FIX: added examples folder

Author: pabloinigo

.gitignore

@@ -165,5 +165,3 @@ website/node_modules
 website/vendor
 tfplan
 
-# Examples directory (local testing only)
-examples/

examples/audio/output/prueba_directorio_original.mp3

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+# OpenAI Embeddings Example
+
+This example demonstrates how to generate and use text embeddings with the OpenAI API through the Terraform provider for OpenAI.
+
+## What are embeddings?
+
+Embeddings are vector representations of text that capture their semantic meaning. They are useful for:
+
+- Semantic search
+- Similarity comparison between texts
+- Clustering and classification
+- Recommendation systems
+- And other natural language processing applications
+
+## Prerequisites
+
+1. Terraform installed
+2. An OpenAI API key
+3. The OpenAI provider installed in `~/.terraform.d/plugins/`
+
+## Configuration
+
+1. Make sure you have the OpenAI provider correctly installed:
+   ```
+   mkdir -p ~/.terraform.d/plugins/registry.terraform.io/fjcorp/openai/1.0.0/darwin_arm64
+   cp ~/path/to/binary/terraform-provider-openai ~/.terraform.d/plugins/registry.terraform.io/fjcorp/openai/1.0.0/darwin_arm64/
+   ```
+
+2. Configure the necessary environment variables:
+   ```
+   export OPENAI_API_KEY="your-api-key"
+   # If you belong to an organization:
+   export OPENAI_ORGANIZATION_ID="your-organization-id"
+   ```
+
+## Usage
+
+This example includes:
+
+1. **Basic Embedding**: Embedding generation for a single text
+2. **Base64 Format Embedding**: Example of using an alternative format
+3. **Multiple Embeddings**: Generating embeddings for multiple texts in a single request
+4. **Embeddings with Custom Dimensions**: Example of using newer models with specific dimensions
+
+To run the example:
+
+```
+terraform init
+terraform apply
+```
+
+## Understanding the code
+
+The `main.tf` file demonstrates:
+
+- How to configure the OpenAI provider
+- How to use the embeddings module for different use cases
+- How to work with different parameters (model, format, dimensions)
+- How to handle multiple texts in a single request
+
+## Important notes
+
+- The generated embeddings can be large, so they are not shown directly in the Terraform output
+- The `text-embedding-ada-002` model has a limit of 8192 input tokens
+- The total number of embeddings is limited per request and per model
+- For newer models like `text-embedding-3-small`, you can specify the number of dimensions of the resulting vector
+
+## API and Provider Limitations
+
+**Important**: The OpenAI API does not currently provide a way to list or retrieve existing embeddings. As a result, this provider only supports creating embeddings as a resource (`openai_embedding`) and does not include a data source for retrieving previously created embeddings.
+
+### Import Limitations
+
+When importing existing embeddings, you'll face the following limitations:
+
+1. **Partial Resource State**: Only basic metadata is imported (ID, created date, etc.), but the actual embedding vectors are not available
+2. **No Retrieval API**: The OpenAI API has no endpoint to retrieve previously created embeddings, so the import process cannot fetch the original vector data
+3. **Resource Replacement**: After import, applying the configuration will replace the imported resource with a newly created one
+
+### Import Workaround
+
+This module handles imports by:
+1. Using simulated embeddings rather than the actual vectors (which can't be retrieved)
+2. Providing a fault-tolerant structure that works with both new and imported resources
+3. Accepting that imports are primarily for tracking existing resources, not for retrieving the actual embedding vectors
+
+To import an existing embedding resource:
+
+```bash
+terraform import module.my_embedding.openai_chat_completion.embedding_simulation chatcmpl-XXXXXXXXXXXXXXXXXXXX
+```
+
+After import, a subsequent `terraform apply` will replace the imported resource with a newly created one, since the original embedding vectors cannot be retrieved from the API.
+
+The provider's implementation supports all the official OpenAI API parameters for embeddings:
+- `input`: Required - The text to embed (string or array of strings)
+- `model`: Required - ID of the model to use (e.g., "text-embedding-ada-002")
+- `dimensions`: Optional - The number of dimensions for the embeddings (only for text-embedding-3 and later models)
+- `encoding_format`: Optional - Format for the embeddings, either "float" (default) or "base64"
+- `user`: Optional - A unique identifier representing your end-user
+
+Unlike other OpenAI resources, embeddings cannot be retrieved after creation, so store the results as needed in your application.
+
+## Example of use in real applications
+
+The generated embeddings can be exported and used in:
+
+- Vector databases like Pinecone, Milvus, or Weaviate
+- Semantic search systems
+- Sentiment analysis and text classification
+- Content similarity or duplication detection 

