File size: 4,351 Bytes
71c916b f1fd3e0 765ede8 0b57010 765ede8 c1ca5a1 1e22681 71c916b 765ede8 72ba547 f1fd3e0 765ede8 215277a 1e22681 f1fd3e0 765ede8 1e22681 f1fd3e0 1e22681 f1fd3e0 97c22f1 f1fd3e0 1e22681 765ede8 f1fd3e0 71c916b 67c6e4d f1fd3e0 765ede8 1e22681 765ede8 1e22681 f1fd3e0 765ede8 97c22f1 f1fd3e0 d29b8ab b6d31e4 d29b8ab f1fd3e0 97c22f1 f1fd3e0 765ede8 71c916b 1e22681 f1fd3e0 |
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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
import os
import shutil
import gradio as gr
import qdrant_client
from getpass import getpass
openai_api_key = os.getenv('OPENAI_API_KEY')
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.4)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core.memory import ChatMemoryBuffer
chat_engine = None
index = None
query_engine = None
memory = None
client = None
vector_store = None
storage_context = None
def process_upload(files):
upload_dir = "uploaded_files"
if not os.path.exists(upload_dir):
os.makedirs(upload_dir)
else:
for f in os.listdir(upload_dir):
os.remove(os.path.join(upload_dir, f))
for file_path in files:
file_name = os.path.basename(file_path)
dest = os.path.join(upload_dir, file_name)
shutil.copy(file_path, dest)
documents = SimpleDirectoryReader(upload_dir).load_data()
global client, vector_store, storage_context, index, query_engine, memory, chat_engine
client = qdrant_client.QdrantClient(location=":memory:")
vector_store = QdrantVectorStore(
collection_name="paper",
client=client,
enable_hybrid=True,
batch_size=20,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
query_engine = index.as_query_engine(vector_store_query_mode="hybrid")
memory = ChatMemoryBuffer.from_defaults(token_limit=3000)
chat_engine = index.as_chat_engine(
chat_mode="context",
memory=memory,
system_prompt=(
"You are an AI assistant who answers the user questions,"
),
)
return "Documents uploaded and index built successfully!"
def chat_with_ai(user_input, chat_history):
global chat_engine
if chat_engine is None:
return chat_history, "Please upload documents first."
response = chat_engine.chat(user_input)
references = response.source_nodes
ref, pages = [], []
for node in references:
file_name = node.metadata.get('file_name')
if file_name and file_name not in ref:
ref.append(file_name)
complete_response = str(response) + "\n\n"
if ref or pages:
chat_history.append((user_input, complete_response))
else:
chat_history.append((user_input, str(response)))
return chat_history, ""
def clear_history():
return [], ""
def gradio_interface():
with gr.Blocks() as demo:
gr.Markdown("# AI Assistant")
with gr.Tab("Upload Documents"):
gr.Markdown("Upload PDF, Excel, CSV, DOC/DOCX, or TXT files below:")
file_upload = gr.File(
label="Upload Files",
file_count="multiple",
file_types=[".pdf", ".csv", ".txt", ".xlsx", ".xls", ".doc", ".docx"],
type="filepath" )
upload_status = gr.Textbox(label="Upload Status", interactive=False)
upload_button = gr.Button("Process Upload")
upload_button.click(process_upload, inputs=file_upload, outputs=upload_status)
with gr.Tab("Chat"):
chatbot = gr.Chatbot(label="AI Assistant Chat Interface")
user_input = gr.Textbox(
placeholder="Ask a question...", label="Enter your question"
)
submit_button = gr.Button("Send")
btn_clear = gr.Button("Clear History")
# A State to hold the chat history.
chat_history = gr.State([])
submit_button.click(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
user_input.submit(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
btn_clear.click(clear_history, outputs=[chatbot, user_input])
return demo
gradio_interface().launch(debug=True)
|