mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-29 16:08:32 +02:00
Merge branch 'master' into neo4j-int
This commit is contained in:
David Myriel
@@ -44,9 +44,9 @@ FastEmbed already had support for embeddings, but re-ranking with cross-encoders
|
||||
One thing I had to ensure was that the new class hierarchy was user-friendly. Users should be able to work with cross-encoders without needing to know the complexities of the underlying models. For instance, they should be able to just write:
|
||||
|
||||
```python
|
||||
from fastembed import TextCrossEncoder
|
||||
from fastembed.rerank.cross_encoder import TextCrossEncoder
|
||||
|
||||
encoder = TextCrossEncoder(model_name='Xenova/ms-marco-MiniLM-L-6-v2')
|
||||
encoder = TextCrossEncoder(model_name="Xenova/ms-marco-MiniLM-L-6-v2")
|
||||
scores = encoder.rerank(query, documents)
|
||||
```
|
||||
|
||||
|
||||
@@ -41,7 +41,7 @@ Imagine you have a PDF packed with complex layouts, tables, and images, and you
|
||||
|
||||
### Why is ColPali Better?
|
||||
|
||||
This entire process can be time-consuming, especially for complex documents, with each page often taking over seven seconds to process. For text-heavy documents, this approach might suffice, but real-world data is often rich and complex, making traditional extraction methods less effective.
|
||||
This entire process can require too many steps, especially for complex documents, with each page often taking over seven seconds to process. For text-heavy documents, this approach might suffice, but real-world data is often rich and complex, making traditional extraction methods less effective.
|
||||
|
||||
This is where ColPali comes into play. **ColPali, or Contextualized Late Interaction Over PaliGemma**, uses a vision language model (VLM) to simplify and enhance the document retrieval process.
|
||||
|
||||
@@ -50,7 +50,7 @@ Instead of relying on text-only methods, ColPali generates contextualized **mult
|
||||
## How ColPali Works Under the Hood
|
||||
|
||||
|
||||
Rather than relying on OCR, ColPali **processes the entire document as an image** using a Vision Encoder. It creates multi-vector embeddings that capture both the textual content and the visual structure of the document which are then passed through a Language Model (LLM), which integrates the information into a representation that retains both text and visual features.
|
||||
Rather than relying on OCR, ColPali **processes the entire document as an image** using a Vision Encoder. It creates multi-vector embeddings that capture both the textual content and the visual structure of the document which are then passed through a Large Language Model (LLM), which integrates the information into a representation that retains both text and visual features.
|
||||
|
||||
Here’s a step-by-step look at the ColPali architecture and how it enhances document retrieval:
|
||||
|
||||
@@ -91,7 +91,7 @@ Our goal is to go through a dataset of multilingual newspaper articles like the
|
||||
|
||||
### Results
|
||||
|
||||
> In our testing, the search time was reduced to 0.81 seconds.
|
||||
> Success! Tests shows that search time is 2x faster than with Scalar Quantization.
|
||||
|
||||
This is significantly faster than with Scalar Quantization, and we still retrieved the top document matches with remarkable accuracy.
|
||||
|
||||
|
||||
@@ -2,7 +2,74 @@
|
||||
title: Home
|
||||
weight: 2
|
||||
hideTOC: true
|
||||
breadcrumb: false
|
||||
content:
|
||||
- partial: "documentation/banners/banner-a"
|
||||
title: Qdrant Documentation
|
||||
description: Qdrant is an AI-native vector database and a semantic search engine. You can use it to extract meaningful information from unstructured data.
|
||||
linkDescription: <a href="https://github.com/qdrant/qdrant_demo/" target="_blank">Clone this repo now</a> and build a search engine in five minutes.
|
||||
cloudButton:
|
||||
text: Cloud Quickstart
|
||||
url: /documentation/quickstart-cloud/
|
||||
localButton:
|
||||
text: Local Quickstart
|
||||
url: /documentation/quickstart/
|
||||
contained: true
|
||||
- partial: documentation/banners/banner-d
|
||||
developingTitle: Ready to start developing?
|
||||
developingDescription: Qdrant is open-source and can be self-hosted. However, the quickest way to get started is with our <a href="https://qdrant.to/cloud" target="_blank">free tier</a> on Qdrant Cloud. It scales easily and provides a UI where you can interact with data.
|
||||
developingBlock:
|
||||
title: Create your first Qdrant Cloud cluster today
|
||||
button:
|
||||
text: Get Started
|
||||
url: https://qdrant.to/cloud
|
||||
image:
|
||||
src: /img/rocket.svg
|
||||
alt: Rocket
|
||||
- partial: documentation/sections/cards-section
|
||||
title: Optimize Qdrant's performance
|
||||
description: Boost search speed, reduce latency, and improve the accuracy and memory usage of your Qdrant deployment.
|
||||
button:
|
||||
text: Learn More
|
||||
url: /documentation/guides/optimize/
|
||||
cardsPartial: documentation/cards/docs-cards
|
||||
cards:
|
||||
- id: 1
|
||||
tag: Documents
|
||||
icon:
|
||||
src: /icons/outline/documentation-blue.svg
|
||||
alt: Documents
|
||||
title: Distributed Deployment
|
||||
description: Scale Qdrant beyond a single node and optimize for high availability, fault tolerance, and billion-scale performance.
