Merge branch 'HKUDS:main' into main
This commit is contained in:
Yannick Stephan
GitHub
105
.env.example
105
.env.example
@@ -16,80 +16,79 @@
|
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# WORKING_DIR=<absolute_path_for_working_dir>
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# INPUT_DIR=<absolute_path_for_doc_input_dir>
|
||||
|
||||
### Logging level
|
||||
LOG_LEVEL=INFO
|
||||
VERBOSE=False
|
||||
|
||||
### Optional Timeout
|
||||
TIMEOUT=300
|
||||
|
||||
# Ollama Emulating Model Tag
|
||||
### Ollama Emulating Model Tag
|
||||
# OLLAMA_EMULATING_MODEL_TAG=latest
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||||
|
||||
### RAG Configuration
|
||||
MAX_ASYNC=4
|
||||
EMBEDDING_DIM=1024
|
||||
MAX_EMBED_TOKENS=8192
|
||||
### Settings relative to query
|
||||
HISTORY_TURNS=3
|
||||
COSINE_THRESHOLD=0.2
|
||||
TOP_K=60
|
||||
MAX_TOKEN_TEXT_CHUNK=4000
|
||||
MAX_TOKEN_RELATION_DESC=4000
|
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MAX_TOKEN_ENTITY_DESC=4000
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### Settings relative to indexing
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CHUNK_SIZE=1200
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CHUNK_OVERLAP_SIZE=100
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MAX_TOKENS=32768
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MAX_TOKEN_SUMMARY=500
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SUMMARY_LANGUAGE=English
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### Logging level
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# LOG_LEVEL=INFO
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# VERBOSE=False
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|
||||
### LLM Configuration (Use valid host. For local services, you can use host.docker.internal)
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### Ollama example
|
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### Max async calls for LLM
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# MAX_ASYNC=4
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### Optional Timeout for LLM
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# TIMEOUT=150 # Time out in seconds, None for infinite timeout
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### Settings for RAG query
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# HISTORY_TURNS=3
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# COSINE_THRESHOLD=0.2
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# TOP_K=60
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# MAX_TOKEN_TEXT_CHUNK=4000
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# MAX_TOKEN_RELATION_DESC=4000
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# MAX_TOKEN_ENTITY_DESC=4000
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|
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### Settings for document indexing
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# CHUNK_SIZE=1200
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# CHUNK_OVERLAP_SIZE=100
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# MAX_TOKENS=32768 # Max tokens send to LLM for summarization
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# MAX_TOKEN_SUMMARY=500 # Max tokens for entity or relations summary
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# SUMMARY_LANGUAGE=English
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# MAX_EMBED_TOKENS=8192
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### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
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LLM_BINDING=ollama
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LLM_BINDING_HOST=http://host.docker.internal:11434
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LLM_MODEL=mistral-nemo:latest
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LLM_BINDING_API_KEY=your_api_key
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### Ollama example
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LLM_BINDING_HOST=http://localhost:11434
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### OpenAI alike example
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# LLM_BINDING=openai
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# LLM_MODEL=deepseek-chat
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# LLM_BINDING_HOST=https://api.deepseek.com
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# LLM_MODEL=gpt-4o
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# LLM_BINDING_HOST=https://api.openai.com/v1
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# LLM_BINDING_API_KEY=your_api_key
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### lollms example
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# LLM_BINDING=lollms
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# LLM_MODEL=mistral-nemo:latest
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# LLM_BINDING_HOST=http://localhost:9600
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# LLM_BINDING_API_KEY=your_api_key
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### for OpenAI LLM (LLM_BINDING_API_KEY take priority)
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# OPENAI_API_KEY=your_api_key
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|
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### Lollms example
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# LLM_BINDING=lollms
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# LLM_BINDING_HOST=http://host.docker.internal:9600
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# LLM_MODEL=mistral-nemo:latest
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|
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### Embedding Configuration (Use valid host. For local services, you can use host.docker.internal)
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# Ollama example
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EMBEDDING_BINDING=ollama
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EMBEDDING_BINDING_HOST=http://host.docker.internal:11434
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### Embedding Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
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EMBEDDING_MODEL=bge-m3:latest
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EMBEDDING_DIM=1024
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# EMBEDDING_BINDING_API_KEY=your_api_key
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### ollama example
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||||
EMBEDDING_BINDING=ollama
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EMBEDDING_BINDING_HOST=http://localhost:11434
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### OpenAI alike example
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# EMBEDDING_BINDING=openai
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# LLM_BINDING_HOST=https://api.openai.com/v1
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### Lollms example
|
||||
# EMBEDDING_BINDING=lollms
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# EMBEDDING_BINDING_HOST=http://host.docker.internal:9600
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# EMBEDDING_MODEL=bge-m3:latest
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# EMBEDDING_BINDING_HOST=http://localhost:9600
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|
||||
### Optional for Azure (LLM_BINDING_HOST, LLM_BINDING_API_KEY take priority)
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# AZURE_OPENAI_API_VERSION=2024-08-01-preview
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# AZURE_OPENAI_DEPLOYMENT=gpt-4o
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# AZURE_OPENAI_API_KEY=myapikey
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# AZURE_OPENAI_API_KEY=your_api_key
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||||
# AZURE_OPENAI_ENDPOINT=https://myendpoint.openai.azure.com
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|
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# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
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# AZURE_EMBEDDING_API_VERSION=2023-05-15
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|
||||
### Data storage selection
|
||||
# LIGHTRAG_KV_STORAGE=PGKVStorage
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# LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
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# LIGHTRAG_GRAPH_STORAGE=PGGraphStorage
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||||
# LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
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||||
LIGHTRAG_KV_STORAGE=JsonKVStorage
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||||
LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage
|
||||
LIGHTRAG_GRAPH_STORAGE=NetworkXStorage
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||||
LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage
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||||
|
||||
### Oracle Database Configuration
|
||||
ORACLE_DSN=localhost:1521/XEPDB1
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@@ -138,4 +137,4 @@ MONGODB_GRAPH=false # deprecated (keep for backward compatibility)
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|
||||
### Qdrant
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||||
QDRANT_URL=http://localhost:16333
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QDRANT_API_KEY=your-api-key # 可选
|
||||
# QDRANT_API_KEY=your-api-key
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||||
|
||||
3
.gitignore
vendored
3
.gitignore
vendored
@@ -60,3 +60,6 @@ dickens/
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book.txt
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lightrag-dev/
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gui/
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||||
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||||
# unit-test files
|
||||
test_*
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||||
|
||||
13
Dockerfile
13
Dockerfile
@@ -3,19 +3,6 @@ FROM python:3.11-slim as builder
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
# Install build dependencies
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
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||||
build-essential \
|
||||
curl \
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||||
pkg-config \
|
||||
libssl-dev \
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||||
&& curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y \
|
||||
&& . "$HOME/.cargo/env" \
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||||
&& rustup default stable \
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||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
ENV PATH="/root/.cargo/bin:${PATH}"
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||||
|
||||
# Copy only requirements files first to leverage Docker cache
|
||||
COPY requirements.txt .
|
||||
COPY lightrag/api/requirements.txt ./lightrag/api/
|
||||
|
||||
16
README.md
16
README.md
@@ -3,7 +3,7 @@
|
||||
<table border="0" width="100%">
|
||||
<tr>
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||||
<td width="100" align="center">
|
||||
<img src="https://i-blog.csdnimg.cn/direct/0d97ea81439442a19ac3972ad537a811.png" width="80" height="80" alt="lightrag">
|
||||
<img src="./assets/logo.png" width="80" height="80" alt="lightrag">
|
||||
</td>
|
||||
<td>
|
||||
<div>
|
||||
@@ -545,6 +545,20 @@ The `insert_batch_size` parameter in `addon_params` controls how many documents
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary> <b> Insert with ID </b></summary>
|
||||
|
||||
If you want to provide your own IDs for your documents, number of documents and number of IDs must be the same.
|
||||
|
||||
```python
|
||||
# Insert single text, and provide ID for it
|
||||
rag.insert("TEXT1", ids=["ID_FOR_TEXT1"])
|
||||
|
||||
# Insert multiple texts, and provide IDs for them
|
||||
rag.insert(["TEXT1", "TEXT2",...], ids=["ID_FOR_TEXT1", "ID_FOR_TEXT2"])
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
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||||
<summary><b>Incremental Insert</b></summary>
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||||
|
||||
BIN
assets/logo.png
BIN
assets/logo.png
Binary file not shown.
|
Before Width: | Height: | Size: 185 KiB After Width: | Height: | Size: 155 KiB |
@@ -6,16 +6,8 @@ services:
|
||||
volumes:
|
||||
- ./data/rag_storage:/app/data/rag_storage
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||||
- ./data/inputs:/app/data/inputs
|
||||
- .env:/app/.env
|
||||
- ./config.ini:/app/config.ini
|
||||
- ./.env:/app/.env
|
||||
env_file:
|
||||
- .env
|
||||
environment:
|
||||
- TZ=UTC
|
||||
restart: unless-stopped
|
||||
networks:
|
||||
- lightrag_net
|
||||
extra_hosts:
|
||||
- "host.docker.internal:host-gateway"
|
||||
networks:
|
||||
lightrag_net:
|
||||
driver: bridge
|
||||
|
||||
@@ -37,20 +37,22 @@ async def main():
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||||
llm_model_max_token_size=32768,
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||||
enable_llm_cache_for_entity_extract=True,
|
||||
embedding_func=EmbeddingFunc(
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embedding_dim=768,
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||||
embedding_dim=1024,
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||||
max_token_size=8192,
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||||
func=lambda texts: ollama_embedding(
|
||||
texts, embed_model="nomic-embed-text", host="http://localhost:11434"
|
||||
texts, embed_model="bge-m3", host="http://localhost:11434"
|
||||
),
|
||||
),
|
||||
kv_storage="PGKVStorage",
|
||||
doc_status_storage="PGDocStatusStorage",
|
||||
graph_storage="PGGraphStorage",
|
||||
vector_storage="PGVectorStorage",
|
||||
auto_manage_storages_states=False,
|
||||
)
|
||||
|
||||
# add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c
|
||||
rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func
|
||||
await rag.initialize_storages()
|
||||
|
||||
with open(f"{ROOT_DIR}/book.txt", "r", encoding="utf-8") as f:
|
||||
await rag.ainsert(f.read())
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
|
||||
|
||||
__version__ = "1.1.11"
|
||||
__version__ = "1.2.1"
|
||||
__author__ = "Zirui Guo"
|
||||
__url__ = "https://github.com/HKUDS/LightRAG"
|
||||
|
||||
@@ -1,14 +1,14 @@
|
||||
## Install with API Support
|
||||
## Install LightRAG as an API Server
|
||||
|
||||
LightRAG provides optional API support through FastAPI servers that add RAG capabilities to existing LLM services. You can install LightRAG with API support in two ways:
|
||||
LightRAG provides optional API support through FastAPI servers that add RAG capabilities to existing LLM services. You can install LightRAG API Server in two ways:
|
||||
|
||||
### 1. Installation from PyPI
|
||||
### Installation from PyPI
|
||||
|
||||
```bash
|
||||
pip install "lightrag-hku[api]"
|
||||
```
|
||||
|
||||
### 2. Installation from Source (Development)
|
||||
### Installation from Source (Development)
|
||||
|
||||
```bash
|
||||
# Clone the repository
|
||||
@@ -22,33 +22,80 @@ cd lightrag
|
||||
pip install -e ".[api]"
|
||||
```
|
||||
|
||||
### Prerequisites
|
||||
### Starting API Server with Default Settings
|
||||
|
||||
LightRAG requires both LLM and Embedding Model to work together to complete document indexing and querying tasks. LightRAG supports binding to various LLM/Embedding backends:
|
||||
|
||||
* ollama
|
||||
* lollms
|
||||
* openai & openai compatible
|
||||
* azure_openai
|
||||
|
||||
Before running any of the servers, ensure you have the corresponding backend service running for both llm and embedding.
|
||||
The new api allows you to mix different bindings for llm/embeddings.
|
||||
For example, you have the possibility to use ollama for the embedding and openai for the llm.
|
||||
The LightRAG API Server provides default parameters for LLM and Embedding, allowing users to easily start the service through command line. These default configurations are:
|
||||
|
||||
#### For LoLLMs Server
|
||||
- LoLLMs must be running and accessible
|
||||
- Default connection: http://localhost:9600
|
||||
- Configure using --llm-binding-host and/or --embedding-binding-host if running on a different host/port
|
||||
* Default endpoint of LLM/Embeding backend(LLM_BINDING_HOST or EMBEDDING_BINDING_HOST)
|
||||
|
||||
#### For Ollama Server
|
||||
- Ollama must be running and accessible
|
||||
- Requires environment variables setup or command line argument provided
|
||||
- Environment variables: LLM_BINDING=ollama, LLM_BINDING_HOST, LLM_MODEL
|
||||
- Command line arguments: --llm-binding=ollama, --llm-binding-host, --llm-model
|
||||
- Default connection is http://localhost:11434 if not priveded
|
||||
```
|
||||
# for lollms backend
|
||||
LLM_BINDING_HOST=http://localhost:11434
|
||||
EMBEDDING_BINDING_HOST=http://localhost:11434
|
||||
|
||||
> The default MAX_TOKENS(num_ctx) for Ollama is 32768. If your Ollama server is lacking or GPU memory, set it to a lower value.
|
||||
# for lollms backend
|
||||
LLM_BINDING_HOST=http://localhost:9600
|
||||
EMBEDDING_BINDING_HOST=http://localhost:9600
|
||||
|
||||
#### For OpenAI Alike Server
|
||||
- Requires environment variables setup or command line argument provided
|
||||
- Environment variables: LLM_BINDING=ollama, LLM_BINDING_HOST, LLM_MODEL, LLM_BINDING_API_KEY
|
||||
- Command line arguments: --llm-binding=ollama, --llm-binding-host, --llm-model, --llm-binding-api-key
|
||||
- Default connection is https://api.openai.com/v1 if not priveded
|
||||
# for openai, openai compatible or azure openai backend
|
||||
LLM_BINDING_HOST=https://api.openai.com/v1
|
||||
EMBEDDING_BINDING_HOST=http://localhost:9600
|
||||
```
|
||||
|
||||
#### For Azure OpenAI Server
|
||||
* Default model config
|
||||
|
||||
```
|
||||
LLM_MODEL=mistral-nemo:latest
|
||||
|
||||
EMBEDDING_MODEL=bge-m3:latest
|
||||
EMBEDDING_DIM=1024
|
||||
MAX_EMBED_TOKENS=8192
|
||||
```
|
||||
|
||||
* API keys for LLM/Embedding backend
|
||||
|
||||
When connecting to backend require API KEY, corresponding environment variables must be provided:
|
||||
|
||||
```
|
||||
LLM_BINDING_API_KEY=your_api_key
|
||||
EMBEDDING_BINDING_API_KEY=your_api_key
|
||||
```
|
||||
|
||||
* Use command line arguments to choose LLM/Embeding backend
|
||||
|
||||
Use `--llm-binding` to select LLM backend type, and use `--embedding-binding` to select the embedding backend type. All the supported backend types are:
|
||||
|
||||
```
|
||||
openai: LLM default type
|
||||
ollama: Embedding defult type
|
||||
lollms:
|
||||
azure_openai:
|
||||
openai-ollama: select openai for LLM and ollama for embedding(only valid for --llm-binding)
|
||||
```
|
||||
|
||||
The LightRAG API Server allows you to mix different bindings for llm/embeddings. For example, you have the possibility to use ollama for the embedding and openai for the llm.With the above default parameters, you can start API Server with simple CLI arguments like these:
|
||||
|
||||
```
|
||||
# start with openai llm and ollama embedding
|
||||
LLM_BINDING_API_KEY=your_api_key Light_server
|
||||
LLM_BINDING_API_KEY=your_api_key Light_server --llm-binding openai-ollama
|
||||
|
||||
# start with openai llm and openai embedding
|
||||
LLM_BINDING_API_KEY=your_api_key Light_server --llm-binding openai --embedding-binding openai
|
||||
|
||||
# start with ollama llm and ollama embedding (no apikey is needed)
|
||||
Light_server --llm-binding ollama --embedding-binding ollama
|
||||
```
|
||||
|
||||
### For Azure OpenAI Backend
|
||||
Azure OpenAI API can be created using the following commands in Azure CLI (you need to install Azure CLI first from [https://docs.microsoft.com/en-us/cli/azure/install-azure-cli](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli)):
|
||||
```bash
|
||||
# Change the resource group name, location and OpenAI resource name as needed
|
||||
@@ -68,13 +115,18 @@ az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_
|
||||
The output of the last command will give you the endpoint and the key for the OpenAI API. You can use these values to set the environment variables in the `.env` file.
|
||||
|
||||
```
|
||||
# Azure OpenAI Configuration in .env
|
||||
LLM_BINDING=azure_openai
|
||||
LLM_BINDING_HOST=endpoint_of_azure_ai
|
||||
LLM_MODEL=model_name_of_azure_ai
|
||||
LLM_BINDING_API_KEY=api_key_of_azure_ai
|
||||
LLM_BINDING_HOST=your-azure-endpoint
|
||||
LLM_MODEL=your-model-deployment-name
|
||||
LLM_BINDING_API_KEY=your-azure-api-key
|
||||
AZURE_OPENAI_API_VERSION=2024-08-01-preview # optional, defaults to latest version
|
||||
EMBEDDING_BINDING=azure_openai # if using Azure OpenAI for embeddings
|
||||
EMBEDDING_MODEL=your-embedding-deployment-name
|
||||
|
||||
```
|
||||
|
||||
### 3. Install Lightrag as a Linux Service
|
||||
### Install Lightrag as a Linux Service
|
||||
|
||||
Create a your service file `lightrag.sevice` from the sample file : `lightrag.sevice.example`. Modified the WorkingDirectoryand EexecStart in the service file:
|
||||
|
||||
@@ -105,40 +157,36 @@ sudo systemctl status lightrag.service
|
||||
sudo systemctl enable lightrag.service
|
||||
```
|
||||
|
||||
### Automatic Document Indexing
|
||||
|
||||
When starting any of the servers with the `--auto-scan-at-startup` parameter, the system will automatically:
|
||||
|
||||
## Configuration
|
||||
1. Scan for new files in the input directory
|
||||
2. Indexing new documents that aren't already in the database
|
||||
3. Make all content immediately available for RAG queries
|
||||
|
||||
LightRAG can be configured using either command-line arguments or environment variables. When both are provided, command-line arguments take precedence over environment variables.
|
||||
> The `--input-dir` parameter specify the input directory to scan for.
|
||||
|
||||
Default `TOP_K` is set to `60`. Default `COSINE_THRESHOLD` are set to `0.2`.
|
||||
## API Server Configuration
|
||||
|
||||
### Environment Variables
|
||||
API Server can be config in three way (highest priority first):
|
||||
|
||||
You can configure LightRAG using environment variables by creating a `.env` file in your project root directory. A sample file `.env.example` is provided for your convenience.
|
||||
* Command line arguments
|
||||
* Enviroment variables or .env file
|
||||
* Config.ini (Only for storage configuration)
|
||||
|
||||
### Config.ini
|
||||
Most of the configurations come with a default settings, check out details in sample file: `.env.example`. Datastorage configuration can be also set by config.ini. A sample file `config.ini.example` is provided for your convenience.
|
||||
|
||||
Datastorage configuration can be also set by config.ini. A sample file `config.ini.example` is provided for your convenience.
|
||||
### LLM and Embedding Backend Supported
|
||||
|
||||
### Configuration Priority
|
||||
LightRAG supports binding to various LLM/Embedding backends:
|
||||
|
||||
The configuration values are loaded in the following order (highest priority first):
|
||||
1. Command-line arguments
|
||||
2. Environment variables
|
||||
3. Config.ini
|
||||
4. Defaul values
|
||||
* ollama
|
||||
* lollms
|
||||
* openai & openai compatible
|
||||
* azure_openai
|
||||
|
||||
For example:
|
||||
```bash
|
||||
# This command-line argument will override both the environment variable and default value
|
||||
python lightrag.py --port 8080
|
||||
|
||||
# The environment variable will override the default value but not the command-line argument
|
||||
PORT=7000 python lightrag.py
|
||||
```
|
||||
|
||||
> Best practices: you can set your database setting in Config.ini while testing, and you use .env for production.
|
||||
Use environment variables `LLM_BINDING ` or CLI argument `--llm-binding` to select LLM backend type. Use environment variables `EMBEDDING_BINDING ` or CLI argument `--embedding-binding` to select LLM backend type.
|
||||
|
||||
### Storage Types Supported
|
||||
|
||||
@@ -199,7 +247,16 @@ MongoDocStatusStorage MongoDB
|
||||
|
||||
### How Select Storage Implementation
|
||||
|
||||
You can select storage implementation by enviroment variables or command line arguments. You can not change storage implementation selection after you add documents to LightRAG. Data migration from one storage implementation to anthor is not supported yet. For further information please read the sample env file or config.ini file.
|
||||
You can select storage implementation by environment variables. Your can set the following environmental variables to a specific storage implement-name before the your first start of the API Server:
|
||||
|
||||
```
|
||||
LIGHTRAG_KV_STORAGE=PGKVStorage
|
||||
LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
|
||||
LIGHTRAG_GRAPH_STORAGE=PGGraphStorage
|
||||
LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
|
||||
```
|
||||
|
||||
You can not change storage implementation selection after you add documents to LightRAG. Data migration from one storage implementation to anthor is not supported yet. For further information please read the sample env file or config.ini file.
|
||||
|
||||
### LightRag API Server Comand Line Options
|
||||
|
||||
@@ -207,32 +264,22 @@ You can select storage implementation by enviroment variables or command line a
|
||||
|-----------|---------|-------------|
|
||||
| --host | 0.0.0.0 | Server host |
|
||||
| --port | 9621 | Server port |
|
||||
| --llm-binding | ollama | LLM binding to be used. Supported: lollms, ollama, openai |
|
||||
| --llm-binding-host | (dynamic) | LLM server host URL. Defaults based on binding: http://localhost:11434 (ollama), http://localhost:9600 (lollms), https://api.openai.com/v1 (openai) |
|
||||
| --llm-model | mistral-nemo:latest | LLM model name |
|
||||
| --llm-binding-api-key | None | API Key for OpenAI Alike LLM |
|
||||
| --embedding-binding | ollama | Embedding binding to be used. Supported: lollms, ollama, openai |
|
||||
| --embedding-binding-host | (dynamic) | Embedding server host URL. Defaults based on binding: http://localhost:11434 (ollama), http://localhost:9600 (lollms), https://api.openai.com/v1 (openai) |
|
||||
| --embedding-model | bge-m3:latest | Embedding model name |
|
||||
| --working-dir | ./rag_storage | Working directory for RAG storage |
|
||||
| --input-dir | ./inputs | Directory containing input documents |
|
||||
| --max-async | 4 | Maximum async operations |
|
||||
| --max-tokens | 32768 | Maximum token size |
|
||||
| --embedding-dim | 1024 | Embedding dimensions |
|
||||
| --max-embed-tokens | 8192 | Maximum embedding token size |
|
||||
| --timeout | None | Timeout in seconds (useful when using slow AI). Use None for infinite timeout |
|
||||
| --timeout | 150 | Timeout in seconds. None for infinite timeout(not recommended) |
|
||||
| --log-level | INFO | Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) |
|
||||
| --verbose | False | Verbose debug output (True, Flase) |
|
||||
| --verbose | - | Verbose debug output (True, Flase) |
|
||||
| --key | None | API key for authentication. Protects lightrag server against unauthorized access |
|
||||
| --ssl | False | Enable HTTPS |
|
||||
| --ssl-certfile | None | Path to SSL certificate file (required if --ssl is enabled) |
|
||||
| --ssl-keyfile | None | Path to SSL private key file (required if --ssl is enabled) |
|
||||
| --top-k | 50 | Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode. |
|
||||
| --cosine-threshold | 0.4 | The cossine threshold for nodes and relations retrieval, works with top-k to control the retrieval of nodes and relations. |
|
||||
| --kv-storage | JsonKVStorage | implement-name of KV_STORAGE |
|
||||
| --graph-storage | NetworkXStorage | implement-name of GRAPH_STORAGE |
|
||||
| --vector-storage | NanoVectorDBStorage | implement-name of VECTOR_STORAGE |
|
||||
| --doc-status-storage | JsonDocStatusStorage | implement-name of DOC_STATUS_STORAGE |
|
||||
| --llm-binding | ollama | LLM binding type (lollms, ollama, openai, openai-ollama, azure_openai) |
|
||||
| --embedding-binding | ollama | Embedding binding type (lollms, ollama, openai, azure_openai) |
|
||||
| auto-scan-at-startup | - | Scan input directory for new files and start indexing |
|
||||
|
||||
### Example Usage
|
||||
|
||||
@@ -244,57 +291,49 @@ Ollama is the default backend for both llm and embedding, so by default you can
|
||||
# Run lightrag with ollama, mistral-nemo:latest for llm, and bge-m3:latest for embedding
|
||||
lightrag-server
|
||||
|
||||
# Using specific models (ensure they are installed in your ollama instance)
|
||||
lightrag-server --llm-model adrienbrault/nous-hermes2theta-llama3-8b:f16 --embedding-model nomic-embed-text --embedding-dim 1024
|
||||
|
||||
# Using an authentication key
|
||||
lightrag-server --key my-key
|
||||
|
||||
# Using lollms for llm and ollama for embedding
|
||||
lightrag-server --llm-binding lollms
|
||||
```
|
||||
|
||||
#### Running a Lightrag server with lollms default local server as llm and embedding backends
|
||||
|
||||
```bash
|
||||
# Run lightrag with lollms, mistral-nemo:latest for llm, and bge-m3:latest for embedding, use lollms for both llm and embedding
|
||||
lightrag-server --llm-binding lollms --embedding-binding lollms
|
||||
|
||||
# Using specific models (ensure they are installed in your ollama instance)
|
||||
lightrag-server --llm-binding lollms --llm-model adrienbrault/nous-hermes2theta-llama3-8b:f16 --embedding-binding lollms --embedding-model nomic-embed-text --embedding-dim 1024
|
||||
# Run lightrag with lollms, mistral-nemo:latest for llm, and bge-m3:latest for embedding
|
||||
# Configure LLM_BINDING=lollms and EMBEDDING_BINDING=lollms in .env or config.ini
|
||||
lightrag-server
|
||||
|
||||
# Using an authentication key
|
||||
lightrag-server --key my-key
|
||||
|
||||
# Using lollms for llm and openai for embedding
|
||||
lightrag-server --llm-binding lollms --embedding-binding openai --embedding-model text-embedding-3-small
|
||||
```
|
||||
|
||||
|
||||
#### Running a Lightrag server with openai server as llm and embedding backends
|
||||
|
||||
```bash
|
||||
# Run lightrag with lollms, GPT-4o-mini for llm, and text-embedding-3-small for embedding, use openai for both llm and embedding
|
||||
lightrag-server --llm-binding openai --llm-model GPT-4o-mini --embedding-binding openai --embedding-model text-embedding-3-small
|
||||
# Run lightrag with openai, GPT-4o-mini for llm, and text-embedding-3-small for embedding
|
||||
# Configure in .env or config.ini:
|
||||
# LLM_BINDING=openai
|
||||
# LLM_MODEL=GPT-4o-mini
|
||||
# EMBEDDING_BINDING=openai
|
||||
# EMBEDDING_MODEL=text-embedding-3-small
|
||||
lightrag-server
|
||||
|
||||
# Using an authentication key
|
||||
lightrag-server --llm-binding openai --llm-model GPT-4o-mini --embedding-binding openai --embedding-model text-embedding-3-small --key my-key
|
||||
|
||||
# Using lollms for llm and openai for embedding
|
||||
lightrag-server --llm-binding lollms --embedding-binding openai --embedding-model text-embedding-3-small
|
||||
lightrag-server --key my-key
|
||||
```
|
||||
|
||||
#### Running a Lightrag server with azure openai server as llm and embedding backends
|
||||
|
||||
```bash
|
||||
# Run lightrag with lollms, GPT-4o-mini for llm, and text-embedding-3-small for embedding, use openai for both llm and embedding
|
||||
lightrag-server --llm-binding azure_openai --llm-model GPT-4o-mini --embedding-binding openai --embedding-model text-embedding-3-small
|
||||
# Run lightrag with azure_openai
|
||||
# Configure in .env or config.ini:
|
||||
# LLM_BINDING=azure_openai
|
||||
# LLM_MODEL=your-model
|
||||
# EMBEDDING_BINDING=azure_openai
|
||||
# EMBEDDING_MODEL=your-embedding-model
|
||||
lightrag-server
|
||||
|
||||
# Using an authentication key
|
||||
lightrag-server --llm-binding azure_openai --llm-model GPT-4o-mini --embedding-binding azure_openai --embedding-model text-embedding-3-small --key my-key
|
||||
|
||||
# Using lollms for llm and azure_openai for embedding
|
||||
lightrag-server --llm-binding lollms --embedding-binding azure_openai --embedding-model text-embedding-3-small
|
||||
lightrag-server --key my-key
|
||||
```
|
||||
|
||||
**Important Notes:**
|
||||
@@ -315,7 +354,18 @@ pip install lightrag-hku
|
||||
|
||||
## API Endpoints
|
||||
|
||||
All servers (LoLLMs, Ollama, OpenAI and Azure OpenAI) provide the same REST API endpoints for RAG functionality.
|
||||
All servers (LoLLMs, Ollama, OpenAI and Azure OpenAI) provide the same REST API endpoints for RAG functionality. When API Server is running, visit:
|
||||
|
||||
- Swagger UI: http://localhost:9621/docs
|
||||
- ReDoc: http://localhost:9621/redoc
|
||||
|
||||
You can test the API endpoints using the provided curl commands or through the Swagger UI interface. Make sure to:
|
||||
|
||||
1. Start the appropriate backend service (LoLLMs, Ollama, or OpenAI)
|
||||
2. Start the RAG server
|
||||
3. Upload some documents using the document management endpoints
|
||||
4. Query the system using the query endpoints
|
||||
5. Trigger document scan if new files is put into inputs directory
|
||||
|
||||
### Query Endpoints
|
||||
|
||||
@@ -452,63 +502,3 @@ A query prefix in the query string can determines which LightRAG query mode is u
|
||||
For example, chat message "/mix 唐僧有几个徒弟" will trigger a mix mode query for LighRAG. A chat message without query prefix will trigger a hybrid mode query by default。
|
||||
|
||||
"/bypass" is not a LightRAG query mode, it will tell API Server to pass the query directly to the underlying LLM with chat history. So user can use LLM to answer question base on the chat history. If you are using Open WebUI as front end, you can just switch the model to a normal LLM instead of using /bypass prefix.
