Merge branch 'main' into main
This commit is contained in:
wiltshirek
GitHub
30
.github/workflows/linting.yaml
vendored
Normal file
30
.github/workflows/linting.yaml
vendored
Normal file
@@ -0,0 +1,30 @@
|
||||
name: Linting and Formatting
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
pull_request:
|
||||
branches:
|
||||
- main
|
||||
|
||||
jobs:
|
||||
lint-and-format:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v2
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v2
|
||||
with:
|
||||
python-version: '3.x'
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install pre-commit
|
||||
|
||||
- name: Run pre-commit
|
||||
run: pre-commit run --all-files
|
||||
3
.gitignore
vendored
3
.gitignore
vendored
@@ -8,4 +8,5 @@ dist/
|
||||
env/
|
||||
local_neo4jWorkDir/
|
||||
neo4jWorkDir/
|
||||
ignore_this.txt
|
||||
ignore_this.txt
|
||||
.venv/
|
||||
|
||||
227
README.md
227
README.md
@@ -8,7 +8,7 @@
|
||||
<a href='https://lightrag.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
|
||||
<a href='https://youtu.be/oageL-1I0GE'><img src='https://badges.aleen42.com/src/youtube.svg'></a>
|
||||
<a href='https://arxiv.org/abs/2410.05779'><img src='https://img.shields.io/badge/arXiv-2410.05779-b31b1b'></a>
|
||||
<a href='https://discord.gg/mvsfu2Tg'><img src='https://discordapp.com/api/guilds/1296348098003734629/widget.png?style=shield'></a>
|
||||
<a href='https://discord.gg/rdE8YVPm'><img src='https://discordapp.com/api/guilds/1296348098003734629/widget.png?style=shield'></a>
|
||||
</p>
|
||||
<p>
|
||||
<img src='https://img.shields.io/github/stars/hkuds/lightrag?color=green&style=social' />
|
||||
@@ -22,11 +22,17 @@ This repository hosts the code of LightRAG. The structure of this code is based
|
||||
</div>
|
||||
|
||||
## 🎉 News
|
||||
- [x] [2024.10.20]🎯🎯📢📢We’ve added a new feature to LightRAG: Graph Visualization.
|
||||
- [x] [2024.10.18]🎯🎯📢📢We’ve added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
|
||||
- [x] [2024.10.17]🎯🎯📢📢We have created a [Discord channel](https://discord.gg/mvsfu2Tg)! Welcome to join for sharing and discussions! 🎉🎉
|
||||
- [x] [2024.10.16]🎯🎯📢📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
|
||||
- [x] [2024.10.15]🎯🎯📢📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
|
||||
- [x] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
|
||||
- [x] [2024.10.20]🎯📢We’ve added a new feature to LightRAG: Graph Visualization.
|
||||
- [x] [2024.10.18]🎯📢We’ve added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
|
||||
- [x] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/mvsfu2Tg)! Welcome to join for sharing and discussions! 🎉🎉
|
||||
- [x] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
|
||||
- [x] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
|
||||
|
||||
## Algorithm Flowchart
|
||||
|
||||
|
||||
|
||||
|
||||
## Install
|
||||
|
||||
@@ -58,8 +64,8 @@ from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
|
||||
|
||||
#########
|
||||
# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
|
||||
# import nest_asyncio
|
||||
# nest_asyncio.apply()
|
||||
# import nest_asyncio
|
||||
# nest_asyncio.apply()
|
||||
#########
|
||||
|
||||
WORKING_DIR = "./dickens"
|
||||
@@ -190,8 +196,11 @@ see test_neo4j.py for a working example.
|
||||
|
||||
<details>
|
||||
<summary> Using Ollama Models </summary>
|
||||
|
||||
* If you want to use Ollama models, you only need to set LightRAG as follows:
|
||||
|
||||
### Overview
|
||||
If you want to use Ollama models, you need to pull model you plan to use and embedding model, for example `nomic-embed-text`.