|
||||
link:
|
||||
url: /documentation/guides/distributed_deployment/
|
||||
text: Read More
|
||||
- id: 2
|
||||
tag: Documents
|
||||
icon:
|
||||
src: /icons/outline/documentation-blue.svg
|
||||
alt: Documents
|
||||
title: Multitenancy
|
||||
description: Build vector search apps that serve millions of users. Learn about data isolation, security, and performance tuning.
|
||||
link:
|
||||
url: /documentation/guides/multiple-partitions/
|
||||
text: Read More
|
||||
- id: 3
|
||||
tag: Blog
|
||||
tagColor: violet
|
||||
icon:
|
||||
src: /icons/outline/blog-purple.svg
|
||||
alt: Blog
|
||||
title: Vector Quantization
|
||||
description: Learn about cutting-edge techniques for vector quantization and how they can be used to improve search performance.
|
||||
link:
|
||||
url: /articles/what-is-vector-quantization/
|
||||
text: Read More
|
||||
partition: qdrant
|
||||
---
|
||||
|
||||
THIS CONTENT IS GOING TO BE IGNORED FOR NOW
|
||||
|
||||
# Documentation
|
||||
|
||||
Qdrant is an AI-native vector database and a semantic search engine. You can use it to extract meaningful information from unstructured data. Want to see how it works? [Clone this repo now](https://github.com/qdrant/qdrant_demo/) and build a search engine in five minutes.
|
||||
|
||||
@@ -0,0 +1,49 @@
|
||||
---
|
||||
title: Build World-Class Applications
|
||||
slug: build
|
||||
breadcrumb: false
|
||||
content:
|
||||
- partial: documentation/banners/banner-c
|
||||
title: Build World-Class Applications
|
||||
description: Dev-portal Build
|
||||
image:
|
||||
src: /img/dev-portal-build/spanish-ai-app-hero.png
|
||||
alt: Spanish AI app.png
|
||||
startedButton:
|
||||
text: Get Started
|
||||
url: https://qdrant.to/cloud
|
||||
- partial: documentation/sections/cards-section
|
||||
title: Start Building
|
||||
description: Deploy and manage high-performance vector search clusters across cloud environments. Easily scale with fully managed cloud solutions, integrate seamlessly across hybrid setups, or maintain complete control with private cloud deployments in Kubernetes.
|
||||
cardsPartial: documentation/cards/docs-cards
|
||||
cards:
|
||||
- id: 1
|
||||
image:
|
||||
src: /img/dev-portal-build/search.png
|
||||
alt: Search
|
||||
title: Search
|
||||
description: Build a simple neural search service with Qdrant and FastEmbed. Learn how to upload data, create indexes, and run search queries.
|
||||
link:
|
||||
url: /documentation/tutorials/hybrid-search-fastembed/
|
||||
text: Read More
|
||||
- id: 2
|
||||
image:
|
||||
src: /img/dev-portal-build/rag.png
|
||||
alt: RAG
|
||||
title: RAG
|
||||
description: Build end-to-end prototype chatbots. Learn how Qdrant integrates with popular RAG frameworks like LangChain and Llamaindex.
|
||||
link:
|
||||
url: /documentation/frameworks/langchain/
|
||||
text: Read More
|
||||
- id: 3
|
||||
image:
|
||||
src: /img/dev-portal-build/pipelines.png
|
||||
alt: Pipelines
|
||||
title: Pipelines
|
||||
description: Integrate Qdrant into your data infrastructure by connecting with popular data engineering tools.
|
||||
link:
|
||||
url: /documentation/send-data/
|
||||
text: Read More
|
||||
partition: build
|
||||
hideInSidebar: true
|
||||
---
|
||||
@@ -0,0 +1,73 @@
|
||||
---
|
||||
title: Welcome to Qdrant Cloud
|
||||
slug: cloud-intro
|
||||
breadcrumb: false
|
||||
content:
|
||||
- partial: documentation/banners/banner-b
|
||||
title: Welcome to Qdrant Cloud
|
||||
description: Dev-portal Cloud
|
||||
image:
|
||||
src: /img/dev-portal-cloud/dev-portal-cloud-hero.png
|
||||
alt: Qdrant cloud dashboard
|
||||
startedButton:
|
||||
text: Get Started
|
||||
url: https://qdrant.to/cloud
|
||||
- partial: documentation/sections/cards-section
|
||||
title: Managed Services
|
||||
description: Deploy and manage high-performance vector search clusters across cloud environments. Easily scale with fully managed cloud solutions, integrate seamlessly across hybrid setups, or maintain complete control with private cloud deployments in Kubernetes.
|
||||
cardsPartial: documentation/cards/docs-cards
|
||||
cards:
|
||||
- id: 1
|
||||
image:
|
||||
src: /img/dev-portal-cloud/managed-cloud.png
|
||||
alt: Qdrant Cloud
|
||||
title: Qdrant Cloud
|
||||
description: Qdrant Managed Cloud is our SaaS solution, providing managed Qdrant database clusters on the cloud.
|
||||
link:
|
||||
url: /documentation/cloud/
|
||||
text: Read More
|
||||
- id: 2
|
||||
image:
|
||||
src: /img/dev-portal-cloud/hybrid-cloud.png
|
||||
alt: Hybrid Cloud
|
||||
title: Hybrid Cloud
|
||||
description: Deploy and manage your vector database across diverse environments, ensuring performance, security, and cost efficiency.
