add embeddings

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

@@ -1,3 +1,4 @@
+
 ---
 title: Embeddings
 weight: 15
@@ -14,18 +15,30 @@ Additionally, [any open-source embeddings from HuggingFace](https://huggingface.
 
 ## Integration code samples:
 
-| Embeddings Providers          |
+| Embeddings Providers          | Description |
-| ----------------------------- |
+| ----------------------------- | ----------- |
-| [Aleph Alpha](./aleph-alpha/) |
+| [Aleph Alpha](./aleph-alpha/) | Multilingual embeddings focused on European languages. |
-| [Bedrock](./bedrock/)         |
+| [Bedrock](./bedrock/)         | AWS managed service for foundation models and embeddings. |
-| [Cohere](./cohere/)           |
+| [BGE](./bge/)                 | Chinese embeddings for various NLP tasks. |
-| [Gemini](./gemini/)           |
+| [Clarifai](./clarifai/)       | Embeddings for image and video recognition. |
-| [Jina](./jina-emebddngs/)     |
+| [Clip](./clip/)               | Aligns images and text, created by OpenAI. |
-| [Mistral](./mistral/)         |
+| [Cohere](./cohere/)           | Language model embeddings for NLP tasks. |
-| [Nomic](./nomic/)             |
+| [Databricks](./databricks/)   | Scalable embeddings integrated with Apache Spark. |
-| [Nvidia](./nvidia/)           |
+| [Gemini](./gemini/)           | Google’s multimodal embeddings for text and vision. |
-| [OpenAI](./openai/)           |
+| [GPT4All](./gpt4all/)         | Open-source, local embeddings for privacy-focused use. |
-| [Prem AI](./premai/)          |
+| [Instruct](./instruct/)       | Embeddings tuned for following instructions. |
-| [Snowflake](./snowflake/)     |
+| [Jina](./jina-emebddngs/)     | Customizable embeddings for neural search. |
-| [Upstage](./upstage/)        |
+| [John Snow Labs](./johnsnow/) | Medical and clinical embeddings. |
-| [Voyage AI](./voyage/)        |
+| [Mistral](./mistral/)         | Open-source, efficient language model embeddings. |
+| [MixedBread](./mixedbread/)   | Lightweight embeddings for constrained environments. |
+| [Nomic](./nomic/)             | Embeddings for data visualization. |
+| [Nvidia](./nvidia_nemo/)      | GPU-optimized embeddings from Nvidia. |
+| [OCI](./oci/)                 | Oracle Cloud’s AI service with embeddings. |
+| [Ollama](./ollama/)           | Embeddings for conversational AI. |
+| [OpenAI](./openai/)           | Industry-leading embeddings for NLP. |
+| [Prem AI](./premai/)          | Precise language embeddings. |
+| [Snowflake](./snowflake/)     | Scalable embeddings for big data. |
+| [Together AI](./together_ai/) | Community-driven, open-source embeddings. |
+| [Upstage](./upstage/)         | Embeddings for speech and language tasks. |
+| [Voyage AI](./voyage/)        | Navigation and spatial understanding embeddings. |
+| [Watsonx](./watsonx/)         | IBM's enterprise-grade embeddings. |

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

@@ -0,0 +1,52 @@
+
+---
+title: Clarifai
+weight: 1200
+aliases:
+  - /documentation/examples/clarifai-search/
+  - /documentation/tutorials/clarifai-search/
+  - /documentation/integrations/clarifai/ 
+---
+
+# Using Clarifai Embeddings with Qdrant 
+
+Clarifai is a leading provider of visual embeddings, which are particularly strong in image and video analysis. Clarifai offers an API that allows you to create embeddings for various media types, which can be integrated into Qdrant for efficient vector search and retrieval. 
+
+You can install the Clarifai Python client with pip:
+
+```bash
+pip install clarifai-client
+```
+
+## Integration Example
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from clarifai.rest import ClarifaiApp
+
+# Initialize Clarifai client
+clarifai_app = ClarifaiApp(api_key="<< your_api_key >>")
+
+# Choose the model for embeddings
+model = clarifai_app.public_models.general_embedding_model
+
+# Upload and get embeddings for an image
+image_path = "./path/to/the/image.jpg"
+response = model.predict_by_filename(image_path)
+
+# Extract the embedding from the response
+embedding = response['outputs'][0]['data']['embeddings'][0]['vector']
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient()
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="MyCollection",
+    points=Batch(
+        ids=[1],
+        vectors=[embedding],
+    )
+)
+```

