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app.py

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+from src.app import *
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+langchain==1.2.2
+langchain-anthropic==1.3.1
+langchain-pinecone==0.2.13
+langchain-text-splitters==1.1.0
+pinecone==7.3.0
+python-dotenv==1.2.1
+requests==2.32.5
+sentence-transformers==5.2.0

src/app.py

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+from pinecone import Pinecone
+from sentence_transformers import SentenceTransformer
+from anthropic import Anthropic
+import gradio as gr
+from langchain.agents import create_agent
+from langchain.tools import tool
+from langchain.chat_models import init_chat_model
+from src.config import *
+
+# Initialize Pinecone
+print("Connecting to Pinecone...")
+pc = Pinecone(api_key=PINECONE_API_KEY)
+index = pc.Index(PINECONE_INDEX_NAME)
+
+# Load embedding model
+print("Loading embedding model...")
+embedding_model = SentenceTransformer(EMBEDDING_MODEL)
+
+# Get portfolio stats
+stats = index.describe_index_stats()
+total_vectors = stats['total_vector_count']
+print(f"Connected! Index contains {total_vectors} vectors")
+
+
+@tool(response_format="content_and_artifact")
+def retrieve_code_context(query: str):
+    """Search through my GitHub repositories to find relevant code and project information."""
+    # Convert query to embedding
+    query_embedding = embedding_model.encode(query).tolist()
+
+    # Search Pinecone
+    results = index.query(
+        vector=query_embedding,
+        top_k=3,
+        include_metadata=True
+    )
+
+    # Format results for the LLM
+    context_parts = []
+    for match in results['matches']:
+        repo = match['metadata']['repo']
+        path = match['metadata']['path']
+        text = match['metadata']['text']
+        score = match['score']
+        context_parts.append(f"[Repo: {repo}, File: {path}, Relevance: {score:.2f}]\n{text}")
+
+    serialized = "\n\n---\n\n".join(context_parts)
+    return serialized, results['matches']
+
+
+# Initialize Claude for LangChain
+print("Initializing Claude agent...")
+model = init_chat_model(
+    "claude-sonnet-4-20250514",
+    model_provider="anthropic",
+    api_key=ANTHROPIC_API_KEY
+)
+
+# Create RAG agent with retrieval tool
+tools = [retrieve_code_context]
+system_prompt = f"""You are knightscode139's AI portfolio assistant. You have access to a tool that searches through {total_vectors} code chunks from his GitHub repositories.
+
+CRITICAL RULES:
+1. Use the search tool to find relevant code before answering technical questions
+2. Answer in FIRST PERSON as knightscode139
+3. ONLY state what is EXPLICITLY shown in the retrieved code
+4. If code doesn't contain specific details, say "I don't see that in my code"
+5. Be CONCISE (2-4 sentences unless asked for more detail)
+6. Decline off-topic questions politely
+
+When you retrieve code, cite the repo and file name naturally in your response."""
+
+agent = create_agent(model, tools, system_prompt=system_prompt)
+print("Agent ready!")
+
+
+def answer_question(question, history):
+    """Handle user questions with the RAG agent."""
+
+    try:
+        # Convert Gradio history to LangChain messages
+        messages = []
+        for msg in history:
+            messages.append({
+                "role": msg['role'],
+                "content": msg['content'][0]['text']  # Extract text from nested structure
+            })
+            
+        # Add current question
+        messages.append({"role": "user", "content": question})
+        
+        # Stream agent responses
+        response_text = ""
+        for event in agent.stream(
+            {"messages": messages},
+            stream_mode="values"
+        ):
+            last_message = event["messages"][-1]
+            if hasattr(last_message, 'content') and isinstance(last_message.content, str):
+                response_text = last_message.content
+        
+        return response_text
+        
+    except Exception as e:
+        return f"Error: {str(e)}. Please try again."
+
+
+# Create Gradio ChatInterface
+demo = gr.ChatInterface(
+    fn=answer_question,
+    title="🤖 knightscode139's GitHub Portfolio Assistant",
+    description=f"""Ask questions about my code and projects! Powered by LangChain RAG Agent + Claude Sonnet 4.
+    
+**Currently indexed:** {total_vectors} code chunks from my GitHub repositories.
+    
+The agent can search through my code multiple times to give you accurate answers.""",
+    examples=[
+        "What projects do you have?",
+        "How did you handle data preprocessing?",
+        "Show me your experience with machine learning",
+        "What accuracy did you achieve in your models?",
+        "Do you have any NLP projects?"
+    ],
+)
+
+
+if __name__ == "__main__":
+    demo.launch()

src/config.py

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+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
+
+# GitHub Configuration
+GITHUB_USERNAME = "knightscode139"
+TOKEN_GITHUB = os.getenv("TOKEN_GITHUB")
+
+# Pinecone Configuration
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+PINECONE_INDEX_NAME = "github-repos"
+
+# Anthropic Configuration
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
+
+# OPENAI Configuration
+# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# Embedding Model
+EMBEDDING_MODEL = "all-MiniLM-L6-v2"