|
||||
|
||||
## Development
|
||||
|
||||
Contribute to the project: [Guide](contributor-readme.MD)
|
||||
|
||||
### Running in Development Mode
|
||||
|
||||
For LoLLMs:
|
||||
```bash
|
||||
uvicorn lollms_lightrag_server:app --reload --port 9621
|
||||
```
|
||||
|
||||
For Ollama:
|
||||
```bash
|
||||
uvicorn ollama_lightrag_server:app --reload --port 9621
|
||||
```
|
||||
|
||||
For OpenAI:
|
||||
```bash
|
||||
uvicorn openai_lightrag_server:app --reload --port 9621
|
||||
```
|
||||
For Azure OpenAI:
|
||||
```bash
|
||||
uvicorn azure_openai_lightrag_server:app --reload --port 9621
|
||||
```
|
||||
### API Documentation
|
||||
|
||||
When any server is running, visit:
|
||||
- Swagger UI: http://localhost:9621/docs
|
||||
- ReDoc: http://localhost:9621/redoc
|
||||
|
||||
### Testing API Endpoints
|
||||
|
||||
You can test the API endpoints using the provided curl commands or through the Swagger UI interface. Make sure to:
|
||||
1. Start the appropriate backend service (LoLLMs, Ollama, or OpenAI)
|
||||
2. Start the RAG server
|
||||
3. Upload some documents using the document management endpoints
|
||||
4. Query the system using the query endpoints
|
||||
5. Trigger document scan if new files is put into inputs directory
|
||||
|
||||
### Important Features
|
||||
|
||||
#### Automatic Document Vectorization
|
||||
When starting any of the servers with the `--input-dir` parameter, the system will automatically:
|
||||
1. Check for existing vectorized content in the database
|
||||
2. Only vectorize new documents that aren't already in the database
|
||||
3. Make all content immediately available for RAG queries
|
||||
|
||||
This intelligent caching mechanism:
|
||||
- Prevents unnecessary re-vectorization of existing documents
|
||||
- Reduces startup time for subsequent runs
|
||||
- Preserves system resources
|
||||
- Maintains consistency across restarts
|
||||
|
||||
**Important Notes:**
|
||||
- The `--input-dir` parameter enables automatic document processing at startup
|
||||
- Documents already in the database are not re-vectorized
|
||||
- Only new documents in the input directory will be processed
|
||||
- This optimization significantly reduces startup time for subsequent runs
|
||||
- The working directory (`--working-dir`) stores the vectorized documents database
|
||||
|
||||
@@ -57,10 +57,9 @@ ALTER USER your_new_role WITH PASSWORD 'your_secure_password';
|
||||
\q
|
||||
```
|
||||
|
||||
### 3. Install PGVector Extension
|
||||
|
||||
Install necessary dependencies and compile the extension:
|
||||
### 3. Install PGVector and Age Extensions
|
||||
|
||||
Install PGVector:
|
||||
```bash
|
||||
sudo apt install postgresql-server-dev-all
|
||||
cd /tmp
|
||||
@@ -69,6 +68,15 @@ cd pgvector
|
||||
make
|
||||
sudo make install
|
||||
```
|
||||
Install age:
|
||||
```bash
|
||||
sudo apt-get install build-essential libpq-dev
|
||||
cd /tmp
|
||||
git clone https://github.com/apache/age.git
|
||||
cd age
|
||||
make
|
||||
sudo make install
|
||||
```
|
||||
|
||||
### 4. Create a Database for LightRAG
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
10
lightrag/api/routers/__init__.py
Normal file
10
lightrag/api/routers/__init__.py
Normal file
@@ -0,0 +1,10 @@
|
||||
"""
|
||||
This module contains all the routers for the LightRAG API.
|
||||
"""
|
||||
|
||||
from .document_routes import router as document_router
|
||||
from .query_routes import router as query_router
|
||||
from .graph_routes import router as graph_router
|
||||
from .ollama_api import OllamaAPI
|
||||
|
||||
__all__ = ["document_router", "query_router", "graph_router", "OllamaAPI"]
|
||||
770
lightrag/api/routers/document_routes.py
Normal file
770
lightrag/api/routers/document_routes.py
Normal file
@@ -0,0 +1,770 @@
|
||||
"""
|
||||
This module contains all document-related routes for the LightRAG API.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
import aiofiles
|
||||
import shutil
|
||||
import traceback
|
||||
import pipmaster as pm
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Any
|
||||
|
||||
from fastapi import APIRouter, BackgroundTasks, Depends, File, HTTPException, UploadFile
|
||||
from pydantic import BaseModel, Field, field_validator
|
||||
|
||||
from lightrag import LightRAG
|
||||
from lightrag.base import DocProcessingStatus, DocStatus
|
||||
from ..utils_api import get_api_key_dependency
|
||||
|
||||
|
||||
router = APIRouter(prefix="/documents", tags=["documents"])
|
||||
|
||||
# Global progress tracker
|
||||
scan_progress: Dict = {
|
||||
"is_scanning": False,
|
||||
"current_file": "",
|
||||
"indexed_count": 0,
|
||||
"total_files": 0,
|
||||
"progress": 0,
|
||||
}
|
||||
|
||||
# Lock for thread-safe operations
|
||||
progress_lock = asyncio.Lock()
|
||||
|
||||
# Temporary file prefix
|
||||
temp_prefix = "__tmp__"
|
||||
|
||||
|
||||
class InsertTextRequest(BaseModel):
|
||||
text: str = Field(
|
||||
min_length=1,
|
||||
description="The text to insert",
|
||||
)
|
||||
|
||||
@field_validator("text", mode="after")
|
||||
@classmethod
|
||||
def strip_after(cls, text: str) -> str:
|
||||
return text.strip()
|
||||
|
||||
|
||||
class InsertTextsRequest(BaseModel):
|
||||
texts: list[str] = Field(
|
||||
min_length=1,
|
||||
description="The texts to insert",
|
||||
)
|
||||
|
||||
@field_validator("texts", mode="after")
|
||||
@classmethod
|
||||
def strip_after(cls, texts: list[str]) -> list[str]:
|
||||
return [text.strip() for text in texts]
|
||||
|
||||
|
||||
class InsertResponse(BaseModel):
|
||||
status: str = Field(description="Status of the operation")
|
||||
message: str = Field(description="Message describing the operation result")
|
||||
|
||||
|
||||
class DocStatusResponse(BaseModel):
|
||||
@staticmethod
|
||||
def format_datetime(dt: Any) -> Optional[str]:
|
||||
if dt is None:
|
||||
return None
|
||||
if isinstance(dt, str):
|
||||
return dt
|
||||
return dt.isoformat()
|
||||
|
||||
"""Response model for document status
|
||||
|
||||
Attributes:
|
||||
id: Document identifier
|
||||
content_summary: Summary of document content
|
||||
content_length: Length of document content
|
||||
status: Current processing status
|
||||
created_at: Creation timestamp (ISO format string)
|
||||
updated_at: Last update timestamp (ISO format string)
|
||||
chunks_count: Number of chunks (optional)
|
||||
error: Error message if any (optional)
|
||||
metadata: Additional metadata (optional)
|
||||
"""
|
||||
|
||||
id: str
|
||||
content_summary: str
|
||||
content_length: int
|
||||
status: DocStatus
|
||||
created_at: str
|
||||
updated_at: str
|
||||
chunks_count: Optional[int] = None
|
||||
error: Optional[str] = None
|
||||
metadata: Optional[dict[str, Any]] = None
|
||||
|
||||
|
||||
class DocsStatusesResponse(BaseModel):
|
||||
statuses: Dict[DocStatus, List[DocStatusResponse]] = {}
|
||||
|
||||
|
||||
class DocumentManager:
|
||||
def __init__(
|
||||
self,
|
||||
input_dir: str,
|
||||
supported_extensions: tuple = (
|
||||
".txt",
|
||||
".md",
|
||||
".pdf",
|
||||
".docx",
|
||||
".pptx",
|
||||
".xlsx",
|
||||
".rtf", # Rich Text Format
|
||||
".odt", # OpenDocument Text
|
||||
".tex", # LaTeX
|
||||
".epub", # Electronic Publication
|
||||
".html", # HyperText Markup Language
|
||||
".htm", # HyperText Markup Language
|
||||
".csv", # Comma-Separated Values
|
||||
".json", # JavaScript Object Notation
|
||||
".xml", # eXtensible Markup Language
|
||||
".yaml", # YAML Ain't Markup Language
|
||||
".yml", # YAML
|
||||
".log", # Log files
|
||||
".conf", # Configuration files
|
||||
".ini", # Initialization files
|
||||
".properties", # Java properties files
|
||||
".sql", # SQL scripts
|
||||
".bat", # Batch files
|
||||
".sh", # Shell scripts
|
||||
".c", # C source code
|
||||
".cpp", # C++ source code
|
||||
".py", # Python source code
|
||||
".java", # Java source code
|
||||
".js", # JavaScript source code
|
||||
".ts", # TypeScript source code
|
||||
".swift", # Swift source code
|
||||
".go", # Go source code
|
||||
".rb", # Ruby source code
|
||||
".php", # PHP source code
|
||||
".css", # Cascading Style Sheets
|
||||
".scss", # Sassy CSS
|
||||
".less", # LESS CSS
|
||||
),
|
||||
):
|
||||
self.input_dir = Path(input_dir)
|
||||
self.supported_extensions = supported_extensions
|
||||
self.indexed_files = set()
|
||||
|
||||
# Create input directory if it doesn't exist
|
||||
self.input_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def scan_directory_for_new_files(self) -> List[Path]:
|
||||
"""Scan input directory for new files"""
|
||||
new_files = []
|
||||
for ext in self.supported_extensions:
|
||||
logging.debug(f"Scanning for {ext} files in {self.input_dir}")
|
||||
for file_path in self.input_dir.rglob(f"*{ext}"):
|
||||
if file_path not in self.indexed_files:
|
||||
new_files.append(file_path)
|
||||
return new_files
|
||||
|
||||
# def scan_directory(self) -> List[Path]:
|
||||
# new_files = []
|
||||
# for ext in self.supported_extensions:
|
||||
# for file_path in self.input_dir.rglob(f"*{ext}"):
|
||||
# new_files.append(file_path)
|
||||
# return new_files
|
||||
|
||||
def mark_as_indexed(self, file_path: Path):
|
||||
self.indexed_files.add(file_path)
|
||||
|
||||
def is_supported_file(self, filename: str) -> bool:
|
||||
return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
|
||||
|
||||
|
||||
async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool:
|
||||
"""Add a file to the queue for processing
|
||||
|
||||
Args:
|
||||
rag: LightRAG instance
|
||||
file_path: Path to the saved file
|
||||
Returns:
|
||||
bool: True if the file was successfully enqueued, False otherwise
|
||||
"""
|
||||
|
||||
try:
|
||||
content = ""
|
||||
ext = file_path.suffix.lower()
|
||||
|
||||
file = None
|
||||
async with aiofiles.open(file_path, "rb") as f:
|
||||
file = await f.read()
|
||||
|
||||
# Process based on file type
|
||||
match ext:
|
||||
case (
|
||||
".txt"
|
||||
| ".md"
|
||||
| ".html"
|
||||
| ".htm"
|
||||
| ".tex"
|
||||
| ".json"
|
||||
| ".xml"
|
||||
| ".yaml"
|
||||
| ".yml"
|
||||
| ".rtf"
|
||||
| ".odt"
|
||||
| ".epub"
|
||||
| ".csv"
|
||||
| ".log"
|
||||
| ".conf"
|
||||
| ".ini"
|
||||
| ".properties"
|
||||
| ".sql"
|
||||
| ".bat"
|
||||
| ".sh"
|
||||
| ".c"
|
||||
| ".cpp"
|
||||
| ".py"
|
||||
| ".java"
|
||||
| ".js"
|
||||
| ".ts"
|
||||
| ".swift"
|
||||
| ".go"
|
||||
| ".rb"
|
||||
| ".php"
|
||||
| ".css"
|
||||
| ".scss"
|
||||
| ".less"
|
||||
):
|
||||
content = file.decode("utf-8")
|
||||
case ".pdf":
|
||||
if not pm.is_installed("pypdf2"):
|
||||
pm.install("pypdf2")
|
||||
from PyPDF2 import PdfReader # type: ignore
|
||||
from io import BytesIO
|
||||
|
||||
pdf_file = BytesIO(file)
|
||||
reader = PdfReader(pdf_file)
|
||||
for page in reader.pages:
|
||||
content += page.extract_text() + "\n"
|
||||
case ".docx":
|
||||
if not pm.is_installed("docx"):
|
||||
pm.install("docx")
|
||||
from docx import Document
|
||||
from io import BytesIO
|
||||
|
||||
docx_file = BytesIO(file)
|
||||
doc = Document(docx_file)
|
||||
content = "\n".join([paragraph.text for paragraph in doc.paragraphs])
|
||||
case ".pptx":
|
||||
if not pm.is_installed("pptx"):
|
||||
pm.install("pptx")
|
||||
from pptx import Presentation
|
||||
from io import BytesIO
|
||||
|
||||
pptx_file = BytesIO(file)
|
||||
prs = Presentation(pptx_file)
|
||||
for slide in prs.slides:
|
||||
for shape in slide.shapes:
|
||||
if hasattr(shape, "text"):
|
||||
content += shape.text + "\n"
|
||||
case ".xlsx":
|
||||
if not pm.is_installed("openpyxl"):
|
||||
pm.install("openpyxl")
|
||||
from openpyxl import load_workbook
|
||||
from io import BytesIO
|
||||
|
||||
xlsx_file = BytesIO(file)
|
||||
wb = load_workbook(xlsx_file)
|
||||
for sheet in wb:
|
||||
content += f"Sheet: {sheet.title}\n"
|
||||
for row in sheet.iter_rows(values_only=True):
|
||||
content += (
|
||||
"\t".join(
|
||||
str(cell) if cell is not None else "" for cell in row
|
||||
)
|
||||
+ "\n"
|
||||
)
|
||||
content += "\n"
|
||||
case _:
|
||||
logging.error(
|
||||
f"Unsupported file type: {file_path.name} (extension {ext})"
|
||||
)
|
||||
return False
|
||||
|
||||
# Insert into the RAG queue
|
||||
if content:
|
||||
await rag.apipeline_enqueue_documents(content)
|
||||
logging.info(f"Successfully fetched and enqueued file: {file_path.name}")
|
||||
return True
|
||||
else:
|
||||
logging.error(f"No content could be extracted from file: {file_path.name}")
|
||||
|
||||
except Exception as e:
|
||||
logging.error(f"Error processing or enqueueing file {file_path.name}: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
finally:
|
||||
if file_path.name.startswith(temp_prefix):
|
||||
try:
|
||||
file_path.unlink()
|
||||
except Exception as e:
|
||||
logging.error(f"Error deleting file {file_path}: {str(e)}")
|
||||
return False
|
||||
|
||||
|
||||
async def pipeline_index_file(rag: LightRAG, file_path: Path):
|
||||
"""Index a file
|
||||
|
||||
Args:
|
||||
rag: LightRAG instance
|
||||
file_path: Path to the saved file
|
||||
"""
|
||||
try:
|
||||
if await pipeline_enqueue_file(rag, file_path):
|
||||
await rag.apipeline_process_enqueue_documents()
|
||||
|
||||
except Exception as e:
|
||||
logging.error(f"Error indexing file {file_path.name}: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
|
||||
|
||||
async def pipeline_index_files(rag: LightRAG, file_paths: List[Path]):
|
||||
"""Index multiple files concurrently
|
||||
|
||||
Args:
|
||||
rag: LightRAG instance
|
||||
file_paths: Paths to the files to index
|
||||
"""
|
||||
if not file_paths:
|
||||
return
|
||||
try:
|
||||
enqueued = False
|
||||
|
||||
if len(file_paths) == 1:
|
||||
enqueued = await pipeline_enqueue_file(rag, file_paths[0])
|
||||
else:
|
||||
tasks = [pipeline_enqueue_file(rag, path) for path in file_paths]
|
||||
enqueued = any(await asyncio.gather(*tasks))
|
||||
|
||||
if enqueued:
|
||||
await rag.apipeline_process_enqueue_documents()
|
||||
except Exception as e:
|
||||
logging.error(f"Error indexing files: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
|
||||
|
||||
async def pipeline_index_texts(rag: LightRAG, texts: List[str]):
|
||||
"""Index a list of texts
|
||||
|
||||
Args:
|
||||
rag: LightRAG instance
|
||||
texts: The texts to index
|
||||
"""
|
||||
if not texts:
|
||||
return
|
||||
await rag.apipeline_enqueue_documents(texts)
|
||||
await rag.apipeline_process_enqueue_documents()
|
||||
|
||||
|
||||
async def save_temp_file(input_dir: Path, file: UploadFile = File(...)) -> Path:
|
||||
"""Save the uploaded file to a temporary location
|
||||
|
||||
Args:
|
||||
file: The uploaded file
|
||||
|
||||
Returns:
|
||||
Path: The path to the saved file
|
||||
"""
|
||||
# Generate unique filename to avoid conflicts
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
unique_filename = f"{temp_prefix}{timestamp}_{file.filename}"
|
||||
|
||||
# Create a temporary file to save the uploaded content
|
||||
temp_path = input_dir / "temp" / unique_filename
|
||||
temp_path.parent.mkdir(exist_ok=True)
|
||||
|
||||
# Save the file
|
||||
with open(temp_path, "wb") as buffer:
|
||||
shutil.copyfileobj(file.file, buffer)
|
||||
return temp_path
|
||||
|
||||
|
||||
async def run_scanning_process(rag: LightRAG, doc_manager: DocumentManager):
|
||||
"""Background task to scan and index documents"""
|
||||
try:
|
||||
new_files = doc_manager.scan_directory_for_new_files()
|
||||
scan_progress["total_files"] = len(new_files)
|
||||
|
||||
logging.info(f"Found {len(new_files)} new files to index.")
|
||||
for file_path in new_files:
|
||||
try:
|
||||
async with progress_lock:
|
||||
scan_progress["current_file"] = os.path.basename(file_path)
|
||||
|
||||
await pipeline_index_file(rag, file_path)
|
||||
|
||||
async with progress_lock:
|
||||
scan_progress["indexed_count"] += 1
|
||||
scan_progress["progress"] = (
|
||||
scan_progress["indexed_count"] / scan_progress["total_files"]
|
||||
) * 100
|
||||
|
||||
except Exception as e:
|
||||
logging.error(f"Error indexing file {file_path}: {str(e)}")
|
||||
|
||||
except Exception as e:
|
||||
logging.error(f"Error during scanning process: {str(e)}")
|
||||
finally:
|
||||
async with progress_lock:
|
||||
scan_progress["is_scanning"] = False
|
||||
|
||||
|
||||
def create_document_routes(
|
||||
rag: LightRAG, doc_manager: DocumentManager, api_key: Optional[str] = None
|
||||
):
|
||||
optional_api_key = get_api_key_dependency(api_key)
|
||||
|
||||
@router.post("/scan", dependencies=[Depends(optional_api_key)])
|
||||
async def scan_for_new_documents(background_tasks: BackgroundTasks):
|
||||
"""
|
||||
Trigger the scanning process for new documents.
|
||||
|
||||
This endpoint initiates a background task that scans the input directory for new documents
|
||||
and processes them. If a scanning process is already running, it returns a status indicating
|
||||
that fact.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the scanning status
|
||||
"""
|
||||
async with progress_lock:
|
||||
if scan_progress["is_scanning"]:
|
||||
return {"status": "already_scanning"}
|
||||
|
||||
scan_progress["is_scanning"] = True
|
||||
scan_progress["indexed_count"] = 0
|
||||
scan_progress["progress"] = 0
|
||||
|
||||
# Start the scanning process in the background
|
||||
background_tasks.add_task(run_scanning_process, rag, doc_manager)
|
||||
return {"status": "scanning_started"}
|
||||
|
||||
@router.get("/scan-progress")
|
||||
async def get_scan_progress():
|
||||
"""
|
||||
Get the current progress of the document scanning process.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the current scanning progress information including:
|
||||
- is_scanning: Whether a scan is currently in progress
|
||||
- current_file: The file currently being processed
|
||||
- indexed_count: Number of files indexed so far
|
||||
- total_files: Total number of files to process
|
||||
- progress: Percentage of completion
|
||||
"""
|
||||
async with progress_lock:
|
||||
return scan_progress
|
||||
|
||||
@router.post("/upload", dependencies=[Depends(optional_api_key)])
|
||||
async def upload_to_input_dir(
|
||||
background_tasks: BackgroundTasks, file: UploadFile = File(...)
|
||||
):
|
||||
"""
|
||||
Upload a file to the input directory and index it.
|
||||
|
||||
This API endpoint accepts a file through an HTTP POST request, checks if the
|
||||
uploaded file is of a supported type, saves it in the specified input directory,
|
||||
indexes it for retrieval, and returns a success status with relevant details.
|
||||
|
||||
Args:
|
||||
background_tasks: FastAPI BackgroundTasks for async processing
|
||||
file (UploadFile): The file to be uploaded. It must have an allowed extension.
|
||||
|
||||
Returns:
|
||||
InsertResponse: A response object containing the upload status and a message.
|
||||
|
||||
Raises:
|
||||
HTTPException: If the file type is not supported (400) or other errors occur (500).
|
||||
"""
|
||||
try:
|
||||
if not doc_manager.is_supported_file(file.filename):
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
|
||||
)
|
||||
|
||||
file_path = doc_manager.input_dir / file.filename
|
||||
with open(file_path, "wb") as buffer:
|
||||
shutil.copyfileobj(file.file, buffer)
|
||||
|
||||
# Add to background tasks
|
||||
background_tasks.add_task(pipeline_index_file, rag, file_path)
|
||||
|
||||
return InsertResponse(
|
||||
status="success",
|
||||
message=f"File '{file.filename}' uploaded successfully. Processing will continue in background.",
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Error /documents/upload: {file.filename}: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.post(
|
||||
"/text", response_model=InsertResponse, dependencies=[Depends(optional_api_key)]
|
||||
)
|
||||
async def insert_text(
|
||||
request: InsertTextRequest, background_tasks: BackgroundTasks
|
||||
):
|
||||
"""
|
||||
Insert text into the RAG system.
|
||||
|
||||
This endpoint allows you to insert text data into the RAG system for later retrieval
|
||||
and use in generating responses.
|
||||
|
||||
Args:
|
||||
request (InsertTextRequest): The request body containing the text to be inserted.
|
||||
background_tasks: FastAPI BackgroundTasks for async processing
|
||||
|
||||
Returns:
|
||||
InsertResponse: A response object containing the status of the operation.
|
||||
|
||||
Raises:
|
||||
HTTPException: If an error occurs during text processing (500).
|
||||
"""
|
||||
try:
|
||||
background_tasks.add_task(pipeline_index_texts, rag, [request.text])
|
||||
return InsertResponse(
|
||||
status="success",
|
||||
message="Text successfully received. Processing will continue in background.",
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Error /documents/text: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.post(
|
||||
"/texts",
|
||||
response_model=InsertResponse,
|
||||
dependencies=[Depends(optional_api_key)],
|
||||
)
|
||||
async def insert_texts(
|
||||
request: InsertTextsRequest, background_tasks: BackgroundTasks
|
||||
):
|
||||
"""
|
||||
Insert multiple texts into the RAG system.
|
||||
|
||||
This endpoint allows you to insert multiple text entries into the RAG system
|
||||
in a single request.
|
||||
|
||||
Args:
|
||||
request (InsertTextsRequest): The request body containing the list of texts.
|
||||
background_tasks: FastAPI BackgroundTasks for async processing
|
||||
|
||||
Returns:
|
||||
InsertResponse: A response object containing the status of the operation.
|
||||
|
||||
Raises:
|
||||
HTTPException: If an error occurs during text processing (500).
|
||||
"""
|
||||
try:
|
||||
background_tasks.add_task(pipeline_index_texts, rag, request.texts)
|
||||
return InsertResponse(
|
||||
status="success",
|
||||
message="Text successfully received. Processing will continue in background.",
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Error /documents/text: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.post(
|
||||
"/file", response_model=InsertResponse, dependencies=[Depends(optional_api_key)]
|
||||
)
|
||||
async def insert_file(
|
||||
background_tasks: BackgroundTasks, file: UploadFile = File(...)
|
||||
):
|
||||
"""
|
||||
Insert a file directly into the RAG system.
|
||||
|
||||
This endpoint accepts a file upload and processes it for inclusion in the RAG system.
|
||||
The file is saved temporarily and processed in the background.
|
||||
|
||||
Args:
|
||||
background_tasks: FastAPI BackgroundTasks for async processing
|
||||
file (UploadFile): The file to be processed
|
||||
|
||||
Returns:
|
||||
InsertResponse: A response object containing the status of the operation.
|
||||
|
||||
Raises:
|
||||
HTTPException: If the file type is not supported (400) or other errors occur (500).
|
||||
"""
|
||||
try:
|
||||
if not doc_manager.is_supported_file(file.filename):
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
|
||||
)
|
||||
|
||||
temp_path = await save_temp_file(doc_manager.input_dir, file)
|
||||
|
||||
# Add to background tasks
|
||||
background_tasks.add_task(pipeline_index_file, rag, temp_path)
|
||||
|
||||
return InsertResponse(
|
||||
status="success",
|
||||
message=f"File '{file.filename}' saved successfully. Processing will continue in background.",
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Error /documents/file: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.post(
|
||||
"/file_batch",
|
||||
response_model=InsertResponse,
|
||||
dependencies=[Depends(optional_api_key)],
|
||||
)
|
||||
async def insert_batch(
|
||||
background_tasks: BackgroundTasks, files: List[UploadFile] = File(...)
|
||||
):
|
||||
"""
|
||||
Process multiple files in batch mode.
|
||||
|
||||
This endpoint allows uploading and processing multiple files simultaneously.
|
||||
It handles partial successes and provides detailed feedback about failed files.
|
||||
|
||||
Args:
|
||||
background_tasks: FastAPI BackgroundTasks for async processing
|
||||
files (List[UploadFile]): List of files to process
|
||||
|
||||
Returns:
|
||||
InsertResponse: A response object containing:
|
||||
- status: "success", "partial_success", or "failure"
|
||||
- message: Detailed information about the operation results
|
||||
|
||||
Raises:
|
||||
HTTPException: If an error occurs during processing (500).
|
||||
"""
|
||||
try:
|
||||
inserted_count = 0
|
||||
failed_files = []
|
||||
temp_files = []
|
||||
|
||||
for file in files:
|
||||
if doc_manager.is_supported_file(file.filename):
|
||||
# Create a temporary file to save the uploaded content
|
||||
temp_files.append(await save_temp_file(doc_manager.input_dir, file))
|
||||
inserted_count += 1
|
||||
else:
|
||||
failed_files.append(f"{file.filename} (unsupported type)")
|
||||
|
||||
if temp_files:
|
||||
background_tasks.add_task(pipeline_index_files, rag, temp_files)
|
||||
|
||||
# Prepare status message
|
||||
if inserted_count == len(files):
|
||||
status = "success"
|
||||
status_message = f"Successfully inserted all {inserted_count} documents"
|
||||
elif inserted_count > 0:
|
||||
status = "partial_success"
|
||||
status_message = f"Successfully inserted {inserted_count} out of {len(files)} documents"
|
||||
if failed_files:
|
||||
status_message += f". Failed files: {', '.join(failed_files)}"
|
||||
else:
|
||||
status = "failure"
|
||||
status_message = "No documents were successfully inserted"
|
||||
if failed_files:
|
||||
status_message += f". Failed files: {', '.join(failed_files)}"
|
||||
|
||||
return InsertResponse(status=status, message=status_message)
|
||||
except Exception as e:
|
||||
logging.error(f"Error /documents/batch: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.delete(
|
||||
"", response_model=InsertResponse, dependencies=[Depends(optional_api_key)]
|
||||
)
|
||||
async def clear_documents():
|
||||
"""
|
||||
Clear all documents from the RAG system.
|
||||
|
||||
This endpoint deletes all text chunks, entities vector database, and relationships
|
||||
vector database, effectively clearing all documents from the RAG system.
|
||||
|
||||
Returns:
|
||||
InsertResponse: A response object containing the status and message.
|
||||
|
||||
Raises:
|
||||
HTTPException: If an error occurs during the clearing process (500).
|
||||
"""
|
||||
try:
|
||||
rag.text_chunks = []
|
||||
rag.entities_vdb = None
|
||||
rag.relationships_vdb = None
|
||||
return InsertResponse(
|
||||
status="success", message="All documents cleared successfully"
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Error DELETE /documents: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.get("", dependencies=[Depends(optional_api_key)])
|
||||
async def documents() -> DocsStatusesResponse:
|
||||
"""
|
||||
Get the status of all documents in the system.
|
||||
|
||||
This endpoint retrieves the current status of all documents, grouped by their
|
||||
processing status (PENDING, PROCESSING, PROCESSED, FAILED).
|
||||
|
||||
Returns:
|
||||
DocsStatusesResponse: A response object containing a dictionary where keys are
|
||||
DocStatus values and values are lists of DocStatusResponse
|
||||
objects representing documents in each status category.
|
||||
|
||||
Raises:
|
||||
HTTPException: If an error occurs while retrieving document statuses (500).
|
||||
"""
|
||||
try:
|
||||
statuses = (
|
||||
DocStatus.PENDING,
|
||||
DocStatus.PROCESSING,
|
||||
DocStatus.PROCESSED,
|
||||
DocStatus.FAILED,
|
||||
)
|
||||
|
||||
tasks = [rag.get_docs_by_status(status) for status in statuses]
|
||||
results: List[Dict[str, DocProcessingStatus]] = await asyncio.gather(*tasks)
|
||||
|
||||
response = DocsStatusesResponse()
|
||||
|
||||
for idx, result in enumerate(results):
|
||||
status = statuses[idx]
|
||||
for doc_id, doc_status in result.items():
|
||||
if status not in response.statuses:
|
||||
response.statuses[status] = []
|
||||
response.statuses[status].append(
|
||||
DocStatusResponse(
|
||||
id=doc_id,
|
||||
content_summary=doc_status.content_summary,
|
||||
content_length=doc_status.content_length,
|
||||
status=doc_status.status,
|
||||
created_at=DocStatusResponse.format_datetime(
|
||||
doc_status.created_at
|
||||
),
|
||||
updated_at=DocStatusResponse.format_datetime(
|
||||
doc_status.updated_at
|
||||
),
|
||||
chunks_count=doc_status.chunks_count,
|
||||
error=doc_status.error,
|
||||
metadata=doc_status.metadata,
|
||||
)
|
||||
)
|
||||
return response
|
||||
except Exception as e:
|
||||
logging.error(f"Error GET /documents: {str(e)}")
|
||||
logging.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
return router
|
||||
27
lightrag/api/routers/graph_routes.py
Normal file
27
lightrag/api/routers/graph_routes.py
Normal file
@@ -0,0 +1,27 @@
|
||||
"""
|
||||
This module contains all graph-related routes for the LightRAG API.
