I am exploring the sql query engine. For the SQL one I get: I'm sorry, but I can't execute SQL queries or access databases to provide real-time results. However, I can guide you on how to write the SQL query for the question you've asked. -- the pandas one works fine

I am creating an OpenAI assistant using a) llama index and b) native OpenAI API. In both cases I choose to upload some grounding documents, instruction prompt and enable the retrieval toolkit. However, in the case of (a) Llamaindex - quite bad texts are generated. Variant (b) openai native works like a charm though. What can be causeing such issues? I was expecting to get similar results - as internally the openAI APIs should be called in both cases

I am facing an issue with llamaindex version 0.8.66 when trying to create an embedding with OpenAIEmbedding

I am using the Azure Openai 1.2.2 (client binaries)

-> llamaindex fails with POST https://myname.openai.azure.com/embeddings "HTTP/1.1 404 Resource Not Found"

-> plain binary (from openai import AzureOpenAI) works for POST https://myname.openai.azure.com//openai/deployments/text-embedding-ada-002/embeddings?api-version=2023-05-15 "HTTP/1.1 200 OK"

How can this be fixed?

As you can see the native client does not have this problem

Why is the embedding null when uploading it to qdrant? What can I do to debug it? manually executing the embedding computation yields non-null results

Are there any plans to upgrade to numpy2? I have issues like: llama-index-core==0.11.10 depends on numpy<2.0.0

Can we use llama_cpp embeddings as well? i.e. instead of HuggingFaceEmbedding use https://github.com/abetlen/llama-cpp-python/blob/main/examples/high_level_api/high_level_api_embedding.py llm.create_embedding("Hello world!")? Does it make sense to standardize on one serving layer and not mix them?

how can I use AutoModelForCausalLM.from_pretrained('TheBloke/leo-hessianai-7B-chat-GGUF', model_file="leo-hessianai-7b-chat.Q4_K_M.gguf", model_type="llama") based on ctransformers with llamaindex? this is currently failing with TheBloke/leo-hessianai-7B-chat-GGUF does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack. for the AutoModelForCausalLM which is coming from the normal transformers library which is used by llamaindex?

How can I utilize a function calling api with any huggingface model? https://platform.openai.com/docs/guides/gpt/function-calling

How can I use llama index for data cleaning? Are there some examples available? So far I have seen function calling examples like https://wandb.ai/darek/llmapps/reports/Using-LLMs-to-Extract-Structured-Data-OpenAI-Function-Calling-in-Action--Vmlldzo0Nzc0MzQ3 which use the native libraries but something more generic basded on llama iindex would be nice

Is an async version of https://gpt-index.readthedocs.io/en/latest/core_modules/query_modules/query_engine/streaming.html streaming chat engine available?