app setup basic

.gitignore

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+.env
+venv/
+chroma_db
+.vscode
+chroma
+/trash
+uploads/

app.py

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+from flask import Flask,request, jsonify
+from flask import render_template
+from werkzeug.utils import secure_filename
+
+from langchain.document_loaders import DirectoryLoader,PyPDFLoader,UnstructuredWordDocumentLoader,TextLoader,UnstructuredHTMLLoader,UnstructuredMarkdownLoader
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from langchain.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
+from langchain_chroma import Chroma
+
+import google.generativeai as genai
+
+from dotenv import load_dotenv
+import os
+import shutil
+
+load_dotenv()
+
+HF_TOKEN = os.getenv('HF_TOKEN')
+GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
+CHROMA_PATH = "chroma"
+UPLOAD_FOLDER = "uploads"
+
+# Initialize Hugging Face embedding
+hugging_face_ef = HuggingFaceInferenceAPIEmbeddings(
+    api_key=HF_TOKEN,
+    model_name="sentence-transformers/all-MiniLM-L6-v2"
+)
+genai.configure(api_key=GOOGLE_API_KEY)
+llm_model = genai.GenerativeModel("gemini-1.5-flash")
+
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER)
+    
+app = Flask(__name__)
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')  # Serve the HTML file we created
+
+
+@app.route("/ai",methods=["POST"])
+def aiPost():
+    print("Post /ai called")
+    json_content = request.json
+    query = json_content.get("query")
+    
+    print("Query:",query)
+    
+    response_answer = llm_model.generate_content(query)
+    return response_answer.text
+
+# add files
+@app.route('/upload', methods=['POST'])
+def upload_files():
+    if 'files' not in request.files:
+        return jsonify({'error': 'No files in request'}), 400
+    
+    files = request.files.getlist('files')
+    
+    uploaded_files = []
+    for file in files:
+        if file.filename:
+            # Secure the filename
+            filename = secure_filename(file.filename)
+            file_path = os.path.join(UPLOAD_FOLDER, filename)
+            file.save(file_path)
+            uploaded_files.append(filename)
+            
+            # Here you can call your RAG pipeline processing function
+            # process_pdf(file_path)
+    
+    return jsonify({
+        'message': 'Files uploaded successfully',
+        'files': uploaded_files
+    })
+
+
+def main():
+    app.run(host="0.0.0.0",port=8000,debug=True)
+
+if __name__ == "__main__":
+    main()
+    
+    

requirements.txt

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+pandas
+numpy
+langchain
+langchain-community
+unstructured
+chromadb
+transformers
+huggingface-hub
+requests
+python-dotenv
+langchain-chroma
+sentence_transformers
+google-generativeai
+markdown
+python-docx 
+flask
+werkzeug