|
||||
link:
|
||||
url: /documentation/hybrid-cloud/
|
||||
text: Read More
|
||||
- id: 3
|
||||
image:
|
||||
src: /img/dev-portal-cloud/private-cloud.png
|
||||
alt: Private Cloud
|
||||
title: Private Cloud
|
||||
description: Qdrant Private Cloud allows you to manage Qdrant database clusters in any Kubernetes cluster on any infrastructure.
|
||||
link:
|
||||
url: /documentation/private-cloud/
|
||||
text: Read More
|
||||
- partial: documentation/sections/cards-section
|
||||
title: Customer Support
|
||||
description: Stream, index, and migrate data to Qdrant with these essential tools and strategies.
|
||||
cardsPartial: documentation/cards/docs-cards
|
||||
cardsPerRow: 2
|
||||
cards:
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/discord-purple.svg
|
||||
alt: Discord icon
|
||||
title: Community Support
|
||||
description: Join 6,000+ active members to learn, collaborate, and participate in Qdrant’s latest activities.
|
||||
link:
|
||||
text: Join our Discord
|
||||
url: https://qdrant.to/discord
|
||||
- id: 2
|
||||
icon:
|
||||
src: /icons/outline/support-blue.svg
|
||||
alt: Support icon
|
||||
title: Qdrant Cloud Support
|
||||
description: Paying customers have access to our Support team. Links to the support portal are available in the Qdrant Cloud Console.
|
||||
link:
|
||||
text: Join Qdrant
|
||||
url: https://qdrant.to/cloud
|
||||
partition: cloud
|
||||
hideInSidebar: true
|
||||
---
|
||||
@@ -3,6 +3,7 @@ title: Managed Cloud
|
||||
weight: 12
|
||||
aliases:
|
||||
- /documentation/overview/qdrant-alternatives/documentation/cloud/
|
||||
partition: cloud
|
||||
---
|
||||
|
||||
# About Qdrant Managed Cloud
|
||||
|
||||
@@ -2,6 +2,7 @@
|
||||
title: Concepts
|
||||
weight: 8
|
||||
# If the index.md file is empty, the link to the section will be hidden from the sidebar
|
||||
partition: qdrant
|
||||
---
|
||||
|
||||
# Concepts
|
||||
|
||||
@@ -859,7 +859,7 @@ curl -X PATCH http://localhost:6333/collections/{collection_name} \
|
||||
```python
|
||||
client.update_collection(
|
||||
collection_name="{collection_name}",
|
||||
optimizer_config=models.OptimizersConfigDiff(indexing_threshold=10000),
|
||||
optimizers_config=models.OptimizersConfigDiff(indexing_threshold=10000),
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Data Management
|
||||
weight: 18
|
||||
partition: build
|
||||
---
|
||||
|
||||
## Data Management Integrations
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Practice Datasets
|
||||
weight: 29
|
||||
partition: build
|
||||
---
|
||||
|
||||
# Common Datasets in Snapshot Format
|
||||
|
||||
@@ -0,0 +1,11 @@
|
||||
---
|
||||
#Delimiter files are used to separate the list of documentation pages into sections.
|
||||
title: "Interfaces"
|
||||
type: delimiter
|
||||
weight: 25 # Change this weight to change order of sections
|
||||
sitemapExclude: True
|
||||
_build:
|
||||
publishResources: false
|
||||
render: never
|
||||
partition: cloud
|
||||
---
|
||||
+2
-1
@@ -2,9 +2,10 @@
|
||||
#Delimiter files are used to separate the list of documentation pages into sections.
|
||||
title: "Support"
|
||||
type: delimiter
|
||||
weight: 27 # Change this weight to change order of sections
|
||||
weight: 30 # Change this weight to change order of sections
|
||||
sitemapExclude: True
|
||||
_build:
|
||||
publishResources: false
|
||||
render: never
|
||||
partition: cloud
|
||||
---
|
||||
+1
@@ -3,6 +3,7 @@
|
||||
title: "Examples"
|
||||
type: delimiter
|
||||
weight: 24 # Change this weight to change order of sections
|
||||
partition: build
|
||||
sitemapExclude: True
|
||||
_build:
|
||||
publishResources: false
|
||||
+2
-1
@@ -7,4 +7,5 @@ sitemapExclude: True
|
||||
_build:
|
||||
publishResources: false
|
||||
render: never
|
||||
---
|
||||
partition: qdrant
|
||||
---
|
||||
+1
@@ -7,4 +7,5 @@ sitemapExclude: True
|
||||
_build:
|
||||
publishResources: false
|
||||
render: never
|
||||
partition: build
|
||||
---
|
||||
+1
@@ -7,4 +7,5 @@ sitemapExclude: True
|
||||
_build:
|
||||
publishResources: false
|
||||
render: never
|
||||
partition: cloud
|
||||
---
|
||||
@@ -0,0 +1,11 @@
|
||||
---
|
||||
#Delimiter files are used to separate the list of documentation pages into sections.
