diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 00000000..c7707166
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+lightrag/api/webui/** -diff
diff --git a/.gitignore b/.gitignore
index a4afe4ea..dd1c386b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -64,3 +64,6 @@ gui/
# unit-test files
test_*
+
+# Cline files
+memory-bank/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 169a7cc7..0629b743 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,16 +3,21 @@ repos:
rev: v5.0.0
hooks:
- id: trailing-whitespace
+ exclude: ^lightrag/api/webui/
- id: end-of-file-fixer
+ exclude: ^lightrag/api/webui/
- id: requirements-txt-fixer
+ exclude: ^lightrag/api/webui/
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.6.4
hooks:
- id: ruff-format
+ exclude: ^lightrag/api/webui/
- id: ruff
args: [--fix, --ignore=E402]
+ exclude: ^lightrag/api/webui/
- repo: https://github.com/mgedmin/check-manifest
@@ -20,3 +25,4 @@ repos:
hooks:
- id: check-manifest
stages: [manual]
+ exclude: ^lightrag/api/webui/
diff --git a/README.md b/README.md
index 018a94e6..61e7b20f 100644
--- a/README.md
+++ b/README.md
@@ -37,28 +37,30 @@ This repository hosts the code of LightRAG. The structure of this code is based
+
+
+
🎉 News
-
-- [x] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
-- [x] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
-- [x] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage).
-- [x] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
-- [x] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise.
-- [x] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
-- [x] [2024.11.12]🎯📢LightRAG now supports [Oracle Database 23ai for all storage types (KV, vector, and graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py).
-- [x] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
-- [x] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge.
-- [x] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
-- [x] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
-- [x] [2024.10.20]🎯📢We've added a new feature to LightRAG: Graph Visualization.
-- [x] [2024.10.18]🎯📢We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
-- [x] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
-- [x] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
-- [x] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
+- [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
+- [X] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
+- [X] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage).
+- [X] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
+- [X] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise.
+- [X] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
+- [X] [2024.11.12]🎯📢LightRAG now supports [Oracle Database 23ai for all storage types (KV, vector, and graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py).
+- [X] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
+- [X] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge.
+- [X] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
+- [X] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
+- [X] [2024.10.20]🎯📢We've added a new feature to LightRAG: Graph Visualization.
+- [X] [2024.10.18]🎯📢We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
+- [X] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
+- [X] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
+- [X] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
@@ -82,16 +84,20 @@ This repository hosts the code of LightRAG. The structure of this code is based
cd LightRAG
pip install -e .
```
+
* Install from PyPI
+
```bash
pip install lightrag-hku
```
## Quick Start
+
* [Video demo](https://www.youtube.com/watch?v=g21royNJ4fw) of running LightRAG locally.
* All the code can be found in the `examples`.
* Set OpenAI API key in environment if using OpenAI models: `export OPENAI_API_KEY="sk-...".`
* Download the demo text "A Christmas Carol by Charles Dickens":
+
```bash
curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt
```
@@ -187,6 +193,7 @@ class QueryParam:
Using Open AI-like APIs
* LightRAG also supports Open AI-like chat/embeddings APIs:
+
```python
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
@@ -225,6 +232,7 @@ async def initialize_rag():
return rag
```
+
@@ -252,12 +260,14 @@ rag = LightRAG(
),
)
```
+
Using Ollama Models
### Overview
+
If you want to use Ollama models, you need to pull model you plan to use and embedding model, for example `nomic-embed-text`.
Then you only need to set LightRAG as follows:
@@ -281,31 +291,37 @@ rag = LightRAG(
```
### Increasing context size
+
In order for LightRAG to work context should be at least 32k tokens. By default Ollama models have context size of 8k. You can achieve this using one of two ways:
#### Increasing the `num_ctx` parameter in Modelfile.
1. Pull the model:
+
```bash
ollama pull qwen2
```
2. Display the model file:
+
```bash
ollama show --modelfile qwen2 > Modelfile
```
3. Edit the Modelfile by adding the following line:
+
```bash
PARAMETER num_ctx 32768
```
4. Create the modified model:
+
```bash
ollama create -f Modelfile qwen2m
```
#### Setup `num_ctx` via Ollama API.
+
Tiy can use `llm_model_kwargs` param to configure ollama:
```python
@@ -325,6 +341,7 @@ rag = LightRAG(
),
)
```
+
#### Low RAM GPUs
In order to run this experiment on low RAM GPU you should select small model and tune context window (increasing context increase memory consumption). For example, running this ollama example on repurposed mining GPU with 6Gb of RAM required to set context size to 26k while using `gemma2:2b`. It was able to find 197 entities and 19 relations on `book.txt`.
@@ -402,6 +419,7 @@ if __name__ == "__main__":
```
#### For detailed documentation and examples, see:
+
- [LlamaIndex Documentation](lightrag/llm/Readme.md)
- [Direct OpenAI Example](examples/lightrag_llamaindex_direct_demo.py)
- [LiteLLM Proxy Example](examples/lightrag_llamaindex_litellm_demo.py)
@@ -483,13 +501,16 @@ print(response_custom)
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.
##### How It Works?
+
The function operates by dividing the input into two parts:
+
- `User Query`
- `Prompt`
It then performs keyword extraction exclusively on the `user query`. This separation ensures that the extraction process is focused and relevant, unaffected by any additional language in the `prompt`. It also allows the `prompt` to serve purely for response formatting, maintaining the intent and clarity of the user's original question.
##### Usage Example
+
This `example` shows how to tailor the function for educational content, focusing on detailed explanations for older students.
```python
@@ -563,6 +584,7 @@ custom_kg = {
rag.insert_custom_kg(custom_kg)
```
+
## Insert
@@ -593,6 +615,7 @@ rag.insert(["TEXT1", "TEXT2", "TEXT3", ...]) # Documents will be processed in b
```
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
@@ -647,6 +670,7 @@ text_content = textract.process(file_path)
rag.insert(text_content.decode('utf-8'))
```
+
## Storage
@@ -685,6 +709,7 @@ async def initialize_rag():
return rag
```
+
see test_neo4j.py for a working example.
@@ -693,6 +718,7 @@ see test_neo4j.py for a working example.
Using PostgreSQL for Storage
For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE).
+
* PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac.
* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag
* How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
@@ -735,6 +761,7 @@ For production level scenarios you will most likely want to leverage an enterpri
> It is a known issue of the release version: https://github.com/apache/age/pull/1721
>
> You can Compile the AGE from source code and fix it.
+ >
@@ -742,9 +769,11 @@ For production level scenarios you will most likely want to leverage an enterpri
Using Faiss for Storage
- Install the required dependencies:
+
```
pip install faiss-cpu
```
+
You can also install `faiss-gpu` if you have GPU support.
- Here we are using `sentence-transformers` but you can also use `OpenAIEmbedding` model with `3072` dimensions.
@@ -810,6 +839,7 @@ relation = rag.create_relation("Google", "Gmail", {
"weight": 2.0
})
```
+
@@ -835,6 +865,7 @@ updated_relation = rag.edit_relation("Google", "Google Mail", {
"weight": 3.0
})
```
+
All operations are available in both synchronous and asynchronous versions. The asynchronous versions have the prefix "a" (e.g., `acreate_entity`, `aedit_relation`).
@@ -851,6 +882,55 @@ All operations are available in both synchronous and asynchronous versions. The
These operations maintain data consistency across both the graph database and vector database components, ensuring your knowledge graph remains coherent.
+## Data Export Functions
+
+## Overview
+
+LightRAG allows you to export your knowledge graph data in various formats for analysis, sharing, and backup purposes. The system supports exporting entities, relations, and relationship data.
