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Runtime error
Abhilashvj
commited on
Commit
•
7ba9210
1
Parent(s):
26add68
Update app.py
Browse files
app.py
CHANGED
@@ -1,3 +1,4 @@
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import json
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import logging
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import os
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@@ -6,9 +7,9 @@ import sys
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import uuid
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from json import JSONDecodeError
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from pathlib import Path
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from tqdm.auto import tqdm
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import datetime
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from time import sleep
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import pandas as pd
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import pinecone
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import streamlit as st
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@@ -27,8 +28,7 @@ from haystack.nodes import (
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from haystack.pipelines import ExtractiveQAPipeline, Pipeline
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from markdown import markdown
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from sentence_transformers import SentenceTransformer
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-
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import openai
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# get API key from top-right dropdown on OpenAI website
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openai.api_key = st.secrets["OPENAI_API_KEY"]
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@@ -36,10 +36,7 @@ index_name = "qa_demo"
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# connect to pinecone environment
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pinecone.init(
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api_key=st.secrets["pinecone_apikey"],
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environment="us-east1-gcp"
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)
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index_name = "qa-demo"
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embed_model = "text-embedding-ada-002"
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@@ -49,7 +46,7 @@ preprocessor = PreProcessor(
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clean_header_footer=False,
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split_by="word",
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split_length=200,
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split_respect_sentence_boundary=True
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)
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file_type_classifier = FileTypeClassifier()
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text_converter = TextConverter()
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@@ -59,58 +56,37 @@ docx_converter = DocxToTextConverter()
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# check if the abstractive-question-answering index exists
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if index_name not in pinecone.list_indexes():
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# create the index if it does not exist
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pinecone.create_index(
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index_name,
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dimension=1536,
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metric="cosine"
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)
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# connect to abstractive-question-answering index we created
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index = pinecone.Index(index_name)
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FILE_UPLOAD_PATH= "./data/uploads/"
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os.makedirs(FILE_UPLOAD_PATH, exist_ok=True)
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limit = 3750
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def retrieve(query):
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res = openai.Embedding.create(
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input=[query],
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engine=embed_model
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)
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# retrieve from Pinecone
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xq = res[
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# get relevant contexts
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res = index.query(xq, top_k=3, include_metadata=True)
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contexts = [
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x['metadata']['text'] for x in res['matches']
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]
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# build our prompt with the retrieved contexts included
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prompt_start =
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"Context:\n"
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)
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prompt_end = (
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f"\n\nQuestion: {query}\nAnswer:"
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)
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# append contexts until hitting limit
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for i in range(1, len(contexts)):
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if len("\n\n---\n\n".join(contexts[:i])) >= limit:
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prompt = (
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prompt_start +
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"\n\n---\n\n".join(contexts[:i-1]) +
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prompt_end
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)
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break
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elif i == len(contexts)-1:
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prompt = (
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prompt_start +
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"\n\n---\n\n".join(contexts) +
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prompt_end
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)
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return prompt, contexts
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@@ -118,17 +94,18 @@ def retrieve(query):
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def complete(prompt):
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# query text-davinci-003
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res = openai.Completion.create(
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engine=
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prompt=prompt,
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temperature=0,
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max_tokens=400,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0,
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stop=None
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)
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return res[
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def query(question, top_k_reader, top_k_retriever):
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# first we retrieve relevant items from Pinecone
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query_with_contexts, contexts = retrieve(question)
