Spaces:
Runtime error
Runtime error
File size: 11,618 Bytes
7ba9210 b77a2d3 90b5d0f 7ba9210 b77a2d3 7ba9210 2b9b8a4 f0c7e0c b77a2d3 7ba9210 b77a2d3 2b9b8a4 b77a2d3 26add68 7ba9210 b77a2d3 641a396 7ba9210 b77a2d3 7ba9210 b77a2d3 946061f 2b9b8a4 b77a2d3 7ba9210 2b9b8a4 7ba9210 2b9b8a4 7ba9210 2b9b8a4 df49b45 2b9b8a4 7ba9210 2b9b8a4 7ba9210 2b9b8a4 7ba9210 26e317f 2b9b8a4 7ba9210 2b9b8a4 7ba9210 2b9b8a4 7ba9210 26add68 2b9b8a4 26e317f b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 7ba9210 9f43bd1 7ba9210 b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 e85e71c b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 2b9b8a4 26add68 7ba9210 2b9b8a4 7ba9210 2b9b8a4 26add68 2b9b8a4 7ba9210 2b9b8a4 7ba9210 69c1d32 7ba9210 69c1d32 aab8068 552baab 69c1d32 7ba9210 2b9b8a4 b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 7ba9210 b77a2d3 9bc6376 26e317f 65dea60 26e317f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 |
import datetime
import json
import logging
import os
import shutil
import sys
import uuid
from json import JSONDecodeError
from pathlib import Path
from time import sleep
import openai
import pandas as pd
import pinecone
import streamlit as st
from annotated_text import annotation
from haystack import Document
from haystack.document_stores import PineconeDocumentStore
from haystack.nodes import (
DocxToTextConverter,
EmbeddingRetriever,
FARMReader,
FileTypeClassifier,
PDFToTextConverter,
PreProcessor,
TextConverter,
)
from haystack.pipelines import ExtractiveQAPipeline, Pipeline
from markdown import markdown
from sentence_transformers import SentenceTransformer
from tqdm.auto import tqdm
# get API key from top-right dropdown on OpenAI website
openai.api_key = st.secrets["OPENAI_API_KEY"]
index_name = "openai-ada-002-index"
# connect to pinecone environment
pinecone.init(api_key=st.secrets["pinecone_apikey"], environment="us-east1-gcp")
embed_model = "text-embedding-ada-002"
preprocessor = PreProcessor(
clean_empty_lines=True,
clean_whitespace=True,
clean_header_footer=False,
split_by="word",
split_length=200,
split_respect_sentence_boundary=True,
)
file_type_classifier = FileTypeClassifier()
text_converter = TextConverter()
pdf_converter = PDFToTextConverter()
docx_converter = DocxToTextConverter()
# check if the abstractive-question-answering index exists
if index_name not in pinecone.list_indexes():
# delete the current index and create the new index if it does not exist
for delete_index in pinecone.list_indexes():
pinecone.delete_index(delete_index)
pinecone.create_index(index_name, dimension=1536, metric="cosine")
# connect to abstractive-question-answering index we created
index = pinecone.Index(index_name)
FILE_UPLOAD_PATH = "./data/uploads/"
os.makedirs(FILE_UPLOAD_PATH, exist_ok=True)
limit = 3750
def retrieve(query):
res = openai.Embedding.create(input=[query], engine=embed_model)
# retrieve from Pinecone
xq = res["data"][0]["embedding"]
# get relevant contexts
res = index.query(xq, top_k=3, include_metadata=True)
contexts = [x["metadata"].get("text", "") for x in res["matches"]]
# build our prompt with the retrieved contexts included
prompt_start = "Answer the question based on the context below.\n\n" + "Context:\n"