|
||||
title: "Support"
|
||||
type: delimiter
|
||||
weight: 30 # Change this weight to change order of sections
|
||||
sitemapExclude: True
|
||||
_build:
|
||||
publishResources: false
|
||||
render: never
|
||||
partition: qdrant
|
||||
---
|
||||
+1
@@ -7,4 +7,5 @@ sitemapExclude: True
|
||||
_build:
|
||||
publishResources: false
|
||||
render: never
|
||||
partition: qdrant
|
||||
---
|
||||
@@ -2,6 +2,7 @@
|
||||
---
|
||||
title: Embeddings
|
||||
weight: 19
|
||||
partition: build
|
||||
---
|
||||
# Supported Embedding Providers & Models
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Build Prototypes
|
||||
weight: 26
|
||||
partition: build
|
||||
---
|
||||
# Examples
|
||||
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
---
|
||||
title: FAQ
|
||||
weight: 28
|
||||
weight: 31
|
||||
partition: qdrant
|
||||
is_empty: true
|
||||
---
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: "FastEmbed"
|
||||
weight: 6
|
||||
partition: qdrant
|
||||
---
|
||||
|
||||
# What is FastEmbed?
|
||||
|
||||
@@ -1,31 +1,33 @@
|
||||
---
|
||||
title: Frameworks
|
||||
weight: 20
|
||||
partition: build
|
||||
---
|
||||
|
||||
## Framework Integrations
|
||||
|
||||
| Framework | Description |
|
||||
| ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
|
||||
| [AutoGen](/documentation/frameworks/autogen/) | Framework from Microsoft building LLM applications using multiple conversational agents. |
|
||||
| [Canopy](/documentation/frameworks/canopy/) | Framework from Pinecone for building RAG applications using LLMs and knowledge bases. |
|
||||
| [Cheshire Cat](/documentation/frameworks/cheshire-cat/) | Framework to create personalized AI assistants using custom data. |
|
||||
| [DocArray](/documentation/frameworks/docarray/) | Python library for managing data in multi-modal AI applications. |
|
||||
| [DSPy](/documentation/frameworks/dspy/) | Framework for algorithmically optimizing LM prompts and weights. |
|
||||
| [Fifty-One](/documentation/frameworks/fifty-one/) | Toolkit for building high-quality datasets and computer vision models. |
|
||||
| [Genkit](/documentation/frameworks/genkit/) | Framework to build, deploy, and monitor production-ready AI-powered apps. |
|
||||
| [Haystack](/documentation/frameworks/haystack/) | LLM orchestration framework to build customizable, production-ready LLM applications. |
|
||||
| [Lakechain](/documentation/frameworks/lakechain/) | Python framework for deploying document processing pipelines on AWS using infrastructure-as-code. |
|
||||
| [Langchain](/documentation/frameworks/langchain/) | Python framework for building context-aware, reasoning applications using LLMs. |
|
||||
| [Langchain-Go](/documentation/frameworks/langchain-go/) | Go framework for building context-aware, reasoning applications using LLMs. |
|
||||
| [Langchain4j](/documentation/frameworks/langchain4j/) | Java framework for building context-aware, reasoning applications using LLMs. |
|
||||
| [LlamaIndex](/documentation/frameworks/llama-index/) | A data framework for building LLM applications with modular integrations. |
|
||||
| [Mem0](/documentation/frameworks/mem0/) | Self-improving memory layer for LLM applications, enabling personalized AI experiences. |
|
||||
| [MemGPT](/documentation/frameworks/memgpt/) | System to build LLM agents with long term memory & custom tools |
|
||||
| [Neo4j GraphRAG](/documentation/frameworks/neo4j-graphrag/) | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. |
|
||||
| [Pandas-AI](/documentation/frameworks/pandas-ai/) | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language |
|
||||
| [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search. |
|
||||
| [Spring AI](/documentation/frameworks/spring-ai/) | Java AI framework for building with Spring design principles such as portability and modular design. |
|
||||
| [Sycamore](/documentation/frameworks/sycamore/) | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
|
||||
| [txtai](/documentation/frameworks/txtai/) | Python library for semantic search, LLM orchestration and language model workflows. |
|
||||
| [Vanna AI](/documentation/frameworks/vanna-ai/) | Python RAG framework for SQL generation and querying. |
|
||||
| Framework | Description |
|
||||
| ------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |
|
||||
| [AutoGen](/documentation/frameworks/autogen/) | Framework from Microsoft building LLM applications using multiple conversational agents. |
|
||||
| [Canopy](/documentation/frameworks/canopy/) | Framework from Pinecone for building RAG applications using LLMs and knowledge bases. |
|
||||
| [Cheshire Cat](/documentation/frameworks/cheshire-cat/) | Framework to create personalized AI assistants using custom data. |
|
||||
| [DocArray](/documentation/frameworks/docarray/) | Python library for managing data in multi-modal AI applications. |
|
||||
| [DSPy](/documentation/frameworks/dspy/) | Framework for algorithmically optimizing LM prompts and weights. |
|
||||
| [Fifty-One](/documentation/frameworks/fifty-one/) | Toolkit for building high-quality datasets and computer vision models. |
|
||||
| [Genkit](/documentation/frameworks/genkit/) | Framework to build, deploy, and monitor production-ready AI-powered apps. |
|
||||
| [Haystack](/documentation/frameworks/haystack/) | LLM orchestration framework to build customizable, production-ready LLM applications. |
|
||||
| [Lakechain](/documentation/frameworks/lakechain/) | Python framework for deploying document processing pipelines on AWS using infrastructure-as-code. |
|
||||
| [Langchain](/documentation/frameworks/langchain/) | Python framework for building context-aware, reasoning applications using LLMs. |
