krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-10-04 10:28:29 +02:00
Code Issues Packages Projects Releases Wiki Activity

docs: fix broken links,typos,and standardize framework casing

Browse Source
This commit is contained in:
goodnight
2025-12-20 15:59:32 +01:00
parent 72668d521e
commit d176436408
12 changed files with 32 additions and 31 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/build-tab.md
+1 -1
View File
@@ -31,7 +31,7 @@ content:
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.
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
qdrant-landing/content/documentation/examples/cohere-rag-connector.md
+1 -1
View File
@@ -30,7 +30,7 @@ This tutorial guides you step by step on building such a service around Qdrant.
## Qdrant connector
You probably already have some collections you would like to bring to the LLM. Maybe your pipeline was set up using some
of the popular libraries such as Langchain, Llama Index, or Haystack. Cohere connectors may implement even more complex
of the popular libraries such as LangChain, LlamaIndex, or Haystack. Cohere connectors may implement even more complex
logic, e.g. hybrid search. In our case, we are going to start with a fresh Qdrant collection, index data using Cohere
Embed v3, build the connector, and finally connect it with the [Command-R model](https://txt.cohere.com/command-r/).
qdrant-landing/content/documentation/examples/hybrid-search-llamaindex-jinaai.md
+1 -1
View File
@@ -88,7 +88,7 @@ load_dotenv('./.env')
LlamaIndex provides built-in support for the [Jina Embeddings API](https://jina.ai/embeddings/#apiform). To use it, you need to initialize the `JinaEmbedding` object with your API Key and model name.
For the LLM, you need wrap it in a subclass of `llama_index.llms.CustomLLM` to make it compatible with LlamaIndex.
For the LLM, you need to wrap it in a subclass of `llama_index.llms.CustomLLM` to make it compatible with LlamaIndex.
```python
# connect embeddings
qdrant-landing/content/documentation/examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md
+5 -5
View File
@@ -26,7 +26,7 @@ are working with confidential or sensitive data.
## Building up the application
Our application will consist of two main processes: indexing and searching. Langchain will glue everything together,
Our application will consist of two main processes: indexing and searching. LangChain will glue everything together,
as we will use a few components, including Cohere and Qdrant, as well as some OCI services. Here is a high-level
overview of the architecture:
@@ -38,7 +38,7 @@ Before we dive into the implementation, make sure to set up all the necessary ac
#### Libraries
We are going to use a few Python libraries. Of course, Langchain will be our main framework, but the Cohere models on
We are going to use a few Python libraries. Of course, LangChain will be our main framework, but the Cohere models on
OCI are accessible via the [OCI SDK](https://docs.oracle.com/en-us/iaas/tools/python/2.125.1/). Let's install all the
necessary libraries:
@@ -125,7 +125,7 @@ client.create_collection(
### Indexing process
We have all the necessary tools set up, so let's start with the indexing process. We will use the Cohere Embedding
models to convert the text into vectors, and then store them in Qdrant. Langchain is integrated with OCI Generative AI
models to convert the text into vectors, and then store them in Qdrant. LangChain is integrated with OCI Generative AI
Service, so we can easily access the models.
Our dataset will be fairly simple, as it will consist of the questions and answers from the [Oracle Cloud Free Tier
@@ -284,7 +284,7 @@ source documents used to generate the response. This might be useful for debuggi
#### Other experiments
Asking the basic questions is just the beginning. What you want to avoid is a hallucination, where the model generates
an answer that is not based on the actual content. The default prompt of Langchain should already prevent this, but you
an answer that is not based on the actual content. The default prompt of LangChain should already prevent this, but you
might still want to check it. Let's ask a question that is not directly answered on the FAQ page:
```python
@@ -306,6 +306,6 @@ not hallucinating in that case.
## Wrapping up
This tutorial has shown how to integrate Cohere's language models with Qdrant to enable natural language search on your
website. We have used Langchain as an orchestrator, and everything was hosted on Oracle Cloud Infrastructure (OCI).
website. We have used LangChain as an orchestrator, and everything was hosted on Oracle Cloud Infrastructure (OCI).
