Update the model information (#416)

Author: bilgeyucel

integrations/google-genai.md

@@ -28,18 +28,25 @@ toc: true
 - [Overview](#overview)
 - [Installation](#installation)
 - [Usage](#usage)
-  - [Chat Generation with `gemini-2.0-flash`](#chat-generation-with-gemini-20-flash)
+  - [Chat Generation with `gemini-3.1-flash-lite-preview`](#chat-generation-with-gemini-31-flash-lite-preview)
   - [Streaming Chat Generation](#streaming-chat-generation)
   - [Function calling](#function-calling)
-  - [Document Embedding](#document-embedding)
-  - [Text Embedding](#text-embedding)
+  - [Embeddings](#embeddings)
+  - [Multimodal Embeddings with `gemini-embedding-2-preview`](#multimodal-embeddings-with-gemini-embedding-2-preview)
+- [License](#license)
 
 ## Overview
 
 [Google Gen AI](https://ai.google.dev/) provides access to Google's Gemini models through the new Google Gen AI SDK. This integration enables the usage of Google's latest generative models via the updated API interface.
 Google Gen AI is compatible with both the Gemini Developer API and the Vertex AI API.
 
-Haystack supports the latest [Gemini models](https://ai.google.dev/models/gemini) like `gemini-2.0-flash` and `text-embedding-004` for tasks such as **chat completion**, **function calling**, **streaming responses** and **embedding generation**.
+Haystack supports the latest [Gemini models](https://ai.google.dev/models/gemini) for tasks such as **chat completion**, **function calling**, **streaming responses** and **embedding generation**.
+
+**Generative models:** `gemini-3.1-flash-lite-preview`, `gemini-3.1-pro-preview`, `gemini-3-flash-preview`, `gemini-2.5-flash`, `gemini-2.5-pro`, `gemini-2.5-flash-lite`, and the Gemini 2.0 series (e.g. `gemini-2.0-flash`).
+
+**Embedding models:** `gemini-embedding-2-preview` (multimodal, multilingual) and `gemini-embedding-001` (multilingual).
+
+> 🖼️ Learn how to build multimodal search systems using Gemini Embedding 2 to embed text, images, video, audio, and PDFs in [Multimodal Search with Gemini Embedding 2 in Haystack](https://haystack.deepset.ai/blog/multimodal-embeddings-gemini-haystack). 
 
 ## Installation
 
@@ -53,9 +60,10 @@ pip install google-genai-haystack
 
 Once installed, you will have access to the Haystack Chat components:
 
-- [`GoogleGenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/googlegenaichatgenerator): Use this component with [Gemini models](https://ai.google.dev/gemini-api/docs/models/gemini#model-variations), such as '**gemini-2.0-flash**' for chat completion and function calling.
-- `GoogleGenAIDocumentEmbedder`: Use this component with [Google GenAI models](https://ai.google.dev/gemini-api/docs/embeddings#embeddings-models), such as '**text-embedding-004**' for generating embeddings for documents.
-- `GoogleGenAITextEmbedder`: Use this component with [Google GenAI models](https://ai.google.dev/gemini-api/docs/embeddings#embeddings-models), such as '**text-embedding-004**' for generating embeddings for text.
+- [`GoogleGenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/googlegenaichatgenerator): Use this component with [Gemini models](https://ai.google.dev/gemini-api/docs/models/gemini#model-variations), such as **gemini-3.1-flash-lite-preview** or **gemini-2.5-pro** for chat completion and function calling.
+- [`GoogleGenAIDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/googlegenaidocumentembedder): Use this component with [Google GenAI embedding models](https://ai.google.dev/gemini-api/docs/embeddings#embeddings-models), such as **gemini-embedding-001** for generating embeddings for documents.
+- [`GoogleGenAITextEmbedder`](https://docs.haystack.deepset.ai/docs/googlegenaitextembedder): Use this component with [Google GenAI embedding models](https://ai.google.dev/gemini-api/docs/embeddings#embeddings-models), such as **gemini-embedding-001** for generating embeddings for text.
+- [`GoogleGenAIMultimodalDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/googlegenaimultimodaldocumentembedder): Use this component with [Google GenAI multimodal embedding models](https://ai.google.dev/gemini-api/docs/embeddings#embeddings-models), such as **gemini-embedding-2-preview** for generating embeddings for text, image, PDF, video and audio.
 
 To use this component with the Gemini Developer API and get an API key, visit [Google AI Studio](https://aistudio.google.com/).
 To use this component with the Vertex AI API, visit [Google Cloud > Vertex AI](https://cloud.google.com/vertex-ai).
@@ -93,9 +101,9 @@ from haystack_integrations.components.generators.google_genai import GoogleGenAI
 chat_generator = GoogleGenAIChatGenerator(api="vertex")
 ```
 
-### Chat Generation with `gemini-2.0-flash`
+### Chat Generation with `gemini-3.1-flash-lite-preview`
 
-To use Gemini model for chat generation, set the `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable and then initialize a `GoogleGenAIChatGenerator` with `"gemini-2.0-flash"`:
+To use Gemini model for chat generation, set the `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable and then initialize a `GoogleGenAIChatGenerator` with `"gemini-3.1-flash-lite-preview"`:
 
 ```python
 import os
@@ -105,7 +113,7 @@ from haystack_integrations.components.generators.google_genai import GoogleGenAI
 os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
 
 # Initialize the chat generator
-chat_generator = GoogleGenAIChatGenerator(model="gemini-2.0-flash")
+chat_generator = GoogleGenAIChatGenerator(model="gemini-3.1-flash-lite-preview")
 
