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Add Gemini Embedding Model 001 (#457)
* * feat(gemini.md): add documentation for integrating Gemini embeddings with Qdrant * * refactor(integrations): move cohere.md to embeddings folder * refactor(integrations): move openai.md to embeddings folder * refactor(integrations): move autogen.md to frameworks folder * refactor(integrations): move langchain.md to frameworks folder * blacken * * feat(embedding, frameworks): reorganise integrations into embedding and frameworks, add _index.md to both * * chore(gemini.md): remove old Gemini integration documentation * * chore(embedding/_index.md): update weight from 24 to 23 and set is_empty to false * chore(frameworks/_index.md): update weight from 24 to 23 and set is_empty to false * Split integrations into embedding and frameworks * Update heading level for embedding a document * Update Gemini embedding documentation * Update titles for embedding and frameworks sections * Try again with nesting * Add documentation for integrated frameworks and embedding options * Delete integrations documentation file * Add Delimiter; unknown weights * Change all weights to 3x * Delimiter reorg * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * * docs(embedding/gemini.md): update Gemini Embedding Model API documentation * * - Add information about the new Gemini Embedding Model and its compatibility with Qdrant * - Clarify the usage of the `task_type` parameter in the API call * - Provide a list of supported task types and * * docs(embedding): update list of embedding integrations * * refactor(fifty-one.md): Rename file from embedding/fifty-one.md to frameworks/fifty-one.md * refactor(txtai.md): Rename file from embedding/txtai.md to frameworks/txtai.md * * chore(embedding): update is_empty value to true in _index.md * chore(embedding): remove Fifty One from embedding/_index.md * embedding -> embeddings --------- Co-authored-by: Atita Arora <atarora@users.noreply.github.com>
This commit is contained in:
Nirant
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---
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#Delimiter files are used to separate the list of documentation pages into sections.
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title: "Support"
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title: "Integrations"
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type: delimiter
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weight: 30 # Change this weight to change order of sections
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sitemapExclude: True
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---
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#Delimiter files are used to separate the list of documentation pages into sections.
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title: "Support"
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type: delimiter
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weight: 40 # Change this weight to change order of sections
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sitemapExclude: True
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---
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@@ -1,6 +1,6 @@
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---
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title: Community links
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weight: 32
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weight: 42
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---
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# Community Contributions
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---
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title: Embeddings
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weight: 33
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# If the index.md file is empty, the link to the section will be hidden from the sidebar
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is_empty: true
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---
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| Embedding |
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|---|
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| [Gemini](./gemini/) |
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| [Aleph Alpha](./aleph-alpha/) |
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| [Cohere](./cohere/) |
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| [Jina](./jina-emebddngs/) |
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| [OpenAI](./openai/) |
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---
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title: Gemini
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weight: 700
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---
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# Gemini
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Qdrant is compatible with Gemini Embedding Model API and its official Python SDK that can be installed as any other package:
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Gemini is a new family of Google PaLM models, released in December 2023. The new embedding models succeed the previous Gecko Embedding Model.
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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.
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The Embedding Model API supports various task types, outlined as follows:
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1. `retrieval_query`: Specifies the given text is a query in a search/retrieval setting.
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2. `retrieval_document`: Specifies the given text is a document from the corpus being searched.
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3. `semantic_similarity`: Specifies the given text will be used for Semantic Text Similarity.
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4. `classification`: Specifies that the given text will be classified.
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5. `clustering`: Specifies that the embeddings will be used for clustering.
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6. `task_type_unspecified`: Unset value, which will default to one of the other values.
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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.
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The following example shows how to do this with Qdrant:
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## Setup
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```bash
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pip install google-generativeai
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```
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Let's see how to use the Embedding Model API to embed a document for retrieval.