Real world would require integrating this mechanism into your organization's systems, but we built a solid foundation
that can be further developed.
qdrant-landing/content/documentation/examples/rag-chatbot-scaleway.md
+2 -2
View File
@@ -28,7 +28,7 @@ A notebook for this tutorial is available on [GitHub](https://github.com/qdrant/
![Architecture diagram](/documentation/examples/rag-chatbot-scaleway/architecture-diagram.png)
> Langchain [supports a wide range of LLMs](https://python.langchain.com/docs/integrations/chat/), and GPT-4o is used as the main generator in this tutorial. You can easily swap it out for your preferred model that might be launched on your premises to complete the fully private setup. For the sake of simplicity, we used the OpenAI APIs, but LangChain makes the transition seamless.
> LangChain [supports a wide range of LLMs](https://python.langchain.com/docs/integrations/chat/), and GPT-4o is used as the main generator in this tutorial. You can easily swap it out for your preferred model that might be launched on your premises to complete the fully private setup. For the sake of simplicity, we used the OpenAI APIs, but LangChain makes the transition seamless.
## Deploying Qdrant Hybrid Cloud on Scaleway
@@ -134,7 +134,7 @@ The `format_docs` function formats the retrieved documents into a single string,
This chain of operations demonstrates a sophisticated approach to information retrieval and content generation, leveraging both the semantic understanding capabilities of vector search and the generative prowess of large language models.
Now, retrieve and generate data using relevant snippets from the blogL
Now, retrieve and generate data using relevant snippets from the blog:
```python
retriever = vectorstore.as_retriever()
qdrant-landing/content/documentation/frameworks/_index.md
+2 -2
View File
@@ -26,8 +26,8 @@ aliases: ["/documentation/frameworks/memgpt/"]
| [Haystack](/documentation/frameworks/haystack/) | LLM orchestration framework to build customizable, production-ready LLM applications. |
| [HoneyHive](/documentation/frameworks/honeyhive/) | AI observability and evaluation platform that provides tracing and monitoring tools for GenAI pipelines. |
| [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. |
| [Langchain4j](/documentation/frameworks/langchain4j/) | Java framework for building context-aware, reasoning applications using LLMs. |
| [LangChain](/documentation/frameworks/langchain/) | Python framework for building context-aware, reasoning applications using LLMs. |
| [LangChain4j](/documentation/frameworks/langchain4j/) | Java framework for building context-aware, reasoning applications using LLMs. |
| [LangGraph](/documentation/frameworks/langgraph/) | Python, Javascript libraries for building stateful, multi-actor applications. |
| [LlamaIndex](/documentation/frameworks/llama-index/) | A data framework for building LLM applications with modular integrations. |
| [Mastra](/documentation/frameworks/mastra/) | Typescript framework to build AI applications and features quickly. |
qdrant-landing/content/documentation/frameworks/autogen.md
+4 -4
View File
@@ -1,9 +1,9 @@
---
title: Autogen
title: AutoGen
aliases: [ ../integrations/autogen/ ]
---
# Microsoft Autogen
# Microsoft AutoGen
[AutoGen](https://github.com/microsoft/autogen/tree/0.2) is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks.
@@ -13,7 +13,7 @@ aliases: [ ../integrations/autogen/ ]
- Human participation: AutoGen allows human participation. This means that humans can provide input and feedback to the agents as needed.
With the [Autogen-Qdrant integration](https://microsoft.github.io/autogen/0.2/docs/reference/agentchat/contrib/vectordb/qdrant/), you build Autogen workflows backed by Qdrant't performant retrievals.
With the [AutoGen-Qdrant integration](https://microsoft.github.io/autogen/0.2/docs/reference/agentchat/contrib/vectordb/qdrant/), you build AutoGen workflows backed by Qdrant's performant retrievals.
## Installation
@@ -100,5 +100,5 @@ chat_results = ragproxyagent.initiate_chat(assistant, message=ragproxyagent.mess
## Next steps
- AutoGen [documentation](https://microsoft.github.io/autogen/0.2)
- Autogen [examples](https://microsoft.github.io/autogen/0.2/docs/Examples)
- AutoGen [examples](https://microsoft.github.io/autogen/0.2/docs/Examples)
- [Source Code](https://github.com/microsoft/autogen/blob/0.2/autogen/agentchat/contrib/vectordb/qdrant.py)
qdrant-landing/content/documentation/frameworks/langchain.md
+8 -8
View File
@@ -1,16 +1,16 @@
---
title: Langchain
title: LangChain
aliases:
- ../integrations/langchain/
- /documentation/overview/integrations/langchain/
---
# Langchain
# LangChain
Langchain is a library that makes developing Large Language Model-based applications much easier. It unifies the interfaces
to different libraries, including major embedding providers and Qdrant. Using Langchain, you can focus on the business value instead of writing the boilerplate.
