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updated readme

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Yannick Stephan
2025-02-09 15:00:07 +01:00
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@@ -408,6 +408,21 @@ rag = LightRAG(
with open("./newText.txt") as f: with open("./newText.txt") as f:
rag.insert(f.read()) rag.insert(f.read())
``` ```
### Insert using Pipeline
The `apipeline_enqueue_documents` and `apipeline_process_enqueue_documents` functions allow you to perform incremental insertion of documents into the graph.
This is useful for scenarios where you want to process documents in the background while still allowing the main thread to continue executing.
And using a routine to process news documents.
```python
rag = LightRAG(..)
await rag.apipeline_enqueue_documents(string_or_strings)
# Your routine in loop
await rag.apipeline_process_enqueue_documents(string_or_strings)
```
### Separate Keyword Extraction ### Separate Keyword Extraction
We've introduced a new function `query_with_separate_keyword_extraction` to enhance the keyword extraction capabilities. This function separates the keyword extraction process from the user's prompt, focusing solely on the query to improve the relevance of extracted keywords. We've introduced a new function `query_with_separate_keyword_extraction` to enhance the keyword extraction capabilities. This function separates the keyword extraction process from the user's prompt, focusing solely on the query to improve the relevance of extracted keywords.