File size: 2,713 Bytes
b4f404b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | from flask import Flask, render_template, request, jsonify
from flask_cors import CORS
from dotenv import load_dotenv
import os
from langchain_pinecone import PineconeVectorStore
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.chat_models import ChatOllama
from langchain_huggingface import HuggingFaceEmbeddings
# Download the Embeddings from HuggingFace
def download_hugging_face_embeddings():
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') # this model returns 384 dimensions
return embeddings
# Define the system prompt
system_prompt = (
"You are an intelligent Personal Portfolio Assistant that answers questions about the user's background, work, and projects. "
"Use the retrieved context below to provide accurate and natural responses. "
"If the context does not contain the answer, respond with 'I'm not sure about that.' "
"Keep your answer concise."
"\n\n"
"Context:\n{context}"
)
load_dotenv()
pinecone_api_key = os.environ.get("PINECONE_API_KEY")
if not pinecone_api_key:
raise ValueError("Missing PINECONE_API_KEY in environment variables.")
app = Flask(__name__)
CORS(app) # ✅ Allow external web app to access this Flask API
# Load embeddings
embeddings = download_hugging_face_embeddings()
index_name = "portfolio"
docsearch = PineconeVectorStore.from_existing_index(index_name=index_name, embedding=embeddings)
retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
# Model
chatModel = ChatOllama(model="gemma3:1b", temperature=0.1, max_tokens=512)
# Prompt
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
question_answer_chain = create_stuff_documents_chain(chatModel, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
@app.route("/")
def index():
return "✅ RAG server running"
@app.route("/get", methods=["POST"])
def chat():
user_msg = request.form.get("msg") or request.json.get("msg")
if not user_msg:
return jsonify({"error": "No message sent"}), 400
try:
response = rag_chain.invoke({"input": user_msg})
answer = response.get("answer", "Sorry, I couldn’t find an answer.")
return jsonify({"reply": answer})
except Exception as e:
print("Error:", e)
return jsonify({"reply": f"Server Error: {str(e)}"})
if __name__ == '__main__':
port = int(os.environ.get('PORT', 2025))
app.run(host="0.0.0.0", port=port, debug=False)
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