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

@@ -0,0 +1,55 @@
+
+---
+title: Clip
+weight: 1300
+aliases:
+  - /documentation/examples/clip-search/
+  - /documentation/tutorials/clip-search/
+  - /documentation/integrations/clip/ 
+---
+
+# Using Clip with Qdrant 
+
+CLIP (Contrastive Language-Image Pre-Training) provides advanced AI capabilities including natural language processing and computer vision. CLIP is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install clip-client
+```
+## Integration Example
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+
+# Load the CLIP model and processor
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# Load and process the image
+image = Image.open("path/to/image.jpg")
+inputs = processor(images=image, return_tensors="pt")
+
+# Generate embeddings
+with torch.no_grad():
+    embeddings = model.get_image_features(**inputs).numpy().tolist()
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="ImageEmbeddings",
+    points=Batch(
+        ids=[1],
+        vectors=embeddings,
+    )
+)
+
+```
+

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

@@ -1,6 +1,6 @@
 ---
 title: Cohere
-weight: 700
+weight: 1400
 aliases: [ ../integrations/cohere/ ]
 ---
 

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

@@ -0,0 +1,42 @@
+
+---
+title: Databricks Embeddings
+weight: 1500
+aliases:
+  - /documentation/examples/databricks-embeddings-search/
+  - /documentation/tutorials/databricks-embeddings-search/
+  - /documentation/integrations/databricks-embeddings/
+---
+
+# Using Databricks Embeddings with Qdrant
+
+Databricks offers an advanced platform for generating embeddings, especially within large-scale data environments. You can use the following Python code to integrate Databricks-generated embeddings with Qdrant.
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from databricks import sql
+
+# Connect to Databricks SQL endpoint
+connection = sql.connect(server_hostname='your_hostname',
+                         http_path='your_http_path',
+                         access_token='your_access_token')
+
+# Execute a query to get embeddings
+query = "SELECT embedding FROM your_table WHERE id = 1"
+cursor = connection.cursor()
+cursor.execute(query)
+embedding = cursor.fetchone()[0]
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="DatabricksEmbeddings",
+    points=Batch(
+        ids=[1],  # Unique ID for the data point
+        vectors=[embedding],  # Embedding fetched from Databricks
+    )
+)
+```

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

@@ -1,6 +1,6 @@
 ---
 title: Gemini
-weight: 700
+weight: 1600
 ---
 
 | 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)   |

qdrant-landing/content/documentation/embeddings/gpt4all.md

@@ -0,0 +1,52 @@
+
+---
+title: GPT4All
+weight: 1700
+aliases:
+  - /documentation/examples/gpt4all-search/
+  - /documentation/tutorials/gpt4all-search/
+  - /documentation/integrations/gpt4all/ 
+---
+
+# Using GPT4All with Qdrant 
+
+GPT4All offers a range of large language models that can be fine-tuned for various applications. GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.
+
+No API calls or GPUs required - you can just download the application and get started. Use GPT4All in Python to program with LLMs implemented with the llama.cpp backend and Nomic's C backend.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install gpt4all
+```
+
+Here is how you might connect to GPT4ALL using Qdrant:
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from gpt4all import GPT4All
+
+# Initialize GPT4All model
+model = GPT4All("gpt4all-lora-quantized")
+
+# Generate embeddings for a text
+text = "GPT4All enables open-source AI applications."
+embeddings = model.embed(text)
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="OpenSourceAI",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```
+

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

@@ -0,0 +1,47 @@
+
+---
+title: Instruct
+weight: 1800
+aliases:
+  - /documentation/examples/instruct-search/
+  - /documentation/tutorials/instruct-search/
+  - /documentation/integrations/instruct/ 
+---
+
+# Using Instruct with Qdrant 
+
+Instruct is a specialized provider offering detailed embeddings for instructional content, which can be effectively used with Qdrant. With Instruct every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. 
+
+## Installation
+
+```bash
+pip install instruct
+```
+
+Below is an example of how to obtain embeddings using Instruct's API and store them in a Qdrant collection:
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from instruct import Instruct
+
+# Initialize Instruct model
+model = Instruct("instruct-base")
+
+# Generate embeddings for instructional content
+text = "Instruct provides detailed embeddings for learning content."
+embeddings = model.embed(text)
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="LearningContent",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```