|
||||
"""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from fastapi import APIRouter, Depends
|
||||
|
||||
from ..utils_api import get_api_key_dependency
|
||||
|
||||
router = APIRouter(tags=["graph"])
|
||||
|
||||
|
||||
def create_graph_routes(rag, api_key: Optional[str] = None):
|
||||
optional_api_key = get_api_key_dependency(api_key)
|
||||
|
||||
@router.get("/graph/label/list", dependencies=[Depends(optional_api_key)])
|
||||
async def get_graph_labels():
|
||||
"""Get all graph labels"""
|
||||
return await rag.get_graph_labels()
|
||||
|
||||
@router.get("/graphs", dependencies=[Depends(optional_api_key)])
|
||||
async def get_knowledge_graph(label: str, max_depth: int = 3):
|
||||
"""Get knowledge graph for a specific label"""
|
||||
return await rag.get_knowledge_graph(node_label=label, max_depth=max_depth)
|
||||
|
||||
return router
|
||||
@@ -5,31 +5,13 @@ import logging
|
||||
import time
|
||||
import json
|
||||
import re
|
||||
import os
|
||||
from enum import Enum
|
||||
from fastapi.responses import StreamingResponse
|
||||
import asyncio
|
||||
from ascii_colors import trace_exception
|
||||
from lightrag import LightRAG, QueryParam
|
||||
from lightrag.utils import encode_string_by_tiktoken
|
||||
from dotenv import load_dotenv
|
||||
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class OllamaServerInfos:
|
||||
# Constants for emulated Ollama model information
|
||||
LIGHTRAG_NAME = "lightrag"
|
||||
LIGHTRAG_TAG = os.getenv("OLLAMA_EMULATING_MODEL_TAG", "latest")
|
||||
LIGHTRAG_MODEL = f"{LIGHTRAG_NAME}:{LIGHTRAG_TAG}"
|
||||
LIGHTRAG_SIZE = 7365960935 # it's a dummy value
|
||||
LIGHTRAG_CREATED_AT = "2024-01-15T00:00:00Z"
|
||||
LIGHTRAG_DIGEST = "sha256:lightrag"
|
||||
|
||||
|
||||
ollama_server_infos = OllamaServerInfos()
|
||||
from ..utils_api import ollama_server_infos
|
||||
|
||||
|
||||
# query mode according to query prefix (bypass is not LightRAG quer mode)
|
||||
@@ -144,7 +126,7 @@ class OllamaAPI:
|
||||
self.rag = rag
|
||||
self.ollama_server_infos = ollama_server_infos
|
||||
self.top_k = top_k
|
||||
self.router = APIRouter()
|
||||
self.router = APIRouter(tags=["ollama"])
|
||||
self.setup_routes()
|
||||
|
||||
def setup_routes(self):
|
||||
225
lightrag/api/routers/query_routes.py
Normal file
225
lightrag/api/routers/query_routes.py
Normal file
@@ -0,0 +1,225 @@
|
||||
"""
|
||||
This module contains all query-related routes for the LightRAG API.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Dict, List, Literal, Optional
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from lightrag.base import QueryParam
|
||||
from ..utils_api import get_api_key_dependency
|
||||
from pydantic import BaseModel, Field, field_validator
|
||||
|
||||
from ascii_colors import trace_exception
|
||||
|
||||
router = APIRouter(tags=["query"])
|
||||
|
||||
|
||||
class QueryRequest(BaseModel):
|
||||
query: str = Field(
|
||||
min_length=1,
|
||||
description="The query text",
|
||||
)
|
||||
|
||||
mode: Literal["local", "global", "hybrid", "naive", "mix"] = Field(
|
||||
default="hybrid",
|
||||
description="Query mode",
|
||||
)
|
||||
|
||||
only_need_context: Optional[bool] = Field(
|
||||
default=None,
|
||||
description="If True, only returns the retrieved context without generating a response.",
|
||||
)
|
||||
|
||||
only_need_prompt: Optional[bool] = Field(
|
||||
default=None,
|
||||
description="If True, only returns the generated prompt without producing a response.",
|
||||
)
|
||||
|
||||
response_type: Optional[str] = Field(
|
||||
min_length=1,
|
||||
default=None,
|
||||
description="Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'.",
|
||||
)
|
||||
|
||||
top_k: Optional[int] = Field(
|
||||
ge=1,
|
||||
default=None,
|
||||
description="Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode.",
|
||||
)
|
||||
|
||||
max_token_for_text_unit: Optional[int] = Field(
|
||||
gt=1,
|
||||
default=None,
|
||||
description="Maximum number of tokens allowed for each retrieved text chunk.",
|
||||
)
|
||||
|
||||
max_token_for_global_context: Optional[int] = Field(
|
||||
gt=1,
|
||||
default=None,
|
||||
description="Maximum number of tokens allocated for relationship descriptions in global retrieval.",
|
||||
)
|
||||
|
||||
max_token_for_local_context: Optional[int] = Field(
|
||||
gt=1,
|
||||
default=None,
|
||||
description="Maximum number of tokens allocated for entity descriptions in local retrieval.",
|
||||
)
|
||||
|
||||
hl_keywords: Optional[List[str]] = Field(
|
||||
default=None,
|
||||
description="List of high-level keywords to prioritize in retrieval.",
|
||||
)
|
||||
|
||||
ll_keywords: Optional[List[str]] = Field(
|
||||
default=None,
|
||||
description="List of low-level keywords to refine retrieval focus.",
|
||||
)
|
||||
|
||||
conversation_history: Optional[List[Dict[str, Any]]] = Field(
|
||||
default=None,
|
||||
description="Stores past conversation history to maintain context. Format: [{'role': 'user/assistant', 'content': 'message'}].",
|
||||
)
|
||||
|
||||
history_turns: Optional[int] = Field(
|
||||
ge=0,
|
||||
default=None,
|
||||
description="Number of complete conversation turns (user-assistant pairs) to consider in the response context.",
|
||||
)
|
||||
|
||||
@field_validator("query", mode="after")
|
||||
@classmethod
|
||||
def query_strip_after(cls, query: str) -> str:
|
||||
return query.strip()
|
||||
|
||||
@field_validator("hl_keywords", mode="after")
|
||||
@classmethod
|
||||
def hl_keywords_strip_after(cls, hl_keywords: List[str] | None) -> List[str] | None:
|
||||
if hl_keywords is None:
|
||||
return None
|
||||
return [keyword.strip() for keyword in hl_keywords]
|
||||
|
||||
@field_validator("ll_keywords", mode="after")
|
||||
@classmethod
|
||||
def ll_keywords_strip_after(cls, ll_keywords: List[str] | None) -> List[str] | None:
|
||||
if ll_keywords is None:
|
||||
return None
|
||||
return [keyword.strip() for keyword in ll_keywords]
|
||||
|
||||
@field_validator("conversation_history", mode="after")
|
||||
@classmethod
|
||||
def conversation_history_role_check(
|
||||
cls, conversation_history: List[Dict[str, Any]] | None
|
||||
) -> List[Dict[str, Any]] | None:
|
||||
if conversation_history is None:
|
||||
return None
|
||||
for msg in conversation_history:
|
||||
if "role" not in msg or msg["role"] not in {"user", "assistant"}:
|
||||
raise ValueError(
|
||||
"Each message must have a 'role' key with value 'user' or 'assistant'."
|
||||
)
|
||||
return conversation_history
|
||||
|
||||
def to_query_params(self, is_stream: bool) -> "QueryParam":
|
||||
"""Converts a QueryRequest instance into a QueryParam instance."""
|
||||
# Use Pydantic's `.model_dump(exclude_none=True)` to remove None values automatically
|
||||
request_data = self.model_dump(exclude_none=True, exclude={"query"})
|
||||
|
||||
# Ensure `mode` and `stream` are set explicitly
|
||||
param = QueryParam(**request_data)
|
||||
param.stream = is_stream
|
||||
return param
|
||||
|
||||
|
||||
class QueryResponse(BaseModel):
|
||||
response: str = Field(
|
||||
description="The generated response",
|
||||
)
|
||||
|
||||
|
||||
def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60):
|
||||
optional_api_key = get_api_key_dependency(api_key)
|
||||
|
||||
@router.post(
|
||||
"/query", response_model=QueryResponse, dependencies=[Depends(optional_api_key)]
|
||||
)
|
||||
async def query_text(request: QueryRequest):
|
||||
"""
|
||||
Handle a POST request at the /query endpoint to process user queries using RAG capabilities.
|
||||
|
||||
Parameters:
|
||||
request (QueryRequest): The request object containing the query parameters.
|
||||
Returns:
|
||||
QueryResponse: A Pydantic model containing the result of the query processing.
|
||||
If a string is returned (e.g., cache hit), it's directly returned.
|
||||
Otherwise, an async generator may be used to build the response.
|
||||
|
||||
Raises:
|
||||
HTTPException: Raised when an error occurs during the request handling process,
|
||||
with status code 500 and detail containing the exception message.
|
||||
"""
|
||||
try:
|
||||
param = request.to_query_params(False)
|
||||
response = await rag.aquery(request.query, param=param)
|
||||
|
||||
# If response is a string (e.g. cache hit), return directly
|
||||
if isinstance(response, str):
|
||||
return QueryResponse(response=response)
|
||||
|
||||
if isinstance(response, dict):
|
||||
result = json.dumps(response, indent=2)
|
||||
return QueryResponse(response=result)
|
||||
else:
|
||||
return QueryResponse(response=str(response))
|
||||
except Exception as e:
|
||||
trace_exception(e)
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.post("/query/stream", dependencies=[Depends(optional_api_key)])
|
||||
async def query_text_stream(request: QueryRequest):
|
||||
"""
|
||||
This endpoint performs a retrieval-augmented generation (RAG) query and streams the response.
|
||||
|
||||
Args:
|
||||
request (QueryRequest): The request object containing the query parameters.
|
||||
optional_api_key (Optional[str], optional): An optional API key for authentication. Defaults to None.
|
||||
|
||||
Returns:
|
||||
StreamingResponse: A streaming response containing the RAG query results.
|
||||
"""
|
||||
try:
|
||||
param = request.to_query_params(True)
|
||||
response = await rag.aquery(request.query, param=param)
|
||||
|
||||
from fastapi.responses import StreamingResponse
|
||||
|
||||
async def stream_generator():
|
||||
if isinstance(response, str):
|
||||
# If it's a string, send it all at once
|
||||
yield f"{json.dumps({'response': response})}\n"
|
||||
else:
|
||||
# If it's an async generator, send chunks one by one
|
||||
try:
|
||||
async for chunk in response:
|
||||
if chunk: # Only send non-empty content
|
||||
yield f"{json.dumps({'response': chunk})}\n"
|
||||
except Exception as e:
|
||||
logging.error(f"Streaming error: {str(e)}")
|
||||
yield f"{json.dumps({'error': str(e)})}\n"
|
||||
|
||||
return StreamingResponse(
|
||||
stream_generator(),
|
||||
media_type="application/x-ndjson",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Content-Type": "application/x-ndjson",
|
||||
"X-Accel-Buffering": "no", # Ensure proper handling of streaming response when proxied by Nginx
|
||||
},
|
||||
)
|
||||
except Exception as e:
|
||||
trace_exception(e)
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
return router
|
||||
496
lightrag/api/utils_api.py
Normal file
496
lightrag/api/utils_api.py
Normal file
@@ -0,0 +1,496 @@
|
||||
"""
|
||||
Utility functions for the LightRAG API.
|
||||
"""
|
||||
|
||||
import os
|
||||
import argparse
|
||||
from typing import Optional
|
||||
import sys
|
||||
from ascii_colors import ASCIIColors
|
||||
from lightrag.api import __api_version__
|
||||
from fastapi import HTTPException, Security
|
||||
from dotenv import load_dotenv
|
||||
from fastapi.security import APIKeyHeader
|
||||
from starlette.status import HTTP_403_FORBIDDEN
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class OllamaServerInfos:
|
||||
# Constants for emulated Ollama model information
|
||||
LIGHTRAG_NAME = "lightrag"
|
||||
LIGHTRAG_TAG = os.getenv("OLLAMA_EMULATING_MODEL_TAG", "latest")
|
||||
LIGHTRAG_MODEL = f"{LIGHTRAG_NAME}:{LIGHTRAG_TAG}"
|
||||
LIGHTRAG_SIZE = 7365960935 # it's a dummy value
|
||||
LIGHTRAG_CREATED_AT = "2024-01-15T00:00:00Z"
|
||||
LIGHTRAG_DIGEST = "sha256:lightrag"
|
||||
|
||||
|
||||
ollama_server_infos = OllamaServerInfos()
|
||||
|
||||
|
||||
def get_api_key_dependency(api_key: Optional[str]):
|
||||
"""
|
||||
Create an API key dependency for route protection.
|
||||
|
||||
Args:
|
||||
api_key (Optional[str]): The API key to validate against.
|
||||
If None, no authentication is required.
|
||||
|
||||
Returns:
|
||||
Callable: A dependency function that validates the API key.
|
||||
"""
|
||||
if not api_key:
|
||||
# If no API key is configured, return a dummy dependency that always succeeds
|
||||
async def no_auth():
|
||||
return None
|
||||
|
||||
return no_auth
|
||||
|
||||
# If API key is configured, use proper authentication
|
||||
api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
|
||||
|
||||
async def api_key_auth(
|
||||
api_key_header_value: Optional[str] = Security(api_key_header),
|
||||
):
|
||||
if not api_key_header_value:
|
||||
raise HTTPException(
|
||||
status_code=HTTP_403_FORBIDDEN, detail="API Key required"
|
||||
)
|
||||
if api_key_header_value != api_key:
|
||||
raise HTTPException(
|
||||
status_code=HTTP_403_FORBIDDEN, detail="Invalid API Key"
|
||||
)
|
||||
return api_key_header_value
|
||||
|
||||
return api_key_auth
|
||||
|
||||
|
||||
class DefaultRAGStorageConfig:
|
||||
KV_STORAGE = "JsonKVStorage"
|
||||
VECTOR_STORAGE = "NanoVectorDBStorage"
|
||||
GRAPH_STORAGE = "NetworkXStorage"
|
||||
DOC_STATUS_STORAGE = "JsonDocStatusStorage"
|
||||
|
||||
|
||||
def get_default_host(binding_type: str) -> str:
|
||||
default_hosts = {
|
||||
"ollama": os.getenv("LLM_BINDING_HOST", "http://localhost:11434"),
|
||||
"lollms": os.getenv("LLM_BINDING_HOST", "http://localhost:9600"),
|
||||
"azure_openai": os.getenv("AZURE_OPENAI_ENDPOINT", "https://api.openai.com/v1"),
|
||||
"openai": os.getenv("LLM_BINDING_HOST", "https://api.openai.com/v1"),
|
||||
}
|
||||
return default_hosts.get(
|
||||
binding_type, os.getenv("LLM_BINDING_HOST", "http://localhost:11434")
|
||||
) # fallback to ollama if unknown
|
||||
|
||||
|
||||
def get_env_value(env_key: str, default: any, value_type: type = str) -> any:
|
||||
"""
|
||||
Get value from environment variable with type conversion
|
||||
|
||||
Args:
|
||||
env_key (str): Environment variable key
|
||||
default (any): Default value if env variable is not set
|
||||
value_type (type): Type to convert the value to
|
||||
|
||||
Returns:
|
||||
any: Converted value from environment or default
|
||||
"""
|
||||
value = os.getenv(env_key)
|
||||
if value is None:
|
||||
return default
|
||||
|
||||
if value_type is bool:
|
||||
return value.lower() in ("true", "1", "yes", "t", "on")
|
||||
try:
|
||||
return value_type(value)
|
||||
except ValueError:
|
||||
return default
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""
|
||||
Parse command line arguments with environment variable fallback
|
||||
|
||||
Returns:
|
||||
argparse.Namespace: Parsed arguments
|
||||
"""
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="LightRAG FastAPI Server with separate working and input directories"
|
||||
)
|
||||
|
||||
# Server configuration
|
||||
parser.add_argument(
|
||||
"--host",
|
||||
default=get_env_value("HOST", "0.0.0.0"),
|
||||
help="Server host (default: from env or 0.0.0.0)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--port",
|
||||
type=int,
|
||||
default=get_env_value("PORT", 9621, int),
|
||||
help="Server port (default: from env or 9621)",
|
||||
)
|
||||
|
||||
# Directory configuration
|
||||
parser.add_argument(
|
||||
"--working-dir",
|
||||
default=get_env_value("WORKING_DIR", "./rag_storage"),
|
||||
help="Working directory for RAG storage (default: from env or ./rag_storage)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--input-dir",
|
||||
default=get_env_value("INPUT_DIR", "./inputs"),
|
||||
help="Directory containing input documents (default: from env or ./inputs)",
|
||||
)
|
||||
|
||||
def timeout_type(value):
|
||||
if value is None:
|
||||
return 150
|
||||
if value is None or value == "None":
|
||||
return None
|
||||
return int(value)
|
||||
|
||||
parser.add_argument(
|
||||
"--timeout",
|
||||
default=get_env_value("TIMEOUT", None, timeout_type),
|
||||
type=timeout_type,
|
||||
help="Timeout in seconds (useful when using slow AI). Use None for infinite timeout",
|
||||
)
|
||||
|
||||
# RAG configuration
|
||||
parser.add_argument(
|
||||
"--max-async",
|
||||
type=int,
|
||||
default=get_env_value("MAX_ASYNC", 4, int),
|
||||
help="Maximum async operations (default: from env or 4)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max-tokens",
|
||||
type=int,
|
||||
default=get_env_value("MAX_TOKENS", 32768, int),
|
||||
help="Maximum token size (default: from env or 32768)",
|
||||
)
|
||||
|
||||
# Logging configuration
|
||||
parser.add_argument(
|
||||
"--log-level",
|
||||
default=get_env_value("LOG_LEVEL", "INFO"),
|
||||
choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
|
||||
help="Logging level (default: from env or INFO)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--verbose",
|
||||
action="store_true",
|
||||
default=get_env_value("VERBOSE", False, bool),
|
||||
help="Enable verbose debug output(only valid for DEBUG log-level)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--key",
|
||||
type=str,
|
||||
default=get_env_value("LIGHTRAG_API_KEY", None),
|
||||
help="API key for authentication. This protects lightrag server against unauthorized access",
|
||||
)
|
||||
|
||||
# Optional https parameters
|
||||
parser.add_argument(
|
||||
"--ssl",
|
||||
action="store_true",
|
||||
default=get_env_value("SSL", False, bool),
|
||||
help="Enable HTTPS (default: from env or False)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ssl-certfile",
|
||||
default=get_env_value("SSL_CERTFILE", None),
|
||||
help="Path to SSL certificate file (required if --ssl is enabled)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ssl-keyfile",
|
||||
default=get_env_value("SSL_KEYFILE", None),
|
||||
help="Path to SSL private key file (required if --ssl is enabled)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--history-turns",
|
||||
type=int,
|
||||
default=get_env_value("HISTORY_TURNS", 3, int),
|
||||
help="Number of conversation history turns to include (default: from env or 3)",
|
||||
)
|
||||
|
||||
# Search parameters
|
||||
parser.add_argument(
|
||||
"--top-k",
|
||||
type=int,
|
||||
default=get_env_value("TOP_K", 60, int),
|
||||
help="Number of most similar results to return (default: from env or 60)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cosine-threshold",
|
||||
type=float,
|
||||
default=get_env_value("COSINE_THRESHOLD", 0.2, float),
|
||||
help="Cosine similarity threshold (default: from env or 0.4)",
|
||||
)
|
||||
|
||||
# Ollama model name
|
||||
parser.add_argument(
|
||||
"--simulated-model-name",
|
||||
type=str,
|
||||
default=get_env_value(
|
||||
"SIMULATED_MODEL_NAME", ollama_server_infos.LIGHTRAG_MODEL
|
||||
),
|
||||
help="Number of conversation history turns to include (default: from env or 3)",
|
||||
)
|
||||
|
||||
# Namespace
|
||||
parser.add_argument(
|
||||
"--namespace-prefix",
|
||||
type=str,
|
||||
default=get_env_value("NAMESPACE_PREFIX", ""),
|
||||
help="Prefix of the namespace",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--auto-scan-at-startup",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Enable automatic scanning when the program starts",
|
||||
)
|
||||
|
||||
# LLM and embedding bindings
|
||||
parser.add_argument(
|
||||
"--llm-binding",
|
||||
type=str,
|
||||
default=get_env_value("LLM_BINDING", "ollama"),
|
||||
choices=["lollms", "ollama", "openai", "openai-ollama", "azure_openai"],
|
||||
help="LLM binding type (default: from env or ollama)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--embedding-binding",
|
||||
type=str,
|
||||
default=get_env_value("EMBEDDING_BINDING", "ollama"),
|
||||
choices=["lollms", "ollama", "openai", "azure_openai"],
|
||||
help="Embedding binding type (default: from env or ollama)",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# convert relative path to absolute path
|
||||
args.working_dir = os.path.abspath(args.working_dir)
|
||||
args.input_dir = os.path.abspath(args.input_dir)
|
||||
|
||||
# Inject storage configuration from environment variables
|
||||
args.kv_storage = get_env_value(
|
||||
"LIGHTRAG_KV_STORAGE", DefaultRAGStorageConfig.KV_STORAGE
|
||||
)
|
||||
args.doc_status_storage = get_env_value(
|
||||
"LIGHTRAG_DOC_STATUS_STORAGE", DefaultRAGStorageConfig.DOC_STATUS_STORAGE
|
||||
)
|
||||
args.graph_storage = get_env_value(
|
||||
"LIGHTRAG_GRAPH_STORAGE", DefaultRAGStorageConfig.GRAPH_STORAGE
|
||||
)
|
||||
args.vector_storage = get_env_value(
|
||||
"LIGHTRAG_VECTOR_STORAGE", DefaultRAGStorageConfig.VECTOR_STORAGE
|
||||
)
|
||||
|
||||
# Handle openai-ollama special case
|
||||
if args.llm_binding == "openai-ollama":
|
||||
args.llm_binding = "openai"
|
||||
args.embedding_binding = "ollama"
|
||||
|
||||
args.llm_binding_host = get_env_value(
|
||||
"LLM_BINDING_HOST", get_default_host(args.llm_binding)
|
||||
)
|
||||
args.embedding_binding_host = get_env_value(
|
||||
"EMBEDDING_BINDING_HOST", get_default_host(args.embedding_binding)
|
||||
)
|
||||
args.llm_binding_api_key = get_env_value("LLM_BINDING_API_KEY", None)
|
||||
args.embedding_binding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "")
|
||||
|
||||
# Inject model configuration
|
||||
args.llm_model = get_env_value("LLM_MODEL", "mistral-nemo:latest")
|
||||
args.embedding_model = get_env_value("EMBEDDING_MODEL", "bge-m3:latest")
|
||||
args.embedding_dim = get_env_value("EMBEDDING_DIM", 1024, int)
|
||||
args.max_embed_tokens = get_env_value("MAX_EMBED_TOKENS", 8192, int)
|
||||
|
||||
# Inject chunk configuration
|
||||
args.chunk_size = get_env_value("CHUNK_SIZE", 1200, int)
|
||||
args.chunk_overlap_size = get_env_value("CHUNK_OVERLAP_SIZE", 100, int)
|
||||
|
||||
ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name
|
||||
|
||||
return args
|
||||
|
||||
|
||||
def display_splash_screen(args: argparse.Namespace) -> None:
|
||||
"""
|
||||
Display a colorful splash screen showing LightRAG server configuration
|
||||
|
||||
Args:
|
||||
args: Parsed command line arguments
|
||||
"""
|
||||
# Banner
|
||||
ASCIIColors.cyan(f"""
|
||||
╔══════════════════════════════════════════════════════════════╗
|
||||
║ 🚀 LightRAG Server v{__api_version__} ║
|
||||
║ Fast, Lightweight RAG Server Implementation ║
|
||||
╚══════════════════════════════════════════════════════════════╝
|
||||
""")
|
||||
|
||||
# Server Configuration
|
||||
ASCIIColors.magenta("\n📡 Server Configuration:")
|
||||
ASCIIColors.white(" ├─ Host: ", end="")
|
||||
ASCIIColors.yellow(f"{args.host}")
|
||||
ASCIIColors.white(" ├─ Port: ", end="")
|
||||
ASCIIColors.yellow(f"{args.port}")
|
||||
ASCIIColors.white(" ├─ CORS Origins: ", end="")
|
||||
ASCIIColors.yellow(f"{os.getenv('CORS_ORIGINS', '*')}")
|
||||
ASCIIColors.white(" ├─ SSL Enabled: ", end="")
|
||||
ASCIIColors.yellow(f"{args.ssl}")
|
||||
ASCIIColors.white(" └─ API Key: ", end="")
|
||||
ASCIIColors.yellow("Set" if args.key else "Not Set")
|
||||
if args.ssl:
|
||||
ASCIIColors.white(" ├─ SSL Cert: ", end="")
|
||||
ASCIIColors.yellow(f"{args.ssl_certfile}")
|
||||
ASCIIColors.white(" └─ SSL Key: ", end="")
|
||||
ASCIIColors.yellow(f"{args.ssl_keyfile}")
|
||||
|
||||
# Directory Configuration
|
||||
ASCIIColors.magenta("\n📂 Directory Configuration:")
|
||||
ASCIIColors.white(" ├─ Working Directory: ", end="")
|
||||
ASCIIColors.yellow(f"{args.working_dir}")
|
||||
ASCIIColors.white(" └─ Input Directory: ", end="")
|
||||
ASCIIColors.yellow(f"{args.input_dir}")
|
||||
|
||||
# LLM Configuration
|
||||
ASCIIColors.magenta("\n🤖 LLM Configuration:")
|
||||
ASCIIColors.white(" ├─ Binding: ", end="")
|
||||
ASCIIColors.yellow(f"{args.llm_binding}")
|
||||
ASCIIColors.white(" ├─ Host: ", end="")
|
||||
ASCIIColors.yellow(f"{args.llm_binding_host}")
|
||||
ASCIIColors.white(" └─ Model: ", end="")
|
||||
ASCIIColors.yellow(f"{args.llm_model}")
|
||||
|
||||
# Embedding Configuration
|
||||
ASCIIColors.magenta("\n📊 Embedding Configuration:")
|
||||
ASCIIColors.white(" ├─ Binding: ", end="")
|
||||
ASCIIColors.yellow(f"{args.embedding_binding}")
|
||||
ASCIIColors.white(" ├─ Host: ", end="")
|
||||
ASCIIColors.yellow(f"{args.embedding_binding_host}")
|
||||
ASCIIColors.white(" ├─ Model: ", end="")
|
||||
ASCIIColors.yellow(f"{args.embedding_model}")
|
||||
ASCIIColors.white(" └─ Dimensions: ", end="")
|
||||
ASCIIColors.yellow(f"{args.embedding_dim}")
|
||||
|
||||
# RAG Configuration
|
||||
ASCIIColors.magenta("\n⚙️ RAG Configuration:")
|
||||
ASCIIColors.white(" ├─ Max Async Operations: ", end="")
|
||||
ASCIIColors.yellow(f"{args.max_async}")
|
||||
ASCIIColors.white(" ├─ Max Tokens: ", end="")
|
||||
ASCIIColors.yellow(f"{args.max_tokens}")
|
||||
ASCIIColors.white(" ├─ Max Embed Tokens: ", end="")
|
||||
ASCIIColors.yellow(f"{args.max_embed_tokens}")
|
||||
ASCIIColors.white(" ├─ Chunk Size: ", end="")
|
||||
ASCIIColors.yellow(f"{args.chunk_size}")
|
||||
ASCIIColors.white(" ├─ Chunk Overlap Size: ", end="")
|
||||
ASCIIColors.yellow(f"{args.chunk_overlap_size}")
|
||||
ASCIIColors.white(" ├─ History Turns: ", end="")
|
||||
ASCIIColors.yellow(f"{args.history_turns}")
|
||||
ASCIIColors.white(" ├─ Cosine Threshold: ", end="")
|
||||
ASCIIColors.yellow(f"{args.cosine_threshold}")
|
||||
ASCIIColors.white(" └─ Top-K: ", end="")
|
||||
ASCIIColors.yellow(f"{args.top_k}")
|
||||
|
||||
# System Configuration
|
||||
ASCIIColors.magenta("\n💾 Storage Configuration:")
|
||||
ASCIIColors.white(" ├─ KV Storage: ", end="")
|
||||
ASCIIColors.yellow(f"{args.kv_storage}")
|
||||
ASCIIColors.white(" ├─ Vector Storage: ", end="")
|
||||
ASCIIColors.yellow(f"{args.vector_storage}")
|
||||
ASCIIColors.white(" ├─ Graph Storage: ", end="")
|
||||
ASCIIColors.yellow(f"{args.graph_storage}")
|
||||
ASCIIColors.white(" └─ Document Status Storage: ", end="")
|
||||
ASCIIColors.yellow(f"{args.doc_status_storage}")
|
||||
|
||||
ASCIIColors.magenta("\n🛠️ System Configuration:")
|
||||
ASCIIColors.white(" ├─ Ollama Emulating Model: ", end="")
|
||||
ASCIIColors.yellow(f"{ollama_server_infos.LIGHTRAG_MODEL}")
|
||||
ASCIIColors.white(" ├─ Log Level: ", end="")
|
||||
ASCIIColors.yellow(f"{args.log_level}")
|
||||
ASCIIColors.white(" ├─ Verbose Debug: ", end="")
|
||||
ASCIIColors.yellow(f"{args.verbose}")
|
||||
ASCIIColors.white(" └─ Timeout: ", end="")
|
||||
ASCIIColors.yellow(f"{args.timeout if args.timeout else 'None (infinite)'}")
|
||||
|
||||
# Server Status
|
||||
ASCIIColors.green("\n✨ Server starting up...\n")
|
||||
|
||||
# Server Access Information
|
||||
protocol = "https" if args.ssl else "http"
|
||||
if args.host == "0.0.0.0":
|
||||
ASCIIColors.magenta("\n🌐 Server Access Information:")
|
||||
ASCIIColors.white(" ├─ Local Access: ", end="")
|
||||
ASCIIColors.yellow(f"{protocol}://localhost:{args.port}")
|
||||
ASCIIColors.white(" ├─ Remote Access: ", end="")
|
||||
ASCIIColors.yellow(f"{protocol}://<your-ip-address>:{args.port}")
|
||||
ASCIIColors.white(" ├─ API Documentation (local): ", end="")
|
||||
ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/docs")
|
||||
ASCIIColors.white(" ├─ Alternative Documentation (local): ", end="")
|
||||
ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/redoc")
|
||||
ASCIIColors.white(" └─ WebUI (local): ", end="")
|
||||
ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/webui")
|
||||
|
||||
ASCIIColors.yellow("\n📝 Note:")
|
||||
ASCIIColors.white(""" Since the server is running on 0.0.0.0:
|
||||
- Use 'localhost' or '127.0.0.1' for local access
|
||||
- Use your machine's IP address for remote access
|
||||
- To find your IP address:
|
||||
• Windows: Run 'ipconfig' in terminal
|
||||
• Linux/Mac: Run 'ifconfig' or 'ip addr' in terminal
|
||||
""")
|
||||
else:
|
||||
base_url = f"{protocol}://{args.host}:{args.port}"
|
||||
ASCIIColors.magenta("\n🌐 Server Access Information:")
|
||||
ASCIIColors.white(" ├─ Base URL: ", end="")
|
||||
ASCIIColors.yellow(f"{base_url}")
|
||||
ASCIIColors.white(" ├─ API Documentation: ", end="")
|
||||
ASCIIColors.yellow(f"{base_url}/docs")
|
||||
ASCIIColors.white(" └─ Alternative Documentation: ", end="")
|
||||
ASCIIColors.yellow(f"{base_url}/redoc")
|
||||
|
||||
# Usage Examples
|
||||
ASCIIColors.magenta("\n📚 Quick Start Guide:")
|
||||
ASCIIColors.cyan("""
|
||||
1. Access the Swagger UI:
|
||||
Open your browser and navigate to the API documentation URL above
|
||||
|
||||
2. API Authentication:""")
|
||||
if args.key:
|
||||
ASCIIColors.cyan(""" Add the following header to your requests:
|
||||
X-API-Key: <your-api-key>
|
||||
""")
|
||||
else:
|
||||
ASCIIColors.cyan(" No authentication required\n")
|
||||
|
||||
ASCIIColors.cyan(""" 3. Basic Operations:
|
||||
- POST /upload_document: Upload new documents to RAG
|
||||
- POST /query: Query your document collection
|
||||
- GET /collections: List available collections
|
||||
|
||||
4. Monitor the server:
|
||||
- Check server logs for detailed operation information
|
||||
- Use healthcheck endpoint: GET /health
|
||||
""")
|
||||
|
||||
# Security Notice
|
||||
if args.key:
|
||||
ASCIIColors.yellow("\n⚠️ Security Notice:")
|
||||
ASCIIColors.white(""" API Key authentication is enabled.