|
||||
|
||||
Then you only need to set LightRAG as follows:
|
||||
|
||||
```python
|
||||
from lightrag.llm import ollama_model_complete, ollama_embedding
|
||||
@@ -213,28 +222,59 @@ rag = LightRAG(
|
||||
)
|
||||
```
|
||||
|
||||
* Increasing the `num_ctx` parameter:
|
||||
### Increasing context size
|
||||
In order for LightRAG to work context should be at least 32k tokens. By default Ollama models have context size of 8k. You can achieve this using one of two ways:
|
||||
|
||||
#### Increasing the `num_ctx` parameter in Modelfile.
|
||||
|
||||
1. Pull the model:
|
||||
```python
|
||||
```bash
|
||||
ollama pull qwen2
|
||||
```
|
||||
|
||||
2. Display the model file:
|
||||
```python
|
||||
```bash
|
||||
ollama show --modelfile qwen2 > Modelfile
|
||||
```
|
||||
|
||||
3. Edit the Modelfile by adding the following line:
|
||||
```python
|
||||
```bash
|
||||
PARAMETER num_ctx 32768
|
||||
```
|
||||
|
||||
4. Create the modified model:
|
||||
```python
|
||||
```bash
|
||||
ollama create -f Modelfile qwen2m
|
||||
```
|
||||
|
||||
#### Setup `num_ctx` via Ollama API.
|
||||
Tiy can use `llm_model_kwargs` param to configure ollama:
|
||||
|
||||
```python
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=ollama_model_complete, # Use Ollama model for text generation
|
||||
llm_model_name='your_model_name', # Your model name
|
||||
llm_model_kwargs={"options": {"num_ctx": 32768}},
|
||||
# Use Ollama embedding function
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=768,
|
||||
max_token_size=8192,
|
||||
func=lambda texts: ollama_embedding(
|
||||
texts,
|
||||
embed_model="nomic-embed-text"
|
||||
)
|
||||
),
|
||||
)
|
||||
```
|
||||
#### Fully functional example
|
||||
|
||||
There fully functional example `examples/lightrag_ollama_demo.py` that utilizes `gemma2:2b` model, runs only 4 requests in parallel and set context size to 32k.
|
||||
|
||||
#### Low RAM GPUs
|
||||
|
||||
In order to run this experiment on low RAM GPU you should select small model and tune context window (increasing context increase memory consumption). For example, running this ollama example on repurposed mining GPU with 6Gb of RAM required to set context size to 26k while using `gemma2:2b`. It was able to find 197 entities and 19 relations on `book.txt`.
|
||||
|
||||
</details>
|
||||
|
||||
### Query Param
|
||||
@@ -265,12 +305,33 @@ rag.insert(["TEXT1", "TEXT2",...])
|
||||
|
||||
```python
|
||||
# Incremental Insert: Insert new documents into an existing LightRAG instance
|
||||
rag = LightRAG(working_dir="./dickens")
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=llm_model_func,
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=embedding_dimension,
|
||||
max_token_size=8192,
|
||||
func=embedding_func,
|
||||
),
|
||||
)
|
||||
|
||||
with open("./newText.txt") as f:
|
||||
rag.insert(f.read())
|
||||
```
|
||||
|
||||
### Multi-file Type Support
|
||||
|
||||
The `testract` supports reading file types such as TXT, DOCX, PPTX, CSV, and PDF.
|
||||
|
||||
```python
|
||||
import textract
|
||||
|
||||
file_path = 'TEXT.pdf'
|
||||
text_content = textract.process(file_path)
|
||||
|
||||
rag.insert(text_content.decode('utf-8'))
|
||||
```
|
||||
|
||||
### Graph Visualization
|
||||
|
||||
<details>
|
||||
@@ -361,8 +422,8 @@ def main():
|
||||
SET e.entity_type = node.entity_type,
|
||||
e.description = node.description,
|
||||
e.source_id = node.source_id,
|
||||
e.displayName = node.id
|
||||
REMOVE e:Entity
|
||||
e.displayName = node.id
|
||||
REMOVE e:Entity
|
||||
WITH e, node
|
||||
CALL apoc.create.addLabels(e, [node.entity_type]) YIELD node AS labeledNode
|
||||
RETURN count(*)
|
||||
@@ -415,7 +476,7 @@ def main():
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
|
||||
|
||||
finally:
|
||||
driver.close()
|
||||
|
||||
@@ -425,6 +486,125 @@ if __name__ == "__main__":
|
||||
|
||||
</details>
|
||||
|
||||
## API Server Implementation
|
||||
|
||||
LightRAG also provides a FastAPI-based server implementation for RESTful API access to RAG operations. This allows you to run LightRAG as a service and interact with it through HTTP requests.
