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0e6228a 859a762 d0c9bed 0e6228a 2dafffe 0e6228a 38d6177 0e6228a 3034fdd 2dafffe 0e6228a a4951ab 0e6228a a4951ab 0e6228a a4951ab 0e6228a a4951ab 0e6228a 8a40227 0e6228a a4951ab 0e6228a | 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 | import os
import json
import gradio as gr
import openai
from typing import Iterable
from langchain.document_loaders import WebBaseLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
from langchain.tools import Tool
from langchain.utilities import GoogleSearchAPIWrapper
openai.api_key = os.environ['OPENAI_API_KEY']
def save_docs_to_jsonl(array:Iterable[Document], file_path:str)->None:
with open(file_path, 'w') as jsonl_file:
for doc in array:
jsonl_file.write(doc.json() + '\n')
def load_docs_from_jsonl(file_path) -> Iterable[Document]:
if not os.path.exists(file_path):
print("Invalid file path.")
return []
array = []
with open(file_path, 'r') as jsonl_file:
for line in jsonl_file:
data = json.loads(line)
obj = Document(**data)
array.append(obj)
return array
# Loading all the documents if they are not found locally
documents = load_docs_from_jsonl('striim_docs.jsonl')
# Split the documents into smaller chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=500)
docs = text_splitter.split_documents(documents)
# Convert the document chunks to embedding and save them to the vector store
vectordb = FAISS.from_documents(docs, embedding=OpenAIEmbeddings())
# create our Q&A chain
pdf_qa = ConversationalRetrievalChain.from_llm(
ChatOpenAI(temperature=0, model_name='gpt-3.5-turbo'),
retriever=vectordb.as_retriever(search_type="similarity", search_kwargs={'k': 4}),
return_generated_question=True,
return_source_documents=True,
verbose=False,
)
# Function to query Google if user selects allow internet access
def get_query_from_internet(user_query, temperature=0):
delimiter = "```"
# Checking if user query is flagged as inappropriate
response = openai.Moderation.create(input=user_query["question"])
moderation_output = response["results"][0]
if moderation_output["flagged"]:
return "Your query was flagged as inappropriate. Please try again."
search = GoogleSearchAPIWrapper()
tool = Tool(
name="Google Search",
description="Search Google for recent results.",
func=search.run,
)
llm = ChatOpenAI(temperature=0, model_name='gpt-3.5-turbo')
tools = load_tools(["requests_all"])
tools += [tool]
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors="Check your output and make sure it conforms!"
)
return agent_chain.run({'input': user_query})
# Front end web application using Gradio
chat_history = []
CSS ="""
.contain { display: flex; flex-direction: column; }
footer.svelte-1ax1toq.svelte-1ax1toq.svelte-1ax1toq.svelte-1ax1toq { display: none; }
#component-0 { height: 100%; }
#chatbot { flex-grow: 1; overflow: auto;}
"""
with gr.Blocks(theme='samayg/StriimTheme', css=CSS) as demo:
image = gr.Image('striim-logo-light.png', height=48, width=200, show_label=False, show_download_button=False, show_share_button=False)
chatbot = gr.Chatbot(show_label=False, height=300)
msg = gr.Textbox(label="Question:")
examples = gr.Examples(examples=[['What\'s new in Striim version 4.2.0?'], ['My Striim application keeps crashing. What should I do?'], ['How can I improve Striim performance?'], ['It says could not connect to source or target. What should I do?']], inputs=msg, label="Examples")
submit = gr.Button("Submit")
#with gr.Accordion(label="Advanced options", open=False):
#slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="Temperature", info="The temperature of StriimGPT, default at 0. Higher values may allow for better inference but may fabricate false information.")
#internet_access = gr.Checkbox(value=False, label="Allow Internet Access?", info="If the chatbot cannot answer your question, this setting allows for internet access. Warning: this may take longer and produce inaccurate results.")
def user(query, history):
#if allow_internet:
# Get response from internet-based query function
# result = get_query_from_internet({"question": query, "chat_history": chat_history}, temperature=slider.value)
# answer = result
#else:
# Get response from QA chain
result = pdf_qa({"question": query, "chat_history": chat_history})
answer = result["answer"]
# Append user message and response to chat history
chat_history.append((query, answer))
return gr.update(value=""), chat_history
# The msg.submit() now also depends on the status of the internet_access checkbox
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False)
submit.click(user, [msg, chatbot], [msg, chatbot], queue=False)
if __name__ == "__main__":
# demo.launch(debug=True)
demo.launch(debug=True, share=True)
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