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docs: Use gemini-embedding-2 in gemini.md (#2299)

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2026-04-23 21:42:58 +05:30
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qdrant-landing/content/documentation/embeddings/gemini.md
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@@ -22,7 +22,7 @@ The following example shows how to integrate Gemini embeddings with Qdrant:
Let's see how to use the Embedding Model API to embed documents for retrieval.
The following example shows how to embed multiple documents with the `gemini-embedding-2-preview` model using the `RETRIEVAL_DOCUMENT` [task type](#supported-task-types):
The following example shows how to embed multiple documents with the `gemini-embedding-2` model using the `RETRIEVAL_DOCUMENT` [task type](#supported-task-types):
## Embedding a document
@@ -40,7 +40,7 @@ texts = [
]
result = gemini_client.models.embed_content(
model="gemini-embedding-2-preview",
model="gemini-embedding-2",
contents=texts,
config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT"),
)
@@ -59,7 +59,7 @@ const texts = [
];
const result = await geminiClient.models.embedContent({
model: "gemini-embedding-2-preview",
model: "gemini-embedding-2",
contents: texts,
config: { taskType: "RETRIEVAL_DOCUMENT" },
});
@@ -90,7 +90,7 @@ const points = texts.map((text, idx) => ({
### Create Collection
By default, `gemini-embedding-2-preview` outputs a 3072-dimensional embedding vector. You can reduce it to a smaller size (e.g., 768 or 1536) using the `output_dimensionality` configuration to save storage space. In this example, we keep the default 3072 dimensions.
By default, `gemini-embedding-2` outputs a 3072-dimensional embedding vector. You can reduce it to a smaller size (e.g., 768 or 1536) using the `output_dimensionality` configuration to save storage space. In this example, we keep the default 3072 dimensions.
```python
client.create_collection(
@@ -124,7 +124,7 @@ Once the documents are indexed, you can search for the most relevant documents u
```python
query_result = gemini_client.models.embed_content(
model="gemini-embedding-2-preview",
model="gemini-embedding-2",
contents="Is Qdrant compatible with Gemini?",
config=types.EmbedContentConfig(task_type="RETRIEVAL_QUERY"),
)
@@ -137,7 +137,7 @@ client.query_points(
```typescript
const queryResult = await geminiClient.models.embedContent({
model: "gemini-embedding-2-preview",
model: "gemini-embedding-2",
contents: "Is Qdrant compatible with Gemini?",
config: { taskType: "RETRIEVAL_QUERY" },
});
@@ -161,7 +161,7 @@ pdf_part = types.Part.from_bytes(
)
gemini_client.models.embed_content(
model="gemini-embedding-2-preview",
model="gemini-embedding-2",
contents=[pdf_part],
)
```
@@ -173,7 +173,7 @@ const pdfBytes = readFileSync("filename.pdf");
const base64 = pdfBytes.toString("base64");
await geminiClient.models.embedContent({
model: "gemini-embedding-2-preview",
model: "gemini-embedding-2",
contents: [{
parts: [{ inlineData: { mimeType: "application/pdf", data: base64 } }],
}],