|
||||
| [Langchain-Go](/documentation/frameworks/langchain-go/) | Go framework for building context-aware, reasoning applications using LLMs. |
|
||||
| [Langchain4j](/documentation/frameworks/langchain4j/) | Java framework for building context-aware, reasoning applications using LLMs. |
|
||||
| [LlamaIndex](/documentation/frameworks/llama-index/) | A data framework for building LLM applications with modular integrations. |
|
||||
| [Mem0](/documentation/frameworks/mem0/) | Self-improving memory layer for LLM applications, enabling personalized AI experiences. |
|
||||
| [MemGPT](/documentation/frameworks/memgpt/) | System to build LLM agents with long term memory & custom tools |
|
||||
| [Neo4j GraphRAG](/documentation/frameworks/neo4j-graphrag/) | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. |
|
||||
| [Pandas-AI](/documentation/frameworks/pandas-ai/) | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language |
|
||||
| [Ragbits](/documentation/frameworks/ragbits/) | Python package that offers essential "bits" for building powerful Retrieval-Augmented Generation (RAG) applications. |
|
||||
| [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search. |
|
||||
| [Spring AI](/documentation/frameworks/spring-ai/) | Java AI framework for building with Spring design principles such as portability and modular design. |
|
||||
| [Sycamore](/documentation/frameworks/sycamore/) | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
|
||||
| [txtai](/documentation/frameworks/txtai/) | Python library for semantic search, LLM orchestration and language model workflows. |
|
||||
| [Vanna AI](/documentation/frameworks/vanna-ai/) | Python RAG framework for SQL generation and querying. |
|
||||
|
||||
@@ -0,0 +1,83 @@
|
||||
---
|
||||
title: Ragbits
|
||||
---
|
||||
|
||||
# Ragbits
|
||||
|
||||
[Ragbit](https://ragbits.deepsense.ai) is a Python package that offers essential "bits" for building powerful Retrieval-Augmented Generation (RAG) applications. It prioritizes developer experience by providing a simple and intuitive API. It also includes a comprehensive set of tools for seamlessly building, testing, and deploying your RAG applications efficiently.
|
||||
|
||||
Qdrant is available as a vectorstore in Ragbits to ingest and search search documents from a collection.
|
||||
|
||||
## Installation
|
||||
|
||||
Install the Python package that comes bundled with the Qdrant integration.
|
||||
|
||||
```bash
|
||||
pip install ragbits
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
An example usage of Ragbits and Qdrant would look something like this:
|
||||
|
||||
The following example uses [OpenAI embeddings](https://platform.openai.com/docs/guides/embeddings) via [LiteLLM](https://www.litellm.ai).
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
from qdrant_client import AsyncQdrantClient
|
||||
|
||||
from ragbits.core.embeddings.litellm import LiteLLMEmbeddings
|
||||
from ragbits.core.vector_stores.qdrant import QdrantVectorStore
|
||||
from ragbits.document_search import DocumentSearch, SearchConfig
|
||||
from ragbits.document_search.documents.document import DocumentMeta
|
||||
|
||||
documents = [
|
||||
DocumentMeta.create_text_document_from_literal(
|
||||
"RIP boiled water. You will be mist."
|
||||
),
|
||||
DocumentMeta.create_text_document_from_literal(
|
||||
"Why programmers don't like to swim? Because they're scared of the floating points."
|
||||
),
|
||||
DocumentMeta.create_text_document_from_literal("This one is completely unrelated."),
|
||||
]
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
embedder = LiteLLMEmbeddings(
|
||||
model="text-embedding-3-small",
|
||||
)
|
||||
vector_store = QdrantVectorStore(
|
||||
client=AsyncQdrantClient(url="http://localhost:6333"),
|
||||
collection_name="{collection_name}",
|
||||
)
|
||||
document_search = DocumentSearch(
|
||||
embedder=embedder,
|
||||
vector_store=vector_store,
|
||||
)
|
||||
|
||||
await document_search.ingest(documents)
|
||||
|
||||
all_documents = await vector_store.list()
|
||||
print([doc.metadata["content"] for doc in all_documents])
|
||||
|
||||
query = "I write computer software. Tell me something."
|
||||
vector_store_kwargs = {
|
||||
"k": 1,
|
||||
"max_distance": None,
|
||||
}
|
||||
results = await document_search.search(
|
||||
query,
|
||||
config=SearchConfig(vector_store_kwargs=vector_store_kwargs),
|
||||
)
|
||||
|
||||
print(f"Documents similar to: {query}")
|
||||
print([element.get_key() for element in results])
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## 📚 Further Reading
|
||||
|
||||
- Ragbits [Documentation](http://ragbits.deepsense.ai)
|
||||
- [Source Code](https://github.com/deepsense-ai/ragbits)
|
||||
@@ -3,4 +3,5 @@ title: Guides
|
||||
weight: 9
|
||||
# If the index.md file is empty, the link to the section will be hidden from the sidebar
|
||||
is_empty: true
|
||||
partition: qdrant
|
||||
---
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Hybrid Cloud
|
||||
weight: 13
|
||||
partition: cloud
|
||||
---
|
||||
|
||||
# Qdrant Hybrid Cloud
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Infrastructure
|
||||
weight: 21
|
||||
partition: build
|
||||
---
|
||||
|
||||
## Infrastructure Integrations
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: API & SDKs
|
||||
weight: 6
|
||||
partition: qdrant
|
||||
aliases:
|
||||
- /documentation/interfaces/
|
||||
---
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Observability
|
||||
weight: 22
|
||||
partition: build
|
||||
---
|
||||
|
||||
## Observability Integrations
|
||||
|
||||
@@ -3,6 +3,7 @@ title: What is Qdrant?
