Added Supabase DB functionality

.gitignore

@@ -6,3 +6,8 @@ __pycache__/
 models/
 .cache/
 .env
+*.pdf
+
+# macOS
+.DS_Store
+data/.DS_Store

app/rag.py

@@ -9,25 +9,31 @@ from langchain_qdrant import QdrantVectorStore
 from langchain_huggingface import HuggingFaceEmbeddings
 from langgraph.graph import StateGraph, END
 from langchain_openai import ChatOpenAI
+from langchain_ollama.chat_models import ChatOllama
 
+import time
 import os
+from dotenv import load_dotenv
 
 from qdrant_client import QdrantClient
 
-import os
-from dotenv import load_dotenv
+from supabase import create_client, Client
+
 load_dotenv()
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
+
 session_histories: dict[str, list] = {}
 # %%
 LLM_MODEL = "mistralai/mistral-7b-instruct:free"
+LLM_MODEL = "mistral:latest"
 OPENROUTER_API_KEY = os.getenv('OPENROUTER_API_KEY')
 COLLECTION_NAME = "wellness_docs"
 EMBEDDING_MODEL = "intfloat/e5-large-v2"
 QDRANT_URL = os.getenv('QDRANT_URL')
 QDRANT_API_KEY = os.getenv('QDRANT_API_KEY')
-
+SUPABASE_URL = os.getenv('SUPABASE_URL')
+SUPABASE_KEY = os.getenv('SUPABASE_KEY')
 
 FAQ_COLLECTION = "faqs"
 BLOGS_COLLECTION = "blogs"
@@ -35,7 +41,7 @@ TECHNOLOGY_COLLECTION = "technology"
 REVOLUTION_COLLECTION = "revolution"
 
 # %%
-llm = ChatOpenAI(
+"""llm = ChatOpenAI(
     model_name=LLM_MODEL,
     openai_api_key=OPENROUTER_API_KEY,
     base_url="https://openrouter.ai/api/v1",
@@ -43,8 +49,12 @@ llm = ChatOpenAI(
         "HTTP-Referer": "http://localhost",
         "X-Title": "Auro Wellness Chatbot",
     }
-)
+)"""
+
+llm = ChatOllama(model=LLM_MODEL)
+
 embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
+supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
 
 try:
     client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
@@ -69,7 +79,23 @@ class ToolInput(BaseModel):
     prompt: str
     iteration: int = 1
     
-
+def log_chat(session_id: str, query: str, answer: str, metadata: dict=None):
+    """
+    Logs a chat interaction to the Supabase 'chat_logs' table.
+    """
+    data = {
+        "session_id": session_id,
+        "query": query,
+        "answer": answer,
+        "metadata": metadata or {}
+    }
+    try:
+        response = supabase.table("chat_logs").insert(data).execute()
+        print("Logged chat interaction:", response)
+    except Exception as e:
+        print("Failed to log chat interaction:", e)
+    
+    
 # %%
 template = """
 You are Auro-Chat, a wellness assistant for Auro Wellness, here to answer any questions anyone has on wellness using the provided context from Auro Wellness's websites which contained company-approved documents, blogs, and FAQs
@@ -258,6 +284,22 @@ async def get_response(query: str, config) -> dict:
         "input": query,
         "history": history
     }
+    start_time = time.time()
     result =  await app.ainvoke(input_data, config=config)
+    latency_ms = int((time.time() - start_time) * 1000)
     session_histories[session_id] = result.get("history", [])
+    
+    metadata = {
+        "retrieved_docs": result.get("tool_results", {}),
+        "model": LLM_MODEL,
+        "embedding_model": EMBEDDING_MODEL,
+        "latency_ms": latency_ms
+    }
+
+    log_chat(
+        session_id=session_id,
+        query=query,
+        answer=result.get("response", ""),
+        metadata=metadata
+    )
     return result