|
||||
|
||||
### Setting up the API Server
|
||||
<details>
|
||||
<summary>Click to expand setup instructions</summary>
|
||||
|
||||
1. First, ensure you have the required dependencies:
|
||||
```bash
|
||||
pip install fastapi uvicorn pydantic
|
||||
```
|
||||
|
||||
2. Set up your environment variables:
|
||||
```bash
|
||||
export RAG_DIR="your_index_directory" # Optional: Defaults to "index_default"
|
||||
```
|
||||
|
||||
3. Run the API server:
|
||||
```bash
|
||||
python examples/lightrag_api_openai_compatible_demo.py
|
||||
```
|
||||
|
||||
The server will start on `http://0.0.0.0:8020`.
|
||||
</details>
|
||||
|
||||
### API Endpoints
|
||||
|
||||
The API server provides the following endpoints:
|
||||
|
||||
#### 1. Query Endpoint
|
||||
<details>
|
||||
<summary>Click to view Query endpoint details</summary>
|
||||
|
||||
- **URL:** `/query`
|
||||
- **Method:** POST
|
||||
- **Body:**
|
||||
```json
|
||||
{
|
||||
"query": "Your question here",
|
||||
"mode": "hybrid" // Can be "naive", "local", "global", or "hybrid"
|
||||
}
|
||||
```
|
||||
- **Example:**
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8020/query" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"query": "What are the main themes?", "mode": "hybrid"}'
|
||||
```
|
||||
</details>
|
||||
|
||||
#### 2. Insert Text Endpoint
|
||||
<details>
|
||||
<summary>Click to view Insert Text endpoint details</summary>
|
||||
|
||||
- **URL:** `/insert`
|
||||
- **Method:** POST
|
||||
- **Body:**
|
||||
```json
|
||||
{
|
||||
"text": "Your text content here"
|
||||
}
|
||||
```
|
||||
- **Example:**
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8020/insert" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"text": "Content to be inserted into RAG"}'
|
||||
```
|
||||
</details>
|
||||
|
||||
#### 3. Insert File Endpoint
|
||||
<details>
|
||||
<summary>Click to view Insert File endpoint details</summary>
|
||||
|
||||
- **URL:** `/insert_file`
|
||||
- **Method:** POST
|
||||
- **Body:**
|
||||
```json
|
||||
{
|
||||
"file_path": "path/to/your/file.txt"
|
||||
}
|
||||
```
|
||||
- **Example:**
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8020/insert_file" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"file_path": "./book.txt"}'
|
||||
```
|
||||
</details>
|
||||
|
||||
#### 4. Health Check Endpoint
|
||||
<details>
|
||||
<summary>Click to view Health Check endpoint details</summary>
|
||||
|
||||
- **URL:** `/health`
|
||||
- **Method:** GET
|
||||
- **Example:**
|
||||
```bash
|
||||
curl -X GET "http://127.0.0.1:8020/health"
|
||||
```
|
||||
</details>
|
||||
|
||||
### Configuration
|
||||
|
||||
The API server can be configured using environment variables:
|
||||
- `RAG_DIR`: Directory for storing the RAG index (default: "index_default")
|
||||
- API keys and base URLs should be configured in the code for your specific LLM and embedding model providers
|
||||
|
||||
### Error Handling
|
||||
<details>
|
||||
<summary>Click to view error handling details</summary>
|
||||
|
||||
The API includes comprehensive error handling:
|
||||
- File not found errors (404)
|
||||
- Processing errors (500)
|
||||
- Supports multiple file encodings (UTF-8 and GBK)
|
||||
</details>
|
||||
|
||||
## Evaluation
|
||||
### Dataset
|
||||
The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain).
|
||||
@@ -671,12 +851,14 @@ def extract_queries(file_path):
|
||||
.
