Update for project_id as optional (#277)

integrations/google-vertex-ai.md

@@ -57,7 +57,8 @@ Once installed, you will have access to various Haystack Generators:
 
 To use Vertex AI models, you need to have a Google Cloud Platform account and be logged in using Application Default Credentials (ADCs). For more info see the [official documentation](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fcloud.google.com%2Fdocs%2Fauthentication%2Fprovide-credentials-adc). 
 
-To start using Vertex AI generators in Haystack, you need to set the `project_id` variable to a valid project ID that have enough authorization to use Vertex AI. Find your `project_id` in the [GCP resource manager](https://console.cloud.google.com/cloud-resource-manager) or locally by running `gcloud projects list` in your terminal. For more info on the gcloud CLI see the [official documentation](https://cloud.google.com/cli).
+To start using Vertex AI generators in Haystack, it is essential that your account has access to a project authorized to use Google Vertex AI endpoints. The `project_id` needed for initialization of Vertex AI generators is set during GCP authentication mentioned above. Additonally, you can also set a different `project_id` by passing it as a variable during initialization of the generator.
+You can find your `project_id` in the [GCP resource manager](https://console.cloud.google.com/cloud-resource-manager) or locally by running `gcloud projects list` in your terminal. For more info on the gcloud CLI see the [official documentation](https://cloud.google.com/cli).
 
 ### Gemini API models
 
@@ -71,7 +72,7 @@ To use Gemini model for text generation, initialize a `VertexAIGeminiGenerator`
 from haystack_integrations.components.generators.google_vertex import VertexAIGeminiGenerator
 
 
-gemini_generator = VertexAIGeminiGenerator(model="gemini-pro", project_id=project_id)
+gemini_generator = VertexAIGeminiGenerator(model="gemini-pro")
 result = gemini_generator.run(parts = ["What is assemblage in art?"])
 print(result["replies"][0])
 ```
@@ -82,7 +83,7 @@ Assemblage in art refers to the creation of a three-dimensional artwork by combi
 
 **Multimodality with `gemini-1.5-flash`** 
 
-To use `gemini-1.5-flash` model for visual question answering, initialize a `VertexAIGeminiGenerator` with `"gemini-1.5-flash"` and `project_id`. Then, run it with the images as well as the prompt:
+To use `gemini-1.5-flash` model for visual question answering, initialize a `VertexAIGeminiGenerator` with `"gemini-1.5-flash"`. Then, run it with the images as well as the prompt:
 
 ```python
 import requests
@@ -99,7 +100,7 @@ images = [
     ByteStream(data=requests.get(url).content, mime_type="image/jpeg")
     for url in URLS
 ]
-gemini_generator = VertexAIGeminiGenerator(model="gemini-1.5-flash", project_id=project_id)
+gemini_generator = VertexAIGeminiGenerator(model="gemini-1.5-flash")
 result = gemini_generator.run(parts = ["What can you tell me about these robots?", *images])
 for answer in result["replies"]:
     print(answer)  
@@ -116,15 +117,15 @@ The fourth image is of Marvin from the 1977 film The Hitchhiker's Guide to the G
 
 ### PaLM API Models
 
-You can leverage PaLM API models `text-bison`, `text-unicorn` and `text-bison-32k` through `VertexAITextGenerator` for task generation. To use PaLM models, initialize a `VertexAITextGenerator` with model name and `project_id`.
+You can leverage PaLM API models `text-bison`, `text-unicorn` and `text-bison-32k` through `VertexAITextGenerator` for task generation. To use PaLM models, initialize a `VertexAITextGenerator` with model name.
 