LangChain is a library that makes developing Large Language Model-based applications much easier. It unifies the interfaces
to different libraries, including major embedding providers and Qdrant. Using LangChain, you can focus on the business value instead of writing the boilerplate.
Langchain distributes the Qdrant integration as a partner package.
LangChain distributes the Qdrant integration as a partner package.
It might be installed with pip:
@@ -184,8 +184,8 @@ Note that if you've added documents with HYBRID mode, you can switch to any retr
## Next steps
If you'd like to know more about running Qdrant in a Langchain-based application, please read our article
[Question Answering with Langchain and Qdrant without boilerplate](/articles/langchain-integration/). Some more information
might also be found in the [Langchain documentation](https://python.langchain.com/docs/integrations/vectorstores/qdrant).
If you'd like to know more about running Qdrant in a LangChain-based application, please read our article
[Question Answering with LangChain and Qdrant without boilerplate](/articles/langchain-integration/). Some more information
might also be found in the [LangChain documentation](https://python.langchain.com/docs/integrations/vectorstores/qdrant).
- [Source Code](https://github.com/langchain-ai/langchain/tree/master/libs%2Fpartners%2Fqdrant)
qdrant-landing/content/documentation/frameworks/langchain4j.md
+4 -4
View File
@@ -1,12 +1,12 @@
---
title: Langchain4J
title: LangChain4j
---
# LangChain for Java
LangChain for Java, also known as [Langchain4J](https://github.com/langchain4j/langchain4j), is a community port of [Langchain](https://www.langchain.com/) for building context-aware AI applications in Java
You can use Qdrant as a vector store in Langchain4J through the [`langchain4j-qdrant`](https://central.sonatype.com/artifact/dev.langchain4j/langchain4j-qdrant) module.
You can use Qdrant as a vector store in LangChain4j through the [`langchain4j-qdrant`](https://central.sonatype.com/artifact/dev.langchain4j/langchain4j-qdrant) module.
## Setup
@@ -47,9 +47,9 @@ EmbeddingStore<TextSegment> embeddingStore =
.build();
```
`QdrantEmbeddingStore` supports all the semantic features of Langchain4J.
`QdrantEmbeddingStore` supports all the semantic features of LangChain4j.
## Further Reading
- You can refer to the [Langchain4J examples](https://github.com/langchain4j/langchain4j-examples/) to get started.
- You can refer to the [LangChain4j examples](https://github.com/langchain4j/langchain4j-examples/) to get started.
- [Source Code](https://github.com/langchain4j/langchain4j/tree/main/langchain4j-qdrant)
qdrant-landing/content/documentation/frameworks/langgraph.md
+2 -2
View File
@@ -1,6 +1,6 @@
---
title: LangGraph
aliases: [ ../integrations/autogen/ ]
aliases: [ ../integrations/langgraph/ ]
---
# LangGraph
@@ -106,4 +106,4 @@ const graph = workflow.compile();
## Further Reading
- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)
- [LangGraph End-to-End Guides](https://langchain-ai.github.io/langgraph/tutorials/)
- [LangGraph Tutorial build basic chatbot](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/)
qdrant-landing/content/documentation/frameworks/llama-index.md
+1 -1
View File
@@ -38,5 +38,5 @@ index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
## Further Reading
- [LlamaIndex Documentation](https://developers.llamaindex.ai/python/examples/vector_stores/qdrantindexdemo/)
- [Example Notebook](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/QdrantIndexDemo.ipynb)
- [Example Notebook](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/QdrantIndexDemo.ipynb)
- [Source Code](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant)
qdrant-landing/content/documentation/frameworks/mem0.md
+1
View File
@@ -63,4 +63,5 @@ history = m.history(memory_id="m1")
## Further Reading
- [Mem0 GitHub Repository](https://github.com/mem0ai/mem0)
- [Mem0 integration with Qdrant](https://docs.mem0.ai/components/vectordbs/dbs/qdrant#qdrant)
- [Mem0 Documentation](https://docs.mem0.ai/)