prompt_end = f"\n\nQuestion: {query}\nAnswer:"
# append contexts until hitting limit
for i in range(1, len(contexts)):
if len("\n\n---\n\n".join(contexts[:i])) >= limit:
prompt = prompt_start + "\n\n---\n\n".join(contexts[: i - 1]) + prompt_end
break
elif i == len(contexts) - 1:
prompt = prompt_start + "\n\n---\n\n".join(contexts) + prompt_end
return prompt, contexts
# first let's make it simpler to get answers
def complete(prompt):
# query text-davinci-003
res = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
temperature=0,
max_tokens=400,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=None,
)
return res["choices"][0]["text"].strip()
def query(question, top_k_reader, top_k_retriever):
# first we retrieve relevant items from Pinecone
query_with_contexts, contexts = retrieve(question)
return complete(query_with_contexts), contexts
indexing_pipeline_with_classification = Pipeline()
indexing_pipeline_with_classification.add_node(
component=file_type_classifier, name="FileTypeClassifier", inputs=["File"]
)
indexing_pipeline_with_classification.add_node(
component=text_converter, name="TextConverter", inputs=["FileTypeClassifier.output_1"]
)
indexing_pipeline_with_classification.add_node(
component=pdf_converter, name="PdfConverter", inputs=["FileTypeClassifier.output_2"]
)
indexing_pipeline_with_classification.add_node(
component=docx_converter, name="DocxConverter", inputs=["FileTypeClassifier.output_4"]
)
indexing_pipeline_with_classification.add_node(
component=preprocessor,
name="Preprocessor",
inputs=["TextConverter", "PdfConverter", "DocxConverter"],
)
def set_state_if_absent(key, value):
if key not in st.session_state:
st.session_state[key] = value
# Adjust to a question that you would like users to see in the search bar when they load the UI:
DEFAULT_QUESTION_AT_STARTUP = os.getenv(
"DEFAULT_QUESTION_AT_STARTUP", "My blog post discusses remote work. Give me statistics."
)
DEFAULT_ANSWER_AT_STARTUP = os.getenv(
"DEFAULT_ANSWER_AT_STARTUP",
"7% more remote workers have been at their current organization for 5 years or fewer",
)
# Sliders
DEFAULT_DOCS_FROM_RETRIEVER = int(os.getenv("DEFAULT_DOCS_FROM_RETRIEVER", "3"))
DEFAULT_NUMBER_OF_ANSWERS = int(os.getenv("DEFAULT_NUMBER_OF_ANSWERS", "3"))
st.set_page_config(
page_title="GPT3 and Langchain Demo"
)
# Persistent state
set_state_if_absent("question", DEFAULT_QUESTION_AT_STARTUP)
set_state_if_absent("answer", DEFAULT_ANSWER_AT_STARTUP)
set_state_if_absent("results", None)
# Small callback to reset the interface in case the text of the question changes
def reset_results(*args):
st.session_state.answer = None
st.session_state.results = None
st.session_state.raw_json = None
# Title
st.write("# GPT3 and Langchain Demo")
st.markdown(
"""
This demo takes its data from the documents uploaded to the Pinecone index through this app. \n
Ask any question from the uploaded documents and Pinecone will retrieve the context for answers and GPT3 will answer them using the retrieved context. \n
*Note: do not use keywords, but full-fledged questions.* The demo is not optimized to deal with keyword queries and might misunderstand you.
