Merge pull request #71 from amosproj/llm_function

Added gemini and llama3 using API

Project/backend/.env.example

@@ -15,5 +15,9 @@ POSTGRES_PASSWORD=password
 POSTGRES_DB=amos
 POSTGRES_PORT=5432
 POSTGRES_HOST=amos-db
+JANUS_PORT=8182
+
+#API Keys 
+GROQ_API_KEY=API_KEY
+GOOGLE_API_KEY=API_KEY
 
-JANUS_PORT=8182

Project/backend/codebase/requirements.txt

@@ -82,3 +82,4 @@ tenacity==8.3.0
 tomli==2.0.1
 typing-inspect==0.9.0
 urllib3==2.2.1
+google-generativeai==0.5.4

Project/backend/config/llm/gemini.py

@@ -0,0 +1,148 @@
+import os
+import json
+import google.generativeai as genai
+from datetime import datetime
+from dotenv import load_dotenv
+
+
+def configure_genai():
+    """
+    Ensure the API key is set in the environment (set it in the .env file or Linux/Mac: export GOOGLE_API_KEY="Your_API_KEY")
+
+    Raises:
+        ValueError: If the API key is not found in the environment variables
+    """
+    # load the API key from the environment variables (could be remove if the env is loaded in the main file)
+    load_dotenv("../../.env", override=True)
+
+    api_key = os.getenv("GOOGLE_API_KEY")
+    if not api_key:
+        raise ValueError("API key not found in environment variables")
+
+    genai.configure(
+        api_key=api_key,
+    )
+
+
+def create_model():
+    """
+    Create and configure a generative model with specified generation and safety settings.
+
+    Returns:
+        genai.GenerativeModel: A configured generative model instance.
+    """
+    generation_config = {
+        "temperature": 1,
+        "top_p": 0.95,
+        "top_k": 64,
+        "max_output_tokens": 8192,
+        "response_mime_type": "text/plain",
+    }
+    safety_settings = [
+        {
+            "category": "HARM_CATEGORY_HARASSMENT",
+            "threshold": "BLOCK_MEDIUM_AND_ABOVE",
+        },
+        {
+            "category": "HARM_CATEGORY_HATE_SPEECH",
+            "threshold": "BLOCK_MEDIUM_AND_ABOVE",
+        },
+        {
+            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+            "threshold": "BLOCK_MEDIUM_AND_ABOVE",
+        },
+        {
+            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+            "threshold": "BLOCK_MEDIUM_AND_ABOVE",
+        },
+    ]
+
+    return genai.GenerativeModel(
+        model_name="gemini-1.5-pro-latest",
+        safety_settings=safety_settings,
+        generation_config=generation_config,
+    )
+
+
+def serialize_chat_history(history):
+    """
+    Convert the chat history to a serializable format.
+
+    Args:
+        history (list): A list of chat entries where each entry is an object containing message details.
+
+    Returns:
+        list: A list of dictionaries, each containing the serialized chat entry with message, timestamp, and role.
+    """
+    serialized_history = []
+    for entry in history:
+        # Extract relevant information from the entry object
+        serialized_entry = {
+            "message": str(entry),
+            "timestamp": datetime.now().isoformat(),
+            "role": entry.role if hasattr(entry, "role") else None,
+        }
+        serialized_history.append(serialized_entry)
+    return serialized_history
+
+
+def generate_response(text_content, prompt_template):
+    """
+    Generate a response from a generative model based on provided text content and a prompt template.
+
+    Args:
+        text_content (str): The main text content to be used within the prompt template.
+        prompt_template (str): A template string for the prompt, containing a placeholder for the text content.
+
+    Returns:
+        str: A JSON-formatted string containing the response text and the serialized chat history.
+
+    Raises:
+        ValueError: If the API key is not found in the environment variables.
+        Exception: If there are issues in configuring the model or generating the response.
+    """
+    configure_genai()
+    model = create_model()
+
+    chat_session = model.start_chat(history=[])
+
+    # Combine text content and prompt template to form the message
+    message = prompt_template.format(text_content=text_content)
+
+    # Send the message to the chat session
+    response = chat_session.send_message(message)
+
+    # Extract the response text and the chat history
+    response_data = {
+        "response_text": response.text,
+        "chat_history": serialize_chat_history(chat_session.history),
+    }
+
+    # Return the response data in JSON format
+    return json.dumps(response_data, indent=2)
+
+
+if __name__ == "__main__":
+    text_content = (
+        "When one thinks about what a holiday means for students, "
+        "we notice how important it is for the kids. It is a time "
+        "when they finally get the chance to take a break from studies "
+        "and pursue their hobbies. They can join courses which give them "
+        "special training to specialize in it. They can get expert in arts, "
+        "craft, pottery, candle making and more. Furthermore, they also make "
+        "new friends there who have the same interests. In addition, students "
+        "get to visit new places on holiday. Like during summer or winter holidays, "
+        "they go with their families to different cities and countries. Through holidays, "
+        "they get new experiences and memories which they remember for a lifetime. "
+        "Furthermore, it also gives them time to relax with their families. Other cousins "
+        "also visit each other’s places and spend time there. They play games and go out "
+        "with each other. Moreover, students also get plenty of time to complete their homework "
+        "and revise the syllabus."
+    )
+    prompt_template = "Give important 3 words which is: {text_content}"
+
+    # text_content = input("Input your paragraph: ")
+    # prompt_template = input("Enter your prompt: ")
+
+    response_json = generate_response(text_content, prompt_template)
+    print(response_json)