|
||||
├── examples
|
||||
│ ├── batch_eval.py
|
||||
│ ├── generate_query.py
|
||||
│ ├── graph_visual_with_html.py
|
||||
│ ├── graph_visual_with_neo4j.py
|
||||
│ ├── generate_query.py
|
||||
│ ├── lightrag_api_openai_compatible_demo.py
|
||||
│ ├── lightrag_azure_openai_demo.py
|
||||
│ ├── lightrag_bedrock_demo.py
|
||||
│ ├── lightrag_hf_demo.py
|
||||
│ ├── lightrag_lmdeploy_demo.py
|
||||
│ ├── lightrag_ollama_demo.py
|
||||
│ ├── lightrag_openai_compatible_demo.py
|
||||
│ ├── lightrag_openai_demo.py
|
||||
@@ -693,8 +875,10 @@ def extract_queries(file_path):
|
||||
│ └── utils.py
|
||||
├── reproduce
|
||||
│ ├── Step_0.py
|
||||
│ ├── Step_1_openai_compatible.py
|
||||
│ ├── Step_1.py
|
||||
│ ├── Step_2.py
|
||||
│ ├── Step_3_openai_compatible.py
|
||||
│ └── Step_3.py
|
||||
├── .gitignore
|
||||
├── .pre-commit-config.yaml
|
||||
@@ -726,3 +910,6 @@ archivePrefix={arXiv},
|
||||
primaryClass={cs.IR}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -3,17 +3,17 @@ from pyvis.network import Network
|
||||
import random
|
||||
|
||||
# Load the GraphML file
|
||||
G = nx.read_graphml('./dickens/graph_chunk_entity_relation.graphml')
|
||||
G = nx.read_graphml("./dickens/graph_chunk_entity_relation.graphml")
|
||||
|
||||
# Create a Pyvis network
|
||||
net = Network(notebook=True)
|
||||
net = Network(height="100vh", notebook=True)
|
||||
|
||||
# Convert NetworkX graph to Pyvis network
|
||||
net.from_nx(G)
|
||||
|
||||
# Add colors to nodes
|
||||
for node in net.nodes:
|
||||
node['color'] = "#{:06x}".format(random.randint(0, 0xFFFFFF))
|
||||
node["color"] = "#{:06x}".format(random.randint(0, 0xFFFFFF))
|
||||
|
||||
# Save and display the network
|
||||
net.show('knowledge_graph.html')
|
||||
net.show("knowledge_graph.html")
|
||||
|
||||
@@ -13,6 +13,7 @@ NEO4J_URI = "bolt://localhost:7687"
|
||||
NEO4J_USERNAME = "neo4j"
|
||||
NEO4J_PASSWORD = "your_password"
|
||||
|
||||
|
||||
def convert_xml_to_json(xml_path, output_path):
|
||||
"""Converts XML file to JSON and saves the output."""
|
||||
if not os.path.exists(xml_path):
|
||||
@@ -21,7 +22,7 @@ def convert_xml_to_json(xml_path, output_path):
|
||||
|
||||
json_data = xml_to_json(xml_path)
|
||||
if json_data:
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
with open(output_path, "w", encoding="utf-8") as f:
|
||||
json.dump(json_data, f, ensure_ascii=False, indent=2)
|
||||
print(f"JSON file created: {output_path}")
|
||||
return json_data
|
||||
@@ -29,16 +30,18 @@ def convert_xml_to_json(xml_path, output_path):
|
||||
print("Failed to create JSON data")
|
||||
return None
|
||||
|
||||
|
||||
def process_in_batches(tx, query, data, batch_size):
|
||||
"""Process data in batches and execute the given query."""
