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Update README.md

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yangdx
2025-04-20 20:33:01 +08:00
parent 733e307a8d
commit dd4f92dae2
2 changed files with 8 additions and 20 deletions
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README.md
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@@ -629,21 +629,15 @@ rag.insert(["TEXT1", "TEXT2",...])
# Batch Insert with custom batch size configuration
rag = LightRAG(
...
working_dir=WORKING_DIR,
addon_params={
"insert_batch_size": 4 # Process 4 documents per batch
}
max_parallel_insert = 4
)
rag.insert(["TEXT1", "TEXT2", "TEXT3", ...]) # Documents will be processed in batches of 4
```
The `insert_batch_size` parameter in `addon_params` controls how many documents are processed in each batch during insertion. This is useful for:
- Managing memory usage with large document collections
- Optimizing processing speed
- Providing better progress tracking
- Default value is 10 if not specified
The `max_parallel_insert` parameter determines the number of documents processed concurrently in the document indexing pipeline. If unspecified, the default value is **2**. We recommend keeping this setting **below 10**, as the performance bottleneck typically lies with the LLM (Large Language Model) processing.The `max_parallel_insert` parameter determines the number of documents processed concurrently in the document indexing pipeline. If unspecified, the default value is **2**. We recommend keeping this setting **below 10**, as the performance bottleneck typically lies with the LLM (Large Language Model) processing.
</details>
@@ -1181,7 +1175,7 @@ Valid modes are:
| **vector_db_storage_cls_kwargs** | `dict` | Additional parameters for vector database, like setting the threshold for nodes and relations retrieval | cosine_better_than_threshold: 0.2（default value changed by env var COSINE_THRESHOLD) |
| **enable_llm_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
| **enable_llm_cache_for_entity_extract** | `bool` | If `TRUE`, stores LLM results in cache for entity extraction; Good for beginners to debug your application | `TRUE` |
| **addon_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"], "insert_batch_size": 10}`: sets example limit, output language, and batch size for document processing | `example_number: all examples, language: English, insert_batch_size: 10` |
| **addon_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"]}`: sets example limit, entiy/relation extraction output language | `example_number: all examples, language: English` |
| **convert_response_to_json_func** | `callable` | Not used | `convert_response_to_json` |
| **embedding_cache_config** | `dict` | Configuration for question-answer caching. Contains three parameters: `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers. `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM. `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |