import streamlit as st
import pinecone
import openai
from openai.embeddings_utils import get_embedding
import json
OPENAI_KEY = st.secrets["OPENAI_KEY"]
PINECONE_KEY = st.secrets["PINECONE_KEY"]
INDEX = 'openai-ml-qa'
instructions = {
"conservative q&a": "Answer the question based on the context below, and if the question can't be answered based on the context, say \"I don't know\"\n\nContext:\n{0}\n\n---\n\nQuestion: {1}\nAnswer:",
"paragraph about a question":"Write a paragraph, addressing the question, and use the text below to obtain relevant information\"\n\nContext:\n{0}\n\n---\n\nQuestion: {1}\nParagraph long Answer:",
"bullet points": "Write a bullet point list of possible answers, addressing the question, and use the text below to obtain relevant information\"\n\nContext:\n{0}\n\n---\n\nQuestion: {1}\nBullet point Answer:",
"summarize problems given a topic": "Write a summary of the problems addressed by the questions below\"\n\n{0}\n\n---\n\n",
"extract key libraries and tools": "Write a list of libraries and tools present in the context below\"\n\nContext:\n{0}\n\n---\n\n",
"simple instructions": "{1} given the common questions and answers below \n\n{0}\n\n---\n\n",
"summarize": "Write an elaborate, paragraph long summary about \"{1}\" given the questions and answers from a public forum on this topic\n\n{0}\n\n---\n\nSummary:",
}
@st.experimental_singleton(show_spinner=False)
def init_openai():
# initialize connection to OpenAI
openai.api_key = OPENAI_KEY
@st.experimental_singleton(show_spinner=False)
def init_pinecone(index_name):
# initialize connection to Pinecone vector DB (app.pinecone.io for API key)
pinecone.init(
api_key=PINECONE_KEY,
environment='us-west1-gcp'
)
index = pinecone.Index(index_name)
stats = index.describe_index_stats()
dims = stats['dimension']
count = stats['namespaces']['']['vector_count']
return index, dims, count
def create_context(question, index, lib_meta, max_len=3750, top_k=5):
"""
Find most relevant context for a question via Pinecone search
"""
q_embed = get_embedding(question, engine=f'text-embedding-ada-002')
res = index.query(
q_embed, top_k=top_k,
include_metadata=True, filter={
'docs': {'$in': lib_meta}
})
cur_len = 0
contexts = []
sources = []
for row in res['matches']:
meta = row['metadata']
text = (
f"Topic: {meta['thread']}\n"+
f"Answer: {meta['context']}"
)
cur_len += len(text)
if cur_len < max_len:
contexts.append(text)
sources.append(row['metadata'])
else:
cur_len -= len(text) + 4
if max_len - cur_len < 200:
break
return "\n\n###\n\n".join(contexts), sources
def answer_question(
index,
fine_tuned_qa_model="text-davinci-003",
question="Am I allowed to publish model outputs to Twitter, without a human review?",
instruction="Answer the question based on the context below, and if the question can't be answered based on the context, say \"I don't know\"\n\nContext:\n{0}\n\n---\n\nQuestion: {1}\nAnswer:",
max_len=3550,
size="curie",
top_k=5,
debug=False,
max_tokens=400,
stop_sequence=None,
domains=["huggingface", "tensorflow", "streamlit", "pytorch"],
):
"""
Answer a question based on the most similar context from the dataframe texts
"""
context, sources = create_context(
question,
index,
lib_meta=domains,
max_len=max_len,
top_k=top_k
)
if debug:
print("Context:\n" + context)
print("\n\n")
try:
# fine-tuned models requires model parameter, whereas other models require engine parameter
model_param = (
{"model": fine_tuned_qa_model}
if ":" in fine_tuned_qa_model
and fine_tuned_qa_model.split(":")[1].startswith("ft")
else {"engine": fine_tuned_qa_model}
)
#print(instruction.format(context, question))
response = openai.Completion.create(
prompt=instruction.format(context, question),
temperature=0,
max_tokens=max_tokens,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=stop_sequence,
**model_param,
)
return response["choices"][0]["text"].strip(), sources
except Exception as e:
print(e)
return ""
def search(index, query, style, top_k, lib_filters):
if query != "":