|
||||
weight: 3
|
||||
aliases:
|
||||
- overview
|
||||
partition: qdrant
|
||||
---
|
||||
|
||||
# Introduction
|
||||
|
||||
@@ -1,16 +1,18 @@
|
||||
---
|
||||
title: Platforms
|
||||
weight: 23
|
||||
partition: build
|
||||
---
|
||||
|
||||
## Platform Integrations
|
||||
|
||||
| Platform | Description |
|
||||
| --------------------------- | ---------------------------------------------------------------------------------------- |
|
||||
| Platform | Description |
|
||||
| -------------------------------------------------- | ---------------------------------------------------------------------------------------- |
|
||||
| [Apify](/documentation/platforms/apify/) | Platform to build web scrapers and automate web browser tasks. |
|
||||
| [Bubble](/documentation/platforms/bubble/) | Development platform for application development with a no-code interface |
|
||||
| [BuildShip](/documentation/platforms/buildship/) | Low-code visual builder to create APIs, scheduled jobs, and backend workflows. |
|
||||
| [Bubble](/documentation/platforms/bubble/) | Development platform for application development with a no-code interface |
|
||||
| [BuildShip](/documentation/platforms/buildship/) | Low-code visual builder to create APIs, scheduled jobs, and backend workflows. |
|
||||
| [DocsGPT](/documentation/platforms/docsgpt/) | Tool for ingesting documentation sources and enabling conversations and queries. |
|
||||
| [Kotaemon](/documentation/platforms/kotaemon/) | Open-source & customizable RAG UI for chatting with your documents. |
|
||||
| [Make](/documentation/platforms/make/) | Cloud platform to build low-code workflows by integrating various software applications. |
|
||||
| [N8N](/documentation/platforms/n8n/) | Platform for node-based, low-code workflow automation. |
|
||||
| [Pipedream](/documentation/platforms/pipedream/) | Platform for connecting apps and developing event-driven automation. |
|
||||
|
||||
@@ -0,0 +1,33 @@
|
||||
---
|
||||
title: Kotaemon
|
||||
---
|
||||
|
||||
# Kotaemon
|
||||
|
||||
[Kotaemon](https://github.com/Cinnamon/kotaemon) is open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind.
|
||||
|
||||
Qdrant is supported as a vectorstore in Kotaemon for ingesting and retrieving documents.
|
||||
|
||||
## Configuration
|
||||
|
||||
- Refer to [Getting started](https://cinnamon.github.io/kotaemon/) guide to set up Kotaemon.
|
||||
|
||||
- To configure Kotaemon to use Qdrant as the vector store, update the `flowsettings.py` as follows.
|
||||
|
||||
```python
|
||||
KH_VECTORSTORE = {
|
||||
"__type__": "kotaemon.storages.QdrantVectorStore",
|
||||
"url": "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
|
||||
"api_key": "<provide-your-own-key>'",
|
||||
"client_kwargs": {} # Additional options to pass to qdrant_client.QdrantClient
|
||||
}
|
||||
```
|
||||
|
||||
- Restart Kotaemon for the changes to take effect.
|
||||
|
||||
The reference for all the Qdrant client options can be found [here](https://python-client.qdrant.tech/qdrant_client.qdrant_client)
|
||||
|
||||
## Further reading
|
||||
|
||||
- [Kotaemon Documentation](https://cinnamon.github.io/kotaemon/)
|
||||
- [Source](https://github.com/Cinnamon/kotaemon)
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Private Cloud
|
||||
weight: 14
|
||||
partition: cloud
|
||||
---
|
||||
|
||||
# Qdrant Private Cloud
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Qdrant Cloud API
|
||||
weight: 15
|
||||
weight: 26
|
||||
partition: cloud
|
||||
---
|
||||
# Qdrant Cloud API
|
||||
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
---
|
||||
title: Cloud Quickstart
|
||||
weight: 4
|
||||
partition: qdrant
|
||||
aliases:
|
||||
- quickstart-cloud
|
||||
- ../cloud-quick-start
|
||||
- cloud-quick-start
|
||||
- cloud-quickstart
|
||||
@@ -124,3 +124,4 @@ When ready, use the Console and our complete REST API to try other operations.
|
||||
|
||||
Now that you have a Qdrant Cloud cluster up and running, you should [test remote access](/documentation/cloud/authentication/#test-cluster-access) with a Qdrant Client.
|
||||
|
||||
For more about Qdrant Cloud, check our [dedicated documentation](/documentation/cloud-intro/).
