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Update google-vertex-ai.md #151

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Jan 26, 2024
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22 changes: 15 additions & 7 deletions integrations/google-vertex-ai.md
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,8 @@ You can leverage Gemini models through two components: [VertexAIGeminiGenerator]
To use Gemini model for text generation, initialize a `VertexAIGeminiGenerator` with `"gemini-pro"` and `project_id`:

```python
from google_vertex_haystack.generators.gemini import VertexAIGeminiGenerator
from haystack_integrations.components.generators.google_vertex import VertexAIGeminiGenerator


gemini_generator = VertexAIGeminiGenerator(model="gemini-pro", project_id=project_id)
result = gemini_generator.run(parts = ["What is assemblage in art?"])
Expand All @@ -87,6 +88,7 @@ To use `gemini-pro-vision` model for visual question answering, initialize a `Ve
import requests
from haystack.dataclasses.byte_stream import ByteStream


URLS = [
"https://raw.githubusercontent.com/silvanocerza/robots/main/robot1.jpg",
"https://raw.githubusercontent.com/silvanocerza/robots/main/robot2.jpg",
Expand Down Expand Up @@ -119,7 +121,8 @@ You can leverage PaLM API models `text-bison`, `text-unicorn` and `text-bison-32
Here'a an example of using `text-unicorn` model with VertexAITextGenerator to extract information as a JSON file:

```python
from google_vertex_haystack.generators.text_generator import VertexAITextGenerator
from haystack_integrations.components.generators.google_vertex import VertexAITextGenerator


palm_llm = VertexAITextGenerator(model="text-unicorn", project_id=project_id)
palm_llm_result = palm_llm.run(
Expand All @@ -136,7 +139,8 @@ You can leverage Codey API models, `code-bison`, `code-bison-32k` and `code-geck

Here'a an example of using `code-bison` model for **code generation**:
```python
from google_vertex_haystack.generators.code_generator import VertexAICodeGenerator
from haystack_integrations.components.generators.google_vertex import VertexAICodeGenerator


codey_llm = VertexAICodeGenerator(model="code-bison", project_id=project_id)
codey_llm_result = codey_llm.run("Write a code for calculating fibonacci numbers in JavaScript")
Expand All @@ -145,7 +149,8 @@ print(codey_llm_result["answers"][0])

Here'a an example of using `code-gecko` model for **code completion**:
```python
from google_vertex_haystack.generators.code_generator import VertexAICodeGenerator
from haystack_integrations.components.generators.google_vertex import VertexAICodeGenerator


codey_llm = VertexAICodeGenerator(model="code-gecko", project_id=project_id)
codey_llm_result = codey_llm.run("""function fibonacci(n) {
Expand All @@ -168,7 +173,8 @@ To generate an image, initialize a VertexAIImageGenerator with the `imagegenerat
```python
import io
import PIL.Image as Image
from google_vertex_haystack.generators.image_generator import VertexAIImageGenerator
from haystack_integrations.components.generators.google_verteximport VertexAIImageGenerator


image_generator = VertexAIImageGenerator(model="imagegeneration", project_id=project_id)
image_generator_result = image_generator.run("magazine style, 4k, photorealistic, modern red armchair, natural lighting")
Expand All @@ -183,7 +189,8 @@ image.save("output.png")
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:

```python
from google_vertex_haystack.generators.captioner import VertexAIImageCaptioner
from haystack_integrations.components.generators.google_vertex import VertexAIImageCaptioner


image_captioner = VertexAIImageCaptioner(model='imagetext', project_id=project_id)
image = ByteStream.from_file_path("output.png") # you can use the generated image
Expand All @@ -198,7 +205,8 @@ To answers questions about an image, initialize a VertexAIImageQA with the `imag

```python
from haystack.dataclasses.byte_stream import ByteStream
from google_vertex_haystack.generators.question_answering import VertexAIImageQA
from haystack_integrations.components.generators.google_vertex import VertexAIImageQA


visual_qa = VertexAIImageQA(model='imagetext', project_id=project_id)
image = ByteStream.from_file_path("output.png") # you can use the generated image
Expand Down