+
+## Export Functions
+
+### Basic Usage
+
+```python
+# Basic CSV export (default format)
+rag.export_data("knowledge_graph.csv")
+
+# Specify any format
+rag.export_data("output.xlsx", file_format="excel")
+```
+
+### Different File Formats supported
+
+```python
+#Export data in CSV format
+rag.export_data("graph_data.csv", file_format="csv")
+
+# Export data in Excel sheet
+rag.export_data("graph_data.xlsx", file_format="excel")
+
+# Export data in markdown format
+rag.export_data("graph_data.md", file_format="md")
+
+# Export data in Text
+rag.export_data("graph_data.txt", file_format="txt")
+```
+## Additional Options
+
+Include vector embeddings in the export (optional):
+
+```python
+rag.export_data("complete_data.csv", include_vector_data=True)
+```
+## Data Included in Export
+
+All exports include:
+
+* Entity information (names, IDs, metadata)
+* Relation data (connections between entities)
+* Relationship information from vector database
+
+
## Entity Merging
@@ -913,6 +993,7 @@ rag.merge_entities(
```
When merging entities:
+
* All relationships from source entities are redirected to the target entity
* Duplicate relationships are intelligently merged
* Self-relationships (loops) are prevented
@@ -946,6 +1027,7 @@ rag.clear_cache(modes=["local"])
```
Valid modes are:
+
- `"default"`: Extraction cache
- `"naive"`: Naive search cache
- `"local"`: Local search cache
@@ -960,33 +1042,33 @@ Valid modes are:
Parameters
-| **Parameter** | **Type** | **Explanation** | **Default** |
-|----------------------------------------------| --- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
-| **working\_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` |
-| **kv\_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`, `OracleKVStorage` | `JsonKVStorage` |
-| **vector\_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`, `OracleVectorDBStorage` | `NanoVectorDBStorage` |
-| **graph\_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
-| **chunk\_token\_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
-| **chunk\_overlap\_token\_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
-| **tiktoken\_model\_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
-| **entity\_extract\_max\_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` |
-| **entity\_summary\_to\_max\_tokens** | `int` | Maximum token size for each entity summary | `500` |
-| **node\_embedding\_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |
-| **node2vec\_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` |
-| **embedding\_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embed` |
-| **embedding\_batch\_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` |
-| **embedding\_func\_max\_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
-| **llm\_model\_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` |
-| **llm\_model\_name** | `str` | LLM model name for generation | `meta-llama/Llama-3.2-1B-Instruct` |
-| **llm\_model\_max\_token\_size** | `int` | Maximum token size for LLM generation (affects entity relation summaries) | `32768`（default value changed by env var MAX_TOKENS) |
-| **llm\_model\_max\_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `16`（default value changed by env var MAX_ASYNC) |
-| **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation | |
-| **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` |
-| **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}` |
+| **Parameter** | **Type** | **Explanation** | **Default** |
+| -------------------------------------------------- | ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+| **working\_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` |
+| **kv\_storage** | `str` | Storage type for documents and text chunks. Supported types:`JsonKVStorage`, `OracleKVStorage` | `JsonKVStorage` |
+| **vector\_storage** | `str` | Storage type for embedding vectors. Supported types:`NanoVectorDBStorage`, `OracleVectorDBStorage` | `NanoVectorDBStorage` |
+| **graph\_storage** | `str` | Storage type for graph edges and nodes. Supported types:`NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
+| **chunk\_token\_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
+| **chunk\_overlap\_token\_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
+| **tiktoken\_model\_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
+| **entity\_extract\_max\_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` |
+| **entity\_summary\_to\_max\_tokens** | `int` | Maximum token size for each entity summary | `500` |
+| **node\_embedding\_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |
+| **node2vec\_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` |
+| **embedding\_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embed` |
+| **embedding\_batch\_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` |
+| **embedding\_func\_max\_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
+| **llm\_model\_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` |
+| **llm\_model\_name** | `str` | LLM model name for generation | `meta-llama/Llama-3.2-1B-Instruct` |
+| **llm\_model\_max\_token\_size** | `int` | Maximum token size for LLM generation (affects entity relation summaries) | `32768`（default value changed by env var MAX_TOKENS) |
+| **llm\_model\_max\_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `16`（default value changed by env var MAX_ASYNC) |
+| **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation | |
+| **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` |
+| **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}` |
@@ -996,12 +1078,15 @@ Valid modes are:
Click to view error handling details
The API includes comprehensive error handling:
+
- File not found errors (404)
- Processing errors (500)
- Supports multiple file encodings (UTF-8 and GBK)
+
## API
+
LightRag can be installed with API support to serve a Fast api interface to perform data upload and indexing/Rag operations/Rescan of the input folder etc..
[LightRag API](lightrag/api/README.md)
@@ -1035,7 +1120,6 @@ net.show('knowledge_graph.html')
Graph visualization with Neo4
-
* The following code can be found in `examples/graph_visual_with_neo4j.py`
```python
@@ -1171,10 +1255,13 @@ LightRag can be installed with Tools support to add extra tools like the graphml
## Evaluation
+
### Dataset
+
The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain).
### Generate Query
+
LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `example/generate_query.py`.
@@ -1203,9 +1290,11 @@ Output the results in the following structure:
- User 5: [user description]
...
```
+
### Batch Eval
+
To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
@@ -1253,37 +1342,40 @@ Output your evaluation in the following JSON format:
}}
}}
```
+
### Overall Performance Table
-| | **Agriculture** | | **CS** | | **Legal** | | **Mix** | |
-|----------------------|-------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
-| | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** |
-| **Comprehensiveness** | 32.4% | **67.6%** | 38.4% | **61.6%** | 16.4% | **83.6%** | 38.8% | **61.2%** |
-| **Diversity** | 23.6% | **76.4%** | 38.0% | **62.0%** | 13.6% | **86.4%** | 32.4% | **67.6%** |
-| **Empowerment** | 32.4% | **67.6%** | 38.8% | **61.2%** | 16.4% | **83.6%** | 42.8% | **57.2%** |
-| **Overall** | 32.4% | **67.6%** | 38.8% | **61.2%** | 15.2% | **84.8%** | 40.0% | **60.0%** |
-| | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** |
-| **Comprehensiveness** | 31.6% | **68.4%** | 38.8% | **61.2%** | 15.2% | **84.8%** | 39.2% | **60.8%** |
-| **Diversity** | 29.2% | **70.8%** | 39.2% | **60.8%** | 11.6% | **88.4%** | 30.8% | **69.2%** |
-| **Empowerment** | 31.6% | **68.4%** | 36.4% | **63.6%** | 15.2% | **84.8%** | 42.4% | **57.6%** |
-| **Overall** | 32.4% | **67.6%** | 38.0% | **62.0%** | 14.4% | **85.6%** | 40.0% | **60.0%** |
-| | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** |
-| **Comprehensiveness** | 26.0% | **74.0%** | 41.6% | **58.4%** | 26.8% | **73.2%** | 40.4% | **59.6%** |
-| **Diversity** | 24.0% | **76.0%** | 38.8% | **61.2%** | 20.0% | **80.0%** | 32.4% | **67.6%** |
-| **Empowerment** | 25.2% | **74.8%** | 40.8% | **59.2%** | 26.0% | **74.0%** | 46.0% | **54.0%** |
-| **Overall** | 24.8% | **75.2%** | 41.6% | **58.4%** | 26.4% | **73.6%** | 42.4% | **57.6%** |
-| | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** |
-| **Comprehensiveness** | 45.6% | **54.4%** | 48.4% | **51.6%** | 48.4% | **51.6%** | **50.4%** | 49.6% |
-| **Diversity** | 22.8% | **77.2%** | 40.8% | **59.2%** | 26.4% | **73.6%** | 36.0% | **64.0%** |
-| **Empowerment** | 41.2% | **58.8%** | 45.2% | **54.8%** | 43.6% | **56.4%** | **50.8%** | 49.2% |
-| **Overall** | 45.2% | **54.8%** | 48.0% | **52.0%** | 47.2% | **52.8%** | **50.4%** | 49.6% |
+| | **Agriculture** | | **CS** | | **Legal** | | **Mix** | |
+| --------------------------- | --------------------- | ------------------ | ------------ | ------------------ | --------------- | ------------------ | --------------- | ------------------ |
+| | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** |
+| **Comprehensiveness** | 32.4% | **67.6%** | 38.4% | **61.6%** | 16.4% | **83.6%** | 38.8% | **61.2%** |
+| **Diversity** | 23.6% | **76.4%** | 38.0% | **62.0%** | 13.6% | **86.4%** | 32.4% | **67.6%** |
+| **Empowerment** | 32.4% | **67.6%** | 38.8% | **61.2%** | 16.4% | **83.6%** | 42.8% | **57.2%** |
+| **Overall** | 32.4% | **67.6%** | 38.8% | **61.2%** | 15.2% | **84.8%** | 40.0% | **60.0%** |
+| | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** |
+| **Comprehensiveness** | 31.6% | **68.4%** | 38.8% | **61.2%** | 15.2% | **84.8%** | 39.2% | **60.8%** |
+| **Diversity** | 29.2% | **70.8%** | 39.2% | **60.8%** | 11.6% | **88.4%** | 30.8% | **69.2%** |
+| **Empowerment** | 31.6% | **68.4%** | 36.4% | **63.6%** | 15.2% | **84.8%** | 42.4% | **57.6%** |
+| **Overall** | 32.4% | **67.6%** | 38.0% | **62.0%** | 14.4% | **85.6%** | 40.0% | **60.0%** |
+| | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** |
+| **Comprehensiveness** | 26.0% | **74.0%** | 41.6% | **58.4%** | 26.8% | **73.2%** | 40.4% | **59.6%** |
+| **Diversity** | 24.0% | **76.0%** | 38.8% | **61.2%** | 20.0% | **80.0%** | 32.4% | **67.6%** |
+| **Empowerment** | 25.2% | **74.8%** | 40.8% | **59.2%** | 26.0% | **74.0%** | 46.0% | **54.0%** |
+| **Overall** | 24.8% | **75.2%** | 41.6% | **58.4%** | 26.4% | **73.6%** | 42.4% | **57.6%** |
+| | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** |
+| **Comprehensiveness** | 45.6% | **54.4%** | 48.4% | **51.6%** | 48.4% | **51.6%** | **50.4%** | 49.6% |
+| **Diversity** | 22.8% | **77.2%** | 40.8% | **59.2%** | 26.4% | **73.6%** | 36.0% | **64.0%** |
+| **Empowerment** | 41.2% | **58.8%** | 45.2% | **54.8%** | 43.6% | **56.4%** | **50.8%** | 49.2% |
+| **Overall** | 45.2% | **54.8%** | 48.0% | **52.0%** | 47.2% | **52.8%** | **50.4%** | 49.6% |
## Reproduce
+
All the code can be found in the `./reproduce` directory.
### Step-0 Extract Unique Contexts
+
First, we need to extract unique contexts in the datasets.
@@ -1340,9 +1432,11 @@ def extract_unique_contexts(input_directory, output_directory):
print("All files have been processed.")
```
+
### Step-1 Insert Contexts
+
For the extracted contexts, we insert them into the LightRAG system.