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@@ -154,20 +131,29 @@ indexing_pipeline_with_classification.add_node(
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inputs=["TextConverter", "PdfConverter", "DocxConverter"],
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)
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def set_state_if_absent(key, value):
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if key not in st.session_state:
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st.session_state[key] = value
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# Adjust to a question that you would like users to see in the search bar when they load the UI:
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DEFAULT_QUESTION_AT_STARTUP = os.getenv(
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# Sliders
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DEFAULT_DOCS_FROM_RETRIEVER = int(os.getenv("DEFAULT_DOCS_FROM_RETRIEVER", "3"))
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DEFAULT_NUMBER_OF_ANSWERS = int(os.getenv("DEFAULT_NUMBER_OF_ANSWERS", "3"))
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st.set_page_config(
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# Persistent state
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set_state_if_absent("question", DEFAULT_QUESTION_AT_STARTUP)
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@@ -181,6 +167,7 @@ def reset_results(*args):
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st.session_state.results = None
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st.session_state.raw_json = None
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# Title
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st.write("# GPT3 and Langchain Demo")
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st.markdown(
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@@ -208,46 +195,52 @@ for data_file in data_files:
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f.write(data_file.getbuffer())
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ALL_FILES.append(file_path)
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st.sidebar.write(str(data_file.name) + " ✅ ")
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META_DATA.append({"filename":data_file.name})
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if len(ALL_FILES) > 0:
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# document_store.update_embeddings(retriever, update_existing_embeddings=False)
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docs = indexing_pipeline_with_classification.run(file_paths=ALL_FILES, meta=META_DATA)[
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index_name = "qa_demo"
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# we will use batches of 64
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batch_size = 200
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# docs = docs['documents']
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with st.spinner(
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"🧠 Performing indexing of uplaoded documents... \n "
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):
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for i in range(0, len(docs), batch_size):
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# find end of batch
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i_end = min(i+batch_size, len(docs))
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# extract batch
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batch = [doc.content for doc in docs[i:i_end]]
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# generate embeddings for batch
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try:
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res = openai.Embedding.create(input=batch, engine=embed_model)
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except:
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done = False
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sleep(5)
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try:
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res = openai.Embedding.create(input=batch, engine=embed_model)
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done = True
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except:
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pass
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# get metadata
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meta = [doc.meta for doc in docs[i:i_end]]
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# create unique IDs
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ids = [doc.id for doc in docs[i:i_end]]
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# add all to upsert list
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to_upsert = list(zip(ids,
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# upsert/insert these records to pinecone
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_ = index.upsert(vectors=to_upsert)
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# top_k_reader = st.sidebar.slider(
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# "Max. number of answers",
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# min_value=1,
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# raw_json = upload_doc(data_file)
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question = st.text_input(
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col1, col2 = st.columns(2)
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col1.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True)
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col2.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True)
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@@ -287,23 +280,19 @@ col2.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html
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run_pressed = col1.button("Run")
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if run_pressed:
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run_query =
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run_pressed or question != st.session_state.question
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)
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# Get results for query
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if run_query and question:
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reset_results()
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st.session_state.question = question
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with st.spinner(
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"🧠 Performing neural search on documents... \n "
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):
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try:
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st.session_state.results
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question, top_k_reader=None, top_k_retriever=None
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)
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except JSONDecodeError as je:
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st.error(
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except Exception as e:
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logging.exception(e)
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if "The server is busy processing requests" in str(e) or "503" in str(e):
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@@ -316,7 +305,7 @@ if st.session_state.results:
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st.write("## Results:")
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for result,contexts in st.session_state.results:
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# answer, context = result.answer, result.context
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# start_idx = context.find(answer)
