Anchors
| Anchor text | Ref. domains ▾ | Top DR | Ref. pages | Links to target | Dofollow links |
|---|---|---|---|---|---|
| LangChain4j | 11 | — | 0 | 11 | 10 90.9% |
| Langchain4j | 3 | — | 0 | 4 | 2 50% |
| https://docs.langchain4j.dev/intro/ | 2 | — | 0 | 2 | 2 100% |
| AI Services | 2 | — | 0 | 2 | 2 100% |
| Langchain4J | 2 | — | 0 | 2 | 2 100% |
| AI services | 2 | — | 0 | 2 | 2 100% |
| https://docs.langchain4j.dev/tutorials/ | 1 | — | 0 | 1 | 0 0% |
| Language Models | 1 | — | 0 | 1 | 1 100% |
| LangChain4j docs | 1 | — | 0 | 1 | 1 100% |
| Chat memory | 1 | — | 0 | 1 | 1 100% |
| https://docs.langchain4j.dev/integrations/embedding-stores/ | 1 | — | 0 | 1 | 1 100% |
| here | 1 | — | 0 | 1 | 1 100% |
| Link | 1 | — | 0 | 1 | 1 100% |
| LangChain4J 官方文档 | 1 | — | 0 | 1 | 1 100% |
| LangChain4j documentation | 1 | — | 0 | 1 | 1 100% |
| Guardrailing in LangChain4J (Mistral Model Only) | 1 | — | 0 | 1 | 1 100% |
| ChatMemory | 1 | — | 0 | 1 | 1 100% |
| chat memory | 1 | — | 0 | 1 | 1 100% |
| tutoriels | 1 | — | 0 | 1 | 1 100% |
| the URL | 1 | — | 0 | 2 | 2 100% |
| langchain4j | 1 | — | 0 | 2 | 2 100% |
| 这里 | 1 | — | 0 | 1 | 1 100% |
| https://docs.langchain4j.dev/tutorials/chat-memory | 1 | — | 0 | 1 | 1 100% |
| extraction de données structurées | 1 | — | 0 | 1 | 1 100% |
| Langchain4J | 1 | — | 0 | 1 | 1 100% |
| Tools | 1 | — | 0 | 1 | 1 100% |
| LangChain4j Advanced RAG | 1 | — | 0 | 1 | 1 100% |
| https://docs.langchain4j.dev/integrations/language-models/ | 1 | — | 0 | 1 | 1 100% |
| Comparison Table of all supported Language Models | 1 | — | 0 | 1 | 1 100% |
| 1 | — | 0 | 4 | 4 100% | |
| 对于 Spring Boot | 1 | — | 0 | 1 | 1 100% |
| documentation | 1 | — | 0 | 1 | 1 100% |
| Filter | 1 | — | 0 | 1 | 1 100% |
| different AI models supported by LangChain4j | 1 | — | 0 | 1 | 1 100% |
| https://docs.langchain4j.dev/ | 1 | — | 0 | 1 | 1 100% |
| https://docs.langchain4j.dev/tutorials/ai-services | 1 | — | 0 | 1 | 1 100% |
| Azure OpenAI embedding model | 1 | — | 0 | 1 | 1 100% |
| langchain4j in-process embedding model | 1 | — | 0 | 1 | 1 100% |
| AI 服务 | 1 | — | 0 | 1 | 1 100% |
| Java (LangChain4j) | 1 | — | 0 | 10 | 10 100% |
| langchain4j document splitter API | 1 | — | 0 | 1 | 1 100% |
| Langchain4J Spring | 1 | — | 0 | 1 | 1 100% |
| langchain4j tools | 1 | — | 0 | 1 | 1 100% |
| Embedding Model | 1 | — | 0 | 1 | 1 100% |
| https://docs.langchain4j.dev | 1 | — | 0 | 1 | 1 100% |
| LangChain4J Chat Memory | 1 | — | 0 | 1 | 1 100% |
| Javadoc | 1 | — | 0 | 47 | 47 100% |
| MCP Client powered by LangChain4j | 1 | — | 0 | 1 | 1 100% |
| 向量模型 | 1 | — | 0 | 1 | 1 100% |
| Java（LangChain4j） | 1 | — | 0 | 3 | 3 100% |
Frequently Asked Questions
What anchor texts are used to link to docs.langchain4j.dev?
This page shows all anchor texts found in backlinks pointing to docs.langchain4j.dev, sorted by the number of referring domains using each anchor. Anchor texts range from branded terms (like the domain name itself) to keyword-rich phrases that describe the linked content. The distribution of anchor texts reveals how other websites perceive and describe docs.langchain4j.dev.
What is anchor text?
Anchor text is the visible, clickable text in a hyperlink. Search engines use anchor text as a signal to understand what the linked page is about. For example, if many sites link to a page using the anchor text "best running shoes," search engines infer that the page is relevant to that topic. Anchor text appears in several forms: exact-match (contains target keywords), branded (uses the company or domain name), generic (like "click here"), and naked URLs.
Why is anchor text analysis important for SEO?
Anchor text analysis helps identify potential SEO risks and opportunities. A natural backlink profile has diverse anchor texts including branded terms, generic phrases, and topic-relevant keywords. Over-optimization, where too many backlinks use the same exact-match keyword anchor, can trigger search engine penalties. Conversely, understanding which anchors drive the most authority (measured by referring domain count and DR) helps prioritize link building efforts.
How many unique anchor texts does docs.langchain4j.dev have?
The anchor text report for docs.langchain4j.dev displays all distinct anchor texts grouped by their hash. Each row shows how many unique referring domains use that anchor, the total number of links, and the dofollow percentage. A high number of unique anchors generally indicates a healthy, natural backlink profile with diverse link sources.