@@ -1366,6 +1460,7 @@ def insert_text(rag, file_path):
if retries == max_retries:
print("Insertion failed after exceeding the maximum number of retries")
```
+
### Step-2 Generate Queries
@@ -1390,9 +1485,11 @@ def get_summary(context, tot_tokens=2000):
return summary
```
+
### Step-3 Query
+
For the queries generated in Step-2, we will extract them and query LightRAG.
@@ -1409,6 +1506,7 @@ def extract_queries(file_path):
return queries
```
+
## Star History
@@ -1441,4 +1539,5 @@ archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
+
**Thank you for your interest in our work!**
diff --git a/lightrag/__init__.py b/lightrag/__init__.py
index 382060f7..89475dca 100644
--- a/lightrag/__init__.py
+++ b/lightrag/__init__.py
@@ -1,5 +1,5 @@
from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
-__version__ = "1.2.5"
+__version__ = "1.2.6"
__author__ = "Zirui Guo"
__url__ = "https://github.com/HKUDS/LightRAG"
diff --git a/lightrag/api/gunicorn_config.py b/lightrag/api/gunicorn_config.py
index 0594ceae..23e46807 100644
--- a/lightrag/api/gunicorn_config.py
+++ b/lightrag/api/gunicorn_config.py
@@ -59,7 +59,7 @@ logconfig_dict = {
},
"filters": {
"path_filter": {
- "()": "lightrag.api.lightrag_server.LightragPathFilter",
+ "()": "lightrag.utils.LightragPathFilter",
},
},
"loggers": {
diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py
index fd09a691..24494705 100644
--- a/lightrag/api/lightrag_server.py
+++ b/lightrag/api/lightrag_server.py
@@ -55,41 +55,6 @@ config = configparser.ConfigParser()
config.read("config.ini")
-class LightragPathFilter(logging.Filter):
- """Filter for lightrag logger to filter out frequent path access logs"""
-
- def __init__(self):
- super().__init__()
- # Define paths to be filtered
- self.filtered_paths = ["/documents", "/health", "/webui/"]
-
- def filter(self, record):
- try:
- # Check if record has the required attributes for an access log
- if not hasattr(record, "args") or not isinstance(record.args, tuple):
- return True
- if len(record.args) < 5:
- return True
-
- # Extract method, path and status from the record args
- method = record.args[1]
- path = record.args[2]
- status = record.args[4]
-
- # Filter out successful GET requests to filtered paths
- if (
- method == "GET"
- and (status == 200 or status == 304)
- and path in self.filtered_paths
- ):
- return False
-
- return True
- except Exception:
- # In case of any error, let the message through
- return True
-
-
def create_app(args):
# Setup logging
logger.setLevel(args.log_level)
@@ -177,6 +142,9 @@ def create_app(args):
if api_key
else "",
version=__api_version__,
+ openapi_url="/openapi.json", # Explicitly set OpenAPI schema URL
+ docs_url="/docs", # Explicitly set docs URL
+ redoc_url="/redoc", # Explicitly set redoc URL
openapi_tags=[{"name": "api"}],
lifespan=lifespan,
)
@@ -516,7 +484,7 @@ def configure_logging():
},
"filters": {
"path_filter": {
- "()": "lightrag.api.lightrag_server.LightragPathFilter",
+ "()": "lightrag.utils.LightragPathFilter",
},
},
}
diff --git a/lightrag/api/routers/document_routes.py b/lightrag/api/routers/document_routes.py
index c1666192..7b6f11c1 100644
--- a/lightrag/api/routers/document_routes.py
+++ b/lightrag/api/routers/document_routes.py
@@ -99,6 +99,37 @@ class DocsStatusesResponse(BaseModel):
statuses: Dict[DocStatus, List[DocStatusResponse]] = {}
+class PipelineStatusResponse(BaseModel):
+ """Response model for pipeline status
+
+ Attributes:
+ autoscanned: Whether auto-scan has started
+ busy: Whether the pipeline is currently busy
+ job_name: Current job name (e.g., indexing files/indexing texts)
+ job_start: Job start time as ISO format string (optional)
+ docs: Total number of documents to be indexed
+ batchs: Number of batches for processing documents
+ cur_batch: Current processing batch
+ request_pending: Flag for pending request for processing
+ latest_message: Latest message from pipeline processing
+ history_messages: List of history messages
+ """
+
+ autoscanned: bool = False
+ busy: bool = False
+ job_name: str = "Default Job"
+ job_start: Optional[str] = None
+ docs: int = 0
+ batchs: int = 0
+ cur_batch: int = 0
+ request_pending: bool = False
+ latest_message: str = ""
+ history_messages: Optional[List[str]] = None
+
+ class Config:
+ extra = "allow" # Allow additional fields from the pipeline status
+
+
class DocumentManager:
def __init__(
self,
@@ -247,7 +278,7 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool:
if global_args["main_args"].document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
- from docling.document_converter import DocumentConverter
+ from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
@@ -266,7 +297,7 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool:
if global_args["main_args"].document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
- from docling.document_converter import DocumentConverter
+ from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
@@ -286,7 +317,7 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool:
if global_args["main_args"].document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
- from docling.document_converter import DocumentConverter
+ from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
@@ -307,7 +338,7 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool:
if global_args["main_args"].document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
- from docling.document_converter import DocumentConverter
+ from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
@@ -718,17 +749,33 @@ def create_document_routes(
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
- @router.get("/pipeline_status", dependencies=[Depends(optional_api_key)])
- async def get_pipeline_status():
+ @router.get(
+ "/pipeline_status",
+ dependencies=[Depends(optional_api_key)],
+ response_model=PipelineStatusResponse,
+ )
+ async def get_pipeline_status() -> PipelineStatusResponse:
"""
Get the current status of the document indexing pipeline.
This endpoint returns information about the current state of the document processing pipeline,
- including whether it's busy, the current job name, when it started, how many documents
- are being processed, how many batches there are, and which batch is currently being processed.
+ including the processing status, progress information, and history messages.
Returns:
- dict: A dictionary containing the pipeline status information
+ PipelineStatusResponse: A response object containing:
+ - autoscanned (bool): Whether auto-scan has started
+ - busy (bool): Whether the pipeline is currently busy
+ - job_name (str): Current job name (e.g., indexing files/indexing texts)
+ - job_start (str, optional): Job start time as ISO format string
+ - docs (int): Total number of documents to be indexed
+ - batchs (int): Number of batches for processing documents
+ - cur_batch (int): Current processing batch
+ - request_pending (bool): Flag for pending request for processing
+ - latest_message (str): Latest message from pipeline processing
+ - history_messages (List[str], optional): List of history messages
+
+ Raises:
+ HTTPException: If an error occurs while retrieving pipeline status (500)
"""
try:
from lightrag.kg.shared_storage import get_namespace_data
@@ -746,7 +793,7 @@ def create_document_routes(
if status_dict.get("job_start"):
status_dict["job_start"] = str(status_dict["job_start"])
- return status_dict
+ return PipelineStatusResponse(**status_dict)
except Exception as e:
logger.error(f"Error getting pipeline status: {str(e)}")
logger.error(traceback.format_exc())
diff --git a/lightrag/api/webui/assets/index-CJz72b6Q.js b/lightrag/api/webui/assets/index-BlVvSIic.js
similarity index 97%
rename from lightrag/api/webui/assets/index-CJz72b6Q.js
rename to lightrag/api/webui/assets/index-BlVvSIic.js
