LlamaIndex Vector_Stores Integration: WordLift
WordLift is an AI-powered SEO platform. With our AI we build your own knowledge graph for your business with entities marked up by the different topics, categories and regions. Using this graph, search engines will be able to understand the structure of your content faster and more precisely. To access a WordLift key and unlock our AI-powered SEO tools, visit WordLift.
This integration enables the use of WordLift as a vector store for LlamaIndex, allowing you to work with your knowledge graph directly from your codebase.
Features
- Perform retrieval-augmented generation (RAG) using your knowledge graph data directly in your codebase.
- Add new nodes and search within your knowledge graph effortlessly.
Usage
Please refer to the notebook for usage of WordLift as vector store in LlamaIndex.
WordLift Knowledge Graphs are built on the principles of fully Linked Data, where each entity is assigned a permanent dereferentiable URI.
When adding nodes to an existing Knowledge Graph, it's essential to include an "entity_id" in the metadata of each loaded document.
For further insights into Fully Linked Data, explore these resources:
W3C Linked Data,
5 Star Data.
1 maintainer
Metadata
1 maintainer
Release files for llama-index-vector-stores-wordlift 0.8.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| llama_index_vector_stores_wordlift-0.8.0.tar.gz | 6.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| llama_index_vector_stores_wordlift-0.8.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 13.4 kB
Release files / llama_index_vector_stores_wordlift-0.8.0.tar.gz
| Download URL | llama_index_vector_stores_wordlift-0.8.0.tar.gz |
|---|---|
| Size | 6.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
91b0d6f3126626d8c8fa010548da5f14f05d2f811f533e7f579c837f88a48317
|
|
BLAKE2b-256 checksum How to use checksums |
38020ee7f1f9bd69aec31adf9d42298a954fbccc1655002a1b7b36e0f15d9778
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.12.7 {"installer":{"name":"uv","version":"0.12.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}
|
Release files / llama_index_vector_stores_wordlift-0.8.0-py3-none-any.whl
| Download URL | llama_index_vector_stores_wordlift-0.8.0-py3-none-any.whl |
|---|---|
| Size | 6.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
732aaa24679671d54d8968e2f5a6ca4346da6b7e2f1949017ef9735fac458945
|
|
BLAKE2b-256 checksum How to use checksums |
8b71623588630bf10967e770d9f393b1c018ece202653627fdf8bbcceeeb88c9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.12.7 {"installer":{"name":"uv","version":"0.12.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}
|
