the highest excellence.
+
+The user said: 
+'''
+
+class AgGPT11Pro:
+    def __init__(self, model_path="AgGPT11oPro.gguf"):
+        with suppress_stdout_stderr():
+            self.model = Llama(model_path=model_path, n_ctx=2048, n_gpu_layers=35)
+
+    def Ask(self, question):
+        agent0 = Agent()
+        context_extra = agent0.GatherContext(query=question, num_results=3)
+        messages = [
+            {"role": "system", "content": f"{context_extra}{system_prompt}{question}"},
+        ]
+        with suppress_stdout_stderr():
+            output = self.model.create_chat_completion(messages, max_tokens=1050, temperature=0.7)
+        return output["choices"][0]["message"]["content"]
+
+    def AskAgGPT(self, question):
+        return self.Ask(question)
+    
+    def rawask(self, question):
+        messages = [
+            {"role": "system", "content": f"{question}"},
+        ]
+        with suppress_stdout_stderr():
+            output = self.model.create_chat_completion(messages, max_tokens=1050, temperature=0.7)
+        return output["choices"][0]["message"]["content"]
+
+if __name__ == "__main__":
+    ag_gpt = AgGPT11Pro()
+    question = "What is the capital of France? Also tell me the current date and time."
+    answer = ag_gpt.AskAgGPT(question)
+    print(f"Question: {question}\nAnswer: {answer}")

Agent0.py

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+from duckduckgo_search import DDGS  
+import datetime
+
+class Agent:
+    def GatherContext(self, query="today's news", num_results=3):
+        result = ""
+        with DDGS() as ddgs:
+            for r in ddgs.text(query, max_results=num_results):
+                result += f"{r['title']}: {r['body']}\n"
+
+        todaydate = datetime.datetime.now().strftime("%Y-%m-%d")
+        current_time = datetime.datetime.now().strftime("%H:%M:%S")
+        context =  f'''
+          Context gathered from the web: {result}
+          time is {current_time} 
+          Date is {todaydate}
+         '''
+        return context
+
+if __name__ == "__main__":
+    agent = Agent()
+    context = agent.GatherContext(query="latest AI news", num_results=5)
+    print(f"Web Context:\n{context}")

README.md

@@ -1,3 +1,25 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+---
+
+# AgGPT-11o Pro
+
+AgGPT-11o Pro is a large language model designed to reason and think for longer, resulting in more accurate and reliable outputs for problems that require complex cognition. AgGPT-11o Pro has a wide range of agentic tools and capabilities. 
+
+## Examples: 
+
+```json
+{
+  "input": "What is the capital of France?",
+  "output": "The capital of France is Paris."
+}
+```
+
+```json
+{
+  "input": "Explain the theory of relativity in simple terms.",
+  "output": "The theory of relativity, proposed by Albert Einstein, states that the laws of physics are the same for all observers, regardless of their relative motion. It also introduces the concept that space and time are interconnected, forming a four-dimensional spacetime."
+}
+```

requirements.txt

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+duckduckgo_search