[Question]: AuthenticationError when using OpenAI Embedding API in llama_index

[Abdulk084]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

I'm encountering an AuthenticationError when trying to generate embeddings using the OpenAI API in my application that uses the llama_index library. Here's a snippet of the code where the issue occurs:

from flask import Flask, render_template, request, jsonify
from llama_index import SimpleDirectoryReader, VectorStoreIndex, StorageContext
from llama_index.vector_stores import PineconeVectorStore
import pinecone
import re
import openai
import time
os.environ["OPENAI_API_KEY"] = 'xxx'
pinecone.init(api_key="xxxx", environment="xxx")
index = pinecone.Index("index-name")
vector_store = PineconeVectorStore(pinecone_index=index)
loaded_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

query_engine = loaded_index.as_query_engine(similarity_top_k=3)
response = query_engine.query("walking,standing")

And the error message I get is:

AuthenticationError Traceback (most recent call last)
...
RetryError: RetryError[<Future at 0x1682020d0 state=finished raised AuthenticationError>]

The error is raised when trying to create an embedding using the OpenAI's service, but it fails due to an authentication issue. It seems like the tenacity library is trying to retry the operation, but it fails every time due to the same error.

Any help on how to resolve this would be much appreciated.

/venv/lib/python3.11/site-packages/openai/api_resources/embedding.py:33, in Embedding.create(cls, *args, **kwargs)
     32 try:
---> 33     response = super().create(*args, **kwargs)
     35     # If a user specifies base64, we'll just return the encoded string.
     36     # This is only for the default case.

[dosu-bot]

Answer generated by a 🤖

Answer

I understand that you're encountering an AuthenticationError when trying to generate embeddings using the OpenAI API in your application that utilizes the llama_index library. This error typically arises when the API key provided is not valid or not recognized by the OpenAI API.

In your code, you've set the OpenAI API key using the line os.environ["OPENAI_API_KEY"] = 'xxx'. However, it's possible that the openai.api_key is not being set correctly, which could be causing the AuthenticationError.

To address this issue, I suggest setting the openai.api_key directly with your API key, like so:

openai.api_key = 'xxx'

Please replace 'xxx' with your actual OpenAI API key. If the issue persists, please double-check that your API key is correct and has the necessary permissions.

If you're using custom embeddings, ensure that the model is correctly initialized and the embeddings are correctly generated.

In case you're using Azure OpenAI, you need to set the openai.api_type, openai.api_base, and openai.api_version as shown below:

openai.api_type = "azure"
openai.api_base = "https://<foo-bar>.openai.azure.com"
openai.api_version = "2022-12-01"
os.environ["OPENAI_API_KEY"] = "youcannottellanyone"
openai.api_key = os.getenv("OPENAI_API_KEY")

And initialize the AzureOpenAI with the necessary parameters:

llm = AzureOpenAI(
    engine="<foo-bar-deployment>",
    temperature=0,
    openai_api_version=openai.api_version,
    model_kwargs={
        "api_key": openai.api_key,
        "api_base": openai.api_base,
        "api_type": openai.api_type,
        "api_version": openai.api_version,
    },
)

Please replace <foo-bar> and <foo-bar-deployment> with your actual Azure OpenAI details.

This solution should help resolve the AuthenticationError you're experiencing. However, it's important to note that this is one potential solution based on the information provided. If you find this solution helpful and believe it's not covered by existing documentation, you're encouraged to make a pull request to update the documentation for the benefit of other users.

Sources

• docs/examples/embeddings/OpenAI.ipynb
• docs/examples/embeddings/custom_embeddings.ipynb
• docs/examples/index_structs/knowledge_graph/KnowledgeGraphIndex_vs_VectorStoreIndex_vs_CustomIndex_combined.ipynb

---

This response is meant to be useful, save you time, and share context. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

[pors]

The way llama-index is implemented forces you to set the environment variable before importing the VectorStoreIndex.

So, put os.environ["OPENAI_API_KEY"] = 'xxx' above from llama_index import SimpleDirectoryReader, VectorStoreIndex, StorageContext and it should work.

[bconrad98]

The way llama-index is implemented forces you to set the environment variable before importing the VectorStoreIndex.

So, put os.environ["OPENAI_API_KEY"] = 'xxx' above from llama_index import SimpleDirectoryReader, VectorStoreIndex, StorageContext and it should work.

If this is the case, then the documentation needs to be updated here.

[dosubot[bot]]

To resolve the AuthenticationError when using the OpenAI Embedding API in llama_index, ensure that the OpenAI API key is set correctly before importing the relevant modules. Here’s how you can adjust your code:

1. Set the environment variable for the OpenAI API key before importing any llama_index modules:

import os
os.environ["OPENAI_API_KEY"] = 'xxx'

from flask import Flask, render_template, request, jsonify
from llama_index import SimpleDirectoryReader, VectorStoreIndex, StorageContext
from llama_index.vector_stores import PineconeVectorStore
import pinecone
import re
import openai
import time

pinecone.init(api_key="xxxx", environment="xxx")
index = pinecone.Index("index-name")
vector_store = PineconeVectorStore(pinecone_index=index)
loaded_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

query_engine = loaded_index.as_query_engine(similarity_top_k=3)
response = query_engine.query("walking,standing")

2. Alternatively, you can set the OpenAI API key directly in your code:

import openai
openai.api_key = 'xxx'

Replace 'xxx' with your actual OpenAI API key ^[1].

If you prefer to use a local model instead of OpenAI for embeddings, you can use the Huggingface pipeline. Here’s an example:

from langchain.embeddings import HuggingfaceEmbeddings
from llama_index import GPTSimpleVectorIndex

# Initialize the Huggingface embeddings model
embed_model = HuggingfaceEmbeddings(model_name="your-local-model")

# Create the index with the local embeddings model
index = GPTSimpleVectorIndex(
    documents, llm_predictor=embed_model, prompt_helper=prompt_helper
)

Replace "your-local-model" with the actual model name or path to your local model ^[1].

For using local models, you may need to install additional dependencies:

pip install llama-index-embeddings-huggingface
pip install transformers optimum[exporters]
pip install llama-index-embeddings-huggingface-optimum

Example code for using a Hugging Face model:

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings

Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)

Example code for using Hugging Face Optimum ONNX embeddings:

from llama_index.embeddings.huggingface_optimum import OptimumEmbedding

OptimumEmbedding.create_and_save_optimum_model(
    "BAAI/bge-small-en-v1.5", "./bge_onnx"
)

Settings.embed_model = OptimumEmbedding(folder_name="./bge_onnx")

For more details on embedding options, refer to the LlamaIndex documentation ^[2][3][4].

To continue talking to Dosu, mention @dosu.