examples/audio/output/speech.mp3

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+# OpenAI Embeddings Example
+# This example demonstrates how to generate and use text embeddings with OpenAI
+
+terraform {
+  required_providers {
+    openai = {
+      source  = "fjcorp/openai"
+      version = "1.0.0"
+    }
+  }
+}
+
+# Configure the OpenAI Provider
+provider "openai" {
+  # API key will be sourced from environment variable OPENAI_API_KEY
+  # Organization ID will be sourced from environment variable OPENAI_ORGANIZATION_ID
+}
+
+# Example 1: Basic text embedding
+module "simple_embedding" {
+  source = "../../modules/embeddings"
+
+  input = "The food was delicious and the waiter was very friendly."
+  model = "text-embedding-ada-002"
+}
+
+# Example 2: Embedding with different format (base64)
+module "base64_embedding" {
+  source = "../../modules/embeddings"
+
+  input           = "Convert this text to a base64 embedding."
+  model           = "text-embedding-ada-002"
+  encoding_format = "base64"
+}
+
+# Example 3: Multiple texts in a single request
+locals {
+  multiple_texts = jsonencode([
+    "First text to embed",
+    "Second text to embed",
+    "Third text to embed with different content"
+  ])
+}
+
+module "multiple_embeddings" {
+  source = "../../modules/embeddings"
+
+  input = local.multiple_texts
+  model = "text-embedding-ada-002"
+}
+
+# Example 4: Using a newer model with dimensions specification
+# Note: text-embedding-3 models support specifying dimensions
+module "embedding_with_dimensions" {
+  source = "../../modules/embeddings"
+
+  input      = "Generate an embedding with custom dimensions."
+  model      = "text-embedding-3-small" # Requires OpenAI API that supports this model
+  dimensions = 256                      # Specify custom dimensions (if supported by the model)
+}
+
+# Outputs
+output "simple_embedding_usage" {
+  description = "Token usage for the simple embedding"
+  value       = module.simple_embedding.usage
+}
+
+output "multiple_embeddings_count" {
+  description = "Number of embeddings generated in the batch request"
+  value       = length(module.multiple_embeddings.embeddings)
+}
+
+# The actual embeddings are marked as sensitive to avoid cluttering the output
+output "simple_embedding_id" {
+  description = "ID of the simple embedding"
+  value       = module.simple_embedding.embedding_id
+}
+
+output "base64_embedding_model" {
+  description = "Model used for the base64 encoding"
+  value       = module.base64_embedding.model_used
+} 

examples/audio/output/test_speech.mp3

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+{"model": "gpt-3.5-turbo", "custom_id": "request-1", "messages": [{"role": "user", "content": "Explain quantum computing in simple terms"}]}
+{"model": "gpt-3.5-turbo", "custom_id": "request-2", "messages": [{"role": "user", "content": "Write a short poem about artificial intelligence"}]}
+{"model": "gpt-3.5-turbo", "custom_id": "request-3", "messages": [{"role": "user", "content": "What are the key differences between machine learning and deep learning?"}]} 

examples/audio/samples/speech.mp3

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+{"model": "gpt-3.5-turbo", "custom_id": "request-1", "method": "POST", "messages": [{"role": "user", "content": "Explain quantum computing in simple terms"}]}
+{"model": "gpt-3.5-turbo", "custom_id": "request-2", "method": "POST", "messages": [{"role": "user", "content": "Write a short poem about artificial intelligence"}]}
+{"model": "gpt-3.5-turbo", "custom_id": "request-3", "method": "POST", "messages": [{"role": "user", "content": "What are the key differences between machine learning and deep learning?"}]}