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# end_idx = start_idx + len(answer)
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@@ -328,8 +317,8 @@ if st.session_state.results:
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# unsafe_allow_html=True,
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# )
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st.write(
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-
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)
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except:
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# filename = result.meta.get('filename', "")
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@@ -338,9 +327,6 @@ if st.session_state.results:
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# unsafe_allow_html=True,
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# )
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st.write(
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-
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)
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-
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import datetime
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import json
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import logging
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import os
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import uuid
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from json import JSONDecodeError
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from pathlib import Path
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from time import sleep
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+
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import openai
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import pandas as pd
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import pinecone
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import streamlit as st
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from haystack.pipelines import ExtractiveQAPipeline, Pipeline
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from markdown import markdown
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from sentence_transformers import SentenceTransformer
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from tqdm.auto import tqdm
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# get API key from top-right dropdown on OpenAI website
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openai.api_key = st.secrets["OPENAI_API_KEY"]
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# connect to pinecone environment
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pinecone.init(api_key=st.secrets["pinecone_apikey"], environment="us-east1-gcp")
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index_name = "qa-demo"
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embed_model = "text-embedding-ada-002"
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clean_header_footer=False,
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split_by="word",
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split_length=200,
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split_respect_sentence_boundary=True,
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)
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file_type_classifier = FileTypeClassifier()
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text_converter = TextConverter()
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# check if the abstractive-question-answering index exists
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if index_name not in pinecone.list_indexes():
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# create the index if it does not exist
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pinecone.create_index(index_name, dimension=1536, metric="cosine")
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# connect to abstractive-question-answering index we created
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index = pinecone.Index(index_name)
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FILE_UPLOAD_PATH = "./data/uploads/"
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os.makedirs(FILE_UPLOAD_PATH, exist_ok=True)
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limit = 3750
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def retrieve(query):
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res = openai.Embedding.create(input=[query], engine=embed_model)
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# retrieve from Pinecone
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xq = res["data"][0]["embedding"]
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# get relevant contexts
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res = index.query(xq, top_k=3, include_metadata=True)
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contexts = [x["metadata"]["text"] for x in res["matches"]]
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# build our prompt with the retrieved contexts included
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prompt_start = "Answer the question based on the context below.\n\n" + "Context:\n"
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prompt_end = f"\n\nQuestion: {query}\nAnswer:"
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# append contexts until hitting limit
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for i in range(1, len(contexts)):
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if len("\n\n---\n\n".join(contexts[:i])) >= limit:
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prompt = prompt_start + "\n\n---\n\n".join(contexts[: i - 1]) + prompt_end
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break
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elif i == len(contexts) - 1:
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prompt = prompt_start + "\n\n---\n\n".join(contexts) + prompt_end
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return prompt, contexts
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def complete(prompt):
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# query text-davinci-003
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res = openai.Completion.create(
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engine="text-davinci-003",
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prompt=prompt,
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temperature=0,
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max_tokens=400,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0,
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stop=None,
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)
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return res["choices"][0]["text"].strip()
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def query(question, top_k_reader, top_k_retriever):
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# first we retrieve relevant items from Pinecone
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query_with_contexts, contexts = retrieve(question)
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inputs=["TextConverter", "PdfConverter", "DocxConverter"],
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)
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def set_state_if_absent(key, value):
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if key not in st.session_state:
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st.session_state[key] = value
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+
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# Adjust to a question that you would like users to see in the search bar when they load the UI:
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DEFAULT_QUESTION_AT_STARTUP = os.getenv(
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"DEFAULT_QUESTION_AT_STARTUP", "My blog post discusses remote work. Give me statistics."