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

@@ -1,6 +1,6 @@
 ---
 title: Jina Embeddings
-weight: 800
+weight: 1900
 aliases: 
   - /documentation/embeddings/jina-emebddngs/
   - ../integrations/jina-embeddings/

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

@@ -0,0 +1,54 @@
+
+---
+title: John Snow Labs
+weight: 2000
+aliases:
+  - /documentation/examples/john-snow-labs-search/
+  - /documentation/tutorials/john-snow-labs-search/
+  - /documentation/integrations/john-snow-labs/ 
+---
+
+# Using John Snow Labs with Qdrant 
+
+John Snow Labs offers a variety of models, particularly in the healthcare domain. They have pre-trained models that can generate embeddings for medical text data.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install johnsnowlabs
+```
+
+
+Here is an example of how you mmight obtain embeddings using John Snow Labs's API and store them in a Qdrant collection:
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from johnsnowlabs import nlp
+
+# Load the pre-trained model, for example, a named entity recognition (NER) model
+model = nlp.load_model("ner_jsl")
+
+# Sample text to generate embeddings
+text = "John Snow Labs provides state-of-the-art healthcare NLP solutions."
+
+# Generate embeddings for the text
+document = nlp.DocumentAssembler().setInput(text)
+embeddings = model.transform(document).collectEmbeddings()
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embeddings into Qdrant
+qdrant_client.upsert(
+    collection_name="HealthcareNLP",
+    points=Batch(
+        ids=[1],  # This would be your unique ID for the data point
+        vectors=[embeddings],
+    )
+)
+
+```
+

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

@@ -1,6 +1,6 @@
 ---
 title: Mistral 
-weight: 700
+weight: 2100
 ---
 
 | Time: 10 min | Level: Beginner | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/qdrant/examples/blob/mistral-getting-started/mistral-embed-getting-started/mistral_qdrant_getting_started.ipynb)   |

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

@@ -0,0 +1,51 @@
+
+---
+title: MixedBread
+weight: 2200
+aliases:
+  - /documentation/examples/mixedbread-search/
+  - /documentation/tutorials/mixedbread-search/
+  - /documentation/integrations/mixedbread/ 
+---
+
+# Using MixedBread with Qdrant 
+
+MixedBread is a unique provider offering embeddings across multiple domains. Their models are versatile for various search tasks when integrated with Qdrant. MixedBread is creating state-of-the-art models and tools that make search smarter, faster, and more relevant. Whether you're building a next-gen search engine or RAG (Retrieval Augmented Generation) systems, or whether you're enhancing your existing search solution, they've got the ingredients to make it happen.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install mixedbread
+```
+
+## Integration Example
+
+Below is an example of how to obtain embeddings using MixedBread's API and store them in a Qdrant collection:
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from mixedbread import MixedBreadModel
+
+# Initialize MixedBread model
+model = MixedBreadModel("mixedbread-variant")
+
+# Generate embeddings
+text = "MixedBread provides versatile embeddings for various domains."
+embeddings = model.embed(text)
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="VersatileEmbeddings",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```

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

@@ -1,6 +1,6 @@
 ---
 title: "Nomic"
-weight: 1100
+weight: 2300
 ---
 
 # Nomic

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

@@ -1,6 +1,6 @@
 ---
 title: Nvidia
-weight: 1200
+weight: 2400
 ---
 
 # Nvidia

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

@@ -0,0 +1,54 @@
+
+---
+title: OCI (Oracle Cloud Infrastructure)
+weight: 2500
+aliases:
+  - /documentation/examples/oci-(oracle-cloud-infrastructure)-search/
+  - /documentation/tutorials/oci-(oracle-cloud-infrastructure)-search/
+  - /documentation/integrations/oci-(oracle-cloud-infrastructure)/ 
+---
+
+# Using OCI (Oracle Cloud Infrastructure) with Qdrant 
+
+OCI provides robust cloud-based embeddings for various media types. The Generative AI Embedding Models convert textual input - ranging from phrases and sentences to entire paragraphs - into a structured format known as embeddings. Each piece of text input is transformed into a numerical array consisting of 1024 distinct numbers.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install oci
+```
+
+## Code Example
+
+Below is an example of how to obtain embeddings using OCI (Oracle Cloud Infrastructure)'s API and store them in a Qdrant collection:
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+import oci
+
+# Initialize OCI client
+config = oci.config.from_file()
+ai_client = oci.ai_language.AIServiceLanguageClient(config)
+
+# Generate embeddings using OCI's AI service
+text = "OCI provides cloud-based AI services."
+response = ai_client.batch_detect_language_entities(text)
+embeddings = response.data[0].entities[0].embedding
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="CloudAI",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```
+