with st.spinner("Retrieving, please wait..."):
answer, sources = answer_question(
index,
question=query,
instruction=instructions[style],
top_k=top_k
)
# lowercase relevant lib filters
lib_meta = [lib.lower() for lib in lib_filters.keys() if lib_filters[lib]]
lower_libs = [lib.lower() for lib in libraries]
# display the answer
st.write(answer)
with st.expander("Sources"):
for source in sources:
st.write(f"""
{source['docs']} > {source['category']} > [{source['thread']}]({source['href']})
""")
st.markdown("""
<link
rel="stylesheet"
href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"
/>
""", unsafe_allow_html=True)
#model_name = 'mpnet-discourse'
libraries = [
"Streamlit",
"HuggingFace",
"PyTorch",
"TensorFlow"
]
with st.spinner("Connecting to OpenAI..."):
retriever = init_openai()
with st.spinner("Connecting to Pinecone..."):
index, dims, count = init_pinecone(INDEX)
st.write("# ML Q&A")
search = st.container()
query = search.text_input('Ask a framework-specific question!', "")
with search.expander("Search Options"):
style = st.radio(label='Style', options=[
'Paragraph about a question', 'Conservative Q&A',
'Bullet points', 'Summarize problems given a topic',
'Extract key libraries and tools', 'Simple instructions',
'Summarize'
])
# add section for filters
st.write("""
#### Metadata Filters
**Libraries**
""")
# create two cols
cols = st.columns(2)
# add filtering based on library
lib_filters = {}
for lib in libraries:
i = len(lib_filters.keys()) % 2
with cols[i]:
lib_filters[lib] = st.checkbox(lib, value=True)
st.write("---")
top_k = st.slider(
"top_k",
min_value=1,
max_value=20,
value=5
)
st.sidebar.write(f"""
### Info
**Pinecone index name**: {INDEX}
**Pinecone index size**: {count}
**OpenAI embedding model**: *text-embedding-ada-002*
**Vector dimensionality**: {dims}
**OpenAI generation model**: *text-davinci-003*
---
### How it Works
The Q&A tool takes discussions and docs from some of the best Python ML
libraries and collates their content into a natural language search and Q&A tool.
Ask questions like **"How do I use the gradient tape in tensorflow?"** or **"What is the difference
between Tensorflow and PyTorch?"**, choose a answer style, and return relevant results!
The app is powered using OpenAI's embedding service with Pinecone's vector database. The whole process consists
of *three* steps:
**1**. Questions are fed into OpenAI's embeddings service to generate a {dims}-dimensional query vector.
**2**. We use Pinecone to identify similar context vectors (previously encoded from Q&A pages).
**3**. Relevant pages are passed in a new question to OpenAI's generative model, returning our answer.
**How do I make something like this?**
It's easy! Check out the [source code](https://github.com/pinecone-io/examples/tree/master/integrations/openai/beyond_search_webinar) and learn how to [integrate OpenAI and Pinecone in the docs](https://www.pinecone.io/docs/integrations/openai/)!
---
### Usage
If you'd like to restrict your search to a specific library (such as PyTorch or
Streamlit) you can with the *Advanced Options* dropdown. The source of information
can be switched between official docs and forum discussions too!
If you'd like OpenAI to consider more or less pages, try changing the `top_k` slider.
Want to see the original sources that GPT-3 is using to generate the answer? No problem, just click on the **Sources** box.
""")
#if style.lower() == 'conservative q&a':
# search.info("*Access search options above.*")
if search.button("Go!") or query != "":
with st.spinner("Retrieving, please wait..."):
# lowercase relevant lib filters
lib_meta = [lib.lower() for lib in lib_filters.keys() if lib_filters[lib]]
# ask the question
answer, sources = answer_question(
index,
question=query,
instruction=instructions[style.lower()],
top_k=top_k,
domains=lib_meta
)
# display the answer
st.write(answer)
with st.expander("Sources"):
for source in sources:
st.write(f"""
{source['docs']} > {source['category']} > [{source['thread']}]({source['href']})
""")