prototype

src/main.py

@@ -1,31 +1,13 @@
 import os
 from typing import Dict, List, Any
-import openai
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import SerperDevTool, WebsiteSearchTool
 from dotenv import load_dotenv
-from langchain.agents import load_tools
+from openai import OpenAI
 
-
-# Load environment variables from .env file with the contents: 
-# SERPER_API_KEY=your_serper_api_key
-# OPENAI_API_KEY=your_openai_api_key
+# Load environment variables from .env file
 load_dotenv()
 
-# Set up API keys
-serper_api_key = os.getenv("SERPER_API_KEY")
-#openai_api_key = os.getenv("OPENAI_API_KEY")
-openai_api_key="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"
-
-# Initialize tools
-search_tool = SerperDevTool()
-web_search_tool = WebsiteSearchTool()
-
-#vars 
-llm = "gpt-4o"
-fast_llm = "gpt-3.5-turbo"
-
-langchain_tools = load_tools(["google-serper"], llm = llm)
+# Initialize OpenAI client
+client = OpenAI(api_key="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")
 
 # Example knowledge base json structure
 knowledge_base_example = {
@@ -61,49 +43,26 @@
     }
 }
 
-# This dictionary (stores in pinata as json) represents a page in the system. It includes:
-# - Page ID: A unique identifier for the page.
-# - Title: The title of the page.
-# - Content: A brief description or content of the page.
-# - Links: An array of pointers to related pages and files. Each link has a type (either 'page' or 'file'), an ID, and a title or filename.
-# The frontend can use this dictionary to dynamically fetch and display related content by following the IDs in the links array. This structure allows for easy navigation and organization of interconnected pages and files.
-page_example = {
-    "page_id": "123",
-    "title": "Introduction to BrainButler",
-    "content": "This page provides an overview of BrainButler.",
-    "links": [
-        {
-            "type": "page",
-            "id": "124",
-            "title": "System Architecture"
-        },
-        {
-            "type": "file",
-            "id": "file_001",
-            "filename": "README.md"
-        }
-    ]
-}
-
-import openai
-
+# Function definitions
 def set_openai_api_key(api_key):
-    openai.api_key = api_key
+    client.api_key = api_key
 
 def create_agent_prompt(role, goal, backstory):
     return f"You are a {role}. Your goal is to {goal}. Backstory: {backstory}"
 
 def execute_task(agent_prompt, task_description, context=""):
-    response = openai.ChatCompletion.create(
-        model="gpt-4",
+    response = client.chat.completions.create(
+        model="gpt-4o",
         messages=[
             {"role": "system", "content": agent_prompt},
             {"role": "user", "content": f"Task: {task_description}\nContext: {context}"}
         ]
     )
-    return response.choices[0].message['content']
 
-def knowledge_base_crew(knowledge_base):
+    return response.choices[0].message.content
+    # return response.choices[0].message['content']
+
+def analyze_kb(knowledge_base):
     agent_prompt = create_agent_prompt(
         "Knowledge Base Organizer",
         "Organize and manage knowledge bases",
@@ -112,49 +71,61 @@ def knowledge_base_crew(knowledge_base):
     task = "Analyze the current knowledge base structure and suggest improvements or new knowledge bases."
     return execute_task(agent_prompt, task, str(knowledge_base))
 
-def assimilation_crew(new_info, kb_analysis):
+def summarize_kb(knowledge_base):
+    agent_prompt = create_agent_prompt(
+        "Knowledge Base Summarizer",
+        "Summarize the current knowledge base as concisely as possible",
+        "An expert in summarizing and condensing information into concise and clear summaries."
+    )
+    task = "Provide a summary of the current knowledge base."
+    return execute_task(agent_prompt, task, str(knowledge_base))
+
+
+
+def assimilation(new_info, kb_analysis):
     agent_prompt = create_agent_prompt(
         "Information Assimilator",
         "Decide how to incorporate new information into existing knowledge bases",
         "A skilled analyst with expertise in content curation and information synthesis."
     )
-    task = "Review new information and determine the best way to incorporate it into the existing knowledge base."
+    task = ("Review new information and determine the best way to incorporate it into the existing knowledge base.")
     context = f"New Information: {new_info}\nKnowledge Base Analysis: {kb_analysis}"
     return execute_task(agent_prompt, task, context)
 
-def creation_crew(assimilation_plan):
+def creation(assimilation_plan):
     agent_prompt = create_agent_prompt(
         "Content Creator",
-        "Research and generate comprehensive pages for knowledge bases",
-        "A creative writer and researcher with a talent for producing engaging and informative content."
+        ("Research and generate comprehensive pages for knowledge bases"),
+        ("A creative writer and researcher with a talent for producing engaging and informative content.")
     )
-    task = "Research and generate a new page for the knowledge base based on the assimilation plan."
+    task = ("Research and generate a new page for the knowledge base based on the assimilation plan.")
     return execute_task(agent_prompt, task, assimilation_plan)
 
 def process_new_information(new_info, knowledge_base):
     # Step 1: Analyze knowledge base structure
-    kb_analysis = knowledge_base_crew(knowledge_base)
+    kb_summary = summarize_kb(knowledge_base)
 
     # Step 2: Determine how to incorporate new information
-    assimilation_plan = assimilation_crew(new_info, kb_analysis)
+    assimilation_plan = assimilation(new_info, kb_summary)
 
     # Step 3: Create new content based on the assimilation plan
-    new_content = creation_crew(assimilation_plan)
+    new_content = creation(assimilation_plan)
 
     return new_content
 
 # Example usage
 if __name__ == "__main__":
-    set_openai_api_key("your_openai_api_key_here")
 
-    new_info = "Recent advancements in quantum computing and their potential impact on cryptography."
+    new_info = ("Recent advancements in quantum computing and their potential impact on cryptography.")
+
     knowledge_base = {
-        "title": "Technology Trends",
+        "title": ("Technology Trends"),
         "pages": [
-            {"title": "Quantum Computing Basics", "content": "..."},
-            {"title": "Modern Cryptography", "content": "..."}
+            {"title": ("Quantum Computing Basics"), "content": "..."},
+            {"title": ("Modern Cryptography"), "content": "..."}
         ]
     }
 
     updated_content = process_new_information(new_info, knowledge_base)
-    print("New content generated:", updated_content)
+    print("New content generated:", updated_content)
+