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

@@ -0,0 +1,52 @@
+
+---
+title: Ollama
+weight: 2600
+aliases:
+  - /documentation/examples/ollama-search/
+  - /documentation/tutorials/ollama-search/
+  - /documentation/integrations/ollama/ 
+---
+
+# Using Ollama with Qdrant 
+
+Ollama provides specialized embeddings for niche applications. Ollama supports a variety of embedding models, making it possible to build retrieval augmented generation (RAG) applications that combine text prompts with existing documents or other data in specialized areas.
+
+
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install ollama
+```
+## Integration Example
+
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from ollama import Ollama
+
+# Initialize Ollama model
+model = Ollama("ollama-unique")
+
+# Generate embeddings for niche applications
+text = "Ollama excels in niche applications with specific embeddings."
+embeddings = model.embed(text)
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="NicheApplications",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```
+

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

@@ -1,6 +1,6 @@
 ---
 title: OpenAI 
-weight: 800
+weight: 2700
 aliases: [ ../integrations/openai/ ]
 ---
 

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

@@ -0,0 +1,46 @@
+
+---
+title: OpenCLIP
+weight: 2750
+aliases:
+  - /documentation/examples/openclip-search/
+  - /documentation/tutorials/openclip-search/
+  - /documentation/integrations/openclip/
+---
+
+# Using OpenCLIP with Qdrant
+
+OpenCLIP is an open-source implementation of the CLIP model, allowing for open source generation of multimodal embeddings that link text and images. 
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+import open_clip
+
+# Load the OpenCLIP model and tokenizer
+model, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='openai')
+tokenizer = open_clip.get_tokenizer('ViT-B-32')
+
+# Generate embeddings for a text
+text = "A photo of a cat"
+text_inputs = tokenizer([text])
+
+with torch.no_grad():
+    text_features = model.encode_text(text_inputs)
+
+# Convert tensor to a list
+embeddings = text_features[0].cpu().numpy().tolist()
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="OpenCLIPEmbeddings",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+```
+

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

@@ -1,6 +1,6 @@
 ---
 title: Prem AI
-weight: 1600
+weight: 2800
 ---
 
 # Prem AI

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

@@ -1,6 +1,6 @@
 ---
 title: Snowflake Models
-weight: 1500
+weight: 2900
 ---
 
 # Snowflake

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

@@ -0,0 +1,48 @@
+
+---
+title: Together AI
+weight: 3000
+aliases:
+  - /documentation/examples/together-ai-search/
+  - /documentation/tutorials/together-ai-search/
+  - /documentation/integrations/together-ai/ 
+---
+
+# Using Together AI with Qdrant 
+
+Together AI focuses on collaborative AI embeddings that enhance multi-user search scenarios when integrated with Qdrant.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install togetherai
+```
+## Integration Example
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from togetherai import TogetherAI
+
+# Initialize Together AI model
+model = TogetherAI("togetherai-collab")
+
+# Generate embeddings for collaborative content
+text = "Together AI enhances collaborative content search."
+embeddings = model.embed(text)
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="CollaborativeContent",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```

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

@@ -1,6 +1,6 @@
 ---
 title: Upstage
-weight: 1700
+weight: 3100
 ---
 
 # Upstage

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

@@ -1,6 +1,6 @@
 ---
 title: Voyage AI
-weight: 1300
+weight: 3200
 ---
 
 # Voyage AI

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

@@ -0,0 +1,50 @@
+
+---
+title: Watsonx
+weight: 3000
+aliases:
+  - /documentation/examples/watsonx-search/
+  - /documentation/tutorials/watsonx-search/
+  - /documentation/integrations/watsonx/ 
+---
+
+# Using Watsonx with Qdrant 
+
+Watsonx is IBM's platform for AI embeddings, focusing on enterprise-level text and data analytics. These embeddings are suitable for high-precision vector searches in Qdrant.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install watsonx
+```
+
+## Code Example
+
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from watsonx import Watsonx
+
+# Initialize Watsonx AI model
+model = Watsonx("watsonx-model")
+
+# Generate embeddings for enterprise data
+text = "Watsonx provides enterprise-level NLP solutions."
+embeddings = model.embed(text)
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="EnterpriseData",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```