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* docs: Updated Gemini embeddings

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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. |
| [Bedrock](/documentation/embeddings/bedrock/) | AWS managed service for foundation models and embeddings. |
| [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. |
| [Mistral](/documentation/embeddings/mistral/) | Open-source, efficient language model embeddings. |
| [MixedBread](/documentation/embeddings/mixedbread/) | Lightweight embeddings for constrained environments. |
qdrant-landing/content/documentation/embeddings/gemini.md
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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
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.
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:
The following example shows how to integrate Gemini embeddings with Qdrant:
## Setup
```bash
pip install google-generativeai
```python
# 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
```python
import google.generativeai as gemini_client
from google.genai import Client, types
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, PointStruct, VectorParams
collection_name = "example_collection"
GEMINI_API_KEY = "YOUR GEMINI API KEY" # add your key here
gemini_client = Client() # Needs the GEMINI_API_KEY env to be set
client = QdrantClient(url="http://localhost:6333")
gemini_client.configure(api_key=GEMINI_API_KEY)
texts = [
"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 = [
gemini_client.embed_content(
model="models/embedding-001",
content=sentence,
task_type="retrieval_document",
title="Qdrant x Gemini",
)
for sentence in texts
]
result = gemini_client.models.embed_content(
model="gemini-embedding-2-preview",
contents=texts,
config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT"),
)
```
```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
@@ -74,60 +73,130 @@ results = [
points = [
PointStruct(
id=idx,
vector=response['embedding'],
vector=embedding.values,
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
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
client.create_collection(collection_name, vectors_config=
VectorParams(
size=768,
client.create_collection(
collection_name="{collection_name}",
vectors_config=VectorParams(
size=3072,
distance=Distance.COSINE,
)
)
```
```typescript
await client.createCollection("{collection_name}", {
vectors: { size: 3072, distance: "Cosine" },
});
```
### Add these into the collection
```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
client.search(
collection_name=collection_name,
query_vector=gemini_client.embed_content(
model="models/embedding-001",
content="Is Qdrant compatible with Gemini?",
task_type="retrieval_query",
)["embedding"],
query_result = gemini_client.models.embed_content(
model="gemini-embedding-2-preview",
contents="Is Qdrant compatible with Gemini?",
config=types.EmbedContentConfig(task_type="RETRIEVAL_QUERY"),
)
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 |
|--------------|---------|----------|----------|----------|----------|----------|----------|
| | **Rescore** | False | True | False | True | False | True |
| **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** |
```python
with open("filename.pdf", "rb") as f:
pdf_bytes = f.read()
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)