Project/backend/config/llm/llama3.py

@@ -0,0 +1,109 @@
+# 1. Visit [consol.groq.com](https://consol.groq.com).
+# 2. Navigate to the API Keys section and create a new key.
+# 3. Important: Copy the key immediately as it will only be visible once.
+
+# !pip install groq
+
+import os
+import json
+from datetime import datetime
+from dotenv import load_dotenv
+
+# Assuming 'groq' is the correct library for the API you're using
+from groq import Groq
+
+
+def get_groq_client():
+    """
+    Ensure the API key is set in the environment (set it in the .env file or Linux/Mac: export GROQ_API_KEY="Your_API_KEY")
+
+    Raises:
+        ValueError: If the API key is not found in the environment variables
+    """
+    # load the API key from the environment variables (could be remove if the env is loaded in the main file)
+    load_dotenv("../../.env", override=True)
+
+    api_key = os.getenv("GROQ_API_KEY")
+    if not api_key:
+        raise ValueError("API key not found in environment variables")
+    return Groq(api_key=api_key)
+
+
+def generate_response(text_content, prompt_template):
+    """
+    Generate a response from a Groq AI client based on provided text content and a prompt template.
+
+    This function initializes the Groq client, creates a chat completion request with the given text content
+    and prompt template, and retrieves the response along with the chat history. The response data is returned in JSON format.
+
+    Args:
+        text_content (str): The main text content to be used within the prompt template.
+        prompt_template (str): A template string for the prompt, containing a placeholder for the text content.
+
+    Returns:
+        str: A JSON-formatted string containing the response text and the serialized chat history.
+
+    Raises:
+        ValueError: If the API key is not found in the environment variables.
+    """
+    client = get_groq_client()
+
+    # Combine text content and prompt template to form the message
+    message = prompt_template.format(text_content=text_content)
+
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": message,
+            }
+        ],
+        model="llama3-8b-8192",
+    )
+
+    response_data = {
+        "response_text": chat_completion.choices[0].message.content,
+        "chat_history": [
+            {
+                "message": message,
+                "role": "user",
+                "timestamp": datetime.now().isoformat(),  # You can replace None with actual timestamp if available
+            },
+            {
+                "message": chat_completion.choices[0].message.content,
+                "role": "model",
+                "timestamp": datetime.now().isoformat(),  # You can replace None with actual timestamp if available
+            },
+        ],
+    }
+
+    # Return the response data in JSON format
+    return json.dumps(response_data, indent=2)
+
+
+if __name__ == "__main__":
+    # Take text_content and prompt_template from the user
+    text_content = (
+        "When one thinks about what a holiday means for students, "
+        "we notice how important it is for the kids. It is a time "
+        "when they finally get the chance to take a break from studies "
+        "and pursue their hobbies. They can join courses which give them "
+        "special training to specialize in it. They can get expert in arts, "
+        "craft, pottery, candle making and more. Furthermore, they also make "
+        "new friends there who have the same interests. In addition, students "
+        "get to visit new places on holiday. Like during summer or winter holidays, "
+        "they go with their families to different cities and countries. Through holidays, "
+        "they get new experiences and memories which they remember for a lifetime. "
+        "Furthermore, it also gives them time to relax with their families. Other cousins "
+        "also visit each other’s places and spend time there. They play games and go out "
+        "with each other. Moreover, students also get plenty of time to complete their homework "
+        "and revise the syllabus."
+    )
+    prompt_template = "Give me 3 important words for paragraph"
+
+    # To get the inputs
+    # text_content = input("Enter the paragraph: ")
+    # prompt_template = input("Enter the prompt: ")
+
+    response_json = generate_response(text_content, prompt_template)
+    print(response_json)