File size: 3,739 Bytes
71c916b 765ede8 c1ca5a1 71c916b 765ede8 72ba547 c1ca5a1 765ede8 215277a 765ede8 29c811a 765ede8 71c916b 765ede8 29c811a 765ede8 29c811a 71c916b c1ca5a1 71c916b 765ede8 71c916b 72ba547 765ede8 71c916b 765ede8 c1ca5a1 765ede8 71c916b c1ca5a1 90b92b4 c1ca5a1 765ede8 c1ca5a1 71c916b 67c6e4d c1ca5a1 765ede8 c1ca5a1 765ede8 c1ca5a1 765ede8 c1ca5a1 765ede8 71c916b 765ede8 |
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 |
import os
from getpass import getpass
openai_api_key = os.getenv('OPENAI_API_KEY')
openai_api_key = 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
documents = SimpleDirectoryReader("new_file").load_data()
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client
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"
)
from llama_index.core.memory import ChatMemoryBuffer
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,
use the schema fields to generate appriopriate and valid json queries"""
),
)
# def is_greeting(user_input):
# greetings = ["hello", "hi", "hey", "good morning", "good afternoon", "good evening", "greetings"]
# user_input_lower = user_input.lower().strip()
# return any(greet in user_input_lower for greet in greetings)
# def is_bye(user_input):
# greetings = ["thanks", "thanks you", "thanks a lot", "good answer", "good bye", "bye bye"]
# user_input_lower = user_input.lower().strip()
# return any(greet in user_input_lower for greet in greetings)
import gradio as gr
def chat_with_ai(user_input, chat_history):
# if is_greeting(user_input):
# response = 'hi, how can i help you?'
# chat_history.append((user_input, response))
# return chat_history, ""
# elif is_bye(user_input):
# response = "you're wlocome"
# chat_history.append((user_input, response))
# return chat_history, ""
response = chat_engine.chat(user_input)
references = response.source_nodes
ref,pages = [],[]
for i in range(len(references)):
if references[i].metadata['file_name'] not in ref:
ref.append(references[i].metadata['file_name'])
# pages.append(references[i].metadata['page_label'])
complete_response = str(response) + "\n\n"
if ref !=[] or pages!=[]:
chat_history.append((user_input, complete_response))
ref = []
elif ref==[] or pages==[]:
chat_history.append((user_input,str(response)))
return chat_history, ""
def clear_history():
return [], ""
def gradio_chatbot():
with gr.Blocks() as demo:
gr.Markdown("# Chat Interface for LlamaIndex")
chatbot = gr.Chatbot(label="LlamaIndex Chatbot")
user_input = gr.Textbox(
placeholder="Ask a question...", label="Enter your question"
)
submit_button = gr.Button("Send")
btn_clear = gr.Button("Delete Context")
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(fn=clear_history, outputs=[chatbot, user_input])
return demo
gradio_chatbot().launch(debug=True) |