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The following example shows how to embed a document with the `models/embedding-001` with the `retrieval_document` task type:
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## Embedding a document
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```python
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import pathlib
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import google.generativeai as genai
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import qdrant_client
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GEMINI_API_KEY = "YOUR GEMINI API KEY" # add your key here
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genai.configure(api_key=GEMINI_API_KEY)
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result = genai.embed_content(
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model="models/embedding-001",
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content="Qdrant is the best vector search engine to use with Gemini",
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task_type="retrieval_document",
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title="Qdrant x Gemini",
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)
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```
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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.
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## Indexing documents with Qdrant
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```python
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from qdrant_client.http.models import Batch
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qdrant_client = qdrant_client.QdrantClient()
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qdrant_client.upsert(
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collection_name="GeminiCollection",
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points=Batch(
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ids=[1],
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vectors=genai.embed_content(
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model="models/embedding-001",
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content="Qdrant is the best vector search engine to use with Gemini",
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task_type="retrieval_document",
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title="Qdrant x Gemini",
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)["embedding"],
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),
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)
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```
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## Searching for documents with Qdrant
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Once the documents are indexed, you can search for the most relevant documents using the same model with the `retrieval_query` task type:
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```python
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qdrant_client.search(
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collection_name="GeminiCollection",
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query=genai.embed_content(
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model="models/embedding-001",
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content="What is the best vector database to use with Gemini?",
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task_type="retrieval_query",
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)["embedding"],
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)
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```
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That's it! You can now use Gemini Embedding Models with Qdrant.
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+5
-6
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# OpenAI
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Qdrant can also easily work with [OpenAI embeddings](https://beta.openai.com/docs/guides/embeddings/embeddings). There is an
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official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
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Qdrant can also easily work with [OpenAI embeddings](https://beta.openai.com/docs/guides/embeddings/embeddings).
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There is an official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
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```bash
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pip install openai
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```
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Once installed, the package exposes the method allowing to retrieve the embedding for given text. OpenAI requires an API key
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that has to be provided either as an environmental variable `OPENAI_API_KEY` or set in the source code directly, as
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presented below:
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Once installed, the package exposes the method allowing to retrieve the embedding for given text. OpenAI requires an API key that has to be provided either as an environmental variable `OPENAI_API_KEY` or set in the source code directly, as presented below:
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```python
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import openai
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@@ -38,7 +37,7 @@ qdrant_client.upsert(
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points=Batch(
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ids=[1],
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vectors=[response["data"][0]["embedding"]],
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)
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),
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)
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```
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@@ -1,5 +1,5 @@
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---
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title: FAQ
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weight: 31
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weight: 41
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is_empty: true
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---
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@@ -0,0 +1,24 @@
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---
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title: Frameworks
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weight: 33
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# If the index.md file is empty, the link to the section will be hidden from the sidebar
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is_empty: true
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---
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| Frameworks |
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|---|
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| [AirByte](./airbyte/) |
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| [AutoGen](./autogen/) |
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| [Cheshire Cat](./cheshire-cat/) |
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| [DLT](./dlt/) |
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| [DocArray](./docarray/) |
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| [DSPy](./dspy/) |
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| [Fifty One](./fifty-one/) |
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| [txtai](./txtai/) |
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| [Fondant](./fondant/) |
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| [Haystack](./haystack/) |
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| [Langchain](./langchain/) |
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| [Llama Index](./llama-index/) |
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| [Minds DB](./mindsdb/) |
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| [PrivateGPT](./privategpt/) |
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| [Spark](./spark/) |
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@@ -1,19 +0,0 @@
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---
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title: Integrations
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weight: 24
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# If the index.md file is empty, the link to the section will be hidden from the sidebar
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is_empty: true
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---
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| Integration Instructions | Examples |
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|---|---|
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|AlephAlpha| Multimodal Search |
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|Cohere | Adaptive Q&A Engine|
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|DocArray |
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|FiftyOne|
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|Haystack |
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|LangChain |
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|LlamaIndex | Adaptive Q&A Engine|
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|OpenAI |
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|txtai |
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@@ -1,6 +1,6 @@
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---
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title: Release notes
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weight: 32
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weight: 42
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type: external-link
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external_url: https://github.com/qdrant/qdrant/releases
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sitemapExclude: True
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