index 26bfadd6..379cdc89 100644
--- a/lightrag/api/webui/assets/index-CJz72b6Q.js
+++ b/lightrag/api/webui/assets/index-BlVvSIic.js
@@ -236,23 +236,23 @@ ${(t=e.config)==null?void 0:t.url}`):e});const sq=async(e,t,n)=>(await Mr.get(`/
margin-right: `).concat(u,"px ").concat(r,`;
`),n==="padding"&&"padding-right: ".concat(u,"px ").concat(r,";")].filter(Boolean).join(""),`
}
-
+
.`).concat(hd,` {
right: `).concat(u,"px ").concat(r,`;
}
-
+
.`).concat(md,` {
margin-right: `).concat(u,"px ").concat(r,`;
}
-
+
.`).concat(hd," .").concat(hd,` {
right: 0 `).concat(r,`;
}
-
+
.`).concat(md," .").concat(md,` {
margin-right: 0 `).concat(r,`;
}
-
+
body[`).concat(cl,`] {
`).concat(X8,": ").concat(u,`px;
}
@@ -702,7 +702,7 @@ void main() {
vec2 sourceCompensationVector =
vec2(-sourceDirection * unitNormal.y, sourceDirection * unitNormal.x)
* (webGLSourceRadius + webGLSourceArrowHeadLength);
-
+
// Target arrow head
float targetRadius = a_targetRadius * a_targetRadiusCoef;
float targetDirection = sign(targetRadius);
@@ -1009,7 +1009,7 @@ void main() {
position = viewportToClipspace(viewportOffsetPosition, u_dimensions);
gl_Position = vec4(position, 0, 1);
-
+
`).concat(n?`
v_targetSize = a_targetSize * u_pixelRatio / u_sizeRatio;
v_targetPoint = viewportTarget;
@@ -1038,7 +1038,7 @@ void main() {
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/var aN;function uJ(){if(aN)return am;aN=1;var e=nf();function t(g,b){return g===b&&(g!==0||1/g===1/b)||g!==g&&b!==b}var n=typeof Object.is=="function"?Object.is:t,r=e.useState,a=e.useEffect,i=e.useLayoutEffect,l=e.useDebugValue;function u(g,b){var m=b(),S=r({inst:{value:m,getSnapshot:b}}),v=S[0].inst,A=S[1];return i(function(){v.value=m,v.getSnapshot=b,c(v)&&A({inst:v})},[g,m,b]),a(function(){return c(v)&&A({inst:v}),g(function(){c(v)&&A({inst:v})})},[g]),l(m),m}function c(g){var b=g.getSnapshot;g=g.value;try{var m=b();return!n(g,m)}catch{return!0}}function f(g,b){return b()}var p=typeof window>"u"||typeof window.document>"u"||typeof window.document.createElement>"u"?f:u;return am.useSyncExternalStore=e.useSyncExternalStore!==void 0?e.useSyncExternalStore:p,am}var oN;function cJ(){return oN||(oN=1,rm.exports=uJ()),rm.exports}var dJ=cJ(),Hs='[cmdk-group=""]',om='[cmdk-group-items=""]',fJ='[cmdk-group-heading=""]',w0='[cmdk-item=""]',iN=`${w0}:not([aria-disabled="true"])`,$x="cmdk-item-select",Qo="data-value",pJ=(e,t,n)=>sJ(e,t,n),O3=k.createContext(void 0),ku=()=>k.useContext(O3),D3=k.createContext(void 0),x0=()=>k.useContext(D3),I3=k.createContext(void 0),L3=k.forwardRef((e,t)=>{let n=ol(()=>{var U,L;return{search:"",value:(L=(U=e.value)!=null?U:e.defaultValue)!=null?L:"",filtered:{count:0,items:new Map,groups:new Set}}}),r=ol(()=>new Set),a=ol(()=>new Map),i=ol(()=>new Map),l=ol(()=>new Set),u=M3(e),{label:c,children:f,value:p,onValueChange:g,filter:b,shouldFilter:m,loop:S,disablePointerSelection:v=!1,vimBindings:A=!0,...R}=e,x=En(),T=En(),C=En(),N=k.useRef(null),_=kJ();ii(()=>{if(p!==void 0){let U=p.trim();n.current.value=U,O.emit()}},[p]),ii(()=>{_(6,P)},[]);let O=k.useMemo(()=>({subscribe:U=>(l.current.add(U),()=>l.current.delete(U)),snapshot:()=>n.current,setState:(U,L,Y)=>{var 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0,this._tree=t,this._prefix=n}atPrefix(t){if(!t.startsWith(this._prefix))throw new Error("Mismatched prefix");const[n,r]=Gd(this._tree,t.slice(this._prefix.length));if(n===void 0){const[a,i]=O0(r);for(const l of a.keys())if(l!==fn&&l.startsWith(i)){const u=new Map;return u.set(l.slice(i.length),a.get(l)),new po(u,t)}}return new po(n,t)}clear(){this._size=void 0,this._tree.clear()}delete(t){return this._size=void 0,gee(this._tree,t)}entries(){return new sm(this,fee)}forEach(t){for(const[n,r]of this)t(n,r,this)}fuzzyGet(t,n){return pee(this._tree,t,n)}get(t){const n=qx(this._tree,t);return n!==void 0?n.get(fn):void 0}has(t){const n=qx(this._tree,t);return n!==void 0&&n.has(fn)}keys(){return new sm(this,K3)}set(t,n){if(typeof t!="string")throw new Error("key must be a string");return this._size=void 0,um(this._tree,t).set(fn,n),this}get size(){if(this._size)return this._size;this._size=0;const t=this.entries();for(;!t.next().done;)this._size+=1;return this._size}update(t,n){if(typeof t!="string")throw new Error("key must be a string");this._size=void 0;const r=um(this._tree,t);return r.set(fn,n(r.get(fn))),this}fetch(t,n){if(typeof t!="string")throw new Error("key must be a string");this._size=void 0;const r=um(this._tree,t);let a=r.get(fn);return a===void 0&&r.set(fn,a=n()),a}values(){return new sm(this,X3)}[Symbol.iterator](){return this.entries()}static from(t){const n=new po;for(const[r,a]of t)n.set(r,a);return n}static fromObject(t){return po.from(Object.entries(t))}}const Gd=(e,t,n=[])=>{if(t.length===0||e==null)return[e,n];for(const r of e.keys())if(r!==fn&&t.startsWith(r))return n.push([e,r]),Gd(e.get(r),t.slice(r.length),n);return n.push([e,t]),Gd(void 0,"",n)},qx=(e,t)=>{if(t.length===0||e==null)return e;for(const n of e.keys())if(n!==fn&&t.startsWith(n))return qx(e.get(n),t.slice(n.length))},um=(e,t)=>{const n=t.length;e:for(let r=0;e&&r{const[n,r]=Gd(e,t);if(n!==void 0){if(n.delete(fn),n.size===0)Q3(r);else if(n.size===1){const[a,i]=n.entries().next().value;J3(r,a,i)}}},Q3=e=>{if(e.length===0)return;const[t,n]=O0(e);if(t.delete(n),t.size===0)Q3(e.slice(0,-1));else if(t.size===1){const[r,a]=t.entries().next().value;r!==fn&&J3(e.slice(0,-1),r,a)}},J3=(e,t,n)=>{if(e.length===0)return;const[r,a]=O0(e);r.set(a+t,n),r.delete(a)},O0=e=>e[e.length-1],D0="or",e4="and",hee="and_not";class mo{constructor(t){if((t==null?void 0:t.fields)==null)throw new Error('MiniSearch: option "fields" must be provided');const n=t.autoVacuum==null||t.autoVacuum===!0?fm:t.autoVacuum;this._options={...dm,...t,autoVacuum:n,searchOptions:{...mN,...t.searchOptions||{}},autoSuggestOptions:{...See,...t.autoSuggestOptions||{}}},this._index=new po,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldIds={},this._fieldLength=new Map,this._avgFieldLength=[],this._nextId=0,this._storedFields=new Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=Wx,this.addFields(this._options.fields)}add(t){const{extractField:n,tokenize:r,processTerm:a,fields:i,idField:l}=this._options,u=n(t,l);if(u==null)throw new Error(`MiniSearch: document does not have ID field "${l}"`);if(this._idToShortId.has(u))throw new Error(`MiniSearch: duplicate ID ${u}`);const c=this.addDocumentId(u);this.saveStoredFields(c,t);for(const f of i){const p=n(t,f);if(p==null)continue;const g=r(p.toString(),f),b=this._fieldIds[f],m=new Set(g).size;this.addFieldLength(c,b,this._documentCount-1,m);for(const S of g){const v=a(S,f);if(Array.isArray(v))for(const A of v)this.addTerm(b,c,A);else v&&this.addTerm(b,c,v)}}}addAll(t){for(const n of t)this.add(n)}addAllAsync(t,n={}){const{chunkSize:r=10}=n,a={chunk:[],promise:Promise.resolve()},{chunk:i,promise:l}=t.reduce(({chunk:u,promise:c},f,p)=>(u.push(f),(p+1)%r===0?