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>

qdrant-landing/content/documentation/2-dl.md

@@ -1,6 +1,6 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
-title: "Support"
+title: "Integrations"
 type: delimiter
 weight: 30 # Change this weight to change order of sections
 sitemapExclude: True

qdrant-landing/content/documentation/3-dl.md

@@ -0,0 +1,7 @@
+---
+#Delimiter files are used to separate the list of documentation pages into sections.
+title: "Support"
+type: delimiter
+weight: 40 # Change this weight to change order of sections
+sitemapExclude: True
+---

qdrant-landing/content/documentation/community-links.md

@@ -1,6 +1,6 @@
 ---
 title: Community links
-weight: 32
+weight: 42
 ---
 
 # Community Contributions

qdrant-landing/content/documentation/embeddings/_index.md

@@ -0,0 +1,14 @@
+---
+title: Embeddings
+weight: 33
+# If the index.md file is empty, the link to the section will be hidden from the sidebar
+is_empty: true
+---
+
+| Embedding |
+|---|
+| [Gemini](./gemini/) | 
+| [Aleph Alpha](./aleph-alpha/) | 
+| [Cohere](./cohere/) | 
+| [Jina](./jina-emebddngs/) |
+| [OpenAI](./openai/) |

qdrant-landing/content/documentation/embeddings/gemini.md

@@ -0,0 +1,94 @@
+---
+title: Gemini
+weight: 700
+---
+
+# Gemini
+
+Qdrant is compatible with Gemini Embedding Model API and its official Python SDK that can be installed as any other package:
+
+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`: 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.
+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
+
+```bash
+pip install google-generativeai
+```
+
+Let's see how to use the Embedding Model API to embed a document for retrieval. 
+
+The following example shows how to embed a document with the `models/embedding-001` with the `retrieval_document` task type:
+
+## Embedding a document
+
+```python
+import pathlib
+import google.generativeai as genai
+import qdrant_client
+
+GEMINI_API_KEY = "YOUR GEMINI API KEY"  # add your key here
+
+genai.configure(api_key=GEMINI_API_KEY)
+
+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",
+)
+```
+
+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
+
+```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"],
+    ),
+)
+```
+
+## Searching for documents with Qdrant
+
+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(
+        model="models/embedding-001",
+        content="What is the best vector database to use with Gemini?",
+        task_type="retrieval_query",
+    )["embedding"],
+)
+```
+
+That's it! You can now use Gemini Embedding Models with Qdrant.

qdrant-landing/content/documentation/embeddings/openai.md

@@ -5,16 +5,15 @@ weight: 800
 
 # OpenAI
 
-Qdrant can also easily work with [OpenAI embeddings](https://beta.openai.com/docs/guides/embeddings/embeddings). There is an 
+Qdrant can also easily work with [OpenAI embeddings](https://beta.openai.com/docs/guides/embeddings/embeddings). 
-official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
+
+There is an official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
 
 ```bash
 pip install openai
 ```
 
-Once installed, the package exposes the method allowing to retrieve the embedding for given text. OpenAI requires an API key
+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:
-that has to be provided either as an environmental variable `OPENAI_API_KEY` or set in the source code directly, as 
-presented below:
 
 ```python
 import openai
@@ -38,7 +37,7 @@ qdrant_client.upsert(
     points=Batch(
         ids=[1],
         vectors=[response["data"][0]["embedding"]],
-    )
+    ),
 )
 ```
 

qdrant-landing/content/documentation/faq/_index.md

@@ -1,5 +1,5 @@
 ---
 title: FAQ
-weight: 31
+weight: 41
 is_empty: true
 ---

qdrant-landing/content/documentation/frameworks/_index.md

@@ -0,0 +1,24 @@
+---
+title: Frameworks 
+weight: 33
+# If the index.md file is empty, the link to the section will be hidden from the sidebar
+is_empty: true
+---
+
+| Frameworks |
+|---|
+| [AirByte](./airbyte/) |
+| [AutoGen](./autogen/) |
+| [Cheshire Cat](./cheshire-cat/) |
+| [DLT](./dlt/) |
+| [DocArray](./docarray/) |
+| [DSPy](./dspy/) |
+| [Fifty One](./fifty-one/) |
+| [txtai](./txtai/) |
+| [Fondant](./fondant/) |
+| [Haystack](./haystack/) |
+| [Langchain](./langchain/) |
+| [Llama Index](./llama-index/) |
+| [Minds DB](./mindsdb/) |
+| [PrivateGPT](./privategpt/) |
+| [Spark](./spark/) |

qdrant-landing/content/documentation/integrations/_index.md

@@ -1,19 +0,0 @@
----
-title: Integrations
-weight: 24
-# If the index.md file is empty, the link to the section will be hidden from the sidebar
-is_empty: true
----
-
-| Integration Instructions | Examples  |  
-|---|---| 
-|AlephAlpha| Multimodal Search | 
-|Cohere | Adaptive Q&A Engine|  
-|DocArray |   
-|FiftyOne|  
-|Haystack |  
-|LangChain |  
-|LlamaIndex | Adaptive Q&A Engine|  
-|OpenAI |   
-|txtai  |   
-

qdrant-landing/content/documentation/release-notes.md

@@ -1,6 +1,6 @@
 ---
 title: Release notes
-weight: 32
+weight: 42
 type: external-link
 external_url: https://github.com/qdrant/qdrant/releases
 sitemapExclude: True