|
||||
@@ -1,10 +1,10 @@
|
||||
---
|
||||
title: Local Quickstart
|
||||
weight: 5
|
||||
partition: qdrant
|
||||
aliases:
|
||||
- quick_start
|
||||
- quick-start
|
||||
- quickstart
|
||||
---
|
||||
# How to Get Started with Qdrant Locally
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Release Notes
|
||||
weight: 30
|
||||
weight: 32
|
||||
partition: qdrant
|
||||
type: external-link
|
||||
external_url: https://github.com/qdrant/qdrant/releases
|
||||
sitemapExclude: True
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Send Data to Qdrant
|
||||
weight: 25
|
||||
partition: build
|
||||
---
|
||||
|
||||
## How to Send Your Data to a Qdrant Cluster
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Support
|
||||
weight: 16
|
||||
weight: 35
|
||||
partition: cloud
|
||||
aliases:
|
||||
- /documentation/cloud/support/
|
||||
---
|
||||
|
||||
@@ -6,6 +6,7 @@ is_empty: false
|
||||
aliases:
|
||||
- how-to
|
||||
- tutorials
|
||||
partition: qdrant
|
||||
---
|
||||
|
||||
# Tutorials
|
||||
|
||||
@@ -159,7 +159,7 @@ Image.open(client.search(
|
||||
|
||||
|
||||
|
||||
<h3 style="font-size: 1.25em;">Image-to-Text</h3>
|
||||
### Image-to-Text
|
||||
Now, let's do a reverse search with an image:
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,16 @@
|
||||
---
|
||||
title: Was this page useful?
|
||||
positiveButton:
|
||||
text: "Yes"
|
||||
url: /#
|
||||
icon:
|
||||
src: /icons/outline/thumb-up.svg
|
||||
alt: Thumb up icon
|
||||
negativeButton:
|
||||
text: "No"
|
||||
url: /#
|
||||
icon:
|
||||
src: /icons/outline/thumb-down.svg
|
||||
alt: Thumb down icon
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -0,0 +1,27 @@
|
||||
---
|
||||
logIn:
|
||||
text: Log in
|
||||
url: https://cloud.qdrant.io/
|
||||
startFree:
|
||||
text: Start Free
|
||||
url: https://cloud.qdrant.io/
|
||||
logoLink: /
|
||||
menuItems:
|
||||
- id: menu-0
|
||||
name: Qdrant
|
||||
url: /documentation/
|
||||
- id: menu-1
|
||||
name: Cloud
|
||||
url: /documentation/cloud-intro/
|
||||
- id: menu-2
|
||||
name: Build
|
||||
url: /documentation/build/
|
||||
# - id: menu-3
|
||||
# name: Learn
|
||||
# url: /documentation/learn/
|
||||
- id: menu-4
|
||||
name: API Reference
|
||||
url: https://api.qdrant.tech/api-reference
|
||||
external: true
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -81,7 +81,7 @@ menuItems:
|
||||
url: https://qdrant.to/roadmap
|
||||
- id: 5
|
||||
name: Changelog
|
||||
url: https://github.com/qdrant/qdrant/releases
|
||||
url: https://github.com/qdrant/qdrant/releases
|
||||
- id: 6
|
||||
name: Status Page
|
||||
url: https://status.qdrant.io/
|
||||
@@ -133,5 +133,9 @@ bages:
|
||||
- src: /img/gdpr-badge.png
|
||||
alt: "heyData GDPR"
|
||||
url: https://heydata.eu/
|
||||
question: Ready to get started with Qdrant?
|
||||
questionButton:
|
||||
text: Start Free
|
||||
url: https://qdrant.to/cloud/
|
||||
sitemapExclude: true
|
||||
---
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
stats:
|
||||
githubStars: 20.4k
|
||||
githubStars: 20.5k
|
||||
discordMembers: 6.9k
|
||||
twitterFollowers: 7.5k
|
||||
---
|
||||
@@ -2,7 +2,7 @@
|
||||
title: Why join Qdrant for Startups?
|
||||
mainCard:
|
||||
title: Discount for Qdrant Cloud
|
||||
description: Enjoy a discount on <a href="https://cloud.qdrant.io/" target="_blank">Qdrant Cloud</a> for the first year.
|
||||
description: Enjoy a 20% discount on <a href="https://cloud.qdrant.io/" target="_blank">Qdrant Cloud</a> for the first year.
|
||||
image:
|
||||
src: /img/qdrant-for-startups-benefits/card1.png
|
||||
alt: Qdrant Discount for Startups
|
||||
|
||||
@@ -1,5 +1,17 @@
|
||||
---
|
||||
title: Meet our Stars
|
||||
mainCards:
|
||||
- id: 0
|
||||
headTitle: Distinguished Ambassador
|
||||
headIcon:
|
||||
src: /icons/fill/sparks-purple.svg
|
||||
alt: Sparks
|
||||
image:
|
||||
src: /img/stars/m-k-pavan-kumar.jpg
|
||||
alt: M K Pavan Kumar Photo
|
||||
name: M K Pavan Kumar
|
||||
position: Data Scientist and Lead GenAI
|
||||
description: Kameshwara Pavan Kumar Mantha is a seasoned technology expert with 14 years of extensive experience in full stack development, cloud solutions, and artificial intelligence.<br><br>Specializing in Generative AI and Large Language Models, Pavan has established himself as a leader in these cutting-edge domains.