{chunk:[],promise:c.then(()=>new Promise(g=>setTimeout(g,0))).then(()=>this.addAll(u))}:{chunk:u,promise:c}),a);return l.then(()=>this.addAll(i))}remove(t){const{tokenize:n,processTerm:r,extractField:a,fields:i,idField:l}=this._options,u=a(t,l);if(u==null)throw new Error(`MiniSearch: document does not have ID field "${l}"`);const c=this._idToShortId.get(u);if(c==null)throw new Error(`MiniSearch: cannot remove document with ID ${u}: it is not in the index`);for(const f of i){const p=a(t,f);if(p==null)continue;const g=n(p.toString(),f),b=this._fieldIds[f],m=new Set(g).size;this.removeFieldLength(c,b,this._documentCount,m);for(const S of g){const v=r(S,f);if(Array.isArray(v))for(const A of v)this.removeTerm(b,c,A);else v&&this.removeTerm(b,c,v)}}this._storedFields.delete(c),this._documentIds.delete(c),this._idToShortId.delete(u),this._fieldLength.delete(c),this._documentCount-=1}removeAll(t){if(t)for(const n of t)this.remove(n);else{if(arguments.length>0)throw new Error("Expected documents to be present. Omit the argument to remove all documents.");this._index=new po,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}}discard(t){const n=this._idToShortId.get(t);if(n==null)throw new Error(`MiniSearch: cannot discard document with ID ${t}: it is not in the index`);this._idToShortId.delete(t),this._documentIds.delete(n),this._storedFields.delete(n),(this._fieldLength.get(n)||[]).forEach((r,a)=>{this.removeFieldLength(n,a,this._documentCount,r)}),this._fieldLength.delete(n),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()}maybeAutoVacuum(){if(this._options.autoVacuum===!1)return;const{minDirtFactor:t,minDirtCount:n,batchSize:r,batchWait:a}=this._options.autoVacuum;this.conditionalVacuum({batchSize:r,batchWait:a},{minDirtCount:n,minDirtFactor:t})}discardAll(t){const n=this._options.autoVacuum;try{this._options.autoVacuum=!1;for(const r of t)this.discard(r)}finally{this._options.autoVacuum=n}this.maybeAutoVacuum()}replace(t){const{idField:n,extractField:r}=this._options,a=r(t,n);this.discard(a),this.add(t)}vacuum(t={}){return this.conditionalVacuum(t)}conditionalVacuum(t,n){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&n,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const r=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=Wx,this.performVacuuming(t,r)}),this._enqueuedVacuum)):this.vacuumConditionsMet(n)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(t),this._currentVacuum)}async performVacuuming(t,n){const r=this._dirtCount;if(this.vacuumConditionsMet(n)){const a=t.batchSize||Vx.batchSize,i=t.batchWait||Vx.batchWait;let l=1;for(const[u,c]of this._index){for(const[f,p]of c)for(const[g]of p)this._documentIds.has(g)||(p.size<=1?c.delete(f):p.delete(g));this._index.get(u).size===0&&this._index.delete(u),l%a===0&&await new Promise(f=>setTimeout(f,i)),l+=1}this._dirtCount-=r}await null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null}vacuumConditionsMet(t){if(t==null)return!0;let{minDirtCount:n,minDirtFactor:r}=t;return n=n||fm.minDirtCount,r=r||fm.minDirtFactor,this.dirtCount>=n&&this.dirtFactor>=r}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(t){return this._idToShortId.has(t)}getStoredFields(t){const n=this._idToShortId.get(t);if(n!=null)return this._storedFields.get(n)}search(t,n={}){const{searchOptions:r}=this._options,a={...r,...n},i=this.executeQuery(t,n),l=[];for(const[u,{score:c,terms:f,match:p}]of i){const g=f.length||1,b={id:this._documentIds.get(u),score:c*g,terms:Object.keys(p),queryTerms:f,match:p};Object.assign(b,this._storedFields.get(u)),(a.filter==null||a.filter(b))&&l.push(b)}return t===mo.wildcard&&a.boostDocument==null||l.sort(yN),l}autoSuggest(t,n={}){n={...this._options.autoSuggestOptions,...n};const r=new Map;for(const{score:i,terms:l}of this.search(t,n)){const u=l.join(" "),c=r.get(u);c!=null?(c.score+=i,c.count+=1):r.set(u,{score:i,terms:l,count:1})}const a=[];for(const[i,{score:l,terms:u,count:c}]of r)a.push({suggestion:i,terms:u,score:l/c});return a.sort(yN),a}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(t,n){if(n==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(t),n)}static async loadJSONAsync(t,n){if(n==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJSAsync(JSON.parse(t),n)}static getDefault(t){if(dm.hasOwnProperty(t))return cm(dm,t);throw new Error(`MiniSearch: unknown option "${t}"`)}static loadJS(t,n){const{index:r,documentIds:a,fieldLength:i,storedFields:l,serializationVersion:u}=t,c=this.instantiateMiniSearch(t,n);c._documentIds=td(a),c._fieldLength=td(i),c._storedFields=td(l);for(const[f,p]of c._documentIds)c._idToShortId.set(p,f);for(const[f,p]of r){const g=new Map;for(const b of Object.keys(p)){let m=p[b];u===1&&(m=m.ds),g.set(parseInt(b,10),td(m))}c._index.set(f,g)}return c}static async loadJSAsync(t,n){const{index:r,documentIds:a,fieldLength:i,storedFields:l,serializationVersion:u}=t,c=this.instantiateMiniSearch(t,n);c._documentIds=await nd(a),c._fieldLength=await nd(i),c._storedFields=await nd(l);for(const[p,g]of c._documentIds)c._idToShortId.set(g,p);let f=0;for(const[p,g]of r){const b=new Map;for(const m of Object.keys(g)){let S=g[m];u===1&&(S=S.ds),b.set(parseInt(m,10),await nd(S))}++f%1e3===0&&await t4(0),c._index.set(p,b)}return c}static instantiateMiniSearch(t,n){const{documentCount:r,nextId:a,fieldIds:i,averageFieldLength:l,dirtCount:u,serializationVersion:c}=t;if(c!==1&&c!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const f=new mo(n);return f._documentCount=r,f._nextId=a,f._idToShortId=new Map,f._fieldIds=i,f._avgFieldLength=l,f._dirtCount=u||0,f._index=new po,f}executeQuery(t,n={}){if(t===mo.wildcard)return this.executeWildcardQuery(n);if(typeof t!="string"){const b={...n,...t,queries:void 0},m=t.queries.map(S=>this.executeQuery(S,b));return this.combineResults(m,b.combineWith)}const{tokenize:r,processTerm:a,searchOptions:i}=this._options,l={tokenize:r,processTerm:a,...i,...n},{tokenize:u,processTerm:c}=l,g=u(t).flatMap(b=>c(b)).filter(b=>!!b).map(vee(l)).map(b=>this.executeQuerySpec(b,l));return this.combineResults(g,l.combineWith)}executeQuerySpec(t,n){const r={...this._options.searchOptions,...n},a=(r.fields||this._options.fields).reduce((v,A)=>({...v,[A]:cm(r.boost,A)||1}),{}),{boostDocument:i,weights:l,maxFuzzy:u,bm25:c}=r,{fuzzy:f,prefix:p}={...mN.weights,...l},g=this._index.get(t.term),b=this.termResults(t.term,t.term,1,t.termBoost,g,a,i,c);let m,S;if(t.prefix&&(m=this._index.atPrefix(t.term)),t.fuzzy){const v=t.fuzzy===!0?.2:t.fuzzy,A=v<1?Math.min(u,Math.round(t.term.length*v)):v;A&&(S=this._index.fuzzyGet(t.term,A))}if(m)for(const[v,A]of m){const R=v.length-t.term.length;if(!R)continue;S==null||S.delete(v);const x=p*v.length/(v.length+.3*R);this.termResults(t.term,v,x,t.termBoost,A,a,i,c,b)}if(S)for(const v of S.keys()){const[A,R]=S.get(v);if(!R)continue;const x=f*v.length/(v.length+R);this.termResults(t.term,v,x,t.termBoost,A,a,i,c,b)}return b}executeWildcardQuery(t){const n=new Map,r={...this._options.searchOptions,...t};for(const[a,i]of this._documentIds){const l=r.boostDocument?r.boostDocument(i,"",this._storedFields.get(a)):1;n.set(a,{score:l,terms:[],match:{}})}return n}combineResults(t,n=D0){if(t.length===0)return new Map;const r=n.toLowerCase(),a=mee[r];if(!a)throw new Error(`Invalid combination operator: ${n}`);return t.reduce(a)||new Map}toJSON(){const t=[];for(const[n,r]of this._index){const a={};for(const[i,l]of r)a[i]=Object.fromEntries(l);t.push([n,a])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:t,serializationVersion:2}}termResults(t,n,r,a,i,l,u,c,f=new Map){if(i==null)return f;for(const p of Object.keys(l)){const g=l[p],b=this._fieldIds[p],m=i.get(b);if(m==null)continue;let S=m.size;const v=this._avgFieldLength[b];for(const A of m.keys()){if(!this._documentIds.has(A)){this.removeTerm(b,A,n),S-=1;continue}const R=u?u(this._documentIds.get(A),n,this._storedFields.get(A)):1;if(!R)continue;const x=m.get(A),T=this._fieldLength.get(A)[b],C=yee(x,S,this._documentCount,T,v,c),N=r*a*g*R*C,_=f.get(A);if(_){_.score+=N,Eee(_.terms,t);const O=cm(_.match,n);O?O.push(p):_.match[n]=[p]}else f.set(A,{score:N,terms:[t],match:{[n]:[p]}})}}return f}addTerm(t,n,r){const a=this._index.fetch(r,vN);let i=a.get(t);if(i==null)i=new Map,i.set(n,1),a.set(t,i);else{const l=i.get(n);i.set(n,(l||0)+1)}}removeTerm(t,n,r){if(!this._index.has(r)){this.warnDocumentChanged(n,t,r);return}const a=this._index.fetch(r,vN),i=a.get(t);i==null||i.get(n)==null?this.warnDocumentChanged(n,t,r):i.get(n)<=1?i.size<=1?a.delete(t):i.delete(n):i.set(n,i.get(n)-1),this._index.get(r).size===0&&this._index.delete(r)}warnDocumentChanged(t,n,r){for(const a of Object.keys(this._fieldIds))if(this._fieldIds[a]===n){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(t)} has changed before removal: term "${r}" was not present in field "${a}". Removing a document after it has changed can corrupt the index!