From the main page and official documentation, OpenAI clearly mentioned we can use the response_format as follows: What I'm trying in Postman is the following: { "instructions": "You are a personal math tutor. When asked a question, write and run…
I have built a website with a fairly complex structure and lots of javascript and php functions. I avoided jquery to make it lighter. I now want to add a python function that should return a value to it. Most…
I can install and run Ollama service with GPU in an EC2 instance and make API calls to it from a web app in the following way: First I need to create a docker network, so that the Ollama service…
google gemini_response is string I need in the JSON Structure like this { "Title": "chair", "Description": "A wooden chair with wheels", "Category": "Furniture", "Subcategory": "office stuff", "EstimatedPrice": "$10 - 20 " } I try the json_laod() to desearlize but my…
I'm creating a chatbot RAG using the content from my MongoDB and sending to create vectors on Pinecone. So my user can ask stuff to my chatbot about his reflections. Here is my code: import os from pymongo import MongoClient…
I have finetuned a gemma 7b LLM from HuggingFace using Lora and stored the model as a compressed .tar.gz file. I have finetuned locally in sagemaker. this is my .tar.gz file structure of finetuned model : finetuned_gemma/model-00004-of-00004.safetensors finetuned_gemma/tokenizer_config.json finetuned_gemma/model.safetensors.index.json finetuned_gemma/config.json…
I am currently running code on amazon sagemaker jupyter notebook (not jupyterLab, just a plain jupyter notebook) on the 'ml.t3.2xlarge' instance. There is one line of code shown below, where I am simply deploying an LLM on my data frame.…
I am fresher in the prompt engineering. Suddenly, I am facing a problem in the HuggingFaceInstructEmbeddings. I am using langchain and GoogleGenerativeAI in vscode. My python version 3.10.0 and langchain version 0.1.2. Here is my code- from langchain_google_genai import GoogleGenerativeAI…
Here is a simple code to use Redis and embeddings but It's not clear how can I build and load own embeddings and then pull it from Redis and use in search from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores.redis import Redis…
I'm trying to use functions when calling Azure OpenAI GPT, as documented in https://platform.openai.com/docs/api-reference/chat/create#chat/create-functions I use: import openai openai.api_type = "azure" openai.api_base = "https://XXXXXXXX.openai.azure.com/" openai.api_version = "2023-06-01-preview" openai.api_key = os.getenv("OPENAI_API_KEY") response = openai.ChatCompletion.create( engine="gpt-35-turbo-XXX", model="gpt-35-turbo-0613-XXXX" messages=messages, functions=functions, function_call="auto", ) but…