|
||||
Make sure to include the X-API-Key header in all your requests.
|
||||
""")
|
||||
|
||||
# Ensure splash output flush to system log
|
||||
sys.stdout.flush()
|
||||
File diff suppressed because one or more lines are too long
1120
lightrag/api/webui/assets/index-DbuMPJAD.js
Normal file
1120
lightrag/api/webui/assets/index-DbuMPJAD.js
Normal file
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lightrag/api/webui/assets/index-rP-YlyR1.css
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lightrag/api/webui/assets/index-rP-YlyR1.css
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@@ -2,11 +2,11 @@
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<link rel="icon" type="image/svg+xml" href="./vite.svg" />
|
||||
<link rel="icon" type="image/svg+xml" href="./logo.png" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Lightrag</title>
|
||||
<script type="module" crossorigin src="./assets/index-gr1CNi7P.js"></script>
|
||||
<link rel="stylesheet" crossorigin href="./assets/index-Cq9iD15S.css">
|
||||
<script type="module" crossorigin src="./assets/index-DbuMPJAD.js"></script>
|
||||
<link rel="stylesheet" crossorigin href="./assets/index-rP-YlyR1.css">
|
||||
</head>
|
||||
<body>
|
||||
<div id="root"></div>
|
||||
|
||||
BIN
lightrag/api/webui/logo.png
Normal file
BIN
lightrag/api/webui/logo.png
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Binary file not shown.
|
After Width: | Height: | Size: 155 KiB |
@@ -1 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
|
||||
|
Before Width: | Height: | Size: 1.5 KiB |
@@ -48,11 +48,20 @@ class JsonDocStatusStorage(DocStatusStorage):
|
||||
self, status: DocStatus
|
||||
) -> dict[str, DocProcessingStatus]:
|
||||
"""Get all documents with a specific status"""
|
||||
return {
|
||||
k: DocProcessingStatus(**v)
|
||||
for k, v in self._data.items()
|
||||
if v["status"] == status.value
|
||||
}
|
||||
result = {}
|
||||
for k, v in self._data.items():
|
||||
if v["status"] == status.value:
|
||||
try:
|
||||
# Make a copy of the data to avoid modifying the original
|
||||
data = v.copy()
|
||||
# If content is missing, use content_summary as content
|
||||
if "content" not in data and "content_summary" in data:
|
||||
data["content"] = data["content_summary"]
|
||||
result[k] = DocProcessingStatus(**data)
|
||||
except KeyError as e:
|
||||
logger.error(f"Missing required field for document {k}: {e}")
|
||||
continue
|
||||
return result
|
||||
|
||||
async def index_done_callback(self) -> None:
|
||||
write_json(self._data, self._file_name)
|
||||
|
||||
@@ -5,7 +5,7 @@ from typing import Any, final
|
||||
import numpy as np
|
||||
|
||||
|
||||
from lightrag.types import KnowledgeGraph
|
||||
from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
|
||||
from lightrag.utils import (
|
||||
logger,
|
||||
)
|
||||
@@ -169,9 +169,118 @@ class NetworkXStorage(BaseGraphStorage):
|
||||
self._graph.remove_edge(source, target)
|
||||
|
||||
async def get_all_labels(self) -> list[str]:
|
||||
raise NotImplementedError
|
||||
"""
|
||||
Get all node labels in the graph
|
||||
Returns:
|
||||
[label1, label2, ...] # Alphabetically sorted label list
|
||||
"""
|
||||
labels = set()
|
||||
for node in self._graph.nodes():
|
||||
labels.add(str(node)) # Add node id as a label
|
||||
|
||||
# Return sorted list
|
||||
return sorted(list(labels))
|
||||
|
||||
async def get_knowledge_graph(
|
||||
self, node_label: str, max_depth: int = 5
|
||||
) -> KnowledgeGraph:
|
||||
raise NotImplementedError
|
||||
"""
|
||||
Get complete connected subgraph for specified node (including the starting node itself)
|
||||
|
||||
Args:
|
||||
node_label: Label of the starting node
|
||||
max_depth: Maximum depth of the subgraph
|
||||
|
||||
Returns:
|
||||
KnowledgeGraph object containing nodes and edges
|
||||
"""
|
||||
result = KnowledgeGraph()
|
||||
seen_nodes = set()
|
||||
seen_edges = set()
|
||||
|
||||
# Handle special case for "*" label
|
||||
if node_label == "*":
|
||||
# For "*", return the entire graph including all nodes and edges
|
||||
subgraph = (
|
||||
self._graph.copy()
|
||||
) # Create a copy to avoid modifying the original graph
|
||||
else:
|
||||
# Find nodes with matching node id (partial match)
|
||||
nodes_to_explore = []
|
||||
for n, attr in self._graph.nodes(data=True):
|
||||
if node_label in str(n): # Use partial matching
|
||||
nodes_to_explore.append(n)
|
||||
|
||||
if not nodes_to_explore:
|
||||
logger.warning(f"No nodes found with label {node_label}")
|
||||
return result
|
||||
|
||||
# Get subgraph using ego_graph
|
||||
subgraph = nx.ego_graph(self._graph, nodes_to_explore[0], radius=max_depth)
|
||||
|
||||
# Check if number of nodes exceeds max_graph_nodes
|
||||
max_graph_nodes = 500
|
||||
if len(subgraph.nodes()) > max_graph_nodes:
|
||||
origin_nodes = len(subgraph.nodes())
|
||||
node_degrees = dict(subgraph.degree())
|
||||
top_nodes = sorted(node_degrees.items(), key=lambda x: x[1], reverse=True)[
|
||||
:max_graph_nodes
|
||||
]
|
||||
top_node_ids = [node[0] for node in top_nodes]
|
||||
# Create new subgraph with only top nodes
|
||||
subgraph = subgraph.subgraph(top_node_ids)
|
||||
logger.info(
|
||||
f"Reduced graph from {origin_nodes} nodes to {max_graph_nodes} nodes (depth={max_depth})"
|
||||
)
|
||||
|
||||
# Add nodes to result
|
||||
for node in subgraph.nodes():
|
||||
if str(node) in seen_nodes:
|
||||
continue
|
||||
|
||||
node_data = dict(subgraph.nodes[node])
|
||||
# Get entity_type as labels
|
||||
labels = []
|
||||
if "entity_type" in node_data:
|
||||
if isinstance(node_data["entity_type"], list):
|
||||
labels.extend(node_data["entity_type"])
|
||||
else:
|
||||
labels.append(node_data["entity_type"])
|
||||
|
||||
# Create node with properties
|
||||
node_properties = {k: v for k, v in node_data.items()}
|
||||
|
||||
result.nodes.append(
|
||||
KnowledgeGraphNode(
|
||||
id=str(node), labels=[str(node)], properties=node_properties
|
||||
)
|
||||
)
|
||||
seen_nodes.add(str(node))
|
||||
|
||||
# Add edges to result
|
||||
for edge in subgraph.edges():
|
||||
source, target = edge
|
||||
edge_id = f"{source}-{target}"
|
||||
if edge_id in seen_edges:
|
||||
continue
|
||||
|
||||
edge_data = dict(subgraph.edges[edge])
|
||||
|
||||
# Create edge with complete information
|
||||
result.edges.append(
|
||||
KnowledgeGraphEdge(
|
||||
id=edge_id,
|
||||
type="DIRECTED",
|
||||
source=str(source),
|
||||
target=str(target),
|
||||
properties=edge_data,
|
||||
)
|
||||
)
|
||||
seen_edges.add(edge_id)
|
||||
|
||||
# logger.info(result.edges)
|
||||
|
||||
logger.info(
|
||||
f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
|
||||
)
|
||||
return result
|
||||
|
||||
@@ -254,6 +254,8 @@ class PGKVStorage(BaseKVStorage):
|
||||
db: PostgreSQLDB = field(default=None)
|
||||
|
||||
def __post_init__(self):
|
||||
namespace_prefix = self.global_config.get("namespace_prefix")
|
||||
self.base_namespace = self.namespace.replace(namespace_prefix, "")
|
||||
self._max_batch_size = self.global_config["embedding_batch_num"]
|
||||
|
||||
async def initialize(self):
|
||||
@@ -269,7 +271,7 @@ class PGKVStorage(BaseKVStorage):
|
||||
|
||||
async def get_by_id(self, id: str) -> dict[str, Any] | None:
|
||||
"""Get doc_full data by id."""
|
||||
sql = SQL_TEMPLATES["get_by_id_" + self.namespace]
|
||||
sql = SQL_TEMPLATES["get_by_id_" + self.base_namespace]
|
||||
params = {"workspace": self.db.workspace, "id": id}
|
||||
if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
|
||||
array_res = await self.db.query(sql, params, multirows=True)
|
||||
@@ -283,7 +285,7 @@ class PGKVStorage(BaseKVStorage):
|
||||
|
||||
async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]:
|
||||
"""Specifically for llm_response_cache."""
|
||||
sql = SQL_TEMPLATES["get_by_mode_id_" + self.namespace]
|
||||
sql = SQL_TEMPLATES["get_by_mode_id_" + self.base_namespace]
|
||||
params = {"workspace": self.db.workspace, mode: mode, "id": id}
|
||||
if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
|
||||
array_res = await self.db.query(sql, params, multirows=True)
|
||||
@@ -297,7 +299,7 @@ class PGKVStorage(BaseKVStorage):
|
||||
# Query by id
|
||||
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
|
||||
"""Get doc_chunks data by id"""
|
||||
sql = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
|
||||
sql = SQL_TEMPLATES["get_by_ids_" + self.base_namespace].format(
|
||||
ids=",".join([f"'{id}'" for id in ids])
|
||||
)
|
||||
params = {"workspace": self.db.workspace}
|
||||
@@ -318,7 +320,7 @@ class PGKVStorage(BaseKVStorage):
|
||||
|
||||
async def get_by_status(self, status: str) -> Union[list[dict[str, Any]], None]:
|
||||
"""Specifically for llm_response_cache."""
|
||||
SQL = SQL_TEMPLATES["get_by_status_" + self.namespace]
|
||||
SQL = SQL_TEMPLATES["get_by_status_" + self.base_namespace]
|
||||
params = {"workspace": self.db.workspace, "status": status}
|
||||
return await self.db.query(SQL, params, multirows=True)
|
||||
|
||||
@@ -391,6 +393,8 @@ class PGVectorStorage(BaseVectorStorage):
|
||||
|
||||
def __post_init__(self):
|
||||
self._max_batch_size = self.global_config["embedding_batch_num"]
|
||||
namespace_prefix = self.global_config.get("namespace_prefix")
|
||||
self.base_namespace = self.namespace.replace(namespace_prefix, "")
|
||||
config = self.global_config.get("vector_db_storage_cls_kwargs", {})
|
||||
cosine_threshold = config.get("cosine_better_than_threshold")
|
||||
if cosine_threshold is None:
|
||||
@@ -493,7 +497,9 @@ class PGVectorStorage(BaseVectorStorage):
|
||||
embedding = embeddings[0]
|
||||
embedding_string = ",".join(map(str, embedding))
|
||||
|
||||
sql = SQL_TEMPLATES[self.namespace].format(embedding_string=embedding_string)
|
||||
sql = SQL_TEMPLATES[self.base_namespace].format(
|
||||
embedding_string=embedding_string
|
||||
)
|
||||
params = {
|
||||
"workspace": self.db.workspace,
|
||||
"better_than_threshold": self.cosine_better_than_threshold,
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import configparser
|
||||
import os
|
||||
from dataclasses import asdict, dataclass, field
|
||||
from datetime import datetime
|
||||
from functools import partial
|
||||
@@ -41,13 +41,17 @@ from .utils import (
|
||||
always_get_an_event_loop,
|
||||
compute_mdhash_id,
|
||||
convert_response_to_json,
|
||||
encode_string_by_tiktoken,
|
||||
lazy_external_import,
|
||||
limit_async_func_call,
|
||||
logger,
|
||||
set_logger,
|
||||
encode_string_by_tiktoken,
|
||||
)
|
||||
from .types import KnowledgeGraph
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv(override=True)
|
||||
|
||||
# TODO: TO REMOVE @Yannick
|
||||
config = configparser.ConfigParser()
|
||||
@@ -263,9 +267,8 @@ class LightRAG:
|
||||
_storages_status: StoragesStatus = field(default=StoragesStatus.NOT_CREATED)
|
||||
|
||||
def __post_init__(self):
|
||||
logger.setLevel(self.log_level)
|
||||
os.makedirs(os.path.dirname(self.log_file_path), exist_ok=True)
|
||||
set_logger(self.log_file_path)
|
||||
set_logger(self.log_file_path, self.log_level)
|
||||
logger.info(f"Logger initialized for working directory: {self.working_dir}")
|
||||
|
||||
if not os.path.exists(self.working_dir):
|
||||
@@ -401,16 +404,31 @@ class LightRAG:
|
||||
|
||||
self._storages_status = StoragesStatus.CREATED
|
||||
|
||||
# Initialize storages
|
||||
if self.auto_manage_storages_states:
|
||||
loop = always_get_an_event_loop()
|
||||
loop.run_until_complete(self.initialize_storages())
|
||||
self._run_async_safely(self.initialize_storages, "Storage Initialization")
|
||||
|
||||
def __del__(self):
|
||||
# Finalize storages
|
||||
if self.auto_manage_storages_states:
|
||||
self._run_async_safely(self.finalize_storages, "Storage Finalization")
|
||||
|
||||
def _run_async_safely(self, async_func, action_name=""):
|
||||
"""Safely execute an async function, avoiding event loop conflicts."""
|
||||
try:
|
||||
loop = always_get_an_event_loop()
|
||||
loop.run_until_complete(self.finalize_storages())
|
||||
if loop.is_running():
|
||||
task = loop.create_task(async_func())
|
||||
task.add_done_callback(
|
||||
lambda t: logger.info(f"{action_name} completed!")
|
||||
)
|
||||
else:
|
||||
loop.run_until_complete(async_func())
|
||||
except RuntimeError:
|
||||
logger.warning(
|
||||
f"No running event loop, creating a new loop for {action_name}."
|
||||
)
|
||||
loop = asyncio.new_event_loop()
|
||||
loop.run_until_complete(async_func())
|
||||
loop.close()
|
||||
|
||||
async def initialize_storages(self):
|
||||
"""Asynchronously initialize the storages"""
|
||||
@@ -463,10 +481,10 @@ class LightRAG:
|
||||
return text
|
||||
|
||||
async def get_knowledge_graph(
|
||||
self, nodel_label: str, max_depth: int
|
||||
self, node_label: str, max_depth: int
|
||||
) -> KnowledgeGraph:
|
||||
return await self.chunk_entity_relation_graph.get_knowledge_graph(
|
||||
node_label=nodel_label, max_depth=max_depth
|
||||
node_label=node_label, max_depth=max_depth
|
||||
)
|
||||
|
||||
def _get_storage_class(self, storage_name: str) -> Callable[..., Any]:
|
||||
@@ -474,11 +492,17 @@ class LightRAG:
|
||||
storage_class = lazy_external_import(import_path, storage_name)
|
||||
return storage_class
|
||||
|
||||
@staticmethod
|
||||
def clean_text(text: str) -> str:
|
||||
"""Clean text by removing null bytes (0x00) and whitespace"""
|
||||
return text.strip().replace("\x00", "")
|
||||
|
||||
def insert(
|
||||
self,
|
||||
input: str | list[str],
|
||||
split_by_character: str | None = None,
|
||||
split_by_character_only: bool = False,
|
||||
ids: str | list[str] | None = None,
|
||||
) -> None:
|
||||
"""Sync Insert documents with checkpoint support
|
||||
|
||||
@@ -487,10 +511,11 @@ class LightRAG:
|
||||
split_by_character: if split_by_character is not None, split the string by character, if chunk longer than
|
||||
split_by_character_only: if split_by_character_only is True, split the string by character only, when
|
||||
split_by_character is None, this parameter is ignored.
|
||||
ids: single string of the document ID or list of unique document IDs, if not provided, MD5 hash IDs will be generated
|
||||
"""
|
||||
loop = always_get_an_event_loop()
|
||||
loop.run_until_complete(
|
||||
self.ainsert(input, split_by_character, split_by_character_only)
|
||||
self.ainsert(input, split_by_character, split_by_character_only, ids)
|
||||
)
|
||||
|
||||
async def ainsert(
|
||||
@@ -498,6 +523,7 @@ class LightRAG:
|
||||
input: str | list[str],
|
||||
split_by_character: str | None = None,
|
||||
split_by_character_only: bool = False,
|
||||
ids: str | list[str] | None = None,
|
||||
) -> None:
|
||||
"""Async Insert documents with checkpoint support
|
||||
|
||||
@@ -506,25 +532,34 @@ class LightRAG:
|
||||
split_by_character: if split_by_character is not None, split the string by character, if chunk longer than
|
||||
split_by_character_only: if split_by_character_only is True, split the string by character only, when
|
||||
split_by_character is None, this parameter is ignored.
|
||||
ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated
|
||||
"""
|
||||
await self.apipeline_enqueue_documents(input)
|
||||
await self.apipeline_enqueue_documents(input, ids)
|
||||
await self.apipeline_process_enqueue_documents(
|
||||
split_by_character, split_by_character_only
|
||||
)
|
||||
|
||||
def insert_custom_chunks(self, full_text: str, text_chunks: list[str]) -> None:
|
||||
def insert_custom_chunks(self, full_text: str, text_chunks: list[str], doc_id: str | list[str] | None = None) -> None:
|
||||
loop = always_get_an_event_loop()
|
||||
loop.run_until_complete(self.ainsert_custom_chunks(full_text, text_chunks))
|
||||
loop.run_until_complete(self.ainsert_custom_chunks(full_text, text_chunks, doc_id))
|
||||
|
||||
async def ainsert_custom_chunks(
|
||||
self, full_text: str, text_chunks: list[str]
|
||||
self, full_text: str, text_chunks: list[str], doc_id: str | None = None
|
||||
) -> None:
|
||||
update_storage = False
|
||||
try:
|
||||
doc_key = compute_mdhash_id(full_text.strip(), prefix="doc-")
|
||||
new_docs = {doc_key: {"content": full_text.strip()}}
|
||||
# Clean input texts
|
||||
full_text = self.clean_text(full_text)
|
||||
text_chunks = [self.clean_text(chunk) for chunk in text_chunks]
|
||||
|
||||
_add_doc_keys = await self.full_docs.filter_keys(set(doc_key))
|
||||
# Process cleaned texts
|
||||
if doc_id is None:
|
||||
doc_key = compute_mdhash_id(full_text, prefix="doc-")
|
||||
else:
|
||||
doc_key = doc_id
|
||||
new_docs = {doc_key: {"content": full_text}}
|
||||
|
||||
_add_doc_keys = await self.full_docs.filter_keys({doc_key})
|
||||
new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}
|
||||
if not len(new_docs):
|
||||
logger.warning("This document is already in the storage.")
|
||||
@@ -535,11 +570,10 @@ class LightRAG:
|
||||
|
||||
inserting_chunks: dict[str, Any] = {}
|
||||
for chunk_text in text_chunks:
|
||||
chunk_text_stripped = chunk_text.strip()
|
||||
chunk_key = compute_mdhash_id(chunk_text_stripped, prefix="chunk-")
|
||||
chunk_key = compute_mdhash_id(chunk_text, prefix="chunk-")
|
||||
|
||||
inserting_chunks[chunk_key] = {
|
||||
"content": chunk_text_stripped,
|
||||
"content": chunk_text,
|
||||
"full_doc_id": doc_key,
|
||||
}
|
||||
|
||||
@@ -564,24 +598,52 @@ class LightRAG:
|
||||
if update_storage:
|
||||
await self._insert_done()
|
||||
|
||||
async def apipeline_enqueue_documents(self, input: str | list[str]) -> None:
|
||||
async def apipeline_enqueue_documents(
|
||||
self, input: str | list[str], ids: list[str] | None = None
|
||||
) -> None:
|
||||
"""
|
||||
Pipeline for Processing Documents
|
||||
|
||||
1. Remove duplicate contents from the list
|
||||
2. Generate document IDs and initial status
|
||||
3. Filter out already processed documents
|
||||
4. Enqueue document in status
|
||||
1. Validate ids if provided or generate MD5 hash IDs
|
||||
2. Remove duplicate contents
|
||||
3. Generate document initial status
|
||||
4. Filter out already processed documents
|
||||
5. Enqueue document in status
|
||||
"""
|
||||
if isinstance(input, str):
|
||||
input = [input]
|
||||
if isinstance(ids, str):
|
||||
ids = [ids]
|
||||
|
||||
# 1. Remove duplicate contents from the list
|
||||
unique_contents = list(set(doc.strip() for doc in input))
|
||||
# 1. Validate ids if provided or generate MD5 hash IDs
|
||||
if ids is not None:
|
||||
# Check if the number of IDs matches the number of documents
|
||||
if len(ids) != len(input):
|
||||
raise ValueError("Number of IDs must match the number of documents")
|
||||
|
||||
# 2. Generate document IDs and initial status
|
||||
# Check if IDs are unique
|
||||
if len(ids) != len(set(ids)):
|
||||
raise ValueError("IDs must be unique")
|
||||
|
||||
# Generate contents dict of IDs provided by user and documents
|
||||
contents = {id_: doc for id_, doc in zip(ids, input)}
|
||||
else:
|
||||
# Clean input text and remove duplicates
|
||||
input = list(set(self.clean_text(doc) for doc in input))
|
||||
# Generate contents dict of MD5 hash IDs and documents
|
||||
contents = {compute_mdhash_id(doc, prefix="doc-"): doc for doc in input}
|
||||
|
||||
# 2. Remove duplicate contents
|
||||
unique_contents = {
|
||||
id_: content
|
||||
for content, id_ in {
|
||||
content: id_ for id_, content in contents.items()
|
||||
}.items()
|
||||
}
|
||||
|
||||
# 3. Generate document initial status
|
||||
new_docs: dict[str, Any] = {
|
||||
compute_mdhash_id(content, prefix="doc-"): {
|
||||
id_: {
|
||||
"content": content,
|
||||
"content_summary": self._get_content_summary(content),
|
||||
"content_length": len(content),
|
||||
@@ -589,10 +651,10 @@ class LightRAG:
|
||||
"created_at": datetime.now().isoformat(),
|
||||
"updated_at": datetime.now().isoformat(),
|
||||
}
|
||||
for content in unique_contents
|
||||
for id_, content in unique_contents.items()
|
||||
}
|
||||
|
||||
# 3. Filter out already processed documents
|
||||
# 4. Filter out already processed documents
|
||||
# Get docs ids
|
||||
all_new_doc_ids = set(new_docs.keys())
|
||||
# Exclude IDs of documents that are already in progress
|
||||
@@ -604,7 +666,7 @@ class LightRAG:
|
||||
logger.info("No new unique documents were found.")
|
||||
return
|
||||
|
||||
# 4. Store status document
|
||||
# 5. Store status document
|
||||
await self.doc_status.upsert(new_docs)
|
||||
logger.info(f"Stored {len(new_docs)} new unique documents")
|
||||
|
||||
@@ -661,8 +723,6 @@ class LightRAG:
|
||||
# 4. iterate over batch
|
||||
for doc_id_processing_status in docs_batch:
|
||||
doc_id, status_doc = doc_id_processing_status
|
||||
# Update status in processing
|
||||
doc_status_id = compute_mdhash_id(status_doc.content, prefix="doc-")
|
||||
# Generate chunks from document
|
||||
chunks: dict[str, Any] = {
|
||||
compute_mdhash_id(dp["content"], prefix="chunk-"): {
|
||||
@@ -682,7 +742,7 @@ class LightRAG:
|
||||
tasks = [
|
||||
self.doc_status.upsert(
|
||||
{
|
||||
doc_status_id: {
|
||||
doc_id: {
|
||||
"status": DocStatus.PROCESSING,
|
||||
"updated_at": datetime.now().isoformat(),
|
||||
"content": status_doc.content,
|
||||
@@ -703,7 +763,7 @@ class LightRAG:
|
||||
await asyncio.gather(*tasks)
|
||||
await self.doc_status.upsert(
|
||||
{
|
||||
doc_status_id: {
|
||||
doc_id: {
|
||||
"status": DocStatus.PROCESSED,
|
||||
"chunks_count": len(chunks),
|
||||
"content": status_doc.content,
|
||||
@@ -718,7 +778,7 @@ class LightRAG:
|
||||
logger.error(f"Failed to process document {doc_id}: {str(e)}")
|
||||
await self.doc_status.upsert(
|
||||
{
|
||||
doc_status_id: {
|
||||
doc_id: {
|
||||
"status": DocStatus.FAILED,
|
||||
"error": str(e),
|
||||
"content": status_doc.content,
|
||||
@@ -779,7 +839,7 @@ class LightRAG:
|
||||
all_chunks_data: dict[str, dict[str, str]] = {}
|
||||
chunk_to_source_map: dict[str, str] = {}
|
||||
for chunk_data in custom_kg.get("chunks", {}):
|
||||
chunk_content = chunk_data["content"].strip()
|
||||
chunk_content = self.clean_text(chunk_data["content"])
|
||||
source_id = chunk_data["source_id"]
|
||||
tokens = len(
|
||||
encode_string_by_tiktoken(
|
||||
|
||||
@@ -139,11 +139,14 @@ async def hf_model_complete(
|
||||
|
||||
async def hf_embed(texts: list[str], tokenizer, embed_model) -> np.ndarray:
|
||||
device = next(embed_model.parameters()).device
|
||||
input_ids = tokenizer(
|
||||
encoded_texts = tokenizer(
|
||||
texts, return_tensors="pt", padding=True, truncation=True
|
||||
).input_ids.to(device)
|
||||
).to(device)
|
||||
with torch.no_grad():
|
||||
outputs = embed_model(input_ids)
|
||||
outputs = embed_model(
|
||||
input_ids=encoded_texts["input_ids"],
|
||||
attention_mask=encoded_texts["attention_mask"],
|
||||
)
|
||||
embeddings = outputs.last_hidden_state.mean(dim=1)
|
||||
if embeddings.dtype == torch.bfloat16:
|
||||
return embeddings.detach().to(torch.float32).cpu().numpy()
|
||||
|
||||
@@ -5,6 +5,7 @@ import json
|
||||
import re
|
||||
from typing import Any, AsyncIterator
|
||||
from collections import Counter, defaultdict
|
||||
|
||||
from .utils import (
|
||||
logger,
|
||||
clean_str,
|
||||
@@ -23,6 +24,7 @@ from .utils import (
|
||||
CacheData,
|
||||
statistic_data,
|
||||
get_conversation_turns,
|
||||
verbose_debug,
|
||||
)
|
||||
from .base import (
|
||||
BaseGraphStorage,
|
||||
@@ -33,6 +35,10 @@ from .base import (
|
||||
)
|
||||
from .prompt import GRAPH_FIELD_SEP, PROMPTS
|
||||
import time
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
def chunking_by_token_size(
|
||||
@@ -295,7 +301,7 @@ async def _merge_edges_then_upsert(
|
||||
node_data={
|
||||
"source_id": source_id,
|
||||
"description": description,
|
||||
"entity_type": '"UNKNOWN"',
|
||||
"entity_type": "UNKNOWN",
|
||||
},
|
||||
)
|
||||
description = await _handle_entity_relation_summary(
|
||||
@@ -375,9 +381,8 @@ async def extract_entities(
|
||||
continue_prompt = PROMPTS["entiti_continue_extraction"]
|
||||
if_loop_prompt = PROMPTS["entiti_if_loop_extraction"]
|
||||
|
||||
already_processed = 0
|
||||
already_entities = 0
|
||||
already_relations = 0
|
||||
processed_chunks = 0
|
||||
total_chunks = len(ordered_chunks)
|
||||
|
||||
async def _user_llm_func_with_cache(
|
||||
input_text: str, history_messages: list[dict[str, str]] = None
|
||||
@@ -431,7 +436,7 @@ async def extract_entities(
|
||||
chunk_key_dp (tuple[str, TextChunkSchema]):
|
||||
("chunck-xxxxxx", {"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int})
|
||||
"""
|
||||
nonlocal already_processed, already_entities, already_relations
|
||||
nonlocal processed_chunks
|
||||
chunk_key = chunk_key_dp[0]
|
||||
chunk_dp = chunk_key_dp[1]
|
||||
content = chunk_dp["content"]
|
||||
@@ -488,12 +493,11 @@ async def extract_entities(
|
||||
maybe_edges[(if_relation["src_id"], if_relation["tgt_id"])].append(
|
||||
if_relation
|
||||
)
|
||||
already_processed += 1
|
||||
already_entities += len(maybe_nodes)
|
||||
already_relations += len(maybe_edges)
|
||||
|
||||
logger.debug(
|
||||
f"Processed {already_processed} chunks, {already_entities} entities(duplicated), {already_relations} relations(duplicated)\r",
|
||||
processed_chunks += 1
|
||||
entities_count = len(maybe_nodes)
|
||||
relations_count = len(maybe_edges)
|
||||
logger.info(
|
||||
f" Chunk {processed_chunks}/{total_chunks}: extracted {entities_count} entities and {relations_count} relationships (deduplicated)"
|
||||
)
|
||||
return dict(maybe_nodes), dict(maybe_edges)
|
||||
|
||||
@@ -532,8 +536,12 @@ async def extract_entities(
|
||||
logger.info("Didn't extract any relationships")
|
||||
|
||||
logger.info(
|
||||
f"New entities or relationships extracted, entities:{all_entities_data}, relationships:{all_relationships_data}"
|
||||
f"Extracted {len(all_entities_data)} entities and {len(all_relationships_data)} relationships (deduplicated)"
|
||||
)
|
||||
verbose_debug(
|
||||
f"New entities:{all_entities_data}, relationships:{all_relationships_data}"
|
||||
)
|
||||
verbose_debug(f"New relationships:{all_relationships_data}")
|
||||
|
||||
if entity_vdb is not None:
|
||||
data_for_vdb = {
|
||||
|
||||
@@ -15,8 +15,11 @@ from typing import Any, Callable
|
||||
import xml.etree.ElementTree as ET
|
||||
import numpy as np
|
||||
import tiktoken
|
||||
|
||||
from lightrag.prompt import PROMPTS
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
VERBOSE_DEBUG = os.getenv("VERBOSE", "false").lower() == "true"
|
||||
@@ -25,10 +28,26 @@ VERBOSE_DEBUG = os.getenv("VERBOSE", "false").lower() == "true"
|
||||
def verbose_debug(msg: str, *args, **kwargs):
|
||||
"""Function for outputting detailed debug information.