`,"version_conflict");return}}addDocumentId(t){const n=this._nextId;return this._idToShortId.set(t,n),this._documentIds.set(n,t),this._documentCount+=1,this._nextId+=1,n}addFields(t){for(let n=0;nObject.prototype.hasOwnProperty.call(e,t)?e[t]:void 0,mee={[D0]:(e,t)=>{for(const n of t.keys()){const r=e.get(n);if(r==null)e.set(n,t.get(n));else{const{score:a,terms:i,match:l}=t.get(n);r.score=r.score+a,r.match=Object.assign(r.match,l),bN(r.terms,i)}}return e},[e4]:(e,t)=>{const n=new Map;for(const r of t.keys()){const a=e.get(r);if(a==null)continue;const{score:i,terms:l,match:u}=t.get(r);bN(a.terms,l),n.set(r,{score:a.score+i,terms:a.terms,match:Object.assign(a.match,u)})}return n},[hee]:(e,t)=>{for(const n of t.keys())e.delete(n);return e}},bee={k:1.2,b:.7,d:.5},yee=(e,t,n,r,a,i)=>{const{k:l,b:u,d:c}=i;return Math.log(1+(n-t+.5)/(t+.5))*(c+e*(l+1)/(e+l*(1-u+u*r/a)))},vee=e=>(t,n,r)=>{const a=typeof e.fuzzy=="function"?e.fuzzy(t,n,r):e.fuzzy||!1,i=typeof e.prefix=="function"?e.prefix(t,n,r):e.prefix===!0,l=typeof e.boostTerm=="function"?e.boostTerm(t,n,r):1;return{term:t,fuzzy:a,prefix:i,termBoost:l}},dm={idField:"id",extractField:(e,t)=>e[t],tokenize:e=>e.split(wee),processTerm:e=>e.toLowerCase(),fields:void 0,searchOptions:void 0,storeFields:[],logger:(e,t)=>{typeof(console==null?void 0:console[e])=="function"&&console[e](t)},autoVacuum:!0},mN={combineWith:D0,prefix:!1,fuzzy:!1,maxFuzzy:6,boost:{},weights:{fuzzy:.45,prefix:.375},bm25:bee},See={combineWith:e4,prefix:(e,t,n)=>t===n.length-1},Vx={batchSize:1e3,batchWait:10},Wx={minDirtFactor:.1,minDirtCount:20},fm={...Vx,...Wx},Eee=(e,t)=>{e.includes(t)||e.push(t)},bN=(e,t)=>{for(const n of t)e.includes(n)||e.push(n)},yN=({score:e},{score:t})=>t-e,vN=()=>new Map,td=e=>{const t=new Map;for(const n of Object.keys(e))t.set(parseInt(n,10),e[n]);return t},nd=async e=>{const t=new Map;let n=0;for(const r of Object.keys(e))t.set(parseInt(r,10),e[r]),++n%1e3===0&&await t4(0);return t},t4=e=>new Promise(t=>setTimeout(t,e)),wee=/[\n\r\p{Z}\p{P}]+/u;function Yx(){return Yx=Object.assign?Object.assign.bind():function(e){for(var t=1;te.length)&&(t=e.length);for(var n=0,r=Array(t);nye.createElement("div",{className:"node"},ye.createElement("span",{className:"render "+(n?"circle":"disc"),style:{backgroundColor:t||"#000"}}),ye.createElement("span",{className:`label ${n?"text-muted":""} ${e?"":"text-italic"}`},e||r.no_label||"No label")),Dee=({id:e,labels:t})=>{const n=br(),r=k.useMemo(()=>{const a=n.getGraph().getNodeAttributes(e),i=n.getSetting("nodeReducer");return Object.assign(Object.assign({color:n.getSetting("defaultNodeColor")},a),i?i(e,a):{})},[n,e]);return 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Lightrag
-
+
diff --git a/lightrag/kg/chroma_impl.py b/lightrag/kg/chroma_impl.py
index f668c87a..84d43326 100644
--- a/lightrag/kg/chroma_impl.py
+++ b/lightrag/kg/chroma_impl.py
@@ -156,7 +156,9 @@ class ChromaVectorDBStorage(BaseVectorStorage):
logger.error(f"Error during ChromaDB upsert: {str(e)}")
raise
- async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+ async def query(
+ self, query: str, top_k: int, ids: list[str] | None = None
+ ) -> list[dict[str, Any]]:
try:
embedding = await self.embedding_func([query])
diff --git a/lightrag/kg/faiss_impl.py b/lightrag/kg/faiss_impl.py
index a5716e9c..57b0cae0 100644
--- a/lightrag/kg/faiss_impl.py
+++ b/lightrag/kg/faiss_impl.py
@@ -171,7 +171,9 @@ class FaissVectorDBStorage(BaseVectorStorage):
logger.info(f"Upserted {len(list_data)} vectors into Faiss index.")
return [m["__id__"] for m in list_data]
- async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+ async def query(
+ self, query: str, top_k: int, ids: list[str] | None = None
+ ) -> list[dict[str, Any]]:
"""
Search by a textual query; returns top_k results with their metadata + similarity distance.
"""
diff --git a/lightrag/kg/milvus_impl.py b/lightrag/kg/milvus_impl.py
index 4fb5f012..4b4577ca 100644
--- a/lightrag/kg/milvus_impl.py
+++ b/lightrag/kg/milvus_impl.py
@@ -101,7 +101,9 @@ class MilvusVectorDBStorage(BaseVectorStorage):
results = self._client.upsert(collection_name=self.namespace, data=list_data)
return results
- async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+ async def query(
+ self, query: str, top_k: int, ids: list[str] | None = None
+ ) -> list[dict[str, Any]]:
embedding = await self.embedding_func([query])
results = self._client.search(
collection_name=self.namespace,
diff --git a/lightrag/kg/mongo_impl.py b/lightrag/kg/mongo_impl.py
index da4dc32c..7d43e4f4 100644
--- a/lightrag/kg/mongo_impl.py
+++ b/lightrag/kg/mongo_impl.py
@@ -938,7 +938,9 @@ class MongoVectorDBStorage(BaseVectorStorage):
return list_data
- async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+ async def query(
+ self, query: str, top_k: int, ids: list[str] | None = None
+ ) -> list[dict[str, Any]]:
"""Queries the vector database using Atlas Vector Search."""
# Generate the embedding
embedding = await self.embedding_func([query])
diff --git a/lightrag/kg/nano_vector_db_impl.py b/lightrag/kg/nano_vector_db_impl.py
index ac010f16..4f739091 100644
--- a/lightrag/kg/nano_vector_db_impl.py
+++ b/lightrag/kg/nano_vector_db_impl.py
@@ -120,7 +120,9 @@ class NanoVectorDBStorage(BaseVectorStorage):
f"embedding is not 1-1 with data, {len(embeddings)} != {len(list_data)}"
)
- async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+ async def query(
+ self, query: str, top_k: int, ids: list[str] | None = None
+ ) -> list[dict[str, Any]]:
# Execute embedding outside of lock to avoid long lock times
embedding = await self.embedding_func([query])
embedding = embedding[0]
diff --git a/lightrag/kg/neo4j_impl.py b/lightrag/kg/neo4j_impl.py
index d0c6c779..2df420df 100644
--- a/lightrag/kg/neo4j_impl.py
+++ b/lightrag/kg/neo4j_impl.py
@@ -553,18 +553,6 @@ class Neo4JStorage(BaseGraphStorage):
logger.error(f"Error during upsert: {str(e)}")
raise
- @retry(
- stop=stop_after_attempt(3),
- wait=wait_exponential(multiplier=1, min=4, max=10),
- retry=retry_if_exception_type(
- (
- neo4jExceptions.ServiceUnavailable,
- neo4jExceptions.TransientError,
- neo4jExceptions.WriteServiceUnavailable,
- neo4jExceptions.ClientError,
- )
- ),
- )
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10),
@@ -666,14 +654,14 @@ class Neo4JStorage(BaseGraphStorage):
main_query = """
MATCH (n)
OPTIONAL MATCH (n)-[r]-()
- WITH n, count(r) AS degree
+ WITH n, COALESCE(count(r), 0) AS degree
WHERE degree >= $min_degree
ORDER BY degree DESC
LIMIT $max_nodes
WITH collect({node: n}) AS filtered_nodes
UNWIND filtered_nodes AS node_info
WITH collect(node_info.node) AS kept_nodes, filtered_nodes
- MATCH (a)-[r]-(b)
+ OPTIONAL MATCH (a)-[r]-(b)
WHERE a IN kept_nodes AND b IN kept_nodes
RETURN filtered_nodes AS node_info,
collect(DISTINCT r) AS relationships
@@ -703,7 +691,7 @@ class Neo4JStorage(BaseGraphStorage):
WITH start, nodes, relationships
UNWIND nodes AS node
OPTIONAL MATCH (node)-[r]-()
- WITH node, count(r) AS degree, start, nodes, relationships
+ WITH node, COALESCE(count(r), 0) AS degree, start, nodes, relationships
WHERE node = start OR EXISTS((start)--(node)) OR degree >= $min_degree
ORDER BY
CASE
@@ -716,7 +704,7 @@ class Neo4JStorage(BaseGraphStorage):
WITH collect({node: node}) AS filtered_nodes
UNWIND filtered_nodes AS node_info
WITH collect(node_info.node) AS kept_nodes, filtered_nodes
- MATCH (a)-[r]-(b)
+ OPTIONAL MATCH (a)-[r]-(b)
WHERE a IN kept_nodes AND b IN kept_nodes
RETURN filtered_nodes AS node_info,
collect(DISTINCT r) AS relationships
@@ -744,11 +732,7 @@ class Neo4JStorage(BaseGraphStorage):
result.nodes.append(