|
||||
for i in range(0, len(data), batch_size):
|
||||
batch = data[i:i + batch_size]
|
||||
batch = data[i : i + batch_size]
|
||||
tx.run(query, {"nodes": batch} if "nodes" in query else {"edges": batch})
|
||||
|
||||
|
||||
def main():
|
||||
# Paths
|
||||
xml_file = os.path.join(WORKING_DIR, 'graph_chunk_entity_relation.graphml')
|
||||
json_file = os.path.join(WORKING_DIR, 'graph_data.json')
|
||||
xml_file = os.path.join(WORKING_DIR, "graph_chunk_entity_relation.graphml")
|
||||
json_file = os.path.join(WORKING_DIR, "graph_data.json")
|
||||
|
||||
# Convert XML to JSON
|
||||
json_data = convert_xml_to_json(xml_file, json_file)
|
||||
@@ -46,8 +49,8 @@ def main():
|
||||
return
|
||||
|
||||
# Load nodes and edges
|
||||
nodes = json_data.get('nodes', [])
|
||||
edges = json_data.get('edges', [])
|
||||
nodes = json_data.get("nodes", [])
|
||||
edges = json_data.get("edges", [])
|
||||
|
||||
# Neo4j queries
|
||||
create_nodes_query = """
|
||||
@@ -56,8 +59,8 @@ def main():
|
||||
SET e.entity_type = node.entity_type,
|
||||
e.description = node.description,
|
||||
e.source_id = node.source_id,
|
||||
e.displayName = node.id
|
||||
REMOVE e:Entity
|
||||
e.displayName = node.id
|
||||
REMOVE e:Entity
|
||||
WITH e, node
|
||||
CALL apoc.create.addLabels(e, [node.entity_type]) YIELD node AS labeledNode
|
||||
RETURN count(*)
|
||||
@@ -100,19 +103,24 @@ def main():
|
||||
# Execute queries in batches
|
||||
with driver.session() as session:
|
||||
# Insert nodes in batches
|
||||
session.execute_write(process_in_batches, create_nodes_query, nodes, BATCH_SIZE_NODES)
|
||||
session.execute_write(
|
||||
process_in_batches, create_nodes_query, nodes, BATCH_SIZE_NODES
|
||||
)
|
||||
|
||||
# Insert edges in batches
|
||||
session.execute_write(process_in_batches, create_edges_query, edges, BATCH_SIZE_EDGES)
|
||||
session.execute_write(
|
||||
process_in_batches, create_edges_query, edges, BATCH_SIZE_EDGES
|
||||
)
|
||||
|
||||
# Set displayName and labels
|
||||
session.run(set_displayname_and_labels_query)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
|
||||
|
||||
finally:
|
||||
driver.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
164
examples/lightrag_api_openai_compatible_demo.py
Normal file
164
examples/lightrag_api_openai_compatible_demo.py
Normal file
@@ -0,0 +1,164 @@
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
import os
|
||||
from lightrag import LightRAG, QueryParam
|
||||
from lightrag.llm import openai_complete_if_cache, openai_embedding
|
||||
from lightrag.utils import EmbeddingFunc
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
import asyncio
|
||||
import nest_asyncio
|
||||
|
||||
# Apply nest_asyncio to solve event loop issues
|
||||
nest_asyncio.apply()
|
||||
|
||||
DEFAULT_RAG_DIR = "index_default"
|
||||
app = FastAPI(title="LightRAG API", description="API for RAG operations")
|
||||
|
||||
# Configure working directory
|
||||
WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}")
|
||||
print(f"WORKING_DIR: {WORKING_DIR}")
|
||||
if not os.path.exists(WORKING_DIR):
|
||||
os.mkdir(WORKING_DIR)
|
||||
|
||||
# LLM model function
|
||||
|
||||
|
||||
async def llm_model_func(
|
||||
prompt, system_prompt=None, history_messages=[], **kwargs
|
||||
) -> str:
|
||||
return await openai_complete_if_cache(
|
||||
"gpt-4o-mini",
|
||||
prompt,
|
||||
system_prompt=system_prompt,
|
||||
history_messages=history_messages,
|
||||
api_key="YOUR_API_KEY",
|
||||
base_url="YourURL/v1",
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
# Embedding function
|
||||
|
||||
|
||||
async def embedding_func(texts: list[str]) -> np.ndarray:
|
||||
return await openai_embedding(
|
||||
texts,
|
||||
model="text-embedding-3-large",
|
||||
api_key="YOUR_API_KEY",
|
||||
base_url="YourURL/v1",
|
||||
)
|
||||
|
||||
|
||||