 Here'a an example of using `text-unicorn` model with VertexAITextGenerator to extract information as a JSON file:
 
 ```python
 from haystack_integrations.components.generators.google_vertex import VertexAITextGenerator
 
 
-palm_llm = VertexAITextGenerator(model="text-unicorn", project_id=project_id)
+palm_llm = VertexAITextGenerator(model="text-unicorn")
 palm_llm_result = palm_llm.run(
     """Extract the technical specifications from the text below in a JSON format. Valid fields are name, network, ram, processor, storage, and color.
        Text: Google Pixel 7, 5G network, 8GB RAM, Tensor G2 processor, 128GB of storage, Lemongrass
@@ -135,14 +136,14 @@ print(palm_llm_result["replies"][0])
 
 ### Codey API Models
 
-You can leverage Codey API models, `code-bison`, `code-bison-32k` and `code-gecko`, through `VertexAICodeGenerator` for code generation. To use Codey models, initialize a `VertexAICodeGenerator` with model name and `project_id`.
+You can leverage Codey API models, `code-bison`, `code-bison-32k` and `code-gecko`, through `VertexAICodeGenerator` for code generation. To use Codey models, initialize a `VertexAICodeGenerator` with model name.
 
 Here'a an example of using `code-bison` model for **code generation**:
 ```python
 from haystack_integrations.components.generators.google_vertex import VertexAICodeGenerator
 
 
-codey_llm = VertexAICodeGenerator(model="code-bison", project_id=project_id)
+codey_llm = VertexAICodeGenerator(model="code-bison")
 codey_llm_result = codey_llm.run("Write a code for calculating fibonacci numbers in JavaScript")
 print(codey_llm_result["replies"][0])
 ```
@@ -152,7 +153,7 @@ Here'a an example of using `code-gecko` model for **code completion**:
 from haystack_integrations.components.generators.google_vertex import VertexAICodeGenerator
 
 
-codey_llm = VertexAICodeGenerator(model="code-gecko", project_id=project_id)
+codey_llm = VertexAICodeGenerator(model="code-gecko")
 codey_llm_result = codey_llm.run("""function fibonacci(n) {
   // Base cases
   if (n <= 1) {
@@ -168,15 +169,15 @@ You can leverage Imagen models through three components: [VertexAIImageCaptioner
 
 **Image Generation with `imagegeneration`**
 
-To generate an image, initialize a VertexAIImageGenerator with the `imagegeneration` and the `project_id`, Then, you can run it with a prompt:
+To generate an image, initialize a VertexAIImageGenerator with the `imagegeneration`. Then, you can run it with a prompt:
 
 ```python
 import io
 import PIL.Image as Image
 from haystack_integrations.components.generators.google_verteximport VertexAIImageGenerator
 
 
-image_generator = VertexAIImageGenerator(model="imagegeneration", project_id=project_id)
+image_generator = VertexAIImageGenerator(model="imagegeneration")
 image_generator_result = image_generator.run("magazine style, 4k, photorealistic, modern red armchair, natural lighting")
 
 ## (Optional) Save the generated image
@@ -186,13 +187,13 @@ image.save("output.png")
 
 **Image Captioning with `imagetext`** 
 
-To use generate image captions, initialize a VertexAIImageCaptioner with the `imagetext` model and `project_id`. Then, you can run the VertexAIImageCaptioner with the image that you want to caption: 
+To use generate image captions, initialize a VertexAIImageCaptioner with the `imagetext` model. Then, you can run the VertexAIImageCaptioner with the image that you want to caption: 
 
 ```python
 from haystack_integrations.components.generators.google_vertex import VertexAIImageCaptioner
 
 
-image_captioner = VertexAIImageCaptioner(model='imagetext', project_id=project_id)
+image_captioner = VertexAIImageCaptioner(model='imagetext')
 image = ByteStream.from_file_path("output.png") # you can use the generated image
 
 image_captioner_result = image_captioner.run(image=image)
@@ -201,14 +202,14 @@ print(image_captioner_result["captions"])
 
 **Visual Question Answering (VQA) with `imagetext`** 
 
-To answer questions about an image, initialize a VertexAIImageQA with the `imagetext` model and `project_id`. Then, you can run it with the `image` and the `question`: 
+To answer questions about an image, initialize a VertexAIImageQA with the `imagetext` model. Then, you can run it with the `image` and the `question`: 
 
 ```python
 from haystack.dataclasses.byte_stream import ByteStream
 from haystack_integrations.components.generators.google_vertex import VertexAIImageQA
 
 
-visual_qa = VertexAIImageQA(model='imagetext', project_id=project_id)
+visual_qa = VertexAIImageQA(model='imagetext')
 image = ByteStream.from_file_path("output.png") # you can use the generated image
 question = "what's the color of the furniture?"