|
||||
When VERBOSE_DEBUG=True, outputs the complete message.
|
||||
When VERBOSE_DEBUG=False, outputs only the first 30 characters.
|
||||
When VERBOSE_DEBUG=False, outputs only the first 50 characters.
|
||||
|
||||
Args:
|
||||
msg: The message format string
|
||||
*args: Arguments to be formatted into the message
|
||||
**kwargs: Keyword arguments passed to logger.debug()
|
||||
"""
|
||||
if VERBOSE_DEBUG:
|
||||
logger.debug(msg, *args, **kwargs)
|
||||
else:
|
||||
# Format the message with args first
|
||||
if args:
|
||||
formatted_msg = msg % args
|
||||
else:
|
||||
formatted_msg = msg
|
||||
# Then truncate the formatted message
|
||||
truncated_msg = (
|
||||
formatted_msg[:50] + "..." if len(formatted_msg) > 50 else formatted_msg
|
||||
)
|
||||
logger.debug(truncated_msg, **kwargs)
|
||||
|
||||
|
||||
def set_verbose_debug(enabled: bool):
|
||||
@@ -57,11 +76,17 @@ logger = logging.getLogger("lightrag")
|
||||
logging.getLogger("httpx").setLevel(logging.WARNING)
|
||||
|
||||
|
||||
def set_logger(log_file: str):
|
||||
logger.setLevel(logging.DEBUG)
|
||||
def set_logger(log_file: str, level: int = logging.DEBUG):
|
||||
"""Set up file logging with the specified level.
|
||||
|
||||
Args:
|
||||
log_file: Path to the log file
|
||||
level: Logging level (e.g. logging.DEBUG, logging.INFO)
|
||||
"""
|
||||
logger.setLevel(level)
|
||||
|
||||
file_handler = logging.FileHandler(log_file, encoding="utf-8")
|
||||
file_handler.setLevel(logging.DEBUG)
|
||||
file_handler.setLevel(level)
|
||||
|
||||
formatter = logging.Formatter(
|
||||
"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
|
||||
|
||||
@@ -35,46 +35,48 @@
|
||||
"graphology": "^0.26.0",
|
||||
"graphology-generators": "^0.11.2",
|
||||
"lucide-react": "^0.475.0",
|
||||
"minisearch": "^7.1.1",
|
||||
"minisearch": "^7.1.2",
|
||||
"react": "^19.0.0",
|
||||
"react-dom": "^19.0.0",
|
||||
"react-dropzone": "^14.3.5",
|
||||
"react-markdown": "^9.0.3",
|
||||
"react-dropzone": "^14.3.6",
|
||||
"react-markdown": "^9.1.0",
|
||||
"react-number-format": "^5.4.3",
|
||||
"react-syntax-highlighter": "^15.6.1",
|
||||
"rehype-react": "^8.0.0",
|
||||
"remark-gfm": "^4.0.1",
|
||||
"remark-math": "^6.0.0",
|
||||
"seedrandom": "^3.0.5",
|
||||
"sigma": "^3.0.1",
|
||||
"sonner": "^1.7.4",
|
||||
"tailwind-merge": "^3.0.1",
|
||||
"tailwind-scrollbar": "^4.0.0",
|
||||
"tailwind-merge": "^3.0.2",
|
||||
"tailwind-scrollbar": "^4.0.1",
|
||||
"zustand": "^5.0.3",
|
||||
},
|
||||
"devDependencies": {
|
||||
"@eslint/js": "^9.20.0",
|
||||
"@eslint/js": "^9.21.0",
|
||||
"@stylistic/eslint-plugin-js": "^3.1.0",
|
||||
"@tailwindcss/vite": "^4.0.6",
|
||||
"@types/bun": "^1.2.2",
|
||||
"@types/node": "^22.13.4",
|
||||
"@tailwindcss/vite": "^4.0.8",
|
||||
"@types/bun": "^1.2.3",
|
||||
"@types/node": "^22.13.5",
|
||||
"@types/react": "^19.0.10",
|
||||
"@types/react-dom": "^19.0.4",
|
||||
"@types/react-syntax-highlighter": "^15.5.13",
|
||||
"@types/seedrandom": "^3.0.8",
|
||||
"@vitejs/plugin-react-swc": "^3.8.0",
|
||||
"eslint": "^9.20.1",
|
||||
"eslint": "^9.21.0",
|
||||
"eslint-config-prettier": "^10.0.1",
|
||||
"eslint-plugin-react": "^7.37.4",
|
||||
"eslint-plugin-react-hooks": "^5.1.0",
|
||||
"eslint-plugin-react-refresh": "^0.4.19",
|
||||
"globals": "^15.15.0",
|
||||
"graphology-types": "^0.24.8",
|
||||
"prettier": "^3.5.1",
|
||||
"prettier": "^3.5.2",
|
||||
"prettier-plugin-tailwindcss": "^0.6.11",
|
||||
"tailwindcss": "^4.0.6",
|
||||
"tailwindcss": "^4.0.8",
|
||||
"tailwindcss-animate": "^1.0.7",
|
||||
"typescript": "~5.7.3",
|
||||
"typescript-eslint": "^8.24.0",
|
||||
"vite": "^6.1.0",
|
||||
"typescript-eslint": "^8.24.1",
|
||||
"vite": "^6.1.1",
|
||||
},
|
||||
},
|
||||
},
|
||||
@@ -177,15 +179,15 @@
|
||||
|
||||
"@eslint/config-array": ["@eslint/config-array@0.19.2", "", { "dependencies": { "@eslint/object-schema": "^2.1.6", "debug": "^4.3.1", "minimatch": "^3.1.2" } }, "sha512-GNKqxfHG2ySmJOBSHg7LxeUx4xpuCoFjacmlCoYWEbaPXLwvfIjixRI12xCQZeULksQb23uiA8F40w5TojpV7w=="],
|
||||
|
||||
"@eslint/core": ["@eslint/core@0.11.0", "", { "dependencies": { "@types/json-schema": "^7.0.15" } }, "sha512-DWUB2pksgNEb6Bz2fggIy1wh6fGgZP4Xyy/Mt0QZPiloKKXerbqq9D3SBQTlCRYOrcRPu4vuz+CGjwdfqxnoWA=="],
|
||||
"@eslint/core": ["@eslint/core@0.12.0", "", { "dependencies": { "@types/json-schema": "^7.0.15" } }, "sha512-cmrR6pytBuSMTaBweKoGMwu3EiHiEC+DoyupPmlZ0HxBJBtIxwe+j/E4XPIKNx+Q74c8lXKPwYawBf5glsTkHg=="],
|
||||
|
||||
"@eslint/eslintrc": ["@eslint/eslintrc@3.2.0", "", { "dependencies": { "ajv": "^6.12.4", "debug": "^4.3.2", "espree": "^10.0.1", "globals": "^14.0.0", "ignore": "^5.2.0", "import-fresh": "^3.2.1", "js-yaml": "^4.1.0", "minimatch": "^3.1.2", "strip-json-comments": "^3.1.1" } }, "sha512-grOjVNN8P3hjJn/eIETF1wwd12DdnwFDoyceUJLYYdkpbwq3nLi+4fqrTAONx7XDALqlL220wC/RHSC/QTI/0w=="],
|
||||
"@eslint/eslintrc": ["@eslint/eslintrc@3.3.0", "", { "dependencies": { "ajv": "^6.12.4", "debug": "^4.3.2", "espree": "^10.0.1", "globals": "^14.0.0", "ignore": "^5.2.0", "import-fresh": "^3.2.1", "js-yaml": "^4.1.0", "minimatch": "^3.1.2", "strip-json-comments": "^3.1.1" } }, "sha512-yaVPAiNAalnCZedKLdR21GOGILMLKPyqSLWaAjQFvYA2i/ciDi8ArYVr69Anohb6cH2Ukhqti4aFnYyPm8wdwQ=="],
|
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|
||||
"@eslint/js": ["@eslint/js@9.20.0", "", {}, "sha512-iZA07H9io9Wn836aVTytRaNqh00Sad+EamwOVJT12GTLw1VGMFV/4JaME+JjLtr9fiGaoWgYnS54wrfWsSs4oQ=="],
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"@eslint/js": ["@eslint/js@9.21.0", "", {}, "sha512-BqStZ3HX8Yz6LvsF5ByXYrtigrV5AXADWLAGc7PH/1SxOb7/FIYYMszZZWiUou/GB9P2lXWk2SV4d+Z8h0nknw=="],
|
||||
|
||||
"@eslint/object-schema": ["@eslint/object-schema@2.1.6", "", {}, "sha512-RBMg5FRL0I0gs51M/guSAj5/e14VQ4tpZnQNWwuDT66P14I43ItmPfIZRhO9fUVIPOAQXU47atlywZ/czoqFPA=="],
|
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|
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"@eslint/plugin-kit": ["@eslint/plugin-kit@0.2.5", "", { "dependencies": { "@eslint/core": "^0.10.0", "levn": "^0.4.1" } }, "sha512-lB05FkqEdUg2AA0xEbUz0SnkXT1LcCTa438W4IWTUh4hdOnVbQyOJ81OrDXsJk/LSiJHubgGEFoR5EHq1NsH1A=="],
|
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"@eslint/plugin-kit": ["@eslint/plugin-kit@0.2.7", "", { "dependencies": { "@eslint/core": "^0.12.0", "levn": "^0.4.1" } }, "sha512-JubJ5B2pJ4k4yGxaNLdbjrnk9d/iDz6/q8wOilpIowd6PJPgaxCuHBnBszq7Ce2TyMrywm5r4PnKm6V3iiZF+g=="],
|
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|
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"@faker-js/faker": ["@faker-js/faker@9.5.0", "", {}, "sha512-3qbjLv+fzuuCg3umxc9/7YjrEXNaKwHgmig949nfyaTx8eL4FAsvFbu+1JcFUj1YAXofhaDn6JdEUBTYuk0Ssw=="],
|
||||
|
||||
@@ -203,7 +205,7 @@
|
||||
|
||||
"@humanwhocodes/module-importer": ["@humanwhocodes/module-importer@1.0.1", "", {}, "sha512-bxveV4V8v5Yb4ncFTT3rPSgZBOpCkjfK0y4oVVVJwIuDVBRMDXrPyXRL988i5ap9m9bnyEEjWfm5WkBmtffLfA=="],
|
||||
|
||||
"@humanwhocodes/retry": ["@humanwhocodes/retry@0.4.1", "", {}, "sha512-c7hNEllBlenFTHBky65mhq8WD2kbN9Q6gk0bTk8lSBvc554jpXSkST1iePudpt7+A/AQvuHs9EMqjHDXMY1lrA=="],
|
||||
"@humanwhocodes/retry": ["@humanwhocodes/retry@0.4.2", "", {}, "sha512-xeO57FpIu4p1Ri3Jq/EXq4ClRm86dVF2z/+kvFnyqVYRavTZmaFaUBbWCOuuTh0o/g7DSsk6kc2vrS4Vl5oPOQ=="],
|
||||
|
||||
"@jridgewell/gen-mapping": ["@jridgewell/gen-mapping@0.3.8", "", { "dependencies": { "@jridgewell/set-array": "^1.2.1", "@jridgewell/sourcemap-codec": "^1.4.10", "@jridgewell/trace-mapping": "^0.3.24" } }, "sha512-imAbBGkb+ebQyxKgzv5Hu2nmROxoDOXHh80evxdoXNOrvAnVx7zimzc1Oo5h9RlfV4vPXaE2iM5pOFbvOCClWA=="],
|
||||
|
||||
@@ -383,35 +385,35 @@
|
||||
|
||||
"@swc/types": ["@swc/types@0.1.17", "", { "dependencies": { "@swc/counter": "^0.1.3" } }, "sha512-V5gRru+aD8YVyCOMAjMpWR1Ui577DD5KSJsHP8RAxopAH22jFz6GZd/qxqjO6MJHQhcsjvjOFXyDhyLQUnMveQ=="],
|
||||
|
||||
"@tailwindcss/node": ["@tailwindcss/node@4.0.6", "", { "dependencies": { "enhanced-resolve": "^5.18.0", "jiti": "^2.4.2", "tailwindcss": "4.0.6" } }, "sha512-jb6E0WeSq7OQbVYcIJ6LxnZTeC4HjMvbzFBMCrQff4R50HBlo/obmYNk6V2GCUXDeqiXtvtrQgcIbT+/boB03Q=="],
|
||||
"@tailwindcss/node": ["@tailwindcss/node@4.0.8", "", { "dependencies": { "enhanced-resolve": "^5.18.1", "jiti": "^2.4.2", "tailwindcss": "4.0.8" } }, "sha512-FKArQpbrbwv08TNT0k7ejYXpF+R8knZFAatNc0acOxbgeqLzwb86r+P3LGOjIeI3Idqe9CVkZrh4GlsJLJKkkw=="],
|
||||
|
||||
"@tailwindcss/oxide": ["@tailwindcss/oxide@4.0.6", "", { "optionalDependencies": { "@tailwindcss/oxide-android-arm64": "4.0.6", "@tailwindcss/oxide-darwin-arm64": "4.0.6", "@tailwindcss/oxide-darwin-x64": "4.0.6", "@tailwindcss/oxide-freebsd-x64": "4.0.6", "@tailwindcss/oxide-linux-arm-gnueabihf": "4.0.6", "@tailwindcss/oxide-linux-arm64-gnu": "4.0.6", "@tailwindcss/oxide-linux-arm64-musl": "4.0.6", "@tailwindcss/oxide-linux-x64-gnu": "4.0.6", "@tailwindcss/oxide-linux-x64-musl": "4.0.6", "@tailwindcss/oxide-win32-arm64-msvc": "4.0.6", "@tailwindcss/oxide-win32-x64-msvc": "4.0.6" } }, "sha512-lVyKV2y58UE9CeKVcYykULe9QaE1dtKdxDEdrTPIdbzRgBk6bdxHNAoDqvcqXbIGXubn3VOl1O/CFF77v/EqSA=="],
|
||||
"@tailwindcss/oxide": ["@tailwindcss/oxide@4.0.8", "", { "optionalDependencies": { "@tailwindcss/oxide-android-arm64": "4.0.8", "@tailwindcss/oxide-darwin-arm64": "4.0.8", "@tailwindcss/oxide-darwin-x64": "4.0.8", "@tailwindcss/oxide-freebsd-x64": "4.0.8", "@tailwindcss/oxide-linux-arm-gnueabihf": "4.0.8", "@tailwindcss/oxide-linux-arm64-gnu": "4.0.8", "@tailwindcss/oxide-linux-arm64-musl": "4.0.8", "@tailwindcss/oxide-linux-x64-gnu": "4.0.8", "@tailwindcss/oxide-linux-x64-musl": "4.0.8", "@tailwindcss/oxide-win32-arm64-msvc": "4.0.8", "@tailwindcss/oxide-win32-x64-msvc": "4.0.8" } }, "sha512-KfMcuAu/Iw+DcV1e8twrFyr2yN8/ZDC/odIGta4wuuJOGkrkHZbvJvRNIbQNhGh7erZTYV6Ie0IeD6WC9Y8Hcw=="],
|
||||
|
||||
"@tailwindcss/oxide-android-arm64": ["@tailwindcss/oxide-android-arm64@4.0.6", "", { "os": "android", "cpu": "arm64" }, "sha512-xDbym6bDPW3D2XqQqX3PjqW3CKGe1KXH7Fdkc60sX5ZLVUbzPkFeunQaoP+BuYlLc2cC1FoClrIRYnRzof9Sow=="],
|
||||
"@tailwindcss/oxide-android-arm64": ["@tailwindcss/oxide-android-arm64@4.0.8", "", { "os": "android", "cpu": "arm64" }, "sha512-We7K79+Sm4mwJHk26Yzu/GAj7C7myemm7PeXvpgMxyxO70SSFSL3uCcqFbz9JA5M5UPkrl7N9fkBe/Y0iazqpA=="],
|
||||
|
||||
"@tailwindcss/oxide-darwin-arm64": ["@tailwindcss/oxide-darwin-arm64@4.0.6", "", { "os": "darwin", "cpu": "arm64" }, "sha512-1f71/ju/tvyGl5c2bDkchZHy8p8EK/tDHCxlpYJ1hGNvsYihZNurxVpZ0DefpN7cNc9RTT8DjrRoV8xXZKKRjg=="],
|
||||
"@tailwindcss/oxide-darwin-arm64": ["@tailwindcss/oxide-darwin-arm64@4.0.8", "", { "os": "darwin", "cpu": "arm64" }, "sha512-Lv9Isi2EwkCTG1sRHNDi0uRNN1UGFdEThUAGFrydRmQZnraGLMjN8gahzg2FFnOizDl7LB2TykLUuiw833DSNg=="],
|
||||
|
||||
"@tailwindcss/oxide-darwin-x64": ["@tailwindcss/oxide-darwin-x64@4.0.6", "", { "os": "darwin", "cpu": "x64" }, "sha512-s/hg/ZPgxFIrGMb0kqyeaqZt505P891buUkSezmrDY6lxv2ixIELAlOcUVTkVh245SeaeEiUVUPiUN37cwoL2g=="],
|
||||
"@tailwindcss/oxide-darwin-x64": ["@tailwindcss/oxide-darwin-x64@4.0.8", "", { "os": "darwin", "cpu": "x64" }, "sha512-fWfywfYIlSWtKoqWTjukTHLWV3ARaBRjXCC2Eo0l6KVpaqGY4c2y8snUjp1xpxUtpqwMvCvFWFaleMoz1Vhzlw=="],
|
||||
|
||||
"@tailwindcss/oxide-freebsd-x64": ["@tailwindcss/oxide-freebsd-x64@4.0.6", "", { "os": "freebsd", "cpu": "x64" }, "sha512-Z3Wo8FWZnmio8+xlcbb7JUo/hqRMSmhQw8IGIRoRJ7GmLR0C+25Wq+bEX/135xe/yEle2lFkhu9JBHd4wZYiig=="],
|
||||
"@tailwindcss/oxide-freebsd-x64": ["@tailwindcss/oxide-freebsd-x64@4.0.8", "", { "os": "freebsd", "cpu": "x64" }, "sha512-SO+dyvjJV9G94bnmq2288Ke0BIdvrbSbvtPLaQdqjqHR83v5L2fWADyFO+1oecHo9Owsk8MxcXh1agGVPIKIqw=="],
|
||||
|
||||
"@tailwindcss/oxide-linux-arm-gnueabihf": ["@tailwindcss/oxide-linux-arm-gnueabihf@4.0.6", "", { "os": "linux", "cpu": "arm" }, "sha512-SNSwkkim1myAgmnbHs4EjXsPL7rQbVGtjcok5EaIzkHkCAVK9QBQsWeP2Jm2/JJhq4wdx8tZB9Y7psMzHYWCkA=="],
|
||||
"@tailwindcss/oxide-linux-arm-gnueabihf": ["@tailwindcss/oxide-linux-arm-gnueabihf@4.0.8", "", { "os": "linux", "cpu": "arm" }, "sha512-ZSHggWiEblQNV69V0qUK5vuAtHP+I+S2eGrKGJ5lPgwgJeAd6GjLsVBN+Mqn2SPVfYM3BOpS9jX/zVg9RWQVDQ=="],
|
||||
|
||||
"@tailwindcss/oxide-linux-arm64-gnu": ["@tailwindcss/oxide-linux-arm64-gnu@4.0.6", "", { "os": "linux", "cpu": "arm64" }, "sha512-tJ+mevtSDMQhKlwCCuhsFEFg058kBiSy4TkoeBG921EfrHKmexOaCyFKYhVXy4JtkaeeOcjJnCLasEeqml4i+Q=="],
|
||||
"@tailwindcss/oxide-linux-arm64-gnu": ["@tailwindcss/oxide-linux-arm64-gnu@4.0.8", "", { "os": "linux", "cpu": "arm64" }, "sha512-xWpr6M0OZLDNsr7+bQz+3X7zcnDJZJ1N9gtBWCtfhkEtDjjxYEp+Lr5L5nc/yXlL4MyCHnn0uonGVXy3fhxaVA=="],
|
||||
|
||||
"@tailwindcss/oxide-linux-arm64-musl": ["@tailwindcss/oxide-linux-arm64-musl@4.0.6", "", { "os": "linux", "cpu": "arm64" }, "sha512-IoArz1vfuTR4rALXMUXI/GWWfx2EaO4gFNtBNkDNOYhlTD4NVEwE45nbBoojYiTulajI4c2XH8UmVEVJTOJKxA=="],
|
||||
"@tailwindcss/oxide-linux-arm64-musl": ["@tailwindcss/oxide-linux-arm64-musl@4.0.8", "", { "os": "linux", "cpu": "arm64" }, "sha512-5tz2IL7LN58ssGEq7h/staD7pu/izF/KeMWdlJ86WDe2Ah46LF3ET6ZGKTr5eZMrnEA0M9cVFuSPprKRHNgjeg=="],
|
||||
|
||||
"@tailwindcss/oxide-linux-x64-gnu": ["@tailwindcss/oxide-linux-x64-gnu@4.0.6", "", { "os": "linux", "cpu": "x64" }, "sha512-QtsUfLkEAeWAC3Owx9Kg+7JdzE+k9drPhwTAXbXugYB9RZUnEWWx5x3q/au6TvUYcL+n0RBqDEO2gucZRvRFgQ=="],
|
||||
"@tailwindcss/oxide-linux-x64-gnu": ["@tailwindcss/oxide-linux-x64-gnu@4.0.8", "", { "os": "linux", "cpu": "x64" }, "sha512-KSzMkhyrxAQyY2o194NKVKU9j/c+NFSoMvnHWFaNHKi3P1lb+Vq1UC19tLHrmxSkKapcMMu69D7+G1+FVGNDXQ=="],
|
||||
|
||||
"@tailwindcss/oxide-linux-x64-musl": ["@tailwindcss/oxide-linux-x64-musl@4.0.6", "", { "os": "linux", "cpu": "x64" }, "sha512-QthvJqIji2KlGNwLcK/PPYo7w1Wsi/8NK0wAtRGbv4eOPdZHkQ9KUk+oCoP20oPO7i2a6X1aBAFQEL7i08nNMA=="],
|
||||
"@tailwindcss/oxide-linux-x64-musl": ["@tailwindcss/oxide-linux-x64-musl@4.0.8", "", { "os": "linux", "cpu": "x64" }, "sha512-yFYKG5UtHTRimjtqxUWXBgI4Tc6NJe3USjRIVdlTczpLRxq/SFwgzGl5JbatCxgSRDPBFwRrNPxq+ukfQFGdrw=="],
|
||||
|
||||
"@tailwindcss/oxide-win32-arm64-msvc": ["@tailwindcss/oxide-win32-arm64-msvc@4.0.6", "", { "os": "win32", "cpu": "arm64" }, "sha512-+oka+dYX8jy9iP00DJ9Y100XsqvbqR5s0yfMZJuPR1H/lDVtDfsZiSix1UFBQ3X1HWxoEEl6iXNJHWd56TocVw=="],
|
||||
"@tailwindcss/oxide-win32-arm64-msvc": ["@tailwindcss/oxide-win32-arm64-msvc@4.0.8", "", { "os": "win32", "cpu": "arm64" }, "sha512-tndGujmCSba85cRCnQzXgpA2jx5gXimyspsUYae5jlPyLRG0RjXbDshFKOheVXU4TLflo7FSG8EHCBJ0EHTKdQ=="],
|
||||
|
||||
"@tailwindcss/oxide-win32-x64-msvc": ["@tailwindcss/oxide-win32-x64-msvc@4.0.6", "", { "os": "win32", "cpu": "x64" }, "sha512-+o+juAkik4p8Ue/0LiflQXPmVatl6Av3LEZXpBTfg4qkMIbZdhCGWFzHdt2NjoMiLOJCFDddoV6GYaimvK1Olw=="],
|
||||
"@tailwindcss/oxide-win32-x64-msvc": ["@tailwindcss/oxide-win32-x64-msvc@4.0.8", "", { "os": "win32", "cpu": "x64" }, "sha512-T77jroAc0p4EHVVgTUiNeFn6Nj3jtD3IeNId2X+0k+N1XxfNipy81BEkYErpKLiOkNhpNFjPee8/ZVas29b2OQ=="],
|
||||
|
||||
"@tailwindcss/vite": ["@tailwindcss/vite@4.0.6", "", { "dependencies": { "@tailwindcss/node": "^4.0.6", "@tailwindcss/oxide": "^4.0.6", "lightningcss": "^1.29.1", "tailwindcss": "4.0.6" }, "peerDependencies": { "vite": "^5.2.0 || ^6" } }, "sha512-O25vZ/URWbZ2JHdk2o8wH7jOKqEGCsYmX3GwGmYS5DjE4X3mpf93a72Rn7VRnefldNauBzr5z2hfZptmBNtTUQ=="],
|
||||
"@tailwindcss/vite": ["@tailwindcss/vite@4.0.8", "", { "dependencies": { "@tailwindcss/node": "4.0.8", "@tailwindcss/oxide": "4.0.8", "lightningcss": "^1.29.1", "tailwindcss": "4.0.8" }, "peerDependencies": { "vite": "^5.2.0 || ^6" } }, "sha512-+SAq44yLzYlzyrb7QTcFCdU8Xa7FOA0jp+Xby7fPMUie+MY9HhJysM7Vp+vL8qIp8ceQJfLD+FjgJuJ4lL6nyg=="],
|
||||
|
||||
"@types/bun": ["@types/bun@1.2.2", "", { "dependencies": { "bun-types": "1.2.2" } }, "sha512-tr74gdku+AEDN5ergNiBnplr7hpDp3V1h7fqI2GcR/rsUaM39jpSeKH0TFibRvU0KwniRx5POgaYnaXbk0hU+w=="],
|
||||
"@types/bun": ["@types/bun@1.2.3", "", { "dependencies": { "bun-types": "1.2.3" } }, "sha512-054h79ipETRfjtsCW9qJK8Ipof67Pw9bodFWmkfkaUaRiIQ1dIV2VTlheshlBx3mpKr0KeK8VqnMMCtgN9rQtw=="],
|
||||
|
||||
"@types/debug": ["@types/debug@4.1.12", "", { "dependencies": { "@types/ms": "*" } }, "sha512-vIChWdVG3LG1SMxEvI/AK+FWJthlrqlTu7fbrlywTkkaONwk/UAGaULXRlf8vkzFBLVm0zkMdCquhL5aOjhXPQ=="],
|
||||
|
||||
@@ -429,7 +431,7 @@
|
||||
|
||||
"@types/ms": ["@types/ms@2.1.0", "", {}, "sha512-GsCCIZDE/p3i96vtEqx+7dBUGXrc7zeSK3wwPHIaRThS+9OhWIXRqzs4d6k1SVU8g91DrNRWxWUGhp5KXQb2VA=="],
|
||||
|
||||
"@types/node": ["@types/node@22.13.4", "", { "dependencies": { "undici-types": "~6.20.0" } }, "sha512-ywP2X0DYtX3y08eFVx5fNIw7/uIv8hYUKgXoK8oayJlLnKcRfEYCxWMVE1XagUdVtCJlZT1AU4LXEABW+L1Peg=="],
|
||||
"@types/node": ["@types/node@22.13.5", "", { "dependencies": { "undici-types": "~6.20.0" } }, "sha512-+lTU0PxZXn0Dr1NBtC7Y8cR21AJr87dLLU953CWA6pMxxv/UDc7jYAY90upcrie1nRcD6XNG5HOYEDtgW5TxAg=="],
|
||||
|
||||
"@types/parse-json": ["@types/parse-json@4.0.2", "", {}, "sha512-dISoDXWWQwUquiKsyZ4Ng+HX2KsPL7LyHKHQwgGFEA3IaKac4Obd+h2a/a6waisAoepJlBcx9paWqjA8/HVjCw=="],
|
||||
|
||||
@@ -439,6 +441,8 @@
|
||||
|
||||
"@types/react-dom": ["@types/react-dom@19.0.4", "", { "peerDependencies": { "@types/react": "^19.0.0" } }, "sha512-4fSQ8vWFkg+TGhePfUzVmat3eC14TXYSsiiDSLI0dVLsrm9gZFABjPy/Qu6TKgl1tq1Bu1yDsuQgY3A3DOjCcg=="],
|
||||
|
||||
"@types/react-syntax-highlighter": ["@types/react-syntax-highlighter@15.5.13", "", { "dependencies": { "@types/react": "*" } }, "sha512-uLGJ87j6Sz8UaBAooU0T6lWJ0dBmjZgN1PZTrj05TNql2/XpC6+4HhMT5syIdFUUt+FASfCeLLv4kBygNU+8qA=="],
|
||||
|
||||
"@types/react-transition-group": ["@types/react-transition-group@4.4.12", "", { "peerDependencies": { "@types/react": "*" } }, "sha512-8TV6R3h2j7a91c+1DXdJi3Syo69zzIZbz7Lg5tORM5LEJG7X/E6a1V3drRyBRZq7/utz7A+c4OgYLiLcYGHG6w=="],
|
||||
|
||||
"@types/seedrandom": ["@types/seedrandom@3.0.8", "", {}, "sha512-TY1eezMU2zH2ozQoAFAQFOPpvP15g+ZgSfTZt31AUUH/Rxtnz3H+A/Sv1Snw2/amp//omibc+AEkTaA8KUeOLQ=="],
|
||||
@@ -447,21 +451,21 @@
|
||||
|
||||
"@types/ws": ["@types/ws@8.5.14", "", { "dependencies": { "@types/node": "*" } }, "sha512-bd/YFLW+URhBzMXurx7lWByOu+xzU9+kb3RboOteXYDfW+tr+JZa99OyNmPINEGB/ahzKrEuc8rcv4gnpJmxTw=="],
|
||||
|
||||
"@typescript-eslint/eslint-plugin": ["@typescript-eslint/eslint-plugin@8.24.0", "", { "dependencies": { "@eslint-community/regexpp": "^4.10.0", "@typescript-eslint/scope-manager": "8.24.0", "@typescript-eslint/type-utils": "8.24.0", "@typescript-eslint/utils": "8.24.0", "@typescript-eslint/visitor-keys": "8.24.0", "graphemer": "^1.4.0", "ignore": "^5.3.1", "natural-compare": "^1.4.0", "ts-api-utils": "^2.0.1" }, "peerDependencies": { "@typescript-eslint/parser": "^8.0.0 || ^8.0.0-alpha.0", "eslint": "^8.57.0 || ^9.0.0", "typescript": ">=4.8.4 <5.8.0" } }, "sha512-aFcXEJJCI4gUdXgoo/j9udUYIHgF23MFkg09LFz2dzEmU0+1Plk4rQWv/IYKvPHAtlkkGoB3m5e6oUp+JPsNaQ=="],
|
||||
"@typescript-eslint/eslint-plugin": ["@typescript-eslint/eslint-plugin@8.24.1", "", { "dependencies": { "@eslint-community/regexpp": "^4.10.0", "@typescript-eslint/scope-manager": "8.24.1", "@typescript-eslint/type-utils": "8.24.1", "@typescript-eslint/utils": "8.24.1", "@typescript-eslint/visitor-keys": "8.24.1", "graphemer": "^1.4.0", "ignore": "^5.3.1", "natural-compare": "^1.4.0", "ts-api-utils": "^2.0.1" }, "peerDependencies": { "@typescript-eslint/parser": "^8.0.0 || ^8.0.0-alpha.0", "eslint": "^8.57.0 || ^9.0.0", "typescript": ">=4.8.4 <5.8.0" } }, "sha512-ll1StnKtBigWIGqvYDVuDmXJHVH4zLVot1yQ4fJtLpL7qacwkxJc1T0bptqw+miBQ/QfUbhl1TcQ4accW5KUyA=="],
|
||||
|
||||
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"@typescript-eslint/visitor-keys": ["@typescript-eslint/visitor-keys@8.24.0", "", { "dependencies": { "@typescript-eslint/types": "8.24.0", "eslint-visitor-keys": "^4.2.0" } }, "sha512-kArLq83QxGLbuHrTMoOEWO+l2MwsNS2TGISEdx8xgqpkbytB07XmlQyQdNDrCc1ecSqx0cnmhGvpX+VBwqqSkg=="],
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"@typescript-eslint/visitor-keys": ["@typescript-eslint/visitor-keys@8.24.1", "", { "dependencies": { "@typescript-eslint/types": "8.24.1", "eslint-visitor-keys": "^4.2.0" } }, "sha512-EwVHlp5l+2vp8CoqJm9KikPZgi3gbdZAtabKT9KPShGeOcJhsv4Zdo3oc8T8I0uKEmYoU4ItyxbptjF08enaxg=="],
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"@ungap/structured-clone": ["@ungap/structured-clone@1.3.0", "", {}, "sha512-WmoN8qaIAo7WTYWbAZuG8PYEhn5fkz7dZrqTBZ7dtt//lL2Gwms1IcnQ5yHqjDfX8Ft5j4YzDM23f87zBfDe9g=="],
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||||
@@ -515,7 +519,7 @@
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"bun-types": ["bun-types@1.2.3", "", { "dependencies": { "@types/node": "*", "@types/ws": "~8.5.10" } }, "sha512-P7AeyTseLKAvgaZqQrvp3RqFM3yN9PlcLuSTe7SoJOfZkER73mLdT2vEQi8U64S1YvM/ldcNiQjn0Sn7H9lGgg=="],
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"call-bind": ["call-bind@1.0.8", "", { "dependencies": { "call-bind-apply-helpers": "^1.0.0", "es-define-property": "^1.0.0", "get-intrinsic": "^1.2.4", "set-function-length": "^1.2.2" } }, "sha512-oKlSFMcMwpUg2ednkhQ454wfWiU/ul3CkJe/PEHcTKuiX6RpbehUiFMXu13HalGZxfUwCQzZG747YXBn1im9ww=="],
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||||
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||||
@@ -529,13 +533,13 @@
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"character-entities": ["character-entities@2.0.2", "", {}, "sha512-shx7oQ0Awen/BRIdkjkvz54PnEEI/EjwXDSIZp86/KKdbafHh1Df/RYGBhn4hbe2+uKC9FnT5UCEdyPz3ai9hQ=="],
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"character-entities": ["character-entities@1.2.4", "", {}, "sha512-iBMyeEHxfVnIakwOuDXpVkc54HijNgCyQB2w0VfGQThle6NXn50zU6V/u+LDhxHcDUPojn6Kpga3PTAD8W1bQw=="],
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"character-entities-html4": ["character-entities-html4@2.1.0", "", {}, "sha512-1v7fgQRj6hnSwFpq1Eu0ynr/CDEw0rXo2B61qXrLNdHZmPKgb7fqS1a2JwF0rISo9q77jDI8VMEHoApn8qDoZA=="],
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"character-entities-legacy": ["character-entities-legacy@3.0.0", "", {}, "sha512-RpPp0asT/6ufRm//AJVwpViZbGM/MkjQFxJccQRHmISF/22NBtsHqAWmL+/pmkPWoIUJdWyeVleTl1wydHATVQ=="],
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"character-entities-legacy": ["character-entities-legacy@1.1.4", "", {}, "sha512-3Xnr+7ZFS1uxeiUDvV02wQ+QDbc55o97tIV5zHScSPJpcLm/r0DFPcoY3tYRp+VZukxuMeKgXYmsXQHO05zQeA=="],
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"character-reference-invalid": ["character-reference-invalid@2.0.1", "", {}, "sha512-iBZ4F4wRbyORVsu0jPV7gXkOsGYjGHPmAyv+HiHG8gi5PtC9KI2j1+v8/tlibRvjoWX027ypmG/n0HtO5t7unw=="],
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"character-reference-invalid": ["character-reference-invalid@1.1.4", "", {}, "sha512-mKKUkUbhPpQlCOfIuZkvSEgktjPFIsZKRRbC6KWVEMvlzblj3i3asQv5ODsrwt0N3pHAEvjP8KTQPHkp0+6jOg=="],
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"class-variance-authority": ["class-variance-authority@0.7.1", "", { "dependencies": { "clsx": "^2.1.1" } }, "sha512-Ka+9Trutv7G8M6WT6SeiRWz792K5qEqIGEGzXKhAE6xOWAY6pPH8U+9IY3oCMv6kqTmLsv7Xh/2w2RigkePMsg=="],
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||||
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||||
@@ -619,7 +623,7 @@
