From 9dbd5a10f9e207228863e29e5b52db6470e2f38f Mon Sep 17 00:00:00 2001 From: Anush Date: Thu, 23 Apr 2026 21:42:58 +0530 Subject: [PATCH] docs: Use gemini-embedding-2 in gemini.md (#2299) --- .../content/documentation/embeddings/gemini.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/qdrant-landing/content/documentation/embeddings/gemini.md b/qdrant-landing/content/documentation/embeddings/gemini.md index 9c9613802..d1fe19c90 100644 --- a/qdrant-landing/content/documentation/embeddings/gemini.md +++ b/qdrant-landing/content/documentation/embeddings/gemini.md @@ -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 } }], }],