From what I understand, you opened this issue to seek guidance on customizing the prompt for the zero-shot agent created using the initialize_agent function. There were multiple solutions provided by the community, including using sys_message to change the prompt and using agent_kwargs to set a...
We will be evaluating the text-embedding-ada-002 and text-embedding-3-small (we will call them ada-002 and 3-small in the rest of the tutorial) embedding models from OpenAI, so first, let’s define a function to generate embeddings using OpenAI’s Embeddings API: 1 def get_embeddings(...
Explore how to write serverless Python functions step-by-step. Learn to build, deploy, and optimize AWS Lambda functions using the Serverless Framework.
From AWS Lambda and Azure Functions to Knative and OpenFaaS, we have at least a dozen functions-as-a-service platforms to choose from. Here’s how to navigate the options.
The programming style here is similar to get_sample_relation, but we will not delve into any samples. Now, we define the type of effects that we'll work with and convert its effect into an integer (as it will allow us to use it as an index, for example, matrices). Now, think abou...
We need to import the seaborn and matplotlib are the main libraries, and also, we need to import the NumPy and pandas library. Q3. What is the use of the suptitle function in seaborn subplots? Answer By using the function of suptitle in it, we can define the name of our subplots. ...
This practice is really not too different from what you would need to do in any service oriented architecture before Lambda. There is also the AWS Serverless Application Model, which allows you to define multiple components of your serverless application (API Gateway, S3 events, CloudWatch Events,...
It enables you to define your resilience goals, assess your resilience posture against those goals, and implement recommendations for improvement based on the AWS Well-Architected Framework. This benefits individual teams that want to assess their applications. However, for […]...
6. After projecting the data, we use the range function to define the target name. Code: ids=range(len(tsne.target_names)) Output: 7. After defining the range function, now, in this step, we are plotting the scikit learn tsne figure. Also, we are displaying the same by using the sh...
Next, let’s define our input and output: X = df[['tenure', 'MonthlyCharges']] y = df['Churn'] We can then split our data for training and testing. To do this, we need to import the train_test_split method from the model_selection module in Sklearn. Let’s generate a training...