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Dockerfile

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+# Use a slim Python base image
+FROM python:3.11-slim
+
+# Install build tools just in case a package needs to compile from source
+RUN apt-get update && apt-get install -y build-essential
+
+# Set the working directory
+WORKDIR /code
+
+# Copy the requirements file and install dependencies
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy the application code
+COPY ./app /code/app
+
+# Command to run the Uvicorn server
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

app/main.py

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+import os
+import uuid
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+from typing import List
+
+# Model and DB libraries
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+import chromadb
+from youtube_transcript_api import YouTubeTranscriptApi
+
+# --- 1. Constants and Configuration ---
+MODEL_REPO = "bartowski/Phi-3.5-mini-instruct_Uncensored-GGUF"
+GGUF_FILE = "Phi-3.5-mini-instruct_Uncensored-Q4_K_M.gguf" # Good balance
+CHROMA_PATH = "/app/chroma_db" # Path inside the container for persistent storage
+COLLECTION_NAME = "chat_history"
+
+# --- 2. Initialize FastAPI app ---
+app = FastAPI(
+    title="Enhanced RAG API with Memory",
+    description="An API with chat history (ChromaDB) and YouTube analysis.",
+    version="1.0",
+)
+
+# --- 3. Global Variables (will be loaded on startup) ---
+llm: Llama = None
+chroma_client: chromadb.Client = None
+collection: chromadb.Collection = None
+
+# --- 4. Startup Event: Load models and initialize DB ---
+@app.on_event("startup")
+def load_resources():
+    global llm, chroma_client, collection
+    
+    # Load the LLM
+    print("Downloading and loading LLM...")
+    model_path = hf_hub_download(repo_id=MODEL_REPO, filename=GGUF_FILE)
+    llm = Llama(model_path=model_path, n_ctx=4096, n_gpu_layers=-1, verbose=True)
+    print("LLM loaded.")
+
+    # Initialize ChromaDB client
+    print("Initializing ChromaDB...")
+    # This creates a persistent DB client that stores data in the specified path
+    chroma_client = chromadb.PersistentClient(path=CHROMA_PATH)
+    # Get or create the collection to store chat history
+    collection = chroma_client.get_or_create_collection(name=COLLECTION_NAME)
+    print("ChromaDB initialized.")
+    print("API is ready to go! 🚀")
+
+# --- 5. Pydantic Models for API requests ---
+class ChatRequest(BaseModel):
+    session_id: str = Field(..., description="Unique identifier for a chat session.")
+    message: str = Field(..., description="The user's message.")
+
+class YouTubeRequest(BaseModel):
+    video_url: str = Field(..., description="URL of the YouTube video to analyze.")
+
+# --- 6. API Endpoint for Chat with Memory ---
+@app.post("/chat")
+def chat_with_memory(request: ChatRequest):
+    print(f"Received chat request for session: {request.session_id}")
+    
+    # Step 1: Retrieve relevant chat history from ChromaDB
+    try:
+        history = collection.query(
+            where={"session_id": request.session_id},
+            n_results=5 # Get the last 5 exchanges
+        )
+        # Format history for the prompt
+        context = "\n".join([f"User: {meta['user_message']}\nAI: {doc}" for doc, meta in zip(history['documents'][0], history['metadatas'][0])])
+    except Exception as e:
+        print(f"Error querying ChromaDB: {e}")
+        context = "" # Start fresh if history fails
+
+    # Step 2: Construct the prompt with history
+    prompt_template = (
+        "<s><|system|>\nYou are a helpful AI assistant. "
+        "Use the chat history below to provide a relevant and coherent response.\n\n"
+        "--- Chat History ---\n{chat_history}\n--- End History ---\n<|end|>\n"
+        "<|user|>\n{user_message}<|end|>\n<|assistant|>"
+    )
+    prompt = prompt_template.format(chat_history=context, user_message=request.message)
+
+    # Step 3: Generate a response from the LLM
+    output = llm(prompt=prompt, max_tokens=256, stop=["<|end|>", "User:"], echo=False)
+    ai_response = output["choices"][0]["text"].strip()
+    
+    # Step 4: Save the new exchange to ChromaDB
+    try:
+        # We store the AI response as the document and the user message in metadata
+        doc_id = str(uuid.uuid4())
+        collection.add(
+            ids=[doc_id],
+            documents=[ai_response],
+            metadatas=[{"session_id": request.session_id, "user_message": request.message}]
+        )
+        print(f"Saved new exchange to session {request.session_id}")
+    except Exception as e:
+        print(f"Error saving to ChromaDB: {e}")
+
+    return {"session_id": request.session_id, "response": ai_response}
+
+# --- 7. API Endpoint for YouTube Video Analysis ---
+@app.post("/analyze_youtube")
+def analyze_youtube_video(request: YouTubeRequest):
+    try:
+        # Extract video ID from URL
+        video_id = request.video_url.split("v=")[1].split("&")[0]
+        print(f"Fetching transcript for video ID: {video_id}")
+        
+        # Get transcript
+        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
+        transcript = " ".join([item['text'] for item in transcript_list])
+        print("Transcript fetched successfully.")
+
+        # Create a prompt for summarization
+        prompt = (
+            f"<s><|system|>\nYou are an expert analyst. Summarize the key points of the following YouTube video transcript."
+            f"<|end|>\n<|user|>\nTranscript: {transcript[:3000]}\n\nSummary:<|end|>\n<|assistant|>" # Truncate to fit context
+        )
+        
+        # Get summary from LLM
+        output = llm(prompt, max_tokens=512, stop=["<|end|>"], echo=False)
+        summary = output["choices"][0]["text"].strip()
+        
+        return {"video_url": request.video_url, "summary": summary}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/")
+def read_root():
+    return {"status": "API is running."}

docker-compose.yml

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+# docker-compose.yml
+
+version: '3.8'
+
+services:
+  web:
+    build: .
+    container_name: rag_api_web
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./uploads:/app/uploads
+    environment:
+      - CHROMA_HOST=chroma
+      - CHROMA_PORT=8000
+    depends_on:
+      - chroma
+    restart: unless-stopped
+
+  chroma:
+    image: chromadb/chroma
+    container_name: rag_api_chroma
+    ports:
+      - "8001:8000"
+    volumes:
+      - chroma_data:/chroma/chroma
+    restart: unless-stopped
+
+volumes:
+  chroma_data:
+    driver: local

requirements.txt

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+fastapi
+uvicorn
+llama-cpp-python
+huggingface-hub
+pydantic
+# New additions
+chromadb
+youtube-transcript-api
+uuid

uploads/.gitkeep

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+# ...existing code...