# Initialize RAG instance
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=llm_model_func,
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=3072, max_token_size=8192, func=embedding_func
|
||||
),
|
||||
)
|
||||
|
||||
# Data models
|
||||
|
||||
|
||||
class QueryRequest(BaseModel):
|
||||
query: str
|
||||
mode: str = "hybrid"
|
||||
|
||||
|
||||
class InsertRequest(BaseModel):
|
||||
text: str
|
||||
|
||||
|
||||
class InsertFileRequest(BaseModel):
|
||||
file_path: str
|
||||
|
||||
|
||||
class Response(BaseModel):
|
||||
status: str
|
||||
data: Optional[str] = None
|
||||
message: Optional[str] = None
|
||||
|
||||
|
||||
# API routes
|
||||
|
||||
|
||||
@app.post("/query", response_model=Response)
|
||||
async def query_endpoint(request: QueryRequest):
|
||||
try:
|
||||
loop = asyncio.get_event_loop()
|
||||
result = await loop.run_in_executor(
|
||||
None, lambda: rag.query(request.query, param=QueryParam(mode=request.mode))
|
||||
)
|
||||
return Response(status="success", data=result)
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@app.post("/insert", response_model=Response)
|
||||
async def insert_endpoint(request: InsertRequest):
|
||||
try:
|
||||
loop = asyncio.get_event_loop()
|
||||
await loop.run_in_executor(None, lambda: rag.insert(request.text))
|
||||
return Response(status="success", message="Text inserted successfully")
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@app.post("/insert_file", response_model=Response)
|
||||
async def insert_file(request: InsertFileRequest):
|
||||
try:
|
||||
# Check if file exists
|
||||
if not os.path.exists(request.file_path):
|
||||
raise HTTPException(
|
||||
status_code=404, detail=f"File not found: {request.file_path}"
|
||||
)
|
||||
|
||||
# Read file content
|
||||
try:
|
||||
with open(request.file_path, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
except UnicodeDecodeError:
|
||||
# If UTF-8 decoding fails, try other encodings
|
||||
with open(request.file_path, "r", encoding="gbk") as f:
|
||||
content = f.read()
|
||||
|
||||
# Insert file content
|
||||
loop = asyncio.get_event_loop()
|
||||
await loop.run_in_executor(None, lambda: rag.insert(content))
|
||||
|
||||
return Response(
|
||||
status="success",
|
||||
message=f"File content from {request.file_path} inserted successfully",
|
||||
)
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
return {"status": "healthy"}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
uvicorn.run(app, host="0.0.0.0", port=8020)
|
||||
|
||||
# Usage example
|
||||
# To run the server, use the following command in your terminal:
|
||||
# python lightrag_api_openai_compatible_demo.py
|
||||
|
||||
# Example requests:
|
||||
# 1. Query:
|
||||
# curl -X POST "http://127.0.0.1:8020/query" -H "Content-Type: application/json" -d '{"query": "your query here", "mode": "hybrid"}'
|
||||
|
||||
# 2. Insert text:
|
||||
# curl -X POST "http://127.0.0.1:8020/insert" -H "Content-Type: application/json" -d '{"text": "your text here"}'
|
||||
|
||||
# 3. Insert file:
|
||||
# curl -X POST "http://127.0.0.1:8020/insert_file" -H "Content-Type: application/json" -d '{"file_path": "path/to/your/file.txt"}'
|
||||
|
||||
# 4. Health check:
|
||||
# curl -X GET "http://127.0.0.1:8020/health"
|
||||
75
examples/lightrag_lmdeploy_demo.py
Normal file
75
examples/lightrag_lmdeploy_demo.py
Normal file
@@ -0,0 +1,75 @@
|
||||
import os
|
||||
|
||||
from lightrag import LightRAG, QueryParam
|
||||
from lightrag.llm import lmdeploy_model_if_cache, hf_embedding
|
||||
from lightrag.utils import EmbeddingFunc
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
|
||||
WORKING_DIR = "./dickens"
|
||||
|
||||
if not os.path.exists(WORKING_DIR):
|
||||
os.mkdir(WORKING_DIR)
|
||||
|
||||
|
||||
async def lmdeploy_model_complete(
|
||||
prompt=None, system_prompt=None, history_messages=[], **kwargs
|
||||
) -> str:
|
||||
model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
|
||||
return await lmdeploy_model_if_cache(
|
||||
model_name,
|
||||
prompt,
|
||||
system_prompt=system_prompt,
|
||||
history_messages=history_messages,
|
||||
## please specify chat_template if your local path does not follow original HF file name,
|
||||
## or model_name is a pytorch model on huggingface.co,
|
||||
## you can refer to https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/model.py
|
||||
## for a list of chat_template available in lmdeploy.
|
||||
chat_template="llama3",
|
||||
# model_format ='awq', # if you are using awq quantization model.
|
||||
# quant_policy=8, # if you want to use online kv cache, 4=kv int4, 8=kv int8.
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=lmdeploy_model_complete,
|
||||
llm_model_name="meta-llama/Llama-3.1-8B-Instruct", # please use definite path for local model
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=384,
|
||||
max_token_size=5000,
|
||||
func=lambda texts: hf_embedding(
|
||||
texts,
|
||||
tokenizer=AutoTokenizer.from_pretrained(
|
||||
"sentence-transformers/all-MiniLM-L6-v2"
|
||||
),
|
||||
embed_model=AutoModel.from_pretrained(
|
||||
"sentence-transformers/all-MiniLM-L6-v2"
|
||||
),
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
with open("./book.txt", "r", encoding="utf-8") as f:
|
||||
rag.insert(f.read())
|
||||
|
||||
# Perform naive search
|
||||
print(
|
||||
rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
|
||||
)
|
||||
|
||||
# Perform local search
|
||||
print(
|
||||
rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
|
||||
)
|
||||
|
||||
# Perform global search
|
||||
print(
|
||||
rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
|
||||
)
|
||||
|
||||
# Perform hybrid search
|
||||
print(
|
||||
rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
|
||||
)
|
||||
@@ -1,26 +1,32 @@
|
||||
import os
|
||||
|
||||
import logging
|
||||
from lightrag import LightRAG, QueryParam
|
||||
from lightrag.llm import ollama_model_complete, ollama_embedding
|
||||
from lightrag.utils import EmbeddingFunc
|
||||
|
||||
WORKING_DIR = "./dickens"
|
||||
|
||||
logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
|
||||
|
||||
if not os.path.exists(WORKING_DIR):
|
||||
os.mkdir(WORKING_DIR)
|
||||
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=ollama_model_complete,
|
||||
llm_model_name="your_model_name",
|
||||
llm_model_name="gemma2:2b",
|
||||
llm_model_max_async=4,
|
||||
llm_model_max_token_size=32768,
|
||||
llm_model_kwargs={"host": "http://localhost:11434", "options": {"num_ctx": 32768}},
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=768,
|
||||
max_token_size=8192,
|
||||
func=lambda texts: ollama_embedding(texts, embed_model="nomic-embed-text"),
|
||||
func=lambda texts: ollama_embedding(
|
||||
texts, embed_model="nomic-embed-text", host="http://localhost:11434"
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
with open("./book.txt", "r", encoding="utf-8") as f:
|
||||
rag.insert(f.read())
|
||||
|
||||
|
||||
@@ -34,6 +34,13 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
|
||||
)
|
||||
|
||||
|
||||
async def get_embedding_dim():
|
||||
test_text = ["This is a test sentence."]
|
||||
embedding = await embedding_func(test_text)
|
||||
embedding_dim = embedding.shape[1]
|
||||
return embedding_dim
|
||||
|
||||
|
||||
# function test
|
||||
async def test_funcs():
|
||||
result = await llm_model_func("How are you?")
