Spaces:
Sleeping
Sleeping
File size: 4,307 Bytes
234eac0 cc17218 234eac0 cc17218 234eac0 004bb63 234eac0 cc17218 234eac0 cc17218 234eac0 cc17218 234eac0 cc17218 1aaad7e 234eac0 cc17218 234eac0 004bb63 234eac0 cc17218 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
import os
from typing import List
from chainlit.types import AskFileResponse
from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader
from aimakerspace.openai_utils.prompts import (
UserRolePrompt,
SystemRolePrompt,
AssistantRolePrompt,
)
from aimakerspace.openai_utils.embedding import EmbeddingModel
from aimakerspace.vectordatabase import VectorDatabase
from aimakerspace.openai_utils.chatmodel import ChatOpenAI
import chainlit as cl
import fitz # PyMuPDF for PDF reading
system_template = """\
Use the following context to answer a user's question. If you cannot find the answer in the context, say you don't know the answer."""
system_role_prompt = SystemRolePrompt(system_template)
user_prompt_template = """\
Context:
{context}
Question:
{question}
"""
user_role_prompt = UserRolePrompt(user_prompt_template)
class RetrievalAugmentedQAPipeline:
def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
self.llm = llm
self.vector_db_retriever = vector_db_retriever
async def arun_pipeline(self, user_query: str):
context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
context_prompt = ""
for context in context_list:
context_prompt += context[0] + "\n"
formatted_system_prompt = system_role_prompt.create_message()
formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)
async def generate_response():
async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
yield chunk
return {"response": generate_response(), "context": context_list}
text_splitter = CharacterTextSplitter()
def process_text_file(file: AskFileResponse):
import tempfile
file_extension = os.path.splitext(file.name)[-1].lower()
if file_extension == ".txt":
with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as temp_file:
temp_file_path = temp_file.name
with open(temp_file_path, "wb") as f:
f.write(file.content)
text_loader = TextFileLoader(temp_file_path)
documents = text_loader.load_documents()
elif file_extension == ".pdf":
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file_path = temp_file.name
with open(temp_file_path, "wb") as f:
f.write(file.content)
documents = []
with fitz.open(temp_file_path) as doc:
text = ""
for page in doc:
text += page.get_text("text")
documents.append(text)
else:
raise ValueError("Unsupported file type. Please upload a .txt or .pdf file.")
texts = text_splitter.split_texts(documents)
return texts
@cl.on_chat_start
async def on_chat_start():
files = None
# Wait for the user to upload a file
while files is None:
files = await cl.AskFileMessage(
content="Please upload a Text File or PDF to begin!",
accept=["text/plain", "application/pdf"],
max_size_mb=2,
timeout=180,
).send()
file = files[0]
msg = cl.Message(
content=f"Processing `{file.name}`...", disable_human_feedback=True
)
await msg.send()
# Load the file
texts = process_text_file(file)
print(f"Processing {len(texts)} text chunks")
# Create a dict vector store
vector_db = VectorDatabase()
vector_db = await vector_db.abuild_from_list(texts)
chat_openai = ChatOpenAI()
# Create a chain
retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
vector_db_retriever=vector_db,
llm=chat_openai
)
# Let the user know that the system is ready
msg.content = f"Processing `{file.name}` done. You can now ask questions!"
await msg.update()
cl.user_session.set("chain", retrieval_augmented_qa_pipeline)
@cl.on_message
async def main(message):
chain = cl.user_session.get("chain")
msg = cl.Message(content="")
result = await chain.arun_pipeline(message.content)
async for stream_resp in result["response"]:
await msg.stream_token(stream_resp)
await msg.send()
|