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* docs: Updated Gemini embeddings

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* docs: Updated _index.md

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* docs: gemini-embedding-2-preview

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qdrant-landing/content/documentation/embeddings/_index.md
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@@ -21,7 +21,7 @@ Additionally, [any open-source embeddings from HuggingFace](https://huggingface.
| [Aleph Alpha](/documentation/embeddings/aleph-alpha/) | Multilingual embeddings focused on European languages. | | [Aleph Alpha](/documentation/embeddings/aleph-alpha/) | Multilingual embeddings focused on European languages. |
| [Bedrock](/documentation/embeddings/bedrock/) | AWS managed service for foundation models and embeddings. | | [Bedrock](/documentation/embeddings/bedrock/) | AWS managed service for foundation models and embeddings. |
| [Cohere](/documentation/embeddings/cohere/) | Language model embeddings for NLP tasks. | | [Cohere](/documentation/embeddings/cohere/) | Language model embeddings for NLP tasks. |
| [Gemini](/documentation/embeddings/gemini/) | Google’s multimodal embeddings for text and vision. | | [Gemini](/documentation/embeddings/gemini/) | Google Gemini embeddings for semantic search, classification. |
| [Jina AI](/documentation/embeddings/jina-embeddings/) | Customizable embeddings for neural search. | | [Jina AI](/documentation/embeddings/jina-embeddings/) | Customizable embeddings for neural search. |
| [Mistral](/documentation/embeddings/mistral/) | Open-source, efficient language model embeddings. | | [Mistral](/documentation/embeddings/mistral/) | Open-source, efficient language model embeddings. |
| [MixedBread](/documentation/embeddings/mixedbread/) | Lightweight embeddings for constrained environments. | | [MixedBread](/documentation/embeddings/mixedbread/) | Lightweight embeddings for constrained environments. |
qdrant-landing/content/documentation/embeddings/gemini.md
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title: Gemini title: Gemini
--- ---
| Time: 10 min | Level: Beginner | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/qdrant/examples/blob/gemini-getting-started/gemini-getting-started/gemini-getting-started.ipynb) |
| --- | ----------- | ----------- |
# Gemini # Gemini
Qdrant is compatible with Gemini Embedding Model API and its official Python SDK that can be installed as any other package: [Google Gemini](https://ai.google.dev/gemini-api/docs/embeddings) provides embedding models that are capable of mapping text, image, video, audio, and PDFs and their interleaved combinations thereof into a single, unified vector space. Built on the Gemini architecture, it supports 100+ languages.
Gemini is a new family of Google PaLM models, released in December 2023. The new embedding models succeed the previous Gecko Embedding Model. The following example shows how to integrate Gemini embeddings with Qdrant:
In the latest models, an additional parameter, `task_type`, can be passed to the API call. This parameter serves to designate the intended purpose for the embeddings utilized.
The Embedding Model API supports various task types, outlined as follows:
1. `retrieval_query`: query in a search/retrieval setting
2. `retrieval_document`: document from the corpus being searched
3. `semantic_similarity`: semantic text similarity
4. `classification`: embeddings to be used for text classification
5. `clustering`: the generated embeddings will be used for clustering
6. `task_type_unspecified`: Unset value, which will default to one of the other values.
If you're building a semantic search application, such as RAG, you should use `task_type="retrieval_document"` for the indexed documents and `task_type="retrieval_query"` for the search queries.
The following example shows how to do this with Qdrant:
## Setup ## Setup
```bash ```python
pip install google-generativeai # Install the packages from PyPI
# pip install google-genai qdrant-client
``` ```
Let's see how to use the Embedding Model API to embed a document for retrieval. ```typescript
// Install the packages from npm
// npm install @google/genai @qdrant/js-client-rest
```
The following example shows how to embed a document with the `models/embedding-001` with the `retrieval_document` task type: 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):
## Embedding a document ## Embedding a document
```python ```python
import google.generativeai as gemini_client from google.genai import Client, types
from qdrant_client import QdrantClient from qdrant_client import QdrantClient
from qdrant_client.models import Distance, PointStruct, VectorParams from qdrant_client.models import Distance, PointStruct, VectorParams
collection_name = "example_collection" gemini_client = Client() # Needs the GEMINI_API_KEY env to be set
GEMINI_API_KEY = "YOUR GEMINI API KEY" # add your key here
client = QdrantClient(url="http://localhost:6333") client = QdrantClient(url="http://localhost:6333")
gemini_client.configure(api_key=GEMINI_API_KEY)
texts = [ texts = [
"Qdrant is a vector database that is compatible with Gemini.", "Qdrant is a vector database that is compatible with Gemini.",
"Gemini is a new family of Google PaLM models, released in December 2023.", "Gemini is a family of natively multimodal, large language models (LLMs).",
] ]
results = [ result = gemini_client.models.embed_content(
gemini_client.embed_content( model="gemini-embedding-2-preview",
model="models/embedding-001", contents=texts,
content=sentence, config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT"),
task_type="retrieval_document", )
title="Qdrant x Gemini", ```
)
for sentence in texts ```typescript
] import { GoogleGenAI } from "@google/genai";
import { QdrantClient } from "@qdrant/js-client-rest";
const geminiClient = new GoogleGenAI(); // Needs the GEMINI_API_KEY env to be set
const client = new QdrantClient({ url: "http://localhost:6333" });
