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import os
import queue
import re
import tempfile
import threading
import streamlit as st
from embedchain import App
from embedchain.config import BaseLlmConfig
from embedchain.helpers.callbacks import (StreamingStdOutCallbackHandlerYield,
generate)
def embedchain_bot(db_path, api_key):
return App.from_config(
config={
"llm": {
"provider": "openai",
"config": {
"model": "gpt-3.5-turbo-1106",
"temperature": 0.5,
"max_tokens": 1000,
"top_p": 1,
"stream": True,
"api_key": api_key,
},
},
"vectordb": {
"provider": "chroma",
"config": {"collection_name": "chat-pdf", "dir": db_path, "allow_reset": True},
},
"embedder": {"provider": "openai", "config": {"api_key": api_key}},
"chunker": {"chunk_size": 2000, "chunk_overlap": 0, "length_function": "len"},
}
)
def get_db_path():
tmpdirname = tempfile.mkdtemp()
return tmpdirname
def get_ec_app(api_key):
if "app" in st.session_state:
print("Found app in session state")
app = st.session_state.app
else:
print("Creating app")
db_path = get_db_path()
app = embedchain_bot(db_path, api_key)
st.session_state.app = app
return app
with st.sidebar:
openai_access_token = st.text_input("OpenAI API Key", key="api_key", type="password")
"WE DO NOT STORE YOUR OPENAI KEY."
"Just paste your OpenAI API key here and we'll use it to power the chatbot. [Get your OpenAI API key](https://platform.openai.com/api-keys)" # noqa: E501
if st.session_state.api_key:
app = get_ec_app(st.session_state.api_key)
pdf_files = st.file_uploader("Upload your PDF files", accept_multiple_files=True, type="pdf")
add_pdf_files = st.session_state.get("add_pdf_files", [])
for pdf_file in pdf_files:
file_name = pdf_file.name
if file_name in add_pdf_files:
continue
try:
if not st.session_state.api_key:
st.error("Please enter your OpenAI API Key")
st.stop()
temp_file_name = None
with tempfile.NamedTemporaryFile(mode="wb", delete=False, prefix=file_name, suffix=".pdf") as f:
f.write(pdf_file.getvalue())
temp_file_name = f.name
if temp_file_name:
st.markdown(f"Adding {file_name} to knowledge base...")
app.add(temp_file_name, data_type="pdf_file")
st.markdown("")
add_pdf_files.append(file_name)
os.remove(temp_file_name)
st.session_state.messages.append({"role": "assistant", "content": f"Added {file_name} to knowledge base!"})
except Exception as e:
st.error(f"Error adding {file_name} to knowledge base: {e}")
st.stop()
st.session_state["add_pdf_files"] = add_pdf_files
st.title("📄 Embedchain - Chat with PDF")
styled_caption = '<p style="font-size: 17px; color: #aaa;">🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered by OpenAI!</p>' # noqa: E501
st.markdown(styled_caption, unsafe_allow_html=True)
if "messages" not in st.session_state:
st.session_state.messages = [
{
"role": "assistant",
"content": """
Hi! I'm chatbot powered by Embedchain, which can answer questions about your pdf documents.\n
Upload your pdf documents here and I'll answer your questions about them!
""",
}
]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Ask me anything!"):
if not st.session_state.api_key:
st.error("Please enter your OpenAI API Key", icon="🤖")
st.stop()
app = get_ec_app(st.session_state.api_key)
with st.chat_message("user"):
st.session_state.messages.append({"role": "user", "content": prompt})
st.markdown(prompt)
with st.chat_message("assistant"):
msg_placeholder = st.empty()
msg_placeholder.markdown("Thinking...")
full_response = ""
q = queue.Queue()
def app_response(result):
llm_config = app.llm.config.as_dict()
llm_config["callbacks"] = [StreamingStdOutCallbackHandlerYield(q=q)]
config = BaseLlmConfig(**llm_config)
answer, citations = app.chat(prompt, config=config, citations=True)
result["answer"] = answer
result["citations"] = citations
results = {}
thread = threading.Thread(target=app_response, args=(results,))
thread.start()
for answer_chunk in generate(q):
full_response += answer_chunk
msg_placeholder.markdown(full_response)
thread.join()
answer, citations = results["answer"], results["citations"]
if citations:
full_response += "\n\n**Sources**:\n"
sources = []
for i, citation in enumerate(citations):
source = citation[1]["url"]
pattern = re.compile(r"([^/]+)\.[^\.]+\.pdf$")
match = pattern.search(source)
if match:
source = match.group(1) + ".pdf"
sources.append(source)
sources = list(set(sources))
for source in sources:
full_response += f"- {source}\n"
msg_placeholder.markdown(full_response)
print("Answer: ", full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response})
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