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"eslint": ["eslint@9.20.1", "", { "dependencies": { "@eslint-community/eslint-utils": "^4.2.0", "@eslint-community/regexpp": "^4.12.1", "@eslint/config-array": "^0.19.0", "@eslint/core": "^0.11.0", "@eslint/eslintrc": "^3.2.0", "@eslint/js": "9.20.0", "@eslint/plugin-kit": "^0.2.5", "@humanfs/node": "^0.16.6", "@humanwhocodes/module-importer": "^1.0.1", "@humanwhocodes/retry": "^0.4.1", "@types/estree": "^1.0.6", "@types/json-schema": "^7.0.15", "ajv": "^6.12.4", "chalk": "^4.0.0", "cross-spawn": "^7.0.6", "debug": "^4.3.2", "escape-string-regexp": "^4.0.0", "eslint-scope": "^8.2.0", "eslint-visitor-keys": "^4.2.0", "espree": "^10.3.0", "esquery": "^1.5.0", "esutils": "^2.0.2", "fast-deep-equal": "^3.1.3", "file-entry-cache": "^8.0.0", "find-up": "^5.0.0", "glob-parent": "^6.0.2", "ignore": "^5.2.0", "imurmurhash": "^0.1.4", "is-glob": "^4.0.0", "json-stable-stringify-without-jsonify": "^1.0.1", "lodash.merge": "^4.6.2", "minimatch": "^3.1.2", "natural-compare": "^1.4.0", "optionator": "^0.9.3" }, "peerDependencies": { "jiti": "*" }, "optionalPeers": ["jiti"], "bin": { "eslint": "bin/eslint.js" } }, "sha512-m1mM33o6dBUjxl2qb6wv6nGNwCAsns1eKtaQ4l/NPHeTvhiUPbtdfMyktxN4B3fgHIgsYh1VT3V9txblpQHq+g=="],
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"eslint": ["eslint@9.21.0", "", { "dependencies": { "@eslint-community/eslint-utils": "^4.2.0", "@eslint-community/regexpp": "^4.12.1", "@eslint/config-array": "^0.19.2", "@eslint/core": "^0.12.0", "@eslint/eslintrc": "^3.3.0", "@eslint/js": "9.21.0", "@eslint/plugin-kit": "^0.2.7", "@humanfs/node": "^0.16.6", "@humanwhocodes/module-importer": "^1.0.1", "@humanwhocodes/retry": "^0.4.2", "@types/estree": "^1.0.6", "@types/json-schema": "^7.0.15", "ajv": "^6.12.4", "chalk": "^4.0.0", "cross-spawn": "^7.0.6", "debug": "^4.3.2", "escape-string-regexp": "^4.0.0", "eslint-scope": "^8.2.0", "eslint-visitor-keys": "^4.2.0", "espree": "^10.3.0", "esquery": "^1.5.0", "esutils": "^2.0.2", "fast-deep-equal": "^3.1.3", "file-entry-cache": "^8.0.0", "find-up": "^5.0.0", "glob-parent": "^6.0.2", "ignore": "^5.2.0", "imurmurhash": "^0.1.4", "is-glob": "^4.0.0", "json-stable-stringify-without-jsonify": "^1.0.1", "lodash.merge": "^4.6.2", "minimatch": "^3.1.2", "natural-compare": "^1.4.0", "optionator": "^0.9.3" }, "peerDependencies": { "jiti": "*" }, "optionalPeers": ["jiti"], "bin": { "eslint": "bin/eslint.js" } }, "sha512-KjeihdFqTPhOMXTt7StsDxriV4n66ueuF/jfPNC3j/lduHwr/ijDwJMsF+wyMJethgiKi5wniIE243vi07d3pg=="],
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||||
|
||||
"eslint-config-prettier": ["eslint-config-prettier@10.0.1", "", { "peerDependencies": { "eslint": ">=7.0.0" }, "bin": { "eslint-config-prettier": "build/bin/cli.js" } }, "sha512-lZBts941cyJyeaooiKxAtzoPHTN+GbQTJFAIdQbRhA4/8whaAraEh47Whw/ZFfrjNSnlAxqfm9i0XVAEkULjCw=="],
|
||||
|
||||
@@ -659,6 +663,8 @@
|
||||
|
||||
"fastq": ["fastq@1.19.0", "", { "dependencies": { "reusify": "^1.0.4" } }, "sha512-7SFSRCNjBQIZH/xZR3iy5iQYR8aGBE0h3VG6/cwlbrpdciNYBMotQav8c1XI3HjHH+NikUpP53nPdlZSdWmFzA=="],
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||||
|
||||
"fault": ["fault@1.0.4", "", { "dependencies": { "format": "^0.2.0" } }, "sha512-CJ0HCB5tL5fYTEA7ToAq5+kTwd++Borf1/bifxd9iT70QcXr4MRrO3Llf8Ifs70q+SJcGHFtnIE/Nw6giCtECA=="],
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||||
|
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"file-entry-cache": ["file-entry-cache@8.0.0", "", { "dependencies": { "flat-cache": "^4.0.0" } }, "sha512-XXTUwCvisa5oacNGRP9SfNtYBNAMi+RPwBFmblZEF7N7swHYQS6/Zfk7SRwx4D5j3CH211YNRco1DEMNVfZCnQ=="],
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||||
@@ -679,6 +685,8 @@
|
||||
|
||||
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"format": ["format@0.2.2", "", {}, "sha512-wzsgA6WOq+09wrU1tsJ09udeR/YZRaeArL9e1wPbFg3GG2yDnC2ldKpxs4xunpFF9DgqCqOIra3bc1HWrJ37Ww=="],
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|
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"fsevents": ["fsevents@2.3.3", "", { "os": "darwin" }, "sha512-5xoDfX+fL7faATnagmWPpbFtwh/R77WmMMqqHGS65C3vvB0YHrgF+B1YmZ3441tMj5n63k0212XNoJwzlhffQw=="],
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|
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"function-bind": ["function-bind@1.1.2", "", {}, "sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA=="],
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||||
@@ -743,10 +751,18 @@
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||||
|
||||
"hasown": ["hasown@2.0.2", "", { "dependencies": { "function-bind": "^1.1.2" } }, "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ=="],
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|
||||
"hast-util-parse-selector": ["hast-util-parse-selector@2.2.5", "", {}, "sha512-7j6mrk/qqkSehsM92wQjdIgWM2/BW61u/53G6xmC8i1OmEdKLHbk419QKQUjz6LglWsfqoiHmyMRkP1BGjecNQ=="],
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|
||||
"hast-util-to-jsx-runtime": ["hast-util-to-jsx-runtime@2.3.2", "", { "dependencies": { "@types/estree": "^1.0.0", "@types/hast": "^3.0.0", "@types/unist": "^3.0.0", "comma-separated-tokens": "^2.0.0", "devlop": "^1.0.0", "estree-util-is-identifier-name": "^3.0.0", "hast-util-whitespace": "^3.0.0", "mdast-util-mdx-expression": "^2.0.0", "mdast-util-mdx-jsx": "^3.0.0", "mdast-util-mdxjs-esm": "^2.0.0", "property-information": "^6.0.0", "space-separated-tokens": "^2.0.0", "style-to-object": "^1.0.0", "unist-util-position": "^5.0.0", "vfile-message": "^4.0.0" } }, "sha512-1ngXYb+V9UT5h+PxNRa1O1FYguZK/XL+gkeqvp7EdHlB9oHUG0eYRo/vY5inBdcqo3RkPMC58/H94HvkbfGdyg=="],
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"hast-util-whitespace": ["hast-util-whitespace@3.0.0", "", { "dependencies": { "@types/hast": "^3.0.0" } }, "sha512-88JUN06ipLwsnv+dVn+OIYOvAuvBMy/Qoi6O7mQHxdPXpjy+Cd6xRkWwux7DKO+4sYILtLBRIKgsdpS2gQc7qw=="],
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"hastscript": ["hastscript@6.0.0", "", { "dependencies": { "@types/hast": "^2.0.0", "comma-separated-tokens": "^1.0.0", "hast-util-parse-selector": "^2.0.0", "property-information": "^5.0.0", "space-separated-tokens": "^1.0.0" } }, "sha512-nDM6bvd7lIqDUiYEiu5Sl/+6ReP0BMk/2f4U/Rooccxkj0P5nm+acM5PrGJ/t5I8qPGiqZSE6hVAwZEdZIvP4w=="],
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"highlight.js": ["highlight.js@10.7.3", "", {}, "sha512-tzcUFauisWKNHaRkN4Wjl/ZA07gENAjFl3J/c480dprkGTg5EQstgaNFqBfUqCq54kZRIEcreTsAgF/m2quD7A=="],
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"highlightjs-vue": ["highlightjs-vue@1.0.0", "", {}, "sha512-PDEfEF102G23vHmPhLyPboFCD+BkMGu+GuJe2d9/eH4FsCwvgBpnc9n0pGE+ffKdph38s6foEZiEjdgHdzp+IA=="],
|
||||
|
||||
"hoist-non-react-statics": ["hoist-non-react-statics@3.3.2", "", { "dependencies": { "react-is": "^16.7.0" } }, "sha512-/gGivxi8JPKWNm/W0jSmzcMPpfpPLc3dY/6GxhX2hQ9iGj3aDfklV4ET7NjKpSinLpJ5vafa9iiGIEZg10SfBw=="],
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"html-url-attributes": ["html-url-attributes@3.0.1", "", {}, "sha512-ol6UPyBWqsrO6EJySPz2O7ZSr856WDrEzM5zMqp+FJJLGMW35cLYmmZnl0vztAZxRUoNZJFTCohfjuIJ8I4QBQ=="],
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@@ -761,9 +777,9 @@
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"internal-slot": ["internal-slot@1.1.0", "", { "dependencies": { "es-errors": "^1.3.0", "hasown": "^2.0.2", "side-channel": "^1.1.0" } }, "sha512-4gd7VpWNQNB4UKKCFFVcp1AVv+FMOgs9NKzjHKusc8jTMhd5eL1NqQqOpE0KzMds804/yHlglp3uxgluOqAPLw=="],
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"is-alphabetical": ["is-alphabetical@2.0.1", "", {}, "sha512-FWyyY60MeTNyeSRpkM2Iry0G9hpr7/9kD40mD/cGQEuilcZYS4okz8SN2Q6rLCJ8gbCt6fN+rC+6tMGS99LaxQ=="],
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"is-alphabetical": ["is-alphabetical@1.0.4", "", {}, "sha512-DwzsA04LQ10FHTZuL0/grVDk4rFoVH1pjAToYwBrHSxcrBIGQuXrQMtD5U1b0U2XVgKZCTLLP8u2Qxqhy3l2Vg=="],
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"is-alphanumerical": ["is-alphanumerical@2.0.1", "", { "dependencies": { "is-alphabetical": "^2.0.0", "is-decimal": "^2.0.0" } }, "sha512-hmbYhX/9MUMF5uh7tOXyK/n0ZvWpad5caBA17GsC6vyuCqaWliRG5K1qS9inmUhEMaOBIW7/whAnSwveW/LtZw=="],
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"is-alphanumerical": ["is-alphanumerical@1.0.4", "", { "dependencies": { "is-alphabetical": "^1.0.0", "is-decimal": "^1.0.0" } }, "sha512-UzoZUr+XfVz3t3v4KyGEniVL9BDRoQtY7tOyrRybkVNjDFWyo1yhXNGrrBTQxp3ib9BLAWs7k2YKBQsFRkZG9A=="],
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"is-array-buffer": ["is-array-buffer@3.0.5", "", { "dependencies": { "call-bind": "^1.0.8", "call-bound": "^1.0.3", "get-intrinsic": "^1.2.6" } }, "sha512-DDfANUiiG2wC1qawP66qlTugJeL5HyzMpfr8lLK+jMQirGzNod0B12cFB/9q838Ru27sBwfw78/rdoU7RERz6A=="],
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|
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@@ -783,7 +799,7 @@
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"is-date-object": ["is-date-object@1.1.0", "", { "dependencies": { "call-bound": "^1.0.2", "has-tostringtag": "^1.0.2" } }, "sha512-PwwhEakHVKTdRNVOw+/Gyh0+MzlCl4R6qKvkhuvLtPMggI1WAHt9sOwZxQLSGpUaDnrdyDsomoRgNnCfKNSXXg=="],
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"is-decimal": ["is-decimal@2.0.1", "", {}, "sha512-AAB9hiomQs5DXWcRB1rqsxGUstbRroFOPPVAomNk/3XHR5JyEZChOyTWe2oayKnsSsr/kcGqF+z6yuH6HHpN0A=="],
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"is-decimal": ["is-decimal@1.0.4", "", {}, "sha512-RGdriMmQQvZ2aqaQq3awNA6dCGtKpiDFcOzrTWrDAT2MiWrKQVPmxLGHl7Y2nNu6led0kEyoX0enY0qXYsv9zw=="],
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"is-extglob": ["is-extglob@2.1.1", "", {}, "sha512-SbKbANkN603Vi4jEZv49LeVJMn4yGwsbzZworEoyEiutsN3nJYdbO36zfhGJ6QEDpOZIFkDtnq5JRxmvl3jsoQ=="],
|
||||
|
||||
@@ -793,7 +809,7 @@
|
||||
|
||||
"is-glob": ["is-glob@4.0.3", "", { "dependencies": { "is-extglob": "^2.1.1" } }, "sha512-xelSayHH36ZgE7ZWhli7pW34hNbNl8Ojv5KVmkJD4hBdD3th8Tfk9vYasLM+mXWOZhFkgZfxhLSnrwRr4elSSg=="],
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|
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"is-hexadecimal": ["is-hexadecimal@2.0.1", "", {}, "sha512-DgZQp241c8oO6cA1SbTEWiXeoxV42vlcJxgH+B3hi1AiqqKruZR3ZGF8In3fj4+/y/7rHvlOZLZtgJ/4ttYGZg=="],
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"is-hexadecimal": ["is-hexadecimal@1.0.4", "", {}, "sha512-gyPJuv83bHMpocVYoqof5VDiZveEoGoFL8m3BXNb2VW8Xs+rz9kqO8LOQ5DH6EsuvilT1ApazU0pyl+ytbPtlw=="],
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"is-map": ["is-map@2.0.3", "", {}, "sha512-1Qed0/Hr2m+YqxnM09CjA2d/i6YZNfF6R2oRAOj36eUdS6qIV/huPJNSEpKbupewFs+ZsJlxsjjPbc0/afW6Lw=="],
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|
||||
@@ -883,6 +899,8 @@
|
||||
|
||||
"loose-envify": ["loose-envify@1.4.0", "", { "dependencies": { "js-tokens": "^3.0.0 || ^4.0.0" }, "bin": { "loose-envify": "cli.js" } }, "sha512-lyuxPGr/Wfhrlem2CL/UcnUc1zcqKAImBDzukY7Y5F/yQiNdko6+fRLevlw1HgMySw7f611UIY408EtxRSoK3Q=="],
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|
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"lowlight": ["lowlight@1.20.0", "", { "dependencies": { "fault": "^1.0.0", "highlight.js": "~10.7.0" } }, "sha512-8Ktj+prEb1RoCPkEOrPMYUN/nCggB7qAWe3a7OpMjWQkh3l2RD5wKRQ+o8Q8YuI9RG/xs95waaI/E6ym/7NsTw=="],
|
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|
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"lucide-react": ["lucide-react@0.475.0", "", { "peerDependencies": { "react": "^16.5.1 || ^17.0.0 || ^18.0.0 || ^19.0.0" } }, "sha512-NJzvVu1HwFVeZ+Gwq2q00KygM1aBhy/ZrhY9FsAgJtpB+E4R7uxRk9M2iKvHa6/vNxZydIB59htha4c2vvwvVg=="],
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"markdown-table": ["markdown-table@3.0.4", "", {}, "sha512-wiYz4+JrLyb/DqW2hkFJxP7Vd7JuTDm77fvbM8VfEQdmSMqcImWeeRbHwZjBjIFki/VaMK2BhFi7oUUZeM5bqw=="],
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||||
@@ -991,7 +1009,7 @@
|
||||
|
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"minimatch": ["minimatch@3.1.2", "", { "dependencies": { "brace-expansion": "^1.1.7" } }, "sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw=="],
|
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"minisearch": ["minisearch@7.1.1", "", {}, "sha512-b3YZEYCEH4EdCAtYP7OlDyx7FdPwNzuNwLQ34SfJpM9dlbBZzeXndGavTrC+VCiRWomL21SWfMc6SCKO/U2ZNw=="],
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"minisearch": ["minisearch@7.1.2", "", {}, "sha512-R1Pd9eF+MD5JYDDSPAp/q1ougKglm14uEkPMvQ/05RGmx6G9wvmLTrTI/Q5iPNJLYqNdsDQ7qTGIcNWR+FrHmA=="],
|
||||
|
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"mnemonist": ["mnemonist@0.39.8", "", { "dependencies": { "obliterator": "^2.0.1" } }, "sha512-vyWo2K3fjrUw8YeeZ1zF0fy6Mu59RHokURlld8ymdUPjMlD9EC9ov1/YPqTgqRvUN9nTr3Gqfz29LYAmu0PHPQ=="],
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||||
|
||||
@@ -1029,7 +1047,7 @@
|
||||
|
||||
"parent-module": ["parent-module@1.0.1", "", { "dependencies": { "callsites": "^3.0.0" } }, "sha512-GQ2EWRpQV8/o+Aw8YqtfZZPfNRWZYkbidE9k5rpl/hC3vtHHBfGm2Ifi6qWV+coDGkrUKZAxE3Lot5kcsRlh+g=="],
|
||||
|
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"parse-entities": ["parse-entities@4.0.2", "", { "dependencies": { "@types/unist": "^2.0.0", "character-entities-legacy": "^3.0.0", "character-reference-invalid": "^2.0.0", "decode-named-character-reference": "^1.0.0", "is-alphanumerical": "^2.0.0", "is-decimal": "^2.0.0", "is-hexadecimal": "^2.0.0" } }, "sha512-GG2AQYWoLgL877gQIKeRPGO1xF9+eG1ujIb5soS5gPvLQ1y2o8FL90w2QWNdf9I361Mpp7726c+lj3U0qK1uGw=="],
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"parse-entities": ["parse-entities@2.0.0", "", { "dependencies": { "character-entities": "^1.0.0", "character-entities-legacy": "^1.0.0", "character-reference-invalid": "^1.0.0", "is-alphanumerical": "^1.0.0", "is-decimal": "^1.0.0", "is-hexadecimal": "^1.0.0" } }, "sha512-kkywGpCcRYhqQIchaWqZ875wzpS/bMKhz5HnN3p7wveJTkTtyAB/AlnS0f8DFSqYW1T82t6yEAkEcB+A1I3MbQ=="],
|
||||
|
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"parse-json": ["parse-json@5.2.0", "", { "dependencies": { "@babel/code-frame": "^7.0.0", "error-ex": "^1.3.1", "json-parse-even-better-errors": "^2.3.0", "lines-and-columns": "^1.1.6" } }, "sha512-ayCKvm/phCGxOkYRSCM82iDwct8/EonSEgCSxWxD7ve6jHggsFl4fZVQBPRNgQoKiuV/odhFrGzQXZwbifC8Rg=="],
|
||||
|
||||
@@ -1047,16 +1065,18 @@
|
||||
|
||||
"possible-typed-array-names": ["possible-typed-array-names@1.1.0", "", {}, "sha512-/+5VFTchJDoVj3bhoqi6UeymcD00DAwb1nJwamzPvHEszJ4FpF6SNNbUbOS8yI56qHzdV8eK0qEfOSiodkTdxg=="],
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"postcss": ["postcss@8.5.1", "", { "dependencies": { "nanoid": "^3.3.8", "picocolors": "^1.1.1", "source-map-js": "^1.2.1" } }, "sha512-6oz2beyjc5VMn/KV1pPw8fliQkhBXrVn1Z3TVyqZxU8kZpzEKhBdmCFqI6ZbmGtamQvQGuU1sgPTk8ZrXDD7jQ=="],
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||||
"postcss": ["postcss@8.5.3", "", { "dependencies": { "nanoid": "^3.3.8", "picocolors": "^1.1.1", "source-map-js": "^1.2.1" } }, "sha512-dle9A3yYxlBSrt8Fu+IpjGT8SY8hN0mlaA6GY8t0P5PjIOZemULz/E2Bnm/2dcUOena75OTNkHI76uZBNUUq3A=="],
|
||||
|
||||
"prelude-ls": ["prelude-ls@1.2.1", "", {}, "sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g=="],
|
||||
|
||||
"prettier": ["prettier@3.5.1", "", { "bin": { "prettier": "bin/prettier.cjs" } }, "sha512-hPpFQvHwL3Qv5AdRvBFMhnKo4tYxp0ReXiPn2bxkiohEX6mBeBwEpBSQTkD458RaaDKQMYSp4hX4UtfUTA5wDw=="],
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"prettier": ["prettier@3.5.2", "", { "bin": { "prettier": "bin/prettier.cjs" } }, "sha512-lc6npv5PH7hVqozBR7lkBNOGXV9vMwROAPlumdBkX0wTbbzPu/U1hk5yL8p2pt4Xoc+2mkT8t/sow2YrV/M5qg=="],
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|
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"prettier-plugin-tailwindcss": ["prettier-plugin-tailwindcss@0.6.11", "", { "peerDependencies": { "@ianvs/prettier-plugin-sort-imports": "*", "@prettier/plugin-pug": "*", "@shopify/prettier-plugin-liquid": "*", "@trivago/prettier-plugin-sort-imports": "*", "@zackad/prettier-plugin-twig": "*", "prettier": "^3.0", "prettier-plugin-astro": "*", "prettier-plugin-css-order": "*", "prettier-plugin-import-sort": "*", "prettier-plugin-jsdoc": "*", "prettier-plugin-marko": "*", "prettier-plugin-multiline-arrays": "*", "prettier-plugin-organize-attributes": "*", "prettier-plugin-organize-imports": "*", "prettier-plugin-sort-imports": "*", "prettier-plugin-style-order": "*", "prettier-plugin-svelte": "*" }, "optionalPeers": ["@ianvs/prettier-plugin-sort-imports", "@prettier/plugin-pug", "@shopify/prettier-plugin-liquid", "@trivago/prettier-plugin-sort-imports", "@zackad/prettier-plugin-twig", "prettier-plugin-astro", "prettier-plugin-css-order", "prettier-plugin-import-sort", "prettier-plugin-jsdoc", "prettier-plugin-marko", "prettier-plugin-multiline-arrays", "prettier-plugin-organize-attributes", "prettier-plugin-organize-imports", "prettier-plugin-sort-imports", "prettier-plugin-style-order", "prettier-plugin-svelte"] }, "sha512-YxaYSIvZPAqhrrEpRtonnrXdghZg1irNg4qrjboCXrpybLWVs55cW2N3juhspVJiO0JBvYJT8SYsJpc8OQSnsA=="],
|
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|
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"prism-react-renderer": ["prism-react-renderer@2.4.1", "", { "dependencies": { "@types/prismjs": "^1.26.0", "clsx": "^2.0.0" }, "peerDependencies": { "react": ">=16.0.0" } }, "sha512-ey8Ls/+Di31eqzUxC46h8MksNuGx/n0AAC8uKpwFau4RPDYLuE3EXTp8N8G2vX2N7UC/+IXeNUnlWBGGcAG+Ig=="],
|
||||
|
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"prismjs": ["prismjs@1.29.0", "", {}, "sha512-Kx/1w86q/epKcmte75LNrEoT+lX8pBpavuAbvJWRXar7Hz8jrtF+e3vY751p0R8H9HdArwaCTNDDzHg/ScJK1Q=="],
|
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|
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"prop-types": ["prop-types@15.8.1", "", { "dependencies": { "loose-envify": "^1.4.0", "object-assign": "^4.1.1", "react-is": "^16.13.1" } }, "sha512-oj87CgZICdulUohogVAR7AjlC0327U4el4L6eAvOqCeudMDVU0NThNaV+b9Df4dXgSP1gXMTnPdhfe/2qDH5cg=="],
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"property-information": ["property-information@6.5.0", "", {}, "sha512-PgTgs/BlvHxOu8QuEN7wi5A0OmXaBcHpmCSTehcs6Uuu9IkDIEo13Hy7n898RHfrQ49vKCoGeWZSaAK01nwVig=="],
|
||||
@@ -1071,11 +1091,11 @@
|
||||
|
||||
"react-dom": ["react-dom@19.0.0", "", { "dependencies": { "scheduler": "^0.25.0" }, "peerDependencies": { "react": "^19.0.0" } }, "sha512-4GV5sHFG0e/0AD4X+ySy6UJd3jVl1iNsNHdpad0qhABJ11twS3TTBnseqsKurKcsNqCEFeGL3uLpVChpIO3QfQ=="],
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|
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"react-dropzone": ["react-dropzone@14.3.5", "", { "dependencies": { "attr-accept": "^2.2.4", "file-selector": "^2.1.0", "prop-types": "^15.8.1" }, "peerDependencies": { "react": ">= 16.8 || 18.0.0" } }, "sha512-9nDUaEEpqZLOz5v5SUcFA0CjM4vq8YbqO0WRls+EYT7+DvxUdzDPKNCPLqGfj3YL9MsniCLCD4RFA6M95V6KMQ=="],
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"react-dropzone": ["react-dropzone@14.3.6", "", { "dependencies": { "attr-accept": "^2.2.4", "file-selector": "^2.1.0", "prop-types": "^15.8.1" }, "peerDependencies": { "react": ">= 16.8 || 18.0.0" } }, "sha512-U792j+x0rcwH/U/Slv/OBNU/LGFYbDLHKKiJoPhNaOianayZevCt4Y5S0CraPssH/6/wT6xhKDfzdXUgCBS0HQ=="],
|
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|
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"react-is": ["react-is@16.13.1", "", {}, "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ=="],
|
||||
|
||||
"react-markdown": ["react-markdown@9.0.3", "", { "dependencies": { "@types/hast": "^3.0.0", "devlop": "^1.0.0", "hast-util-to-jsx-runtime": "^2.0.0", "html-url-attributes": "^3.0.0", "mdast-util-to-hast": "^13.0.0", "remark-parse": "^11.0.0", "remark-rehype": "^11.0.0", "unified": "^11.0.0", "unist-util-visit": "^5.0.0", "vfile": "^6.0.0" }, "peerDependencies": { "@types/react": ">=18", "react": ">=18" } }, "sha512-Yk7Z94dbgYTOrdk41Z74GoKA7rThnsbbqBTRYuxoe08qvfQ9tJVhmAKw6BJS/ZORG7kTy/s1QvYzSuaoBA1qfw=="],
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"react-markdown": ["react-markdown@9.1.0", "", { "dependencies": { "@types/hast": "^3.0.0", "@types/mdast": "^4.0.0", "devlop": "^1.0.0", "hast-util-to-jsx-runtime": "^2.0.0", "html-url-attributes": "^3.0.0", "mdast-util-to-hast": "^13.0.0", "remark-parse": "^11.0.0", "remark-rehype": "^11.0.0", "unified": "^11.0.0", "unist-util-visit": "^5.0.0", "vfile": "^6.0.0" }, "peerDependencies": { "@types/react": ">=18", "react": ">=18" } }, "sha512-xaijuJB0kzGiUdG7nc2MOMDUDBWPyGAjZtUrow9XxUeua8IqeP+VlIfAZ3bphpcLTnSZXz6z9jcVC/TCwbfgdw=="],
|
||||
|
||||
"react-number-format": ["react-number-format@5.4.3", "", { "peerDependencies": { "react": "^0.14 || ^15.0.0 || ^16.0.0 || ^17.0.0 || ^18.0.0 || ^19.0.0", "react-dom": "^0.14 || ^15.0.0 || ^16.0.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" } }, "sha512-VCY5hFg/soBighAoGcdE+GagkJq0230qN6jcS5sp8wQX1qy1fYN/RX7/BXkrs0oyzzwqR8/+eSUrqXbGeywdUQ=="],
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||||
|
||||
@@ -1087,10 +1107,14 @@
|
||||
|
||||
"react-style-singleton": ["react-style-singleton@2.2.3", "", { "dependencies": { "get-nonce": "^1.0.0", "tslib": "^2.0.0" }, "peerDependencies": { "@types/react": "*", "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-b6jSvxvVnyptAiLjbkWLE/lOnR4lfTtDAl+eUC7RZy+QQWc6wRzIV2CE6xBuMmDxc2qIihtDCZD5NPOFl7fRBQ=="],
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|
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"react-syntax-highlighter": ["react-syntax-highlighter@15.6.1", "", { "dependencies": { "@babel/runtime": "^7.3.1", "highlight.js": "^10.4.1", "highlightjs-vue": "^1.0.0", "lowlight": "^1.17.0", "prismjs": "^1.27.0", "refractor": "^3.6.0" }, "peerDependencies": { "react": ">= 0.14.0" } }, "sha512-OqJ2/vL7lEeV5zTJyG7kmARppUjiB9h9udl4qHQjjgEos66z00Ia0OckwYfRxCSFrW8RJIBnsBwQsHZbVPspqg=="],
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|
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"react-transition-group": ["react-transition-group@4.4.5", "", { "dependencies": { "@babel/runtime": "^7.5.5", "dom-helpers": "^5.0.1", "loose-envify": "^1.4.0", "prop-types": "^15.6.2" }, "peerDependencies": { "react": ">=16.6.0", "react-dom": ">=16.6.0" } }, "sha512-pZcd1MCJoiKiBR2NRxeCRg13uCXbydPnmB4EOeRrY7480qNWO8IIgQG6zlDkm6uRMsURXPuKq0GWtiM59a5Q6g=="],
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"reflect.getprototypeof": ["reflect.getprototypeof@1.0.10", "", { "dependencies": { "call-bind": "^1.0.8", "define-properties": "^1.2.1", "es-abstract": "^1.23.9", "es-errors": "^1.3.0", "es-object-atoms": "^1.0.0", "get-intrinsic": "^1.2.7", "get-proto": "^1.0.1", "which-builtin-type": "^1.2.1" } }, "sha512-00o4I+DVrefhv+nX0ulyi3biSHCPDe+yLv5o/p6d/UVlirijB8E16FtfwSAi4g3tcqrQ4lRAqQSoFEZJehYEcw=="],
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"refractor": ["refractor@3.6.0", "", { "dependencies": { "hastscript": "^6.0.0", "parse-entities": "^2.0.0", "prismjs": "~1.27.0" } }, "sha512-MY9W41IOWxxk31o+YvFCNyNzdkc9M20NoZK5vq6jkv4I/uh2zkWcfudj0Q1fovjUQJrNewS9NMzeTtqPf+n5EA=="],
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"regenerator-runtime": ["regenerator-runtime@0.14.1", "", {}, "sha512-dYnhHh0nJoMfnkZs6GmmhFknAGRrLznOu5nc9ML+EJxGvrx6H7teuevqVqCuPcPK//3eDrrjQhehXVx9cnkGdw=="],
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"regexp.prototype.flags": ["regexp.prototype.flags@1.5.4", "", { "dependencies": { "call-bind": "^1.0.8", "define-properties": "^1.2.1", "es-errors": "^1.3.0", "get-proto": "^1.0.1", "gopd": "^1.2.0", "set-function-name": "^2.0.2" } }, "sha512-dYqgNSZbDwkaJ2ceRd9ojCGjBq+mOm9LmtXnAnEGyHhN/5R7iDW2TRw3h+o/jCFxus3P2LfWIIiwowAjANm7IA=="],
|
||||
@@ -1179,11 +1203,11 @@
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"supports-preserve-symlinks-flag": ["supports-preserve-symlinks-flag@1.0.0", "", {}, "sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w=="],
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"tailwind-merge": ["tailwind-merge@3.0.1", "", {}, "sha512-AvzE8FmSoXC7nC+oU5GlQJbip2UO7tmOhOfQyOmPhrStOGXHU08j8mZEHZ4BmCqY5dWTCo4ClWkNyRNx1wpT0g=="],