KnowledgeGraphNode(
id=f"{node_id}",
- labels=[
- label
- for label in node.labels
- if label != "base"
- ],
+ labels=[node.get("entity_id")],
properties=dict(node),
)
)
@@ -865,9 +849,7 @@ class Neo4JStorage(BaseGraphStorage):
# Create KnowledgeGraphNode for target
target_node = KnowledgeGraphNode(
id=f"{target_id}",
- labels=[
- label for label in b_node.labels if label != "base"
- ],
+ labels=list(f"{target_id}"),
properties=dict(b_node.properties),
)
@@ -907,9 +889,7 @@ class Neo4JStorage(BaseGraphStorage):
# Create initial KnowledgeGraphNode
start_node = KnowledgeGraphNode(
id=f"{node_record['n'].get('entity_id')}",
- labels=[
- label for label in node_record["n"].labels if label != "base"
- ],
+ labels=list(f"{node_record['n'].get('entity_id')}"),
properties=dict(node_record["n"].properties),
)
finally:
diff --git a/lightrag/kg/oracle_impl.py b/lightrag/kg/oracle_impl.py
index 32790f4f..c42f0f76 100644
--- a/lightrag/kg/oracle_impl.py
+++ b/lightrag/kg/oracle_impl.py
@@ -417,7 +417,9 @@ class OracleVectorDBStorage(BaseVectorStorage):
self.db = None
#################### query method ###############
- async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+ async def query(
+ self, query: str, top_k: int, ids: list[str] | None = None
+ ) -> list[dict[str, Any]]:
embeddings = await self.embedding_func([query])
embedding = embeddings[0]
# 转换精度
diff --git a/lightrag/kg/qdrant_impl.py b/lightrag/kg/qdrant_impl.py
index 53a59c2f..e32c4335 100644
--- a/lightrag/kg/qdrant_impl.py
+++ b/lightrag/kg/qdrant_impl.py
@@ -123,7 +123,9 @@ class QdrantVectorDBStorage(BaseVectorStorage):
)
return results
- async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+ async def query(
+ self, query: str, top_k: int, ids: list[str] | None = None
+ ) -> list[dict[str, Any]]:
embedding = await self.embedding_func([query])
results = self._client.search(
collection_name=self.namespace,
diff --git a/lightrag/kg/tidb_impl.py b/lightrag/kg/tidb_impl.py
index c4485df6..0982c914 100644
--- a/lightrag/kg/tidb_impl.py
+++ b/lightrag/kg/tidb_impl.py
@@ -306,7 +306,9 @@ class TiDBVectorDBStorage(BaseVectorStorage):
await ClientManager.release_client(self.db)
self.db = None
- async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+ async def query(
+ self, query: str, top_k: int, ids: list[str] | None = None
+ ) -> list[dict[str, Any]]:
"""Search from tidb vector"""
embeddings = await self.embedding_func([query])
embedding = embeddings[0]
diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py
index 3a5e4e84..a8b11549 100644
--- a/lightrag/lightrag.py
+++ b/lightrag/lightrag.py
@@ -3,11 +3,14 @@ from __future__ import annotations
import asyncio
import configparser
import os
+import csv
import warnings
from dataclasses import asdict, dataclass, field
from datetime import datetime
from functools import partial
-from typing import Any, AsyncIterator, Callable, Iterator, cast, final
+from typing import Any, AsyncIterator, Callable, Iterator, cast, final, Literal
+import pandas as pd
+
from lightrag.kg import (
STORAGE_ENV_REQUIREMENTS,
@@ -1111,6 +1114,7 @@ class LightRAG:
# Prepare node data
node_data: dict[str, str] = {
+ "entity_id": entity_name,
"entity_type": entity_type,
"description": description,
"source_id": source_id,
@@ -1148,6 +1152,7 @@ class LightRAG:
await self.chunk_entity_relation_graph.upsert_node(
need_insert_id,
node_data={
+ "entity_id": need_insert_id,
"source_id": source_id,
"description": "UNKNOWN",
"entity_type": "UNKNOWN",
@@ -1838,9 +1843,10 @@ class LightRAG:
"""
try:
# 1. Get current entity information
- node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
- if not node_data:
+ node_exists = await self.chunk_entity_relation_graph.has_node(entity_name)
+ if not node_exists:
raise ValueError(f"Entity '{entity_name}' does not exist")
+ node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
# Check if entity is being renamed
new_entity_name = updated_data.get("entity_name", entity_name)
@@ -1853,7 +1859,7 @@ class LightRAG:
"Entity renaming is not allowed. Set allow_rename=True to enable this feature"
)
- existing_node = await self.chunk_entity_relation_graph.get_node(
+ existing_node = await self.chunk_entity_relation_graph.has_node(
new_entity_name
)
if existing_node:
@@ -2035,14 +2041,16 @@ class LightRAG:
"""
try:
# 1. Get current relation information
- edge_data = await self.chunk_entity_relation_graph.get_edge(
+ edge_exists = await self.chunk_entity_relation_graph.has_edge(
source_entity, target_entity
)
- if not edge_data:
+ if not edge_exists:
raise ValueError(
f"Relation from '{source_entity}' to '{target_entity}' does not exist"
)
-
+ edge_data = await self.chunk_entity_relation_graph.get_edge(
+ source_entity, target_entity
+ )
# Important: First delete the old relation record from the vector database
old_relation_id = compute_mdhash_id(
source_entity + target_entity, prefix="rel-"
@@ -2151,12 +2159,13 @@ class LightRAG:
"""
try:
# Check if entity already exists
- existing_node = await self.chunk_entity_relation_graph.get_node(entity_name)
+ existing_node = await self.chunk_entity_relation_graph.has_node(entity_name)
if existing_node:
raise ValueError(f"Entity '{entity_name}' already exists")
# Prepare node data with defaults if missing
node_data = {
+ "entity_id": entity_name,
"entity_type": entity_data.get("entity_type", "UNKNOWN"),
"description": entity_data.get("description", ""),
"source_id": entity_data.get("source_id", "manual"),
@@ -2244,7 +2253,7 @@ class LightRAG:
raise ValueError(f"Target entity '{target_entity}' does not exist")
# Check if relation already exists
- existing_edge = await self.chunk_entity_relation_graph.get_edge(
+ existing_edge = await self.chunk_entity_relation_graph.has_edge(
source_entity, target_entity
)
if existing_edge:
@@ -2377,19 +2386,22 @@ class LightRAG:
# 1. Check if all source entities exist
source_entities_data = {}
for entity_name in source_entities:
- node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
- if not node_data:
+ node_exists = await self.chunk_entity_relation_graph.has_node(
+ entity_name
+ )
+ if not node_exists:
raise ValueError(f"Source entity '{entity_name}' does not exist")
+ node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
source_entities_data[entity_name] = node_data
# 2. Check if target entity exists and get its data if it does
target_exists = await self.chunk_entity_relation_graph.has_node(
target_entity
)
- target_entity_data = {}
+ existing_target_entity_data = {}
if target_exists:
- target_entity_data = await self.chunk_entity_relation_graph.get_node(
- target_entity
+ existing_target_entity_data = (
+ await self.chunk_entity_relation_graph.get_node(target_entity)
)
logger.info(
f"Target entity '{target_entity}' already exists, will merge data"
@@ -2398,7 +2410,7 @@ class LightRAG:
# 3. Merge entity data
merged_entity_data = self._merge_entity_attributes(
list(source_entities_data.values())
- + ([target_entity_data] if target_exists else []),
+ + ([existing_target_entity_data] if target_exists else []),
merge_strategy,
)
@@ -2592,6 +2604,322 @@ class LightRAG:
logger.error(f"Error merging entities: {e}")
raise
+ async def aexport_data(
+ self,
+ output_path: str,
+ file_format: Literal["csv", "excel", "md", "txt"] = "csv",
+ include_vector_data: bool = False,
+ ) -> None:
+ """
+ Asynchronously exports all entities, relations, and relationships to various formats.
+ Args:
+ output_path: The path to the output file (including extension).
+ file_format: Output format - "csv", "excel", "md", "txt".
+ - csv: Comma-separated values file
+ - excel: Microsoft Excel file with multiple sheets
+ - md: Markdown tables
+ - txt: Plain text formatted output
+ - table: Print formatted tables to console
+ include_vector_data: Whether to include data from the vector database.