|
||||
cards:
|
||||
- id: 0
|
||||
image:
|
||||
@@ -30,84 +42,76 @@ cards:
|
||||
position: Founder of SciPhi
|
||||
description: Physics PhD, Quant @ Citadel and Founder at SciPhi
|
||||
- id: 4
|
||||
image:
|
||||
src: /img/stars/m-k-pavan-kumar.jpg
|
||||
alt: M K Pavan Kumar Photo
|
||||
name: M K Pavan Kumar
|
||||
position: Data Scientist and Lead GenAI
|
||||
description: A seasoned technology expert with 14 years of experience in full stack development, cloud solutions, & artificial intelligence
|
||||
distinguished: true
|
||||
- id: 5
|
||||
image:
|
||||
src: /img/stars/niranjan-akella.jpg
|
||||
alt: Niranjan Akella Photo
|
||||
name: Niranjan Akella
|
||||
position: Scientist by Heart & AI Engineer
|
||||
description: I build & deploy AI models like LLMs, Diffusion Models & Vision Models at scale
|
||||
- id: 6
|
||||
- id: 5
|
||||
image:
|
||||
src: /img/stars/bojan-jakimovski.jpg
|
||||
alt: Bojan Jakimovski Photo
|
||||
name: Bojan Jakimovski
|
||||
position: Machine Learning Engineer
|
||||
description: I'm really excited to show the power of the Qdrant as vector database
|
||||
- id: 7
|
||||
- id: 6
|
||||
image:
|
||||
src: /img/stars/haydar-kulekci.jpg
|
||||
alt: Haydar KULEKCI Photo
|
||||
name: Haydar KULEKCI
|
||||
position: Senior Software Engineer
|
||||
description: I am a senior software engineer and consultant with over 10 years of experience in data management, processing, and software development.
|
||||
- id: 8
|
||||
- id: 7
|
||||
image:
|
||||
src: /img/stars/nicola-procopio.jpg
|
||||
alt: Nicola Procopio Photo
|
||||
name: Nicola Procopio
|
||||
position: Senior Data Scientist @ Fincons Group
|
||||
description: Nicola, a data scientist and open-source enthusiast since 2009, has used Qdrant since 2023. He developed fastembed for Haystack, vector search for Cheshire Cat A.I., and shares his expertise through articles, tutorials, and talks.
|
||||
- id: 9
|
||||
- id: 8
|
||||
image:
|
||||
src: /img/stars/eduardo-vasquez.jpg
|
||||
alt: Eduardo Vasquez Photo
|
||||
name: Eduardo Vasquez
|
||||
position: Data Scientist and MLOps Engineer
|
||||
description: I am a Data Scientist and MLOps Engineer exploring generative AI and LLMs, creating YouTube content on RAG workflows and fine-tuning LLMs. I hold an MSc in Statistics and Data Science.
|
||||
- id: 10
|
||||
- id: 9
|
||||
image:
|
||||
src: /img/stars/benito-martin.jpg
|
||||
alt: Benito Martin Photo
|
||||
name: Benito Martin
|
||||
position: Independent Consultant | Data Science, ML and AI Project Implementation | Teacher and Course Content Developer
|
||||
description: Over the past year, Benito developed MLOps and LLM projects. Based in Switzerland, Benito continues to advance his skills.
|
||||
- id: 11
|
||||
- id: 10
|
||||
image:
|
||||
src: /img/stars/nirant-kasliwal.jpg
|
||||
alt: Nirant Kasliwal Photo
|
||||
name: Nirant Kasliwal
|
||||
position: FastEmbed Creator
|
||||
description: I'm a Machine Learning consultant specializing in NLP and Vision systems for early-stage products. I've authored an NLP book recommended by Dr. Andrew Ng to Stanford's CS230 students and maintain FastEmbed at Qdrant for speed.
|
||||
- id: 12
|
||||
- id: 11
|
||||
image:
|
||||
src: /img/stars/denzell-ford.jpg
|
||||
alt: Denzell Ford Photo
|
||||
name: Denzell Ford
|
||||
position: Founder at Trieve, has been using Qdrant since late 2022.
|
||||
description: Denzell Ford, the founder of Trieve, has been using Qdrant since late 2022. He's passionate about helping people in the community.
|
||||
- id: 13
|
||||
- id: 12
|
||||
image:
|
||||
src: /img/stars/pavan-nagula.jpg
|
||||
alt: Pavan Nagula Photo
|
||||
name: Pavan Nagula
|
||||
position: Data Scientist | Machine Learning and Generative AI
|
||||
description: I'm Pavan, a data scientist specializing in AI, ML, and big data analytics. I love experimenting with new technologies in the AI and ML space, and Qdrant is a place where I've seen such innovative implementations recently.
|
||||
- id: 14
|
||||
- id: 13
|
||||
image:
|
||||
src: /img/stars/guohao-li.jpg
|
||||
alt: Guohao Li Photo
|
||||
name: Guohao Li
|
||||
position: Founder of Eigent.AI
|
||||
description: Guohao Li the founder of Eigent.AI, an artificial intelligence researcher and an open-source contributor working on building intelligent agents that can perceive, learn, communicate, reason, and act. He is the core lead of the open source projects CAMEL-AI.org.
|
||||
- id: 15
|
||||
- id: 14
|
||||
image:
|
||||
src: /img/stars/sahin-utar.jpg
|
||||
alt: Şahin Utar Photo
|
||||
|
||||
Reference in New Issue
Block a user