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Update Gemini embedding code and Qdrant indexing (#669)

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* Update Gemini embedding code and Qdrant indexing

* Add Create Collection and Add Points to Gemini embeddings

* Update text

* Update task type descriptions in Gemini embeddings documentation

* Update Gemini query content

* Update classification task description in Gemini embeddings documentation

* Add Colab badge
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Nirant
2024-03-05 20:44:34 +01:00
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parent f42220a3fc
commit 3970fde695
1 changed files with 57 additions and 36 deletions
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qdrant-landing/content/documentation/embeddings/gemini.md
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@@ -3,6 +3,9 @@ title: Gemini
weight: 700
---
| 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:
@@ -13,11 +16,11 @@ In the latest models, an additional parameter, `task_type`, can be passed to the
The Embedding Model API supports various task types, outlined as follows:
1. `retrieval_query`: Specifies the given text is a query in a search/retrieval setting.
2. `retrieval_document`: Specifies the given text is a document from the corpus being searched.
3. `semantic_similarity`: Specifies the given text will be used for Semantic Text Similarity.
4. `classification`: Specifies that the given text will be classified.
5. `clustering`: Specifies that the embeddings will be used for clustering.
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.
@@ -38,42 +41,60 @@ The following example shows how to embed a document with the `models/embedding-0
## Embedding a document
```python
import pathlib
import google.generativeai as genai
import qdrant_client
import google.generativeai as gemini_client
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, PointStruct, VectorParams
collection_name = "example_collection"
GEMINI_API_KEY = "YOUR GEMINI API KEY" # add your key here
genai.configure(api_key=GEMINI_API_KEY)
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.",
]
result = genai.embed_content(
model="models/embedding-001",
content="Qdrant is the best vector search engine to use with Gemini",
task_type="retrieval_document",
title="Qdrant x Gemini",
results = [
gemini_client.embed_content(
model="models/embedding-001",
content=sentence,
task_type="retrieval_document",
title="Qdrant x Gemini",
)
for sentence in texts
]
```
## Creating Qdrant Points and Indexing documents with Qdrant
### Creating Qdrant Points
```python
points = [
PointStruct(
id=idx,
vector=response['embedding'],
payload={"text": text},
)
for idx, (response, text) in enumerate(zip(results, texts))
]
```
### Create Collection
```python
search_client.create_collection(collection_name, vectors_config=
VectorParams(
size=768,
distance=Distance.COSINE,
)
)
```
The returned result is a dictionary with a key: `embedding`. The value of this key is a list of floats representing the embedding of the document.
## Indexing documents with Qdrant
### Add these into the collection
```python
from qdrant_client.http.models import Batch
qdrant_client = qdrant_client.QdrantClient()
qdrant_client.upsert(
collection_name="GeminiCollection",
points=Batch(
ids=[1],
vectors=genai.embed_content(
model="models/embedding-001",
content="Qdrant is the best vector search engine to use with Gemini",
task_type="retrieval_document",
title="Qdrant x Gemini",
)["embedding"],
),
)
search_client.upsert(collection_name, points)
```
## Searching for documents with Qdrant
@@ -81,11 +102,11 @@ qdrant_client.upsert(
Once the documents are indexed, you can search for the most relevant documents using the same model with the `retrieval_query` task type:
```python
qdrant_client.search(
collection_name="GeminiCollection",
query=genai.embed_content(
search_client.search(
collection_name=collection_name,
query_vector=gemini_client.embed_content(
model="models/embedding-001",
content="What is the best vector database to use with Gemini?",
content="Is Qdrant compatible with Gemini?",
task_type="retrieval_query",
)["embedding"],
)