const texts = [
"Qdrant is a vector database that is compatible with Gemini.",
"Gemini is a family of natively multimodal, large language models (LLMs).",
];
const result = await geminiClient.models.embedContent({
model: "gemini-embedding-2-preview",
contents: texts,
config: { taskType: "RETRIEVAL_DOCUMENT" },
});
``` ```
## Creating Qdrant Points and Indexing documents with Qdrant ## Creating Qdrant Points and Indexing documents with Qdrant
@@ -74,60 +73,130 @@ results = [
points = [ points = [
PointStruct( PointStruct(
id=idx, id=idx,
vector=response['embedding'], vector=embedding.values,
payload={"text": text}, payload={"text": text},
) )
for idx, (response, text) in enumerate(zip(results, texts)) for idx, (embedding, text) in enumerate(zip(result.embeddings, texts))
] ]
``` ```
```typescript
const points = texts.map((text, idx) => ({
id: idx,
vector: result.embeddings[idx].values,
payload: { text },
}));
```
### Create Collection ### 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.
```python ```python
client.create_collection(collection_name, vectors_config= client.create_collection(
VectorParams( collection_name="{collection_name}",
size=768, vectors_config=VectorParams(
size=3072,
distance=Distance.COSINE, distance=Distance.COSINE,
) )
) )
``` ```
```typescript
await client.createCollection("{collection_name}", {
vectors: { size: 3072, distance: "Cosine" },
});
```
### Add these into the collection ### Add these into the collection
```python ```python
client.upsert(collection_name, points) client.upsert("{collection_name}", points)
``` ```
## Searching for documents with Qdrant ```typescript
await client.upsert("{collection_name}", { points });
```
Once the documents are indexed, you can search for the most relevant documents using the same model with the `retrieval_query` task type: ### Searching for documents
Once the documents are indexed, you can search for the most relevant documents using the same model with the `RETRIEVAL_QUERY` task type:
```python ```python
client.search( query_result = gemini_client.models.embed_content(
collection_name=collection_name, model="gemini-embedding-2-preview",
query_vector=gemini_client.embed_content( contents="Is Qdrant compatible with Gemini?",
model="models/embedding-001", config=types.EmbedContentConfig(task_type="RETRIEVAL_QUERY"),
content="Is Qdrant compatible with Gemini?", )
task_type="retrieval_query",
)["embedding"], client.query_points(
collection_name="{collection_name}",
query=query_result.embeddings[0].values,
) )
``` ```
## Using Gemini Embedding Models with Binary Quantization ```typescript
const queryResult = await geminiClient.models.embedContent({
model: "gemini-embedding-2-preview",
contents: "Is Qdrant compatible with Gemini?",
config: { taskType: "RETRIEVAL_QUERY" },
});
You can use Gemini Embedding Models with [Binary Quantization](/articles/binary-quantization/) - a technique that allows you to reduce the size of the embeddings by 32 times without losing the quality of the search results too much. const searchResult = await client.query("{collection_name}", {
query: queryResult.embeddings[0].values,
});
```
In this table, you can see the results of the search with the `models/embedding-001` model with Binary Quantization in comparison with the original model: ## Embedding files
At an oversampling of 3 and a limit of 100, we've a 95% recall against the exact nearest neighbors with rescore enabled. You can embed files such as PDFs directly:
| Oversampling | | 1 | 1 | 2 | 2 | 3 | 3 | ```python
|--------------|---------|----------|----------|----------|----------|----------|----------| with open("filename.pdf", "rb") as f:
| | **Rescore** | False | True | False | True | False | True | pdf_bytes = f.read()
| **Limit** | | | | | | | |
| 10 | | 0.523333 | 0.831111 | 0.523333 | 0.915556 | 0.523333 | 0.950000 |
| 20 | | 0.510000 | 0.836667 | 0.510000 | 0.912222 | 0.510000 | 0.937778 |
| 50 | | 0.489111 | 0.841556 | 0.489111 | 0.913333 | 0.488444 | 0.947111 |
| 100 | | 0.485778 | 0.846556 | 0.485556 | 0.929000 | 0.486000 | **0.956333** |
That's it! You can now use Gemini Embedding Models with Qdrant! pdf_part = types.Part.from_bytes(
data=pdf_bytes,
mime_type='application/pdf',
)
gemini_client.models.embed_content(
model="gemini-embedding-2-preview",
contents=[pdf_part],
)
```
```typescript
import { readFileSync } from "fs";
const pdfBytes = readFileSync("filename.pdf");
const base64 = pdfBytes.toString("base64");
await geminiClient.models.embedContent({
model: "gemini-embedding-2-preview",
contents: [{
parts: [{ inlineData: { mimeType: "application/pdf", data: base64 } }],
}],
});
```
## Supported task types
You can specify the `task_type` parameter in the API call. This parameter designates the intended purpose for the embeddings, helping optimize them for the intended relationships.
The Embedding Model API supports various task types, including:
- `RETRIEVAL_DOCUMENT`: Embeddings optimized for document search. Indexing articles, books, or web pages for search.
- `RETRIEVAL_QUERY`: Embeddings optimized for general search queries. Use `RETRIEVAL_QUERY` for queries; `RETRIEVAL_DOCUMENT` for documents to be retrieved.
- `SEMANTIC_SIMILARITY`: Embeddings optimized to assess text similarity.
- `CLASSIFICATION`: Embeddings optimized to classify texts according to preset labels.
- `CLUSTERING`: Embeddings optimized to cluster texts based on their similarities.
- `CODE_RETRIEVAL_QUERY`: Embeddings optimized for retrieval of code blocks based on natural language queries.
- `QUESTION_ANSWERING`: Embeddings for questions in a question-answering system.
- `FACT_VERIFICATION`: Embeddings for statements that need to be verified.
### Further Reading
- [Gemini Embeddings Quickstart](https://github.com/google-gemini/cookbook/blob/main/quickstarts/Embeddings.ipynb)
- [Gemini Embeddings Documentation](https://ai.google.dev/gemini-api/docs/embeddings)
- [Gemini API Documentation](https://ai.google.dev/gemini-api/docs)