Add application file

app.py

@@ -0,0 +1,100 @@
+import openai
+from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
+from llama_index.callbacks.base import CallbackManager
+from llama_index import (
+    LLMPredictor,
+    ServiceContext,
+    StorageContext,
+    load_index_from_storage,
+)
+from langchain.chat_models import ChatOpenAI
+import chainlit as cl
+
+
+import openai
+from llama_index import SimpleDirectoryReader, Document, StorageContext, OpenAIEmbedding, ServiceContext, PromptHelper
+from llama_index.node_parser import SimpleNodeParser
+from llama_index.text_splitter import TokenTextSplitter
+from llama_index.indices.vector_store import VectorStoreIndex
+from llama_index.vector_stores import SupabaseVectorStore
+from llama_index.llms import OpenAI
+from llama_index import GPTVectorStoreIndex, StorageContext
+from llama_index.vector_stores import MilvusVectorStore
+from dotenv import load_dotenv
+import os
+from llama_index import get_response_synthesizer
+
+
+
+
+openai.api_key = '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'
+
+
+# Substitute your connection string here
+DB_CONNECTION = "postgresql://postgres:xnam*P3gMWL9Wgp@db.yfeyscypngqzvtartbca.supabase.co:5432/postgres"
+vector_store = SupabaseVectorStore(
+    postgres_connection_string=DB_CONNECTION, 
+    collection_name='p53',memory = 21096
+)
+
+
+# rebuild storage context
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+prompt_helper = PromptHelper(
+  context_window=4096,
+  num_output=256,
+  chunk_overlap_ratio=0.1,
+  chunk_size_limit=None
+)
+
+llm = OpenAI(model='gpt-3.5-turbo', temperature=0, max_tokens=256)
+embed_model = OpenAIEmbedding()
+node_parser = SimpleNodeParser(
+text_splitter=TokenTextSplitter(chunk_size=1024, chunk_overlap=100)
+)
+
+service_context = ServiceContext.from_defaults(
+  llm=llm,
+  embed_model=embed_model,
+  node_parser=node_parser,
+  prompt_helper=prompt_helper
+)
+
+
+index =  VectorStoreIndex.from_vector_store(vector_store=vector_store)
+from llama_index import get_response_synthesizer
+
+
+
+
+
+@cl.on_chat_start
+async def factory():
+
+
+    query_engine = index.as_query_engine(
+        service_context=service_context,
+        streaming=True,
+    )
+
+    cl.user_session.set("query_engine", query_engine)
+
+
+@cl.on_message
+async def main(message):
+
+
+    query_engine = cl.user_session.get("query_engine")  # type: RetrieverQueryEngine
+    response = await cl.make_async(query_engine.query)(message)
+
+    response_message = cl.Message(content="")
+
+    for token in response.response_gen:
+        response_message.content = response.response_txt
+
+    if response.response_txt:
+        response_message.content = response.response_txt
+
+    await response_message.send()
+