what is the latest evrsion of teh python package for openai
#what is the latest version of the python package for openai
desert mauve
soft frigateBOT
Hi @desert mauve. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
The latest version of the OpenAI Python package, as per the knowledge sources, is 1.30.1. This information was found in a forum post (https://community.openai.com/t/744660) on the OpenAI community. However, please note that this information might be outdated as the sources provided do not specify a date.
To check the latest version, you can visit the OpenAI Python library's release page on GitHub (https://github.com/openai/openai-python/releases) or the OpenAI project page on PyPI (https://pypi.org/project/openai/).
You can also check the installed version of the OpenAI Python package on your system by running the following command in your Python environment:
import openai
print(openai.__version__)
If you want to ensure you have the latest version installed, you can update the package using pip:
pip install --upgrade openai
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desert mauve
@soft frigate im getting this error: {"detail":"'CreateEmbeddingResponse' object is not subscriptable"}
i suspect this line of code is the offender: query_embedding = query_response['data'][0]['embedding']
soft frigateBOT
Let me check the knowledge base for you. One moment! :books:
The error message you're seeing suggests that query_response is not a dictionary-like object, and therefore you can't use the [] operator to access its elements.
In the code snippet you provided, query_response is expected to be a dictionary-like object returned by the openai.Embedding.create() method. This method is used to create embeddings for a given input text, as shown in the OpenAI Cookbook example (https://cookbook.openai.com/examples/vector_databases/pinecone/semantic_search#creating-embeddings).
The expected structure of the response from openai.Embedding.create() is a dictionary-like object with a key 'data' that contains a list of embeddings. Each embedding is also a dictionary-like object with a key 'embedding' that maps to the actual embedding vector.
Here's an example of how you might use this method:
res = openai.Embedding.create(
input=[
"Sample document text goes here",
"there will be several phrases in each batch"
], engine="text-embedding-ada-002"
)
# Accessing the first embedding
first_embedding = res['data'][0]['embedding']
If you're seeing the error message 'CreateEmbeddingResponse' object is not subscriptable, it suggests that query_response is not a dictionary-like object, but an instance of the CreateEmbeddingResponse class.
You might want to check the type of query_response to confirm this:
print(type(query_response))
If query_response is indeed an instance of CreateEmbeddingResponse, you'll need to find out how to access its data. The exact method will depend on the definition of the CreateEmbeddingResponse class, which is not provided in the knowledge sources.
If query_response is supposed to be the result of a call to openai.Embedding.create(), you might want to check that this method call is working as expected.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
Let me check the knowledge base for you. One moment! :books:
The error message you're seeing suggests that the query_response object returned by client.embeddings.create() is not a dictionary-like object, and therefore you can't use the [] operator to access its elements.
In the code snippet you provided, query_response is expected to be a dictionary-like object returned by the openai.Embedding.create() method. This method is used to create embeddings for a given input text, as shown in the OpenAI Cookbook example (https://cookbook.openai.com/examples/vector_databases/pinecone/semantic_search#creating-embeddings).
The expected structure of the response from openai.Embedding.create() is a dictionary-like object with a key 'data' that contains a list of embeddings. Each embedding is also a dictionary-like object with a key 'embedding' that maps to the actual embedding vector.
Here's an example of how you might use this method:
res = openai.Embedding.create(
input=[
"Sample document text goes here",
"there will be several phrases in each batch"
], model="text-embedding-3-small"
)
# Accessing the first embedding
first_embedding = res['data'][0]['embedding']
If you're seeing the error message 'CreateEmbeddingResponse' object is not subscriptable, it suggests that query_response is not a dictionary-like object, but an instance of the CreateEmbeddingResponse class.
You might want to check the type of query_response to confirm this:
print(type(query_response))
If query_response is indeed an instance of CreateEmbeddingResponse, you'll need to find out how to access its data. The exact method will depend on the definition of the CreateEmbeddingResponse class, which is not provided in the knowledge sources.
If query_response is supposed to be the result of a call to openai.Embedding.create(), you might want to check that this method call is working as expected.
It's also worth noting that the OpenAI Python library has undergone several updates, and the method to create embeddings might have changed in more recent versions. You might want to check the latest OpenAI API documentation (https://platform.openai.com/docs/api-reference) for the most up-to-date information.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
desert mauve
@soft frigate give me the code to fix it
soft frigateBOT
Let me check the knowledge base for you. One moment! :books:
Based on the information provided in the knowledge sources, it seems like you're using the OpenAI Python client to create embeddings. However, the exact structure of the response object (CreateEmbeddingResponse) is not clear from the provided sources.
In the OpenAI API documentation (https://platform.openai.com/docs/api-reference), it's mentioned that the response from the createEmbedding method should be a dictionary-like object with a key 'data' that contains a list of embeddings. Each embedding is also a dictionary-like object with a key 'embedding' that maps to the actual embedding vector.
However, the error message you're seeing suggests that query_response is not a dictionary-like object, but an instance of the CreateEmbeddingResponse class.