 # Generate a response
 messages = [ChatMessage.from_user("Tell me about the future of AI")]
@@ -136,7 +144,7 @@ def streaming_callback(chunk: StreamingChunk):
 
 # Initialize with streaming callback
 chat_generator = GoogleGenAIChatGenerator(
-    model="gemini-2.0-flash",
+    model="gemini-3.1-flash-lite-preview",
     streaming_callback=streaming_callback
 )
 
@@ -177,7 +185,7 @@ weather_tool = Tool(
 
 # Initialize chat generator with tools
 chat_generator = GoogleGenAIChatGenerator(
-    model="gemini-2.0-flash",
+    model="gemini-3.1-flash-lite-preview",
     tools=[weather_tool]
 )
 
@@ -195,7 +203,21 @@ Output:
 The weather in Paris is sunny and 25°C.
 ``` 
 
-### Document Embedding
+### Embeddings
+
+Embeddings are vector representations of text that capture semantic meaning. They power use cases like **semantic search**, **retrieval-augmented generation (RAG)**, and **similarity comparison** e.g. finding documents or passages that are closest in meaning to a query.
+
+This integration provides three embedder components:
+
+- **`GoogleGenAIDocumentEmbedder`** — Embeds Haystack `Document` objects (text content). Use it when indexing documents into a vector store or when you need to embed multiple documents in one call. The resulting embeddings are stored on the documents and can be used for document retrieval.
+- **`GoogleGenAITextEmbedder`** — Embeds a single string. Use it when you need to embed a search query or any standalone text to compare against document embeddings (e.g. in a RAG pipeline for the query side).
+- **`GoogleGenAIMultimodalDocumentEmbedder`** — Embeds documents that can contain **images**, **videos**, or **PDFs** (via `meta["file_path"]`). Use the `gemini-embedding-2-preview` model for multimodal retrieval, e.g. searching over mixed media with text or image queries.
+
+For text-only pipelines, use the same model (e.g. `gemini-embedding-001`) for documents and queries so that their embeddings live in the same vector space. For multimodal content, use `gemini-embedding-2-preview` with the appropriate `task_type` in the config.
+
+#### Document Embedding
+
+Use `GoogleGenAIDocumentEmbedder` to create embeddings for documents before storing them in a document store or using them in retrieval:
 
 ```python
 import os
@@ -204,15 +226,17 @@ from haystack_integrations.components.embedders.google_genai import GoogleGenAID
 
 os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
 
-# Initialize the embedder
-embedder = GoogleGenAIDocumentEmbedder()
+# Initialize the embedder (uses gemini-embedding-001 by default)
+embedder = GoogleGenAIDocumentEmbedder(model="gemini-embedding-001")
 
 # Generate a response
 doc = Document(content="some text")
 docs_w_embeddings = embedder.run(documents=[doc])["documents"]
 ```
 
-### Text Embedding
+#### Text Embedding
+
+Use `GoogleGenAITextEmbedder` to embed a single string (e.g. a user query) so you can compare it to document embeddings:
 
 ```python
 import os
@@ -221,9 +245,8 @@ from haystack_integrations.components.embedders.google_genai import GoogleGenAIT
 os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
 
 text_to_embed = "I love pizza!"
-
-# Initialize the text embedder
-text_embedder = GoogleGenAITextEmbedder()
+# Initialize the text embedder (uses gemini-embedding-001 by default)
+text_embedder = GoogleGenAITextEmbedder(model="gemini-embedding-001")
 
 # Generate a response
 print(text_embedder.run(text_to_embed))
@@ -233,5 +256,43 @@ Output:
 
 ```shell
 {'embedding': [-0.052871075, -0.035282962, ...., -0.04802792], 
-'meta': {'model': 'text-embedding-004'}}
+'meta': {'model': 'gemini-embedding-001'}}
+```
+
+The returned embedding is a list of floats (e.g. 3072 dimensions for `gemini-embedding-001`). In a typical RAG pipeline you would pass this query embedding to a retriever to find the most similar document embeddings from your index.
+
+#### Multimodal Embeddings with `gemini-embedding-2-preview`
+
+`GoogleGenAIMultimodalDocumentEmbedder` embeds documents that reference **text**, **images**, **audios**, **videos**, or **PDFs** via `meta["file_path"]`. It uses the `gemini-embedding-2-preview` model, which produces a unified embedding space for text, images, and other modalities—so you can index mixed media and retrieve with text or image queries.
+
+Set `config={"task_type": "RETRIEVAL_DOCUMENT"}` when embedding documents for indexing, and use `RETRIEVAL_QUERY` when embedding queries. Use the same model and task type pairing so document and query embeddings are comparable.
+
+Example: embedding multiple files (text, audio, video, images, PDF) and writing them to a document store:
+
+```python
+import os
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack import Document
+from haystack_integrations.components.embedders.google_genai import GoogleGenAIMultimodalDocumentEmbedder
+
+os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
+
+document_store = InMemoryDocumentStore()
+docs = [
+    Document(meta={"file_path": "kangaroo.mp4"}),
+    Document(meta={"file_path": "tiger.jpg"}),
+    Document(meta={"file_path": "sample.pdf"}),
+    Document(meta={"file_path": "dog.jpg"}),
+    Document(meta={"file_path": "cat.jpg"}),
+]
+
+doc_multimodal_embedder = GoogleGenAIMultimodalDocumentEmbedder(
+    model="gemini-embedding-2-preview",
+    config={"task_type": "RETRIEVAL_DOCUMENT"},
+)
+docs_with_embeddings = doc_multimodal_embedder.run(documents=docs)
+document_store.write_documents(docs_with_embeddings["documents"])
 ```
+
+## License
+`google-genai-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.