OpenAI

PPrince Kumar

Hey guys
Using openai 1.6.1
While doing
Index= VectorStoreIndex.from_documents(documents)

Getting this error
NotFoundError: Error code: 484 ['error': ['code": "404', 'message': 'Resource not found")).

Same error in this also.
index = VectorStoreIndex.from_documents(documents, 11m=11m1, embed_model =embeddings, prompt helper=prompt_helper)

21 comments

WWhiteFang_Jr

Could you share your code if possible

ttheoxd

what ll are you using (llama_index.llms) and how did u set it up

PPrince Kumar

AzureOpenAI

PPrince Kumar

import os
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from llama_index import VectorStoreIndex, SimpleDirectoryReader, PromptHelper
from llama_index.llms import AzureOpenAI
from openai import OpenAI
from llama_index.embeddings import AzureOpenAIEmbeddings
import openai

load_dotenv()

openai.api_key = "YOUR_OPENAI_API_KEY"
openai.api_base = "YOUR_OPENAI_API_BASE"
openai.api_version = "2023-07-01-preview"

llm1 = AzureOpenAI(azure_deployment="gpt-35-turbo", azure_endpoint=openai.api_base, api_key=openai.api_key, api_version="2023-07-01-preview")

embeddings = AzureOpenAIEmbeddings(deployment="text-embedding-ada-002",
model="text-embedding-ada-002",
openai_api_base=openai.api_base,
openai_api_type="azure",
openai_api_key=openai.api_key,
openai_api_version="2023-07-01-preview")

Define prompt helper

max_input_size = 3000
num_output = 256
chunk_size_limit = 1000
max_chunk_overlap = 20

prompt_helper = PromptHelper(context_window=500, num_output=num_output, chunk_size_limit=chunk_size_limit)

documents = SimpleDirectoryReader('../data/qna/').load_data()

index = VectorStoreIndex.from_documents(documents)

ttheoxd

Do you have an azure subscription? are you sure about your deployement name?

ttheoxd

ik i never name them the exact model name

PPrince Kumar

Yess

PPrince Kumar

Using faiss db I have done these things

PPrince Kumar

Now trying it by llama indec

ttheoxd

AZURE_KWARGS: dict = {
"api_key": AZURE_OPENAI_API_KEY,
"azure_endpoint": AZURE_API_BASE_URL,
# "api_type": AZURE_API_TYPE,
"api_version": AZURE_API_VERSION,
"reuse_client": False,
}

Used it like this and it worked
def gpt_35(self) -> AzureOpenAI:
"""Return the GPT_35 model."""
return AzureOpenAI(
engine="OneBotGPT351106",
model="gpt-3.5-turbo-1106",
temperature=self.GPT_TEMP,
additional_kwargs=self.GPT_KWARGS,
**shared_settings.AZURE_KWARGS,
)

ttheoxd

You're missing the engine no?

PPrince Kumar

I tried using engine also inside the AzureOpenAI still facing error

WWhiteFang_Jr

Try and check if your llm and embed model are working.

Plain Text

print(llm.complete("hi"))

print(embeddings.get_text_embedding("hi"))

PPrince Kumar

Is engine different from deployment name?

ttheoxd

no it is the same

PPrince Kumar

It's working

WWhiteFang_Jr

Where is your service_context defined?

PPrince Kumar

I didn't defined it here but I tried it using service context it won't worked last time

WWhiteFang_Jr

Try this once:

Plain Text

from llama_index import ServiceContext
from llama_index import set_global_service_context
service_context = ServiceContext.from_defaults(
    llm=llm,
    embed_model=embed_model)
set_global_service_context(service_context)

WWhiteFang_Jr

Do this after defining the llm and embedding

PPrince Kumar

Its working great man thanks

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OpenAI

Define prompt helper