+ """
+ # Collect data
+ entities_data = []
+ relations_data = []
+ relationships_data = []
+
+ # --- Entities ---
+ all_entities = await self.chunk_entity_relation_graph.get_all_labels()
+ for entity_name in all_entities:
+ entity_info = await self.get_entity_info(
+ entity_name, include_vector_data=include_vector_data
+ )
+ entity_row = {
+ "entity_name": entity_name,
+ "source_id": entity_info["source_id"],
+ "graph_data": str(
+ entity_info["graph_data"]
+ ), # Convert to string to ensure compatibility
+ }
+ if include_vector_data and "vector_data" in entity_info:
+ entity_row["vector_data"] = str(entity_info["vector_data"])
+ entities_data.append(entity_row)
+
+ # --- Relations ---
+ for src_entity in all_entities:
+ for tgt_entity in all_entities:
+ if src_entity == tgt_entity:
+ continue
+
+ edge_exists = await self.chunk_entity_relation_graph.has_edge(
+ src_entity, tgt_entity
+ )
+ if edge_exists:
+ relation_info = await self.get_relation_info(
+ src_entity, tgt_entity, include_vector_data=include_vector_data
+ )
+ relation_row = {
+ "src_entity": src_entity,
+ "tgt_entity": tgt_entity,
+ "source_id": relation_info["source_id"],
+ "graph_data": str(
+ relation_info["graph_data"]
+ ), # Convert to string
+ }
+ if include_vector_data and "vector_data" in relation_info:
+ relation_row["vector_data"] = str(relation_info["vector_data"])
+ relations_data.append(relation_row)
+
+ # --- Relationships (from VectorDB) ---
+ all_relationships = await self.relationships_vdb.client_storage
+ for rel in all_relationships["data"]:
+ relationships_data.append(
+ {
+ "relationship_id": rel["__id__"],
+ "data": str(rel), # Convert to string for compatibility
+ }
+ )
+
+ # Export based on format
+ if file_format == "csv":
+ # CSV export
+ with open(output_path, "w", newline="", encoding="utf-8") as csvfile:
+ # Entities
+ if entities_data:
+ csvfile.write("# ENTITIES\n")
+ writer = csv.DictWriter(csvfile, fieldnames=entities_data[0].keys())
+ writer.writeheader()
+ writer.writerows(entities_data)
+ csvfile.write("\n\n")
+
+ # Relations
+ if relations_data:
+ csvfile.write("# RELATIONS\n")
+ writer = csv.DictWriter(
+ csvfile, fieldnames=relations_data[0].keys()
+ )
+ writer.writeheader()
+ writer.writerows(relations_data)
+ csvfile.write("\n\n")
+
+ # Relationships
+ if relationships_data:
+ csvfile.write("# RELATIONSHIPS\n")
+ writer = csv.DictWriter(
+ csvfile, fieldnames=relationships_data[0].keys()
+ )
+ writer.writeheader()
+ writer.writerows(relationships_data)
+
+ elif file_format == "excel":
+ # Excel export
+ entities_df = (
+ pd.DataFrame(entities_data) if entities_data else pd.DataFrame()
+ )
+ relations_df = (
+ pd.DataFrame(relations_data) if relations_data else pd.DataFrame()
+ )
+ relationships_df = (
+ pd.DataFrame(relationships_data)
+ if relationships_data
+ else pd.DataFrame()
+ )
+
+ with pd.ExcelWriter(output_path, engine="xlsxwriter") as writer:
+ if not entities_df.empty:
+ entities_df.to_excel(writer, sheet_name="Entities", index=False)
+ if not relations_df.empty:
+ relations_df.to_excel(writer, sheet_name="Relations", index=False)
+ if not relationships_df.empty:
+ relationships_df.to_excel(
+ writer, sheet_name="Relationships", index=False
+ )
+
+ elif file_format == "md":
+ # Markdown export
+ with open(output_path, "w", encoding="utf-8") as mdfile:
+ mdfile.write("# LightRAG Data Export\n\n")
+
+ # Entities
+ mdfile.write("## Entities\n\n")
+ if entities_data:
+ # Write header
+ mdfile.write("| " + " | ".join(entities_data[0].keys()) + " |\n")
+ mdfile.write(
+ "| "
+ + " | ".join(["---"] * len(entities_data[0].keys()))
+ + " |\n"
+ )
+
+ # Write rows
+ for entity in entities_data:
+ mdfile.write(
+ "| " + " | ".join(str(v) for v in entity.values()) + " |\n"
+ )
+ mdfile.write("\n\n")
+ else:
+ mdfile.write("*No entity data available*\n\n")
+
+ # Relations
+ mdfile.write("## Relations\n\n")
+ if relations_data:
+ # Write header
+ mdfile.write("| " + " | ".join(relations_data[0].keys()) + " |\n")
+ mdfile.write(
+ "| "
+ + " | ".join(["---"] * len(relations_data[0].keys()))
+ + " |\n"
+ )
+
+ # Write rows
+ for relation in relations_data:
+ mdfile.write(
+ "| "
+ + " | ".join(str(v) for v in relation.values())
+ + " |\n"
+ )
+ mdfile.write("\n\n")
+ else:
+ mdfile.write("*No relation data available*\n\n")
+
+ # Relationships
+ mdfile.write("## Relationships\n\n")
+ if relationships_data:
+ # Write header
+ mdfile.write(
+ "| " + " | ".join(relationships_data[0].keys()) + " |\n"
+ )
+ mdfile.write(
+ "| "
+ + " | ".join(["---"] * len(relationships_data[0].keys()))
+ + " |\n"
+ )
+
+ # Write rows
+ for relationship in relationships_data:
+ mdfile.write(
+ "| "
+ + " | ".join(str(v) for v in relationship.values())
+ + " |\n"
+ )
+ else:
+ mdfile.write("*No relationship data available*\n\n")
+
+ elif file_format == "txt":
+ # Plain text export
+ with open(output_path, "w", encoding="utf-8") as txtfile:
+ txtfile.write("LIGHTRAG DATA EXPORT\n")
+ txtfile.write("=" * 80 + "\n\n")
+
+ # Entities
+ txtfile.write("ENTITIES\n")
+ txtfile.write("-" * 80 + "\n")
+ if entities_data:
+ # Create fixed width columns
+ col_widths = {
+ k: max(len(k), max(len(str(e[k])) for e in entities_data))
+ for k in entities_data[0]
+ }
+ header = " ".join(k.ljust(col_widths[k]) for k in entities_data[0])
+ txtfile.write(header + "\n")
+ txtfile.write("-" * len(header) + "\n")
+
+ # Write rows
+ for entity in entities_data:
+ row = " ".join(
+ str(v).ljust(col_widths[k]) for k, v in entity.items()
+ )
+ txtfile.write(row + "\n")
+ txtfile.write("\n\n")
+ else:
+ txtfile.write("No entity data available\n\n")
+
+ # Relations
+ txtfile.write("RELATIONS\n")
+ txtfile.write("-" * 80 + "\n")
+ if relations_data:
+ # Create fixed width columns
+ col_widths = {
+ k: max(len(k), max(len(str(r[k])) for r in relations_data))
+ for k in relations_data[0]
+ }
+ header = " ".join(
+ k.ljust(col_widths[k]) for k in relations_data[0]
+ )
+ txtfile.write(header + "\n")
+ txtfile.write("-" * len(header) + "\n")
+
+ # Write rows
+ for relation in relations_data:
+ row = " ".join(
+ str(v).ljust(col_widths[k]) for k, v in relation.items()
+ )
+ txtfile.write(row + "\n")
+ txtfile.write("\n\n")
+ else:
+ txtfile.write("No relation data available\n\n")
+
+ # Relationships
+ txtfile.write("RELATIONSHIPS\n")
+ txtfile.write("-" * 80 + "\n")
+ if relationships_data:
+ # Create fixed width columns
+ col_widths = {
+ k: max(len(k), max(len(str(r[k])) for r in relationships_data))
+ for k in relationships_data[0]
+ }
+ header = " ".join(
+ k.ljust(col_widths[k]) for k in relationships_data[0]
+ )
+ txtfile.write(header + "\n")
+ txtfile.write("-" * len(header) + "\n")
+
+ # Write rows
+ for relationship in relationships_data:
+ row = " ".join(
+ str(v).ljust(col_widths[k]) for k, v in relationship.items()
+ )
+ txtfile.write(row + "\n")
+ else:
+ txtfile.write("No relationship data available\n\n")
+
+ else:
+ raise ValueError(
+ f"Unsupported file format: {file_format}. "
+ f"Choose from: csv, excel, md, txt"
+ )
+ if file_format is not None:
+ print(f"Data exported to: {output_path} with format: {file_format}")
+ else:
+ print("Data displayed as table format")
+
+ def export_data(
+ self,
+ output_path: str,
+ file_format: Literal["csv", "excel", "md", "txt"] = "csv",
+ include_vector_data: bool = False,
+ ) -> None:
+ """
+ Synchronously exports all entities, relations, and relationships to various formats.
+ Args:
+ output_path: The path to the output file (including extension).
+ file_format: Output format - "csv", "excel", "md", "txt".
+ - csv: Comma-separated values file
+ - excel: Microsoft Excel file with multiple sheets
+ - md: Markdown tables
+ - txt: Plain text formatted output
+ - table: Print formatted tables to console
+ include_vector_data: Whether to include data from the vector database.
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ except RuntimeError:
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+
+ loop.run_until_complete(
+ self.aexport_data(output_path, file_format, include_vector_data)
+ )
+
def merge_entities(
self,
source_entities: list[str],
diff --git a/lightrag/utils.py b/lightrag/utils.py
index b8f00c5d..362e5531 100644
--- a/lightrag/utils.py
+++ b/lightrag/utils.py
@@ -76,6 +76,7 @@ class LightragPathFilter(logging.Filter):
super().__init__()
# Define paths to be filtered
self.filtered_paths = ["/documents", "/health", "/webui/"]
+ # self.filtered_paths = ["/health", "/webui/"]
def filter(self, record):
try:
diff --git a/lightrag_webui/src/components/graph/PropertiesView.tsx b/lightrag_webui/src/components/graph/PropertiesView.tsx
index 4571b02b..c9b0af0a 100644
--- a/lightrag_webui/src/components/graph/PropertiesView.tsx
+++ b/lightrag_webui/src/components/graph/PropertiesView.tsx
@@ -96,9 +96,9 @@ const refineNodeProperties = (node: RawNodeType): NodeType => {
const neighbour = state.rawGraph.getNode(neighbourId)
if (neighbour) {
relationships.push({
- type: isTarget ? 'Target' : 'Source',
+ type: 'Neighbour',
id: neighbourId,
- label: neighbour.labels.join(', ')
+ label: neighbour.properties['entity_id'] ? neighbour.properties['entity_id'] : neighbour.labels.join(', ')
})
}
}
diff --git a/lightrag_webui/vite.config.ts b/lightrag_webui/vite.config.ts
index 09ecd7ea..b05bf2fa 100644
--- a/lightrag_webui/vite.config.ts
+++ b/lightrag_webui/vite.config.ts
@@ -26,7 +26,9 @@ export default defineConfig({
target: import.meta.env.VITE_BACKEND_URL || 'http://localhost:9621',
changeOrigin: true,
rewrite: endpoint === '/api' ?
- (path) => path.replace(/^\/api/, '') : undefined
+ (path) => path.replace(/^\/api/, '') :
+ endpoint === '/docs' || endpoint === '/openapi.json' ?
+ (path) => path : undefined
}
])
) : {}
diff --git a/requirements.txt b/requirements.txt
index d9a5c68e..d35167fd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,6 +4,12 @@ future
# Basic modules
gensim
+
+# Additional Packages for export Functionality
+pandas>=2.0.0
+
+# Extra libraries are installed when needed using pipmaster
+
pipmaster
pydantic
python-dotenv
@@ -13,5 +19,4 @@ tenacity
# LLM packages
tiktoken
-
-# Extra libraries are installed when needed using pipmaster
+xlsxwriter>=3.1.0