Hopefully not too off topic, but I don´t know where else to ask: Where did https://github.com/microsoft/azurechatgpt go? Was it renamed or removed? Why?

I followed the instructions as follows to run quickstart.ipynb:

In terminal logged in to my azure account by running az login --use-device-code

Post authentication I go back to VScode and run the following code block within quickstart.ipynb which runs fine:
import os
import tiktoken
import openai
import numpy as np
import pandas as pd
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from openai.embeddings_utils import cosine_similarity
from tenacity import retry, wait_random_exponential, stop_after_attempt

Load environment variables

load_dotenv()

Option 1 - Use Azure AD authentication with az cli (use az login in terminal)

default_credential = DefaultAzureCredential()
token = default_credential.get_token("https://cognitiveservices.azure.com/.default")
openai.api_type = "azure_ad"
openai.api_base = os.environ.get("OPENAI_API_BASE")
openai.api_key = token.token
openai.api_version = "2023-06-13"

Option 2 - Using Access Key

openai.api_type = "azure"

openai.api_base = os.environ.get("OPENAI_API_BASE")

openai.api_key = os.environ.get("OPENAI_API_KEY")

openai.api_version = "2022-12-01"

Define embedding model and encoding

EMBEDDING_MODEL = 'text-embedding-ada-002'
COMPLETION_MODEL = 'gpt-4'
encoding = tiktoken.get_encoding('cl100k_base')

Now when I run the following code block I get the error below:
response = openai.Completion.create(engine="gpt-4",
prompt="Knock knock.",
temperature=0)
print(response.choices[0].text)

Error:

MissingSchema Traceback (most recent call last)
File ~/miniconda3/lib/python3.10/site-packages/openai/api_requestor.py:596, in APIRequestor.request_raw(self, method, url, params, supplied_headers, files, stream, request_id, request_timeout)
595 try:
--> 596 result = _thread_context.session.request(
597 method,
598 abs_url,
599 headers=headers,
600 data=data,
601 files=files,
602 stream=stream,
603 timeout=request_timeout if request_timeout else TIMEOUT_SECS,
604 proxies=_thread_context.session.proxies,
605 )
606 except requests.exceptions.Timeout as e:

File ~/miniconda3/lib/python3.10/site-packages/requests/sessions.py:573, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
561 req = Request(
562 method=method.upper(),
563 url=url,
(...)
571 hooks=hooks,
572 )
--> 573 prep = self.prepare_request(req)
575 proxies = proxies or {}

File ~/miniconda3/lib/python3.10/site-packages/requests/sessions.py:484, in Session.prepare_request(self, request)
483 p = PreparedRequest()
--> 484 p.prepare(
485 method=request.method.upper(),
486 url=request.url,
487 files=request.files,
488 data=request.data,
489 json=request.json,
490 headers=merge_setting(
491 request.headers, self.headers, dict_class=CaseInsensitiveDict
492 ),
493 params=merge_setting(request.params, self.params),
494 auth=merge_setting(auth, self.auth),
495 cookies=merged_cookies,
496 hooks=merge_hooks(request.hooks, self.hooks),
497 )
498 return p

File ~/miniconda3/lib/python3.10/site-packages/requests/models.py:368, in PreparedRequest.prepare(self, method, url, headers, files, data, params, auth, cookies, hooks, json)
367 self.prepare_method(method)
--> 368 self.prepare_url(url, params)
369 self.prepare_headers(headers)

File ~/miniconda3/lib/python3.10/site-packages/requests/models.py:439, in PreparedRequest.prepare_url(self, url, params)
438 if not scheme:
--> 439 raise MissingSchema(
440 f"Invalid URL {url!r}: No scheme supplied. "
441 f"Perhaps you meant https://{url}?"
442 )
444 if not host:

MissingSchema: Invalid URL 'None/openai/deployments/gpt-4/completions?api-version=2023-06-13': No scheme supplied. Perhaps you meant https://none/openai/deployments/gpt-4/completions?api-version=2023-06-13?