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"tailwind-merge": ["tailwind-merge@3.0.2", "", {}, "sha512-l7z+OYZ7mu3DTqrL88RiKrKIqO3NcpEO8V/Od04bNpvk0kiIFndGEoqfuzvj4yuhRkHKjRkII2z+KS2HfPcSxw=="],
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"tailwind-scrollbar": ["tailwind-scrollbar@4.0.0", "", { "dependencies": { "prism-react-renderer": "^2.4.1" }, "peerDependencies": { "tailwindcss": "4.x" } }, "sha512-elqx9m09VHY8gkrMiyimFO09JlS3AyLFXT0eaLaWPi7ImwHlbZj1ce/AxSis2LtR+ewBGEyUV7URNEMcjP1Z2w=="],
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"tailwind-scrollbar": ["tailwind-scrollbar@4.0.1", "", { "dependencies": { "prism-react-renderer": "^2.4.1" }, "peerDependencies": { "tailwindcss": "4.x" } }, "sha512-j2ZfUI7p8xmSQdlqaCxEb4Mha8ErvWjDVyu2Ke4IstWprQ/6TmIz1GSLE62vsTlXwnMLYhuvbFbIFzaJGOGtMg=="],
|
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"tailwindcss": ["tailwindcss@4.0.6", "", {}, "sha512-mysewHYJKaXgNOW6pp5xon/emCsfAMnO8WMaGKZZ35fomnR/T5gYnRg2/yRTTrtXiEl1tiVkeRt0eMO6HxEZqw=="],
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"tailwindcss": ["tailwindcss@4.0.8", "", {}, "sha512-Me7N5CKR+D2A1xdWA5t5+kjjT7bwnxZOE6/yDI/ixJdJokszsn2n++mdU5yJwrsTpqFX2B9ZNMBJDwcqk9C9lw=="],
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||||
|
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"tailwindcss-animate": ["tailwindcss-animate@1.0.7", "", { "peerDependencies": { "tailwindcss": ">=3.0.0 || insiders" } }, "sha512-bl6mpH3T7I3UFxuvDEXLxy/VuFxBk5bbzplh7tXI68mwMokNYd1t9qPBHlnyTwfa4JGC4zP516I1hYYtQ/vspA=="],
|
||||
|
||||
@@ -1211,7 +1235,7 @@
|
||||
|
||||
"typescript": ["typescript@5.7.3", "", { "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" } }, "sha512-84MVSjMEHP+FQRPy3pX9sTVV/INIex71s9TL2Gm5FG/WG1SqXeKyZ0k7/blY/4FdOzI12CBy1vGc4og/eus0fw=="],
|
||||
|
||||
"typescript-eslint": ["typescript-eslint@8.24.0", "", { "dependencies": { "@typescript-eslint/eslint-plugin": "8.24.0", "@typescript-eslint/parser": "8.24.0", "@typescript-eslint/utils": "8.24.0" }, "peerDependencies": { "eslint": "^8.57.0 || ^9.0.0", "typescript": ">=4.8.4 <5.8.0" } }, "sha512-/lmv4366en/qbB32Vz5+kCNZEMf6xYHwh1z48suBwZvAtnXKbP+YhGe8OLE2BqC67LMqKkCNLtjejdwsdW6uOQ=="],
|
||||
"typescript-eslint": ["typescript-eslint@8.24.1", "", { "dependencies": { "@typescript-eslint/eslint-plugin": "8.24.1", "@typescript-eslint/parser": "8.24.1", "@typescript-eslint/utils": "8.24.1" }, "peerDependencies": { "eslint": "^8.57.0 || ^9.0.0", "typescript": ">=4.8.4 <5.8.0" } }, "sha512-cw3rEdzDqBs70TIcb0Gdzbt6h11BSs2pS0yaq7hDWDBtCCSei1pPSUXE9qUdQ/Wm9NgFg8mKtMt1b8fTHIl1jA=="],
|
||||
|
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"unbox-primitive": ["unbox-primitive@1.1.0", "", { "dependencies": { "call-bound": "^1.0.3", "has-bigints": "^1.0.2", "has-symbols": "^1.1.0", "which-boxed-primitive": "^1.1.1" } }, "sha512-nWJ91DjeOkej/TA8pXQ3myruKpKEYgqvpw9lz4OPHj/NWFNluYrjbz9j01CJ8yKQd2g4jFoOkINCTW2I5LEEyw=="],
|
||||
|
||||
@@ -1245,7 +1269,7 @@
|
||||
|
||||
"vfile-message": ["vfile-message@4.0.2", "", { "dependencies": { "@types/unist": "^3.0.0", "unist-util-stringify-position": "^4.0.0" } }, "sha512-jRDZ1IMLttGj41KcZvlrYAaI3CfqpLpfpf+Mfig13viT6NKvRzWZ+lXz0Y5D60w6uJIBAOGq9mSHf0gktF0duw=="],
|
||||
|
||||
"vite": ["vite@6.1.0", "", { "dependencies": { "esbuild": "^0.24.2", "postcss": "^8.5.1", "rollup": "^4.30.1" }, "optionalDependencies": { "fsevents": "~2.3.3" }, "peerDependencies": { "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0", "jiti": ">=1.21.0", "less": "*", "lightningcss": "^1.21.0", "sass": "*", "sass-embedded": "*", "stylus": "*", "sugarss": "*", "terser": "^5.16.0", "tsx": "^4.8.1", "yaml": "^2.4.2" }, "optionalPeers": ["@types/node", "jiti", "less", "lightningcss", "sass", "sass-embedded", "stylus", "sugarss", "terser", "tsx", "yaml"], "bin": { "vite": "bin/vite.js" } }, "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ=="],
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"vite": ["vite@6.1.1", "", { "dependencies": { "esbuild": "^0.24.2", "postcss": "^8.5.2", "rollup": "^4.30.1" }, "optionalDependencies": { "fsevents": "~2.3.3" }, "peerDependencies": { "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0", "jiti": ">=1.21.0", "less": "*", "lightningcss": "^1.21.0", "sass": "*", "sass-embedded": "*", "stylus": "*", "sugarss": "*", "terser": "^5.16.0", "tsx": "^4.8.1", "yaml": "^2.4.2" }, "optionalPeers": ["@types/node", "jiti", "less", "lightningcss", "sass", "sass-embedded", "stylus", "sugarss", "terser", "tsx", "yaml"], "bin": { "vite": "bin/vite.js" } }, "sha512-4GgM54XrwRfrOp297aIYspIti66k56v16ZnqHvrIM7mG+HjDlAwS7p+Srr7J6fGvEdOJ5JcQ/D9T7HhtdXDTzA=="],
|
||||
|
||||
"which": ["which@2.0.2", "", { "dependencies": { "isexe": "^2.0.0" }, "bin": { "node-which": "./bin/node-which" } }, "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA=="],
|
||||
|
||||
@@ -1259,6 +1283,8 @@
|
||||
|
||||
"word-wrap": ["word-wrap@1.2.5", "", {}, "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA=="],
|
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|
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"xtend": ["xtend@4.0.2", "", {}, "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ=="],
|
||||
|
||||
"yaml": ["yaml@1.10.2", "", {}, "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg=="],
|
||||
|
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"yocto-queue": ["yocto-queue@0.1.0", "", {}, "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q=="],
|
||||
@@ -1273,8 +1299,6 @@
|
||||
|
||||
"@eslint/eslintrc/globals": ["globals@14.0.0", "", {}, "sha512-oahGvuMGQlPw/ivIYBjVSrWAfWLBeku5tpPE2fOPLi+WHffIWbuh2tCjhyQhTBPMf5E9jDEH4FOmTYgYwbKwtQ=="],
|
||||
|
||||
"@eslint/plugin-kit/@eslint/core": ["@eslint/core@0.10.0", "", { "dependencies": { "@types/json-schema": "^7.0.15" } }, "sha512-gFHJ+xBOo4G3WRlR1e/3G8A6/KZAH6zcE/hkLRCZTi/B9avAG365QhFA8uOGzTMqgTghpn7/fSnscW++dpMSAw=="],
|
||||
|
||||
"@humanfs/node/@humanwhocodes/retry": ["@humanwhocodes/retry@0.3.1", "", {}, "sha512-JBxkERygn7Bv/GbN5Rv8Ul6LVknS+5Bp6RgDC/O8gEBU/yeH5Ui5C/OlWrTb6qct7LjjfT6Re2NxB0ln0yYybA=="],
|
||||
|
||||
"@types/ws/@types/node": ["@types/node@22.13.1", "", { "dependencies": { "undici-types": "~6.20.0" } }, "sha512-jK8uzQlrvXqEU91UxiK5J7pKHyzgnI1Qnl0QDHIgVGuolJhRb9EEl28Cj9b3rGR8B2lhFCtvIm5os8lFnO/1Ew=="],
|
||||
@@ -1285,14 +1309,42 @@
|
||||
|
||||
"babel-plugin-macros/resolve": ["resolve@1.22.10", "", { "dependencies": { "is-core-module": "^2.16.0", "path-parse": "^1.0.7", "supports-preserve-symlinks-flag": "^1.0.0" }, "bin": { "resolve": "bin/resolve" } }, "sha512-NPRy+/ncIMeDlTAsuqwKIiferiawhefFJtkNSW0qZJEqMEb+qBt/77B/jGeeek+F0uOeN05CDa6HXbbIgtVX4w=="],
|
||||
|
||||
"bun-types/@types/node": ["@types/node@22.13.1", "", { "dependencies": { "undici-types": "~6.20.0" } }, "sha512-jK8uzQlrvXqEU91UxiK5J7pKHyzgnI1Qnl0QDHIgVGuolJhRb9EEl28Cj9b3rGR8B2lhFCtvIm5os8lFnO/1Ew=="],
|
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"decode-named-character-reference/character-entities": ["character-entities@2.0.2", "", {}, "sha512-shx7oQ0Awen/BRIdkjkvz54PnEEI/EjwXDSIZp86/KKdbafHh1Df/RYGBhn4hbe2+uKC9FnT5UCEdyPz3ai9hQ=="],
|
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|
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"fast-glob/glob-parent": ["glob-parent@5.1.2", "", { "dependencies": { "is-glob": "^4.0.1" } }, "sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow=="],
|
||||
|
||||
"hastscript/@types/hast": ["@types/hast@2.3.10", "", { "dependencies": { "@types/unist": "^2" } }, "sha512-McWspRw8xx8J9HurkVBfYj0xKoE25tOFlHGdx4MJ5xORQrMGZNqJhVQWaIbm6Oyla5kYOXtDiopzKRJzEOkwJw=="],
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|
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"hastscript/comma-separated-tokens": ["comma-separated-tokens@1.0.8", "", {}, "sha512-GHuDRO12Sypu2cV70d1dkA2EUmXHgntrzbpvOB+Qy+49ypNfGgFQIC2fhhXbnyrJRynDCAARsT7Ou0M6hirpfw=="],
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|
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"hastscript/property-information": ["property-information@5.6.0", "", { "dependencies": { "xtend": "^4.0.0" } }, "sha512-YUHSPk+A30YPv+0Qf8i9Mbfe/C0hdPXk1s1jPVToV8pk8BQtpw10ct89Eo7OWkutrwqvT0eicAxlOg3dOAu8JA=="],
|
||||
|
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"hastscript/space-separated-tokens": ["space-separated-tokens@1.1.5", "", {}, "sha512-q/JSVd1Lptzhf5bkYm4ob4iWPjx0KiRe3sRFBNrVqbJkFaBm5vbbowy1mymoPNLRa52+oadOhJ+K49wsSeSjTA=="],
|
||||
|
||||
"mdast-util-find-and-replace/escape-string-regexp": ["escape-string-regexp@5.0.0", "", {}, "sha512-/veY75JbMK4j1yjvuUxuVsiS/hr/4iHs9FTT6cgTexxdE0Ly/glccBAkloH/DofkjRbZU3bnoj38mOmhkZ0lHw=="],
|
||||
|
||||
"parse-entities/@types/unist": ["@types/unist@2.0.11", "", {}, "sha512-CmBKiL6NNo/OqgmMn95Fk9Whlp2mtvIv+KNpQKN2F4SjvrEesubTRWGYSg+BnWZOnlCaSTU1sMpsBOzgbYhnsA=="],
|
||||
"mdast-util-mdx-jsx/parse-entities": ["parse-entities@4.0.2", "", { "dependencies": { "@types/unist": "^2.0.0", "character-entities-legacy": "^3.0.0", "character-reference-invalid": "^2.0.0", "decode-named-character-reference": "^1.0.0", "is-alphanumerical": "^2.0.0", "is-decimal": "^2.0.0", "is-hexadecimal": "^2.0.0" } }, "sha512-GG2AQYWoLgL877gQIKeRPGO1xF9+eG1ujIb5soS5gPvLQ1y2o8FL90w2QWNdf9I361Mpp7726c+lj3U0qK1uGw=="],
|
||||
|
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"refractor/prismjs": ["prismjs@1.27.0", "", {}, "sha512-t13BGPUlFDR7wRB5kQDG4jjl7XeuH6jbJGt11JHPL96qwsEHNX2+68tFXqc1/k+/jALsbSWJKUOT/hcYAZ5LkA=="],
|
||||
|
||||
"stringify-entities/character-entities-legacy": ["character-entities-legacy@3.0.0", "", {}, "sha512-RpPp0asT/6ufRm//AJVwpViZbGM/MkjQFxJccQRHmISF/22NBtsHqAWmL+/pmkPWoIUJdWyeVleTl1wydHATVQ=="],
|
||||
|
||||
"@typescript-eslint/typescript-estree/minimatch/brace-expansion": ["brace-expansion@2.0.1", "", { "dependencies": { "balanced-match": "^1.0.0" } }, "sha512-XnAIvQ8eM+kC6aULx6wuQiwVsnzsi9d3WxzV3FpWTGA19F621kwdbsAcFKXgKUHZWsy+mY6iL1sHTxWEFCytDA=="],
|
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|
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"hastscript/@types/hast/@types/unist": ["@types/unist@2.0.11", "", {}, "sha512-CmBKiL6NNo/OqgmMn95Fk9Whlp2mtvIv+KNpQKN2F4SjvrEesubTRWGYSg+BnWZOnlCaSTU1sMpsBOzgbYhnsA=="],
|
||||
|
||||
"mdast-util-mdx-jsx/parse-entities/@types/unist": ["@types/unist@2.0.11", "", {}, "sha512-CmBKiL6NNo/OqgmMn95Fk9Whlp2mtvIv+KNpQKN2F4SjvrEesubTRWGYSg+BnWZOnlCaSTU1sMpsBOzgbYhnsA=="],
|
||||
|
||||
"mdast-util-mdx-jsx/parse-entities/character-entities-legacy": ["character-entities-legacy@3.0.0", "", {}, "sha512-RpPp0asT/6ufRm//AJVwpViZbGM/MkjQFxJccQRHmISF/22NBtsHqAWmL+/pmkPWoIUJdWyeVleTl1wydHATVQ=="],
|
||||
|
||||
"mdast-util-mdx-jsx/parse-entities/character-reference-invalid": ["character-reference-invalid@2.0.1", "", {}, "sha512-iBZ4F4wRbyORVsu0jPV7gXkOsGYjGHPmAyv+HiHG8gi5PtC9KI2j1+v8/tlibRvjoWX027ypmG/n0HtO5t7unw=="],
|
||||
|
||||
"mdast-util-mdx-jsx/parse-entities/is-alphanumerical": ["is-alphanumerical@2.0.1", "", { "dependencies": { "is-alphabetical": "^2.0.0", "is-decimal": "^2.0.0" } }, "sha512-hmbYhX/9MUMF5uh7tOXyK/n0ZvWpad5caBA17GsC6vyuCqaWliRG5K1qS9inmUhEMaOBIW7/whAnSwveW/LtZw=="],
|
||||
|
||||
"mdast-util-mdx-jsx/parse-entities/is-decimal": ["is-decimal@2.0.1", "", {}, "sha512-AAB9hiomQs5DXWcRB1rqsxGUstbRroFOPPVAomNk/3XHR5JyEZChOyTWe2oayKnsSsr/kcGqF+z6yuH6HHpN0A=="],
|
||||
|
||||
"mdast-util-mdx-jsx/parse-entities/is-hexadecimal": ["is-hexadecimal@2.0.1", "", {}, "sha512-DgZQp241c8oO6cA1SbTEWiXeoxV42vlcJxgH+B3hi1AiqqKruZR3ZGF8In3fj4+/y/7rHvlOZLZtgJ/4ttYGZg=="],
|
||||
|
||||
"mdast-util-mdx-jsx/parse-entities/is-alphanumerical/is-alphabetical": ["is-alphabetical@2.0.1", "", {}, "sha512-FWyyY60MeTNyeSRpkM2Iry0G9hpr7/9kD40mD/cGQEuilcZYS4okz8SN2Q6rLCJ8gbCt6fN+rC+6tMGS99LaxQ=="],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<link rel="icon" type="image/svg+xml" href="/vite.svg" />
|
||||
<link rel="icon" type="image/svg+xml" href="/logo.png" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Lightrag</title>
|
||||
</head>
|
||||
|
||||
@@ -7,7 +7,10 @@
|
||||
"dev": "bunx --bun vite",
|
||||
"build": "bunx --bun vite build",
|
||||
"lint": "eslint .",
|
||||
"preview": "bunx --bun vite preview"
|
||||
"preview": "bunx --bun vite preview",
|
||||
"dev-no-bun": "vite",
|
||||
"build-no-bun": "vite build --emptyOutDir",
|
||||
"preview-no-bun": "vite preview"
|
||||
},
|
||||
"dependencies": {
|
||||
"@faker-js/faker": "^9.5.0",
|
||||
@@ -41,45 +44,47 @@
|
||||
"graphology": "^0.26.0",
|
||||
"graphology-generators": "^0.11.2",
|
||||
"lucide-react": "^0.475.0",
|
||||
"minisearch": "^7.1.1",
|
||||
"minisearch": "^7.1.2",
|
||||
"react": "^19.0.0",
|
||||
"react-dom": "^19.0.0",
|
||||
"react-dropzone": "^14.3.5",
|
||||
"react-markdown": "^9.0.3",
|
||||
"react-dropzone": "^14.3.6",
|
||||
"react-markdown": "^9.1.0",
|
||||
"react-number-format": "^5.4.3",
|
||||
"react-syntax-highlighter": "^15.6.1",
|
||||
"rehype-react": "^8.0.0",
|
||||
"remark-gfm": "^4.0.1",
|
||||
"remark-math": "^6.0.0",
|
||||
"seedrandom": "^3.0.5",
|
||||
"sigma": "^3.0.1",
|
||||
"sonner": "^1.7.4",
|
||||
"tailwind-merge": "^3.0.1",
|
||||
"tailwind-scrollbar": "^4.0.0",
|
||||
"tailwind-merge": "^3.0.2",
|
||||
"tailwind-scrollbar": "^4.0.1",
|
||||
"zustand": "^5.0.3"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@eslint/js": "^9.20.0",
|
||||
"@eslint/js": "^9.21.0",
|
||||
"@stylistic/eslint-plugin-js": "^3.1.0",
|
||||
"@tailwindcss/vite": "^4.0.6",
|
||||
"@types/bun": "^1.2.2",
|
||||
"@types/node": "^22.13.4",
|
||||
"@tailwindcss/vite": "^4.0.8",
|
||||
"@types/bun": "^1.2.3",
|
||||
"@types/node": "^22.13.5",
|
||||
"@types/react": "^19.0.10",
|
||||
"@types/react-dom": "^19.0.4",
|
||||
"@types/react-syntax-highlighter": "^15.5.13",
|
||||
"@types/seedrandom": "^3.0.8",
|
||||
"@vitejs/plugin-react-swc": "^3.8.0",
|
||||
"eslint": "^9.20.1",
|
||||
"eslint": "^9.21.0",
|
||||
"eslint-config-prettier": "^10.0.1",
|
||||
"eslint-plugin-react": "^7.37.4",
|
||||
"eslint-plugin-react-hooks": "^5.1.0",
|
||||
"eslint-plugin-react-refresh": "^0.4.19",
|
||||
"globals": "^15.15.0",
|
||||
"graphology-types": "^0.24.8",
|
||||
"prettier": "^3.5.1",
|
||||
"prettier": "^3.5.2",
|
||||
"prettier-plugin-tailwindcss": "^0.6.11",
|
||||
"tailwindcss": "^4.0.6",
|
||||
"tailwindcss": "^4.0.8",
|
||||
"tailwindcss-animate": "^1.0.7",
|
||||
"typescript": "~5.7.3",
|
||||
"typescript-eslint": "^8.24.0",
|
||||
"vite": "^6.1.0"
|
||||
"typescript-eslint": "^8.24.1",
|
||||
"vite": "^6.1.1"
|
||||
}
|
||||
}
|
||||
|
||||
BIN
lightrag_webui/public/logo.png
Normal file
BIN
lightrag_webui/public/logo.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 155 KiB |
@@ -1 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
|
||||
|
Before Width: | Height: | Size: 1.5 KiB |
@@ -161,8 +161,8 @@ axiosInstance.interceptors.response.use(
|
||||
)
|
||||
|
||||
// API methods
|
||||
export const queryGraphs = async (label: string): Promise<LightragGraphType> => {
|
||||
const response = await axiosInstance.get(`/graphs?label=${label}`)
|
||||
export const queryGraphs = async (label: string, maxDepth: number): Promise<LightragGraphType> => {
|
||||
const response = await axiosInstance.get(`/graphs?label=${label}&max_depth=${maxDepth}`)
|
||||
return response.data
|
||||
}
|
||||
|
||||
|
||||
@@ -80,7 +80,7 @@ export default function UploadDocumentsDialog() {
|
||||
<FileUploader
|
||||
maxFileCount={Infinity}
|
||||
maxSize={200 * 1024 * 1024}
|
||||
description="supported types: TXT, MD, DOC, PDF, PPTX"
|
||||
description="supported types: TXT, MD, DOCX, PDF, PPTX, RTF, ODT, EPUB, HTML, HTM, TEX, JSON, XML, YAML, YML, CSV, LOG, CONF, INI, PROPERTIES, SQL, BAT, SH, C, CPP, PY, JAVA, JS, TS, SWIFT, GO, RB, PHP, CSS, SCSS, LESS"
|
||||
onUpload={handleDocumentsUpload}
|
||||
progresses={progresses}
|
||||
disabled={isUploading}
|
||||
|
||||
@@ -26,8 +26,10 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean })
|
||||
const registerEvents = useRegisterEvents<NodeType, EdgeType>()
|
||||
const setSettings = useSetSettings<NodeType, EdgeType>()
|
||||
const loadGraph = useLoadGraph<NodeType, EdgeType>()
|
||||
|
||||
const maxIterations = useSettingsStore.use.graphLayoutMaxIterations()
|
||||
const { assign: assignLayout } = useLayoutForceAtlas2({
|
||||
iterations: 20
|
||||
iterations: maxIterations
|
||||
})
|
||||
|
||||
const { theme } = useTheme()
|
||||
|
||||
@@ -1,67 +1,81 @@
|
||||
import { useCallback, useState } from 'react'
|
||||
import { useCallback } from 'react'
|
||||
import { AsyncSelect } from '@/components/ui/AsyncSelect'
|
||||
import { getGraphLabels } from '@/api/lightrag'
|
||||
import { useSettingsStore } from '@/stores/settings'
|
||||
import { useGraphStore } from '@/stores/graph'
|
||||
import { labelListLimit } from '@/lib/constants'
|
||||
import MiniSearch from 'minisearch'
|
||||
|
||||
const lastGraph: any = {
|
||||
graph: null,
|
||||
searchEngine: null,
|
||||
labels: []
|
||||
}
|
||||
|
||||
const GraphLabels = () => {
|
||||
const label = useSettingsStore.use.queryLabel()
|
||||
const [labels, setLabels] = useState<{
|
||||
labels: string[]
|
||||
searchEngine: MiniSearch | null
|
||||
}>({
|
||||
labels: [],
|
||||
searchEngine: null
|
||||
})
|
||||
const [fetched, setFetched] = useState(false)
|
||||
const graph = useGraphStore.use.sigmaGraph()
|
||||
|
||||
const getSearchEngine = useCallback(async () => {
|
||||
if (lastGraph.graph == graph) {
|
||||
return {
|
||||
labels: lastGraph.labels,
|
||||
searchEngine: lastGraph.searchEngine
|
||||
}
|
||||
}
|
||||
const labels = ['*'].concat(await getGraphLabels())
|
||||
|
||||
// Ensure query label exists
|
||||
if (!labels.includes(useSettingsStore.getState().queryLabel)) {
|
||||
useSettingsStore.getState().setQueryLabel(labels[0])
|
||||
}
|
||||
|
||||
// Create search engine
|
||||
const searchEngine = new MiniSearch({
|
||||
idField: 'id',
|
||||
fields: ['value'],
|
||||
searchOptions: {
|
||||
prefix: true,
|
||||
fuzzy: 0.2,
|
||||
boost: {
|
||||
label: 2
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
// Add documents
|
||||
const documents = labels.map((str, index) => ({ id: index, value: str }))
|
||||
searchEngine.addAll(documents)
|
||||
|
||||
lastGraph.graph = graph
|
||||
lastGraph.searchEngine = searchEngine
|
||||
lastGraph.labels = labels
|
||||
|
||||
return {
|
||||
labels,
|
||||
searchEngine
|
||||
}
|
||||
}, [graph])
|
||||
|
||||
const fetchData = useCallback(
|
||||
async (query?: string): Promise<string[]> => {
|
||||
let _labels = labels.labels
|
||||
let _searchEngine = labels.searchEngine
|
||||
const { labels, searchEngine } = await getSearchEngine()
|
||||
|
||||
if (!fetched || !_searchEngine) {
|
||||
_labels = ['*'].concat(await getGraphLabels())
|
||||
|
||||
// Ensure query label exists
|
||||
if (!_labels.includes(useSettingsStore.getState().queryLabel)) {
|
||||
useSettingsStore.getState().setQueryLabel(_labels[0])
|
||||
}
|
||||
|
||||
// Create search engine
|
||||
_searchEngine = new MiniSearch({
|
||||
idField: 'id',
|
||||
fields: ['value'],
|
||||
searchOptions: {
|
||||
prefix: true,
|
||||
fuzzy: 0.2,
|
||||
boost: {
|
||||
label: 2
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
// Add documents
|
||||
const documents = _labels.map((str, index) => ({ id: index, value: str }))
|
||||
_searchEngine.addAll(documents)
|
||||
|
||||
setLabels({
|
||||
labels: _labels,
|
||||
searchEngine: _searchEngine
|
||||
})
|
||||
setFetched(true)
|
||||
}
|
||||
if (!query) {
|
||||
return _labels
|
||||
let result: string[] = labels
|
||||
if (query) {
|
||||
// Search labels
|
||||
result = searchEngine.search(query).map((r) => labels[r.id])
|
||||
}
|
||||
|
||||
// Search labels
|
||||
return _searchEngine.search(query).map((result) => _labels[result.id])
|
||||
return result.length <= labelListLimit
|
||||
? result
|
||||
: [...result.slice(0, labelListLimit), `And ${result.length - labelListLimit} others`]
|
||||
},
|
||||
[labels, fetched, setLabels, setFetched]
|
||||
[getSearchEngine]
|
||||
)
|
||||
|
||||
const setQueryLabel = useCallback((label: string) => {
|
||||
if (label.startsWith('And ') && label.endsWith(' others')) return
|
||||
useSettingsStore.getState().setQueryLabel(label)
|
||||
}, [])
|
||||
|
||||
|
||||
@@ -46,7 +46,7 @@ export const GraphSearchInput = ({
|
||||
}) => {
|
||||
const graph = useGraphStore.use.sigmaGraph()
|
||||
|
||||
const search = useMemo(() => {
|
||||
const searchEngine = useMemo(() => {
|
||||
if (lastGraph.graph == graph) {
|
||||
return lastGraph.searchEngine
|
||||
}
|
||||
@@ -83,9 +83,9 @@ export const GraphSearchInput = ({
|
||||
const loadOptions = useCallback(
|
||||
async (query?: string): Promise<OptionItem[]> => {
|
||||
if (onFocus) onFocus(null)
|
||||
if (!query || !search) return []
|
||||
const result: OptionItem[] = search.search(query).map((result) => ({
|
||||
id: result.id,
|
||||
if (!query || !searchEngine) return []
|
||||
const result: OptionItem[] = searchEngine.search(query).map((r) => ({
|
||||
id: r.id,
|
||||
type: 'nodes'
|
||||
}))
|
||||
|
||||
@@ -101,7 +101,7 @@ export const GraphSearchInput = ({
|
||||
}
|
||||
]
|
||||
},
|
||||
[search, onFocus]
|
||||
[searchEngine, onFocus]
|
||||
)
|
||||
|
||||
return (
|
||||
|
||||
@@ -13,6 +13,7 @@ import Button from '@/components/ui/Button'
|
||||
import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/Popover'
|
||||
import { Command, CommandGroup, CommandItem, CommandList } from '@/components/ui/Command'
|
||||
import { controlButtonVariant } from '@/lib/constants'
|
||||
import { useSettingsStore } from '@/stores/settings'
|
||||
|
||||
import { GripIcon, PlayIcon, PauseIcon } from 'lucide-react'
|
||||
|
||||
@@ -76,12 +77,14 @@ const LayoutsControl = () => {
|
||||
const [layout, setLayout] = useState<LayoutName>('Circular')
|
||||
const [opened, setOpened] = useState<boolean>(false)
|
||||
|
||||
const maxIterations = useSettingsStore.use.graphLayoutMaxIterations()
|
||||
|
||||
const layoutCircular = useLayoutCircular()
|
||||
const layoutCirclepack = useLayoutCirclepack()
|
||||
const layoutRandom = useLayoutRandom()
|
||||
const layoutNoverlap = useLayoutNoverlap({ settings: { margin: 1 } })
|
||||
const layoutForce = useLayoutForce({ maxIterations: 20 })
|
||||
const layoutForceAtlas2 = useLayoutForceAtlas2({ iterations: 20 })
|
||||
const layoutForce = useLayoutForce({ maxIterations: maxIterations })
|
||||
const layoutForceAtlas2 = useLayoutForceAtlas2({ iterations: maxIterations })
|
||||
const workerNoverlap = useWorkerLayoutNoverlap()
|
||||
const workerForce = useWorkerLayoutForce()
|
||||
const workerForceAtlas2 = useWorkerLayoutForceAtlas2()
|
||||
|
||||
@@ -1,9 +1,10 @@
|
||||
import { useState, useCallback, useEffect } from 'react'
|
||||
import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/Popover'
|
||||
import Checkbox from '@/components/ui/Checkbox'
|
||||
import Button from '@/components/ui/Button'
|
||||
import Separator from '@/components/ui/Separator'
|
||||
import Input from '@/components/ui/Input'
|
||||
import { useState, useCallback, useEffect } from 'react'
|
||||
|
||||
import { controlButtonVariant } from '@/lib/constants'
|
||||
import { useSettingsStore } from '@/stores/settings'
|
||||
import { useBackendState } from '@/stores/state'
|
||||
@@ -35,6 +36,74 @@ const LabeledCheckBox = ({
|
||||
)
|
||||
}
|
||||
|
||||
/**
|
||||
* Component that displays a number input with a label.
