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Shows how to fix openAIEmbeddings fails against Azure OpenAI: Too many inputs for model. Use it when you hit this exact problem. Skip it when your error message or symptom looks different.
Diagnoses the Azure OpenAI 400 error where a request exceeds the model's token limit, common when embedding large documents for search ingestion. Use when an agent sees this during bulk embedding or chat calls on Azure OpenAI. Covers token-bounded chunking of oversized texts and retrying failed batches. Not for rate-l…
Pass chunk_size=1 when constructing the embeddings: embeddings = OpenAIEmbeddings(chunk_size=1). This was the confirmed fix in the thread and also works through helpers like vectorstore.from_documents() where you can't pass chunk_size yourself.
The o1/o3-mini models need Azure OpenAI API version 2024-12-01-preview or later. The workaround in the thread is a custom HttpClient handler (AzureOverrideHandler) passed to the connector to override the API version (and the max_completion_tokens limitation). The upstream fix arrived in Azure.AI.OpenAI 2.2.0-beta1, so…
Problem outcome: unknown
Calling an Azure OpenAI o1 deployment (model version 2024-12-17) from Semantic Kernel .NET failed with HTTP 400: "Model o1 is enabled only for api versions 2024-12-01-preview and later". The o1 model name version is not the API version, and SK was sending an older Azure OpenAI API version. Worse, the underlying Azure.…
Problem outcome: unknown
Cause: Giskard's scan evaluates generated answers through its own internal OpenAI client, which read your OPENAI_* environment variables and ignored your Azure config, hence the missing engine/deployment_id. The fix was Azure support for the scan: set AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, `OPENAI_API_VERS…
Problem outcome: unknown
Running a Giskard LLM-as-a-judge test on an Azure OpenAI model fails with Must provide an 'engine' or 'deployment_id' parameter to create a [class 'openai.api_resources.chat_completion.ChatCompletion'], even though deployment_name is set on AzureChatOpenAI. The user's own QA chain runs fine; only the Giskard tes…
Problem outcome: unknown
Configure max_completion_tokens instead of max_tokens: in SK Python, OpenAIChatPromptExecutionSettings (the base class for OpenAI prompt execution settings) has a max_completion_tokens attribute, and the child AzureChatPromptExecutionSettings adds extra_body if needed. Setting that value works fine with re…
Problem outcome: unknown
Invoking Semantic Kernel Python's AzureChatCompletion against an o3-series reasoning model failed with "Unsupported parameter: 'max_tokens' is not supported with this model. Use 'max_completion_tokens' instead." Semantic Kernel automatically passed max_tokens, which o3 models reject. The user expected the standard p…
Problem outcome: unknown
Pass chunk_size=1 when constructing the embeddings: embeddings = OpenAIEmbeddings(chunk_size=1). This was the confirmed fix in the thread and also works through helpers like vectorstore.from_documents() where you can't pass chunk_size yourself. In langchain.js the equivalent option is batchSize (`new OpenAIEmb…
Problem outcome: unknown
Using langchain's OpenAIEmbeddings against Azure OpenAI failed with "InvalidRequestError: Too many inputs for model None. The max number of inputs is 1." Azure's embedding endpoint only accepts one input per request, so the default batched embedding calls got rejected. The reporter also noted that OpenAIEmbeddings nee…
Problem outcome: unknown