The above exception was the direct cause of the following exception:

APIConnectionError Traceback (most recent call last)
Cell In[22], line 1
----> 1 response = openai.Completion.create(engine="gpt-4",
2 prompt="Knock knock.",
3 temperature=0)
4 print(response.choices[0].text)

File ~/miniconda3/lib/python3.10/site-packages/openai/api_resources/completion.py:25, in Completion.create(cls, *args, **kwargs)
23 while True:
24 try:
---> 25 return super().create(*args, **kwargs)
26 except TryAgain as e:
27 if timeout is not None and time.time() > start + timeout:

File ~/miniconda3/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py:153, in EngineAPIResource.create(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)
127 @classmethod
128 def create(
129 cls,
(...)
136 **params,
137 ):
138 (
139 deployment_id,
140 engine,
(...)
150 api_key, api_base, api_type, api_version, organization, **params
151 )
--> 153 response, _, api_key = requestor.request(
154 "post",
155 url,
156 params=params,
157 headers=headers,
158 stream=stream,
159 request_id=request_id,
...
617 request_id=result.headers.get("X-Request-Id"),
618 )
619 # Don't read the whole stream for debug logging unless necessary.

APIConnectionError: Error communicating with OpenAI: Invalid URL 'None/openai/deployments/gpt-4/completions?api-version=2023-06-13': No scheme supplied. Perhaps you meant https://none/openai/deployments/gpt-4/completions?api-version=2023-06-13?
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

When I run, prompt ：The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again. But my deployment already exists.Has anyone encountered it？

qna-quickstart-with-gpt-index/qna-quickstart-with-llama-index.ipynb

got error

TypeError Traceback (most recent call last)
Input In [3], in <cell line: 31>()
28 prompt_helper = PromptHelper(max_input_size=max_input_size, num_output=num_output, max_chunk_overlap=max_chunk_overlap, chunk_size_limit=chunk_size_limit)
30 # Create index
---> 31 index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, embed_model=embedding_llm, prompt_helper=prompt_helper)
32 index.save_to_disk("index.json")

File ~/.local/lib/python3.9/site-packages/llama_index/indices/vector_store/vector_indices.py:73, in GPTSimpleVectorIndex.init(self, nodes, index_struct, service_context, vector_store, **kwargs)
64 def init(
65 self,
66 nodes: Optional[Sequence[Node]] = None,
(...)
70 **kwargs: Any,
71 ) -> None:
72 """Init params."""
---> 73 super().init(
74 nodes=nodes,
75 index_struct=index_struct,
76 service_context=service_context,
77 vector_store=vector_store,
78 **kwargs,
79 )

File ~/.local/lib/python3.9/site-packages/llama_index/indices/vector_store/base.py:54, in GPTVectorStoreIndex.init(self, nodes, index_struct, service_context, vector_store, use_async, **kwargs)
51 self._vector_store = vector_store or SimpleVectorStore()
53 self._use_async = use_async
---> 54 super().init(
55 nodes=nodes,
56 index_struct=index_struct,
57 service_context=service_context,
58 **kwargs,
59 )

TypeError: init() got an unexpected keyword argument 'llm_predictor'

In movie_classification_unsupervised_incl_recommendations_solution.ipynb at cell N°4:
@Retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(10))
def get_embedding(text) -> list[float]:
text = text.replace("\n", " ")
return openai.Embedding.create(input=text, engine=EMBEDDING_MODEL)["data"][0]["embedding"]

get an error:

TypeError Traceback (most recent call last)
Cell In[4], line 2
1 @Retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(10))
----> 2 def get_embedding(text) -> list[float]:
3 text = text.replace("\n", " ")
4 return openai.Embedding.create(input=text, engine=EMBEDDING_MODEL)["data"][0]["embedding"]

TypeError: 'type' object is not subscriptable

As there is a difference in the way we generate embeddings in Azure Open AI vs Open AI as we are not able to create bulk embeddings in single request through Azure Open AI. I have created Azure Open AI embeddings in the below way:-
from langchain.text_splitter import CharacterTextSplitter
#from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.create_documents(contents)
print(texts)
texts_new = [doc.page_content for doc in texts]

Print the extracted texts

list_docs=[]
for text in texts_new:
print(text)
list_docs.append(text)

import time
Embeddings_list=[]
for item in list_docs:
print(item)
while True:
try:
response = openai.Embedding.create(input=item, engine="text-embedding-ada-002")
embeddings = response['data'][0]['embedding']
Embeddings_list.append(embeddings)
break
except Exception as e:
print(e)
time.sleep(15)

How to use this Embedding_list to ingest into vector store and club it to QNA process as this way of embedding integration into the below process has no guided documentation

Create a vectorstore from documents

db = Chroma.from_documents(texts, embeddings)

Create retriever interface

retriever = db.as_retriever()

Create QA chain

qa = RetrievalQA.from_chain_type(llm=OpenAI(openai_api_key=openai_api_key), chain_type='stuff', retriever=retriever)

This looks like a really great workshop. We're doing a corporate Hackathon, and I'm wondering if you have recordings of the presentation parts?