|
||||
*/
|
||||
const LabeledNumberInput = ({
|
||||
value,
|
||||
onEditFinished,
|
||||
label,
|
||||
min,
|
||||
max
|
||||
}: {
|
||||
value: number
|
||||
onEditFinished: (value: number) => void
|
||||
label: string
|
||||
min: number
|
||||
max?: number
|
||||
}) => {
|
||||
const [currentValue, setCurrentValue] = useState<number | null>(value)
|
||||
|
||||
const onValueChange = useCallback(
|
||||
(e: React.ChangeEvent<HTMLInputElement>) => {
|
||||
const text = e.target.value.trim()
|
||||
if (text.length === 0) {
|
||||
setCurrentValue(null)
|
||||
return
|
||||
}
|
||||
const newValue = Number.parseInt(text)
|
||||
if (!isNaN(newValue) && newValue !== currentValue) {
|
||||
if (min !== undefined && newValue < min) {
|
||||
return
|
||||
}
|
||||
if (max !== undefined && newValue > max) {
|
||||
return
|
||||
}
|
||||
setCurrentValue(newValue)
|
||||
}
|
||||
},
|
||||
[currentValue, min, max]
|
||||
)
|
||||
|
||||
const onBlur = useCallback(() => {
|
||||
if (currentValue !== null && value !== currentValue) {
|
||||
onEditFinished(currentValue)
|
||||
}
|
||||
}, [value, currentValue, onEditFinished])
|
||||
|
||||
return (
|
||||
<div className="flex flex-col gap-2">
|
||||
<label
|
||||
htmlFor="terms"
|
||||
className="text-sm leading-none font-medium peer-disabled:cursor-not-allowed peer-disabled:opacity-70"
|
||||
>
|
||||
{label}
|
||||
</label>
|
||||
<Input
|
||||
value={currentValue || ''}
|
||||
onChange={onValueChange}
|
||||
className="h-6 w-full min-w-0"
|
||||
onBlur={onBlur}
|
||||
onKeyDown={(e) => {
|
||||
if (e.key === 'Enter') {
|
||||
onBlur()
|
||||
}
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
/**
|
||||
* Component that displays a popover with settings options.
|
||||
*/
|
||||
@@ -45,11 +114,12 @@ export default function Settings() {
|
||||
const showPropertyPanel = useSettingsStore.use.showPropertyPanel()
|
||||
const showNodeSearchBar = useSettingsStore.use.showNodeSearchBar()
|
||||
const showNodeLabel = useSettingsStore.use.showNodeLabel()
|
||||
|
||||
const enableEdgeEvents = useSettingsStore.use.enableEdgeEvents()
|
||||
const enableNodeDrag = useSettingsStore.use.enableNodeDrag()
|
||||
const enableHideUnselectedEdges = useSettingsStore.use.enableHideUnselectedEdges()
|
||||
const showEdgeLabel = useSettingsStore.use.showEdgeLabel()
|
||||
const graphQueryMaxDepth = useSettingsStore.use.graphQueryMaxDepth()
|
||||
const graphLayoutMaxIterations = useSettingsStore.use.graphLayoutMaxIterations()
|
||||
|
||||
const enableHealthCheck = useSettingsStore.use.enableHealthCheck()
|
||||
const apiKey = useSettingsStore.use.apiKey()
|
||||
@@ -102,6 +172,16 @@ export default function Settings() {
|
||||
[]
|
||||
)
|
||||
|
||||
const setGraphQueryMaxDepth = useCallback((depth: number) => {
|
||||
if (depth < 1) return
|
||||
useSettingsStore.setState({ graphQueryMaxDepth: depth })
|
||||
}, [])
|
||||
|
||||
const setGraphLayoutMaxIterations = useCallback((iterations: number) => {
|
||||
if (iterations < 1) return
|
||||
useSettingsStore.setState({ graphLayoutMaxIterations: iterations })
|
||||
}, [])
|
||||
|
||||
const setApiKey = useCallback(async () => {
|
||||
useSettingsStore.setState({ apiKey: tempApiKey || null })
|
||||
await useBackendState.getState().check()
|
||||
@@ -129,6 +209,14 @@ export default function Settings() {
|
||||
onCloseAutoFocus={(e) => e.preventDefault()}
|
||||
>
|
||||
<div className="flex flex-col gap-2">
|
||||
<LabeledCheckBox
|
||||
checked={enableHealthCheck}
|
||||
onCheckedChange={setEnableHealthCheck}
|
||||
label="Health Check"
|
||||
/>
|
||||
|
||||
<Separator />
|
||||
|
||||
<LabeledCheckBox
|
||||
checked={showPropertyPanel}
|
||||
onCheckedChange={setShowPropertyPanel}
|
||||
@@ -172,11 +260,18 @@ export default function Settings() {
|
||||
/>
|
||||
|
||||
<Separator />
|
||||
|
||||
<LabeledCheckBox
|
||||
checked={enableHealthCheck}
|
||||
onCheckedChange={setEnableHealthCheck}
|
||||
label="Health Check"
|
||||
<LabeledNumberInput
|
||||
label="Max Query Depth"
|
||||
min={1}
|
||||
value={graphQueryMaxDepth}
|
||||
onEditFinished={setGraphQueryMaxDepth}
|
||||
/>
|
||||
<LabeledNumberInput
|
||||
label="Max Layout Iterations"
|
||||
min={1}
|
||||
max={20}
|
||||
value={graphLayoutMaxIterations}
|
||||
onEditFinished={setGraphLayoutMaxIterations}
|
||||
/>
|
||||
|
||||
<Separator />
|
||||
|
||||
112
lightrag_webui/src/components/retrieval/ChatMessage.tsx
Normal file
112
lightrag_webui/src/components/retrieval/ChatMessage.tsx
Normal file
@@ -0,0 +1,112 @@
|
||||
import { ReactNode, useCallback } from 'react'
|
||||
import { Message } from '@/api/lightrag'
|
||||
import useTheme from '@/hooks/useTheme'
|
||||
import Button from '@/components/ui/Button'
|
||||
import { cn } from '@/lib/utils'
|
||||
|
||||
import ReactMarkdown from 'react-markdown'
|
||||
import remarkGfm from 'remark-gfm'
|
||||
import rehypeReact from 'rehype-react'
|
||||
import remarkMath from 'remark-math'
|
||||
|
||||
import type { Element } from 'hast'
|
||||
|
||||
import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter'
|
||||
import { oneLight, oneDark } from 'react-syntax-highlighter/dist/cjs/styles/prism'
|
||||
|
||||
import { LoaderIcon, CopyIcon } from 'lucide-react'
|
||||
|
||||
export type MessageWithError = Message & {
|
||||
isError?: boolean
|
||||
}
|
||||
|
||||
export const ChatMessage = ({ message }: { message: MessageWithError }) => {
|
||||
const handleCopyMarkdown = useCallback(async () => {
|
||||
if (message.content) {
|
||||
try {
|
||||
await navigator.clipboard.writeText(message.content)
|
||||
} catch (err) {
|
||||
console.error('Failed to copy:', err)
|
||||
}
|
||||
}
|
||||
}, [message])
|
||||
|
||||
return (
|
||||
<div
|
||||
className={`max-w-[80%] rounded-lg px-4 py-2 ${
|
||||
message.role === 'user'
|
||||
? 'bg-primary text-primary-foreground'
|
||||
: message.isError
|
||||
? 'bg-red-100 text-red-600 dark:bg-red-950 dark:text-red-400'
|
||||
: 'bg-muted'
|
||||
}`}
|
||||
>
|
||||
<pre className="relative break-words whitespace-pre-wrap">
|
||||
<ReactMarkdown
|
||||
className="dark:prose-invert max-w-none text-base text-sm"
|
||||
remarkPlugins={[remarkGfm, remarkMath]}
|
||||
rehypePlugins={[rehypeReact]}
|
||||
skipHtml={false}
|
||||
components={{
|
||||
code: CodeHighlight
|
||||
}}
|
||||
>
|
||||
{message.content}
|
||||
</ReactMarkdown>
|
||||
{message.role === 'assistant' && message.content.length > 0 && (
|
||||
<Button
|
||||
onClick={handleCopyMarkdown}
|
||||
className="absolute right-0 bottom-0 size-6 rounded-md opacity-20 transition-opacity hover:opacity-100"
|
||||
tooltip="Copy to clipboard"
|
||||
variant="default"
|
||||
size="icon"
|
||||
>
|
||||
<CopyIcon />
|
||||
</Button>
|
||||
)}
|
||||
</pre>
|
||||
{message.content.length === 0 && <LoaderIcon className="animate-spin duration-2000" />}
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
interface CodeHighlightProps {
|
||||
inline?: boolean
|
||||
className?: string
|
||||
children?: ReactNode
|
||||
node?: Element
|
||||
}
|
||||
|
||||
const isInlineCode = (node: Element): boolean => {
|
||||
const textContent = (node.children || [])
|
||||
.filter((child) => child.type === 'text')
|
||||
.map((child) => (child as any).value)
|
||||
.join('')
|
||||
|
||||
return !textContent.includes('\n')
|
||||
}
|
||||
|
||||
const CodeHighlight = ({ className, children, node, ...props }: CodeHighlightProps) => {
|
||||
const { theme } = useTheme()
|
||||
const match = className?.match(/language-(\w+)/)
|
||||
const language = match ? match[1] : undefined
|
||||
const inline = node ? isInlineCode(node) : false
|
||||
|
||||
return !inline ? (
|
||||
<SyntaxHighlighter
|
||||
style={theme === 'dark' ? oneDark : oneLight}
|
||||
PreTag="div"
|
||||
language={language}
|
||||
{...props}
|
||||
>
|
||||
{String(children).replace(/\n$/, '')}
|
||||
</SyntaxHighlighter>
|
||||
) : (
|
||||
<code
|
||||
className={cn(className, 'mx-1 rounded-xs bg-black/10 px-1 dark:bg-gray-100/20')}
|
||||
{...props}
|
||||
>
|
||||
{children}
|
||||
</code>
|
||||
)
|
||||
}
|
||||
@@ -247,7 +247,7 @@ function FileUploader(props: FileUploaderProps) {
|
||||
? ` ${maxFileCount === Infinity ? 'multiple' : maxFileCount}
|
||||
files (up to ${formatBytes(maxSize)} each)`
|
||||
: ` a file with ${formatBytes(maxSize)}`}
|
||||
Supported formats: TXT, MD, DOC, PDF, PPTX
|
||||
Supported formats: TXT, MD, DOCX, PDF, PPTX, RTF, ODT, EPUB, HTML, HTM, TEX, JSON, XML, YAML, YML, CSV, LOG, CONF, INI, PROPERTIES, SQL, BAT, SH, C, CPP, PY, JAVA, JS, TS, SWIFT, GO, RB, PHP, CSS, SCSS, LESS
|
||||
</p>
|
||||
)}
|
||||
</div>
|
||||
|
||||
@@ -1,38 +1,16 @@
|
||||
import Input from '@/components/ui/Input'
|
||||
import Button from '@/components/ui/Button'
|
||||
import { useCallback, useEffect, useRef, useState } from 'react'
|
||||
import { queryText, queryTextStream, Message as ChatMessage } from '@/api/lightrag'
|
||||
import { queryText, queryTextStream, Message } from '@/api/lightrag'
|
||||
import { errorMessage } from '@/lib/utils'
|
||||
import { useSettingsStore } from '@/stores/settings'
|
||||
import { useDebounce } from '@/hooks/useDebounce'
|
||||
import QuerySettings from '@/components/retrieval/QuerySettings'
|
||||
|
||||
import ReactMarkdown from 'react-markdown'
|
||||
import remarkGfm from 'remark-gfm'
|
||||
import rehypeReact from 'rehype-react'
|
||||
import remarkMath from 'remark-math'
|
||||
|
||||
import { EraserIcon, SendIcon, LoaderIcon } from 'lucide-react'
|
||||
|
||||
type Message = ChatMessage & {
|
||||
isError?: boolean
|
||||
}
|
||||
|
||||
const ChatMessageComponent = ({ message }: { message: Message }) => {
|
||||
return (
|
||||
<ReactMarkdown
|
||||
className="prose lg:prose-xs dark:prose-invert max-w-none text-base"
|
||||
remarkPlugins={[remarkGfm, remarkMath]}
|
||||
rehypePlugins={[rehypeReact]}
|
||||
skipHtml={false}
|
||||
>
|
||||
{message.content}
|
||||
</ReactMarkdown>
|
||||
)
|
||||
}
|
||||
import { ChatMessage, MessageWithError } from '@/components/retrieval/ChatMessage'
|
||||
import { EraserIcon, SendIcon } from 'lucide-react'
|
||||
|
||||
export default function RetrievalTesting() {
|
||||
const [messages, setMessages] = useState<Message[]>(
|
||||
const [messages, setMessages] = useState<MessageWithError[]>(
|
||||
() => useSettingsStore.getState().retrievalHistory || []
|
||||
)
|
||||
const [inputValue, setInputValue] = useState('')
|
||||
@@ -147,22 +125,7 @@ export default function RetrievalTesting() {
|
||||
key={idx}
|
||||
className={`flex ${message.role === 'user' ? 'justify-end' : 'justify-start'}`}
|
||||
>
|
||||
<div
|
||||
className={`max-w-[80%] rounded-lg px-4 py-2 ${
|
||||
message.role === 'user'
|
||||
? 'bg-primary text-primary-foreground'
|
||||
: message.isError
|
||||
? 'bg-red-100 text-red-600 dark:bg-red-950 dark:text-red-400'
|
||||
: 'bg-muted'
|
||||
}`}
|
||||
>
|
||||
<pre className="break-words whitespace-pre-wrap">
|
||||
{<ChatMessageComponent message={message} />}
|
||||
</pre>
|
||||
{message.content.length === 0 && (
|
||||
<LoaderIcon className="animate-spin duration-2000" />
|
||||
)}
|
||||
</div>
|
||||
{<ChatMessage message={message} />}
|
||||
</div>
|
||||
))
|
||||
)}
|
||||
|
||||
@@ -55,6 +55,7 @@ export default function SiteHeader() {
|
||||
<header className="border-border/40 bg-background/95 supports-[backdrop-filter]:bg-background/60 sticky top-0 z-50 flex h-10 w-full border-b px-4 backdrop-blur">
|
||||
<a href="/" className="mr-6 flex items-center gap-2">
|
||||
<ZapIcon className="size-4 text-emerald-400" aria-hidden="true" />
|
||||
{/* <img src='/logo.png' className="size-4" /> */}
|
||||
<span className="font-bold md:inline-block">{SiteInfo.name}</span>
|
||||
</a>
|
||||
|
||||
|
||||
@@ -50,11 +50,11 @@ export type NodeType = {
|
||||
}
|
||||
export type EdgeType = { label: string }
|
||||
|
||||
const fetchGraph = async (label: string) => {
|
||||
const fetchGraph = async (label: string, maxDepth: number) => {
|
||||
let rawData: any = null
|
||||
|
||||
try {
|
||||
rawData = await queryGraphs(label)
|
||||
rawData = await queryGraphs(label, maxDepth)
|
||||
} catch (e) {
|
||||
useBackendState.getState().setErrorMessage(errorMessage(e), 'Query Graphs Error!')
|
||||
return null
|
||||
@@ -161,12 +161,13 @@ const createSigmaGraph = (rawGraph: RawGraph | null) => {
|
||||
return graph
|
||||
}
|
||||
|
||||
const lastQueryLabel = { label: '' }
|
||||
const lastQueryLabel = { label: '', maxQueryDepth: 0 }
|
||||
|
||||
const useLightrangeGraph = () => {
|
||||
const queryLabel = useSettingsStore.use.queryLabel()
|
||||
const rawGraph = useGraphStore.use.rawGraph()
|
||||
const sigmaGraph = useGraphStore.use.sigmaGraph()
|
||||
const maxQueryDepth = useSettingsStore.use.graphQueryMaxDepth()
|
||||
|
||||
const getNode = useCallback(
|
||||
(nodeId: string) => {
|
||||
@@ -184,11 +185,13 @@ const useLightrangeGraph = () => {
|
||||
|
||||
useEffect(() => {
|
||||
if (queryLabel) {
|
||||
if (lastQueryLabel.label !== queryLabel) {
|
||||
if (lastQueryLabel.label !== queryLabel || lastQueryLabel.maxQueryDepth !== maxQueryDepth) {
|
||||
lastQueryLabel.label = queryLabel
|
||||
lastQueryLabel.maxQueryDepth = maxQueryDepth
|
||||
|
||||
const state = useGraphStore.getState()
|
||||
state.reset()
|
||||
fetchGraph(queryLabel).then((data) => {
|
||||
fetchGraph(queryLabel, maxQueryDepth).then((data) => {
|
||||
// console.debug('Query label: ' + queryLabel)
|
||||
state.setSigmaGraph(createSigmaGraph(data))
|
||||
data?.buildDynamicMap()
|
||||
@@ -200,7 +203,7 @@ const useLightrangeGraph = () => {
|
||||
state.reset()
|
||||
state.setSigmaGraph(new DirectedGraph())
|
||||
}
|
||||
}, [queryLabel])
|
||||
}, [queryLabel, maxQueryDepth])
|
||||
|
||||
const lightrageGraph = useCallback(() => {
|
||||
if (sigmaGraph) {
|
||||
|
||||
@@ -16,6 +16,7 @@ export const edgeColorSelected = '#F57F17'
|
||||
export const edgeColorHighlighted = '#B2EBF2'
|
||||
|
||||
export const searchResultLimit = 20
|
||||
export const labelListLimit = 40
|
||||
|
||||
export const minNodeSize = 4
|
||||
export const maxNodeSize = 20
|
||||
@@ -26,7 +27,41 @@ export const defaultQueryLabel = '*'
|
||||
|
||||
// reference: https://developer.mozilla.org/en-US/docs/Web/HTTP/MIME_types/Common_types
|
||||
export const supportedFileTypes = {
|
||||
'text/plain': ['.txt', '.md'],
|
||||
'text/plain': [
|
||||
'.txt',
|
||||
'.md',
|
||||
'.html',
|
||||
'.htm',
|
||||
'.tex',
|
||||
'.json',
|
||||
'.xml',
|
||||
'.yaml',
|
||||
'.yml',
|
||||
'.rtf',
|
||||
'.odt',
|
||||
'.epub',
|
||||
'.csv',
|
||||
'.log',
|
||||
'.conf',
|
||||
'.ini',
|
||||
'.properties',
|
||||
'.sql',
|
||||
'.bat',
|
||||
'.sh',
|
||||
'.c',
|
||||
'.cpp',
|
||||
'.py',
|
||||
'.java',
|
||||
'.js',
|
||||
'.ts',
|
||||
'.swift',
|
||||
'.go',
|
||||
'.rb',
|
||||
'.php',
|
||||
'.css',
|
||||
'.scss',
|
||||
'.less'
|
||||
],
|
||||
'application/pdf': ['.pdf'],
|
||||
'application/msword': ['.doc'],
|
||||
'application/vnd.openxmlformats-officedocument.wordprocessingml.document': ['.docx'],
|
||||
|
||||
@@ -8,9 +8,7 @@ type Theme = 'dark' | 'light' | 'system'
|
||||
type Tab = 'documents' | 'knowledge-graph' | 'retrieval' | 'api'
|
||||
|
||||
interface SettingsState {
|
||||
theme: Theme
|
||||
setTheme: (theme: Theme) => void
|
||||
|
||||
// Graph viewer settings
|
||||
showPropertyPanel: boolean
|
||||
showNodeSearchBar: boolean
|
||||
|
||||
@@ -21,23 +19,35 @@ interface SettingsState {
|
||||
enableHideUnselectedEdges: boolean
|
||||
enableEdgeEvents: boolean
|
||||
|
||||
graphQueryMaxDepth: number
|
||||
setGraphQueryMaxDepth: (depth: number) => void
|
||||
|
||||
graphLayoutMaxIterations: number
|
||||
setGraphLayoutMaxIterations: (iterations: number) => void
|
||||
|
||||
// Retrieval settings
|
||||
queryLabel: string
|
||||
setQueryLabel: (queryLabel: string) => void
|
||||
|
||||
enableHealthCheck: boolean
|
||||
setEnableHealthCheck: (enable: boolean) => void
|
||||
|
||||
apiKey: string | null
|
||||
setApiKey: (key: string | null) => void
|
||||
|
||||
currentTab: Tab
|
||||
setCurrentTab: (tab: Tab) => void
|
||||
|
||||
retrievalHistory: Message[]
|
||||
setRetrievalHistory: (history: Message[]) => void
|
||||
|
||||
querySettings: Omit<QueryRequest, 'query'>
|
||||
updateQuerySettings: (settings: Partial<QueryRequest>) => void
|
||||
|
||||
// Auth settings
|
||||
apiKey: string | null
|
||||
setApiKey: (key: string | null) => void
|
||||
|
||||
// App settings
|
||||
theme: Theme
|
||||
setTheme: (theme: Theme) => void
|
||||
|
||||
enableHealthCheck: boolean
|
||||
setEnableHealthCheck: (enable: boolean) => void
|
||||
|
||||
currentTab: Tab
|
||||
setCurrentTab: (tab: Tab) => void
|
||||
}
|
||||
|
||||
const useSettingsStoreBase = create<SettingsState>()(
|
||||
@@ -55,7 +65,11 @@ const useSettingsStoreBase = create<SettingsState>()(
|
||||
enableHideUnselectedEdges: true,
|
||||
enableEdgeEvents: false,
|
||||
|
||||
graphQueryMaxDepth: 3,
|
||||
graphLayoutMaxIterations: 10,
|
||||
|
||||
queryLabel: defaultQueryLabel,
|
||||
|
||||
enableHealthCheck: true,
|
||||
|
||||
apiKey: null,
|
||||
@@ -81,11 +95,18 @@ const useSettingsStoreBase = create<SettingsState>()(
|
||||
|
||||
setTheme: (theme: Theme) => set({ theme }),
|
||||
|
||||
setGraphLayoutMaxIterations: (iterations: number) =>
|
||||
set({
|
||||
graphLayoutMaxIterations: iterations
|
||||
}),
|
||||
|
||||
setQueryLabel: (queryLabel: string) =>
|
||||
set({
|
||||
queryLabel
|
||||
}),
|
||||
|
||||
setGraphQueryMaxDepth: (depth: number) => set({ graphQueryMaxDepth: depth }),
|
||||
|
||||
setEnableHealthCheck: (enable: boolean) => set({ enableHealthCheck: enable }),
|
||||
|
||||
setApiKey: (apiKey: string | null) => set({ apiKey }),
|
||||
@@ -102,7 +123,7 @@ const useSettingsStoreBase = create<SettingsState>()(
|
||||
{
|
||||
name: 'settings-storage',
|
||||
storage: createJSONStorage(() => localStorage),
|
||||
version: 6,
|
||||
version: 7,
|
||||
migrate: (state: any, version: number) => {
|
||||
if (version < 2) {
|
||||
state.showEdgeLabel = false
|
||||
@@ -137,6 +158,10 @@ const useSettingsStoreBase = create<SettingsState>()(
|
||||
}
|
||||
state.retrievalHistory = []
|
||||
}
|
||||
if (version < 7) {
|
||||
state.graphQueryMaxDepth = 3
|
||||
state.graphLayoutMaxIterations = 10
|
||||
}
|
||||
return state
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
aiohttp
|
||||
configparser
|
||||
|
||||
# database packages
|
||||
networkx
|
||||
future
|
||||
|
||||
# Basic modules
|
||||
numpy
|
||||
|
||||
Reference in New Issue
Block a user