""",
unsafe_allow_html=True,
)
# Sidebar
st.sidebar.header("Options")
st.sidebar.write("## File Upload:")
data_files = st.sidebar.file_uploader(
"upload", type=["pdf", "txt", "docx"], accept_multiple_files=True, label_visibility="hidden"
)
ALL_FILES = []
META_DATA = []
for data_file in data_files:
# Upload file
if data_file:
file_path = Path(FILE_UPLOAD_PATH) / f"{uuid.uuid4().hex}_{data_file.name}"
with open(file_path, "wb") as f:
f.write(data_file.getbuffer())
ALL_FILES.append(file_path)
st.sidebar.write(str(data_file.name) + " β
")
META_DATA.append({"filename": data_file.name})
if len(ALL_FILES) > 0:
# document_store.update_embeddings(retriever, update_existing_embeddings=False)
docs = indexing_pipeline_with_classification.run(file_paths=ALL_FILES, meta=META_DATA)[
"documents"
]
index_name = "qa_demo"
# we will use batches of 64
batch_size = 100
# docs = docs['documents']
with st.spinner("π§ Performing indexing of uplaoded documents... \n "):
for i in range(0, len(docs), batch_size):
# find end of batch
i_end = min(i + batch_size, len(docs))
# extract batch
batch = [doc.content for doc in docs[i:i_end]]
# generate embeddings for batch
try:
res = openai.Embedding.create(input=batch, engine=embed_model)
except Exception as e:
done = False
count = 0
while not done and count < 5:
sleep(5)
try:
res = openai.Embedding.create(input=batch, engine=embed_model)
done = True
except:
count += 1
pass
if count >= 5:
res = []
st.error(f"π File indexing failed{str(e)}")
if len(res) > 0:
embeds = [record["embedding"] for record in res["data"]]
# get metadata
meta = []
for doc in docs[i:i_end]:
meta_dict = doc.meta
meta_dict["text"] = doc.content
meta.append(meta_dict)
# create unique IDs
ids = [doc.id for doc in docs[i:i_end]]
# add all to upsert list
to_upsert = list(zip(ids, embeds, meta))
# upsert/insert these records to pinecone
_ = index.upsert(vectors=to_upsert)
# top_k_reader = st.sidebar.slider(
# "Max. number of answers",
# min_value=1,
# max_value=10,
# value=DEFAULT_NUMBER_OF_ANSWERS,
# step=1,
# on_change=reset_results,
# )
# top_k_retriever = st.sidebar.slider(
# "Max. number of documents from retriever",
# min_value=1,
# max_value=10,
# value=DEFAULT_DOCS_FROM_RETRIEVER,
# step=1,
# on_change=reset_results,
# )
# data_files = st.file_uploader(
# "upload", type=["csv"], accept_multiple_files=True, label_visibility="hidden"
# )
# for data_file in data_files:
# # Upload file
# if data_file:
# raw_json = upload_doc(data_file)
question = st.text_input(
value=st.session_state.question,
max_chars=100,
on_change=reset_results,
label="question",
label_visibility="hidden",
)
col1, col2 = st.columns(2)
col1.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True)
col2.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True)
# Run button
run_pressed = col1.button("Run")
if run_pressed:
run_query = run_pressed or question != st.session_state.question
# Get results for query
if run_query and question:
reset_results()
st.session_state.question = question
with st.spinner("π§ Performing neural search on documents... \n "):
try:
st.session_state.results = query(question, top_k_reader=None, top_k_retriever=None)
except JSONDecodeError as je:
st.error(
"π An error occurred reading the results. Is the document store working?"
)
except Exception as e:
logging.exception(e)
if "The server is busy processing requests" in str(e) or "503" in str(e):
st.error("π§βπΎ All our workers are busy! Try again later.")
else:
st.error(f"π An error occurred during the request. {str(e)}")
if st.session_state.results:
st.write("## Results:")
result, contexts = st.session_state.results
# answer, context = result.answer, result.context
# start_idx = context.find(answer)
# end_idx = start_idx + len(answer)
# Hack due to this bug: https://github.com/streamlit/streamlit/issues/3190
try:
# source = f"[{result.meta['Title']}]({result.meta['link']})"
# st.write(
# markdown(f'**Source:** {source} \n {context[:start_idx] } {str(annotation(answer, "ANSWER", "#8ef"))} {context[end_idx:]} \n '),
# unsafe_allow_html=True,
# )
all_contexts = '\n'.join(contexts)
st.write(markdown(f"Answer: \n {result} \n"),
unsafe_allow_html=True,
)
except:
# filename = result.meta.get('filename', "")
# st.write(
# markdown(f'From file: {filename} \n {context[:start_idx] } {str(annotation(answer, "ANSWER", "#8ef"))} {context[end_idx:]} \n '),
# unsafe_allow_html=True,
# )
st.write(
markdown(f"Answer: {result}"),
unsafe_allow_html=True,
)
|