Without knowing the exact structure of the CreateEmbeddingResponse class, it's hard to provide a definitive solution. However, you might try accessing the embedding data using dot notation, like this:
query_embedding = query_response.data[0].embedding
This assumes that CreateEmbeddingResponse has a data attribute that is a list of objects, and each of these objects has an embedding attribute. If this is not the case, you'll need to adjust the code accordingly.
If this doesn't work, I would recommend checking the OpenAI Python client's documentation or source code to find out how to access the data in a CreateEmbeddingResponse object.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
desert mauve
@soft frigate this si what it looks like:
File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from typing import List
from typing_extensions import Literal
from .._models import BaseModel
from .embedding import Embedding
all = ["CreateEmbeddingResponse", "Usage"]
class Usage(BaseModel):
prompt_tokens: int
"""The number of tokens used by the prompt."""
total_tokens: int
"""The total number of tokens used by the request."""
class CreateEmbeddingResponse(BaseModel):
data: List[Embedding]
"""The list of embeddings generated by the model."""
model: str
"""The name of the model used to generate the embedding."""
object: Literal["list"]
"""The object type, which is always "list"."""
usage: Usage
"""The usage information for the request."""
Let me check the knowledge base for you. One moment! :books:
Based on the provided code, it appears that the CreateEmbeddingResponse object is a custom class with several attributes, including data, model, object, and usage. The data attribute is a list of Embedding objects.
Given this, you should be able to access the embedding data using dot notation, like this:
query_embedding = query_response.data[0].embedding
This assumes that each Embedding object in the data list has an embedding attribute. If this is not the case, you'll need to adjust the code accordingly.
Here's how your updated code might look:
@app.post("/query/")
async def query_index(request: Request):
try:
body = await request.json()
query = body.get("query")
if not query:
raise HTTPException(status_code=400, detail="Query parameter is required")
# Embed query using OpenAI
query_response = client.embeddings.create(input=query, model="text-embedding-3-small")
query_embedding = query_response.data[0].embedding # Updated this line
# Search in OpenSearch
search_body = {
"query": {
"knn": {
"embedding": {
"vector": query_embedding,
"k": 5
}
}
}
}
results = opensearch_client.search(index=INDEX_NAME, body=search_body)
# Retrieve the content from the search results
retrieved_docs = [hit['_source']['content'] for hit in results['hits']['hits']]Use retrieved documents to generate a response using OpenAI
combined_docs = "\n\n".join(retrieved_docs)
prompt = f"Use the following documents to answer the query: {query}\n\nDocuments:\n{combined_docs}\n\nAnswer:"
gpt_response = client.Completion.create(
model="gpt-4o",
prompt=prompt,
max_tokens=500
)
answer = gpt_response['choices'][0]['text'].strip()
return JSONResponse(content={"query": query, "answer": answer})
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
Please note that this solution assumes that the `Embedding` objects in the `data` list have an `embedding` attribute. If this is not the case, you'll need to adjust the code accordingly.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------@soft frigate setting query_embedding = query_response.data[0].embedding gives me this error now
{"detail":"RequestError(400, 'search_phase_execution_exception', "failed to create query: Field 'embedding' is not knn_vector type.")"}
soft frigateBOT
Let me check the knowledge base for you. One moment! :books:
The error message you're seeing suggests that the 'embedding' field in your OpenSearch index is not of the type 'knn_vector'. The 'knn_vector' type is required for performing k-nearest neighbors (k-NN) searches, which is what your code is trying to do.
Here's how you might create an OpenSearch index with a 'knn_vector' field:
# This is a hypothetical example and may need to be adjusted based on your specific OpenSearch setup
mapping = {
"properties": {
"embedding": {
"type": "knn_vector",
"dimension": 768 # Adjust this to match the dimension of your embeddings
},
# Include other fields as needed
}
}
opensearch_client.indices.create(index=INDEX_NAME, body=mapping)
This code creates an index with a 'knn_vector' field named 'embedding'. The 'dimension' parameter should match the dimension of your embeddings.
Please note that this is a hypothetical example and may need to be adjusted based on your specific OpenSearch setup. For more information on using k-NN with OpenSearch, you might want to check the OpenSearch documentation (https://opensearch.org/docs/latest/search-plugins/knn/index/).
If you've already created the index and it's not in use, you might need to delete it before you can create a new one with the same name. Be careful with this operation, as it will delete all data in the index:
opensearch_client.indices.delete(index=INDEX_NAME)
After deleting the index, you can create a new one with the 'knn_vector' mapping as shown above.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
desert mauve
@soft frigate how to fix this:
gpt_response = client.Completion.create(
model="gpt-4o",
prompt=prompt,
max_tokens=500
)
OpenAI' object has no attribute 'Completion
soft frigateBOT
Let me check the knowledge base for you. One moment! :books:
The error message you're seeing suggests that the OpenAI object does not have an attribute named Completion. This is likely because the method to create completions in the OpenAI Python client is completions.create(), not Completion.create().
Here's how you might update your code:
gpt_response = client.completions.create(
model="gpt-4o",
prompt=prompt,
max_tokens=500
)
This code calls the completions.create() method on the client object, which is an instance of OpenAI. The completions.create() method is used to generate completions from a model.