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)
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DEFAULT_ANSWER_AT_STARTUP = os.getenv(
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"DEFAULT_ANSWER_AT_STARTUP",
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"7% more remote workers have been at their current organization for 5 years or fewer",
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)
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# Sliders
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DEFAULT_DOCS_FROM_RETRIEVER = int(os.getenv("DEFAULT_DOCS_FROM_RETRIEVER", "3"))
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DEFAULT_NUMBER_OF_ANSWERS = int(os.getenv("DEFAULT_NUMBER_OF_ANSWERS", "3"))
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st.set_page_config(
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page_title="Haystack Demo", page_icon="https://haystack.deepset.ai/img/HaystackIcon.png"
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)
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# Persistent state
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set_state_if_absent("question", DEFAULT_QUESTION_AT_STARTUP)
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st.session_state.results = None
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st.session_state.raw_json = None
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+
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# Title
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st.write("# GPT3 and Langchain Demo")
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st.markdown(
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f.write(data_file.getbuffer())
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ALL_FILES.append(file_path)
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st.sidebar.write(str(data_file.name) + " ✅ ")
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META_DATA.append({"filename": data_file.name})
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+
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if len(ALL_FILES) > 0:
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# document_store.update_embeddings(retriever, update_existing_embeddings=False)
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docs = indexing_pipeline_with_classification.run(file_paths=ALL_FILES, meta=META_DATA)[
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"documents"
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]
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index_name = "qa_demo"
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# we will use batches of 64
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batch_size = 200
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# docs = docs['documents']
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with st.spinner("🧠 Performing indexing of uplaoded documents... \n "):
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for i in range(0, len(docs), batch_size):
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# find end of batch
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i_end = min(i + batch_size, len(docs))
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# extract batch
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batch = [doc.content for doc in docs[i:i_end]]
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# generate embeddings for batch
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try:
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res = openai.Embedding.create(input=batch, engine=embed_model)
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except Exception as e:
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done = False
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count = 0
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while not done and count < 5:
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sleep(5)
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try:
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res = openai.Embedding.create(input=batch, engine=embed_model)
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done = True
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except:
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count += 1
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pass
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if count >= 5:
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st.error(f"🐞 File indexing failed{str(e)}")
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embeds = [record["embedding"] for record in res["data"]]
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# get metadata
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meta = [doc.meta for doc in docs[i:i_end]]
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# create unique IDs
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ids = [doc.id for doc in docs[i:i_end]]
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# add all to upsert list
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to_upsert = list(zip(ids, embeds, meta))
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# upsert/insert these records to pinecone
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_ = index.upsert(vectors=to_upsert)
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+
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# top_k_reader = st.sidebar.slider(
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# "Max. number of answers",
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# min_value=1,
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# raw_json = upload_doc(data_file)
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question = st.text_input(
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value=st.session_state.question,
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max_chars=100,
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on_change=reset_results,
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272 |
+
label="question",
|
273 |
+
label_visibility="hidden",
|
274 |
+
)
|
275 |
col1, col2 = st.columns(2)
|
276 |
col1.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True)
|
277 |
col2.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True)
|
|
|
280 |
run_pressed = col1.button("Run")
|
281 |
if run_pressed:
|
282 |
|
283 |
+
run_query = run_pressed or question != st.session_state.question
|
|
|
|
|
284 |
# Get results for query
|
285 |
if run_query and question:
|
286 |
reset_results()
|
287 |
st.session_state.question = question
|
288 |
|
289 |
+
with st.spinner("🧠 Performing neural search on documents... \n "):
|
|
|
|
|
290 |
try:
|
291 |
+
st.session_state.results = query(question, top_k_reader=None, top_k_retriever=None)
|
|
|
|
|
292 |
except JSONDecodeError as je:
|
293 |
+
st.error(
|
294 |
+
"👓 An error occurred reading the results. Is the document store working?"
|
295 |
+
)
|
296 |
except Exception as e:
|
297 |
logging.exception(e)
|
298 |
if "The server is busy processing requests" in str(e) or "503" in str(e):
|
|
|
305 |
|
306 |
st.write("## Results:")
|
307 |
|
308 |
+
for result, contexts in st.session_state.results:
|
309 |
# answer, context = result.answer, result.context
|
310 |
# start_idx = context.find(answer)
|
311 |
# end_idx = start_idx + len(answer)
|
|
|
317 |
# unsafe_allow_html=True,
|
318 |
# )
|
319 |
st.write(
|
320 |
+
markdown(f"Answer: {result} \n Extracted from context {contexts}"),
|
321 |
+
unsafe_allow_html=True,
|
322 |
)
|
323 |
except:
|
324 |
# filename = result.meta.get('filename', "")
|
|
|
327 |
# unsafe_allow_html=True,
|
328 |
# )
|
329 |
st.write(
|
330 |
+
markdown(f"Answer: {result}"),
|
331 |
+
unsafe_allow_html=True,
|
332 |
)
|
|
|
|
|
|