|
||||
@@ -43,37 +50,59 @@ async def test_funcs():
|
||||
print("embedding_func: ", result)
|
||||
|
||||
|
||||
asyncio.run(test_funcs())
|
||||
# asyncio.run(test_funcs())
|
||||
|
||||
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=llm_model_func,
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=4096, max_token_size=8192, func=embedding_func
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
try:
|
||||
embedding_dimension = await get_embedding_dim()
|
||||
print(f"Detected embedding dimension: {embedding_dimension}")
|
||||
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=llm_model_func,
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=embedding_dimension,
|
||||
max_token_size=8192,
|
||||
func=embedding_func,
|
||||
),
|
||||
)
|
||||
|
||||
with open("./book.txt", "r", encoding="utf-8") as f:
|
||||
await rag.ainsert(f.read())
|
||||
|
||||
# Perform naive search
|
||||
print(
|
||||
await rag.aquery(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="naive")
|
||||
)
|
||||
)
|
||||
|
||||
# Perform local search
|
||||
print(
|
||||
await rag.aquery(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="local")
|
||||
)
|
||||
)
|
||||
|
||||
# Perform global search
|
||||
print(
|
||||
await rag.aquery(
|
||||
"What are the top themes in this story?",
|
||||
param=QueryParam(mode="global"),
|
||||
)
|
||||
)
|
||||
|
||||
# Perform hybrid search
|
||||
print(
|
||||
await rag.aquery(
|
||||
"What are the top themes in this story?",
|
||||
param=QueryParam(mode="hybrid"),
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"An error occurred: {e}")
|
||||
|
||||
|
||||
with open("./book.txt", "r", encoding="utf-8") as f:
|
||||
rag.insert(f.read())
|
||||
|
||||
# Perform naive search
|
||||
print(
|
||||
rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
|
||||
)
|
||||
|
||||
# Perform local search
|
||||
print(
|
||||
rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
|
||||
)
|
||||
|
||||
# Perform global search
|
||||
print(
|
||||
rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
|
||||
)
|
||||
|
||||
# Perform hybrid search
|
||||
print(
|
||||
rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
|
||||
)
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -30,7 +30,7 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
|
||||
texts,
|
||||
model="netease-youdao/bce-embedding-base_v1",
|
||||
api_key=os.getenv("SILICONFLOW_API_KEY"),
|
||||
max_token_size=512
|
||||
max_token_size=512,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -27,11 +27,12 @@ rag = LightRAG(
|
||||
# Read all .txt files from the TEXT_FILES_DIR directory
|
||||
texts = []
|
||||
for filename in os.listdir(TEXT_FILES_DIR):
|
||||
if filename.endswith('.txt'):
|
||||
if filename.endswith(".txt"):
|
||||
file_path = os.path.join(TEXT_FILES_DIR, filename)
|
||||
with open(file_path, 'r', encoding='utf-8') as file:
|
||||
with open(file_path, "r", encoding="utf-8") as file:
|
||||
texts.append(file.read())
|
||||
|
||||
|
||||
# Batch insert texts into LightRAG with a retry mechanism
|
||||
def insert_texts_with_retry(rag, texts, retries=3, delay=5):
|
||||
for _ in range(retries):
|
||||
@@ -39,37 +40,58 @@ def insert_texts_with_retry(rag, texts, retries=3, delay=5):
|
||||
rag.insert(texts)
|
||||
return
|
||||
except Exception as e:
|
||||
print(f"Error occurred during insertion: {e}. Retrying in {delay} seconds...")
|
||||
print(
|
||||
f"Error occurred during insertion: {e}. Retrying in {delay} seconds..."
|
||||
)
|
||||
time.sleep(delay)
|
||||
raise RuntimeError("Failed to insert texts after multiple retries.")
|
||||
|
||||
|
||||
insert_texts_with_retry(rag, texts)
|
||||
|
||||
# Perform different types of queries and handle potential errors
|
||||
try:
|
||||
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
|
||||
print(
|
||||
rag.query(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="naive")
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Error performing naive search: {e}")
|
||||
|
||||
try:
|
||||
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
|
||||
print(
|
||||
rag.query(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="local")
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Error performing local search: {e}")
|
||||
|
||||
try:
|
||||
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
|
||||
print(
|
||||
rag.query(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="global")
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Error performing global search: {e}")
|
||||
|
||||
try:
|
||||
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))
|
||||
print(
|
||||
rag.query(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="hybrid")
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Error performing hybrid search: {e}")
|
||||
|
||||
|
||||
# Function to clear VRAM resources
|
||||
def clear_vram():
|
||||
os.system("sudo nvidia-smi --gpu-reset")
|
||||
|
||||
|
||||
# Regularly clear VRAM to prevent overflow
|
||||
clear_vram_interval = 3600 # Clear once every hour
|
||||
start_time = time.time()
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
|
||||
|
||||
__version__ = "0.0.7"
|
||||
__version__ = "0.0.8"
|
||||
__author__ = "Zirui Guo"
|
||||
__url__ = "https://github.com/HKUDS/LightRAG"
|
||||
|
||||
@@ -109,6 +109,7 @@ class LightRAG:
|
||||
llm_model_name: str = "meta-llama/Llama-3.2-1B-Instruct" #'meta-llama/Llama-3.2-1B'#'google/gemma-2-2b-it'
|
||||
llm_model_max_token_size: int = 32768
|
||||
llm_model_max_async: int = 16
|
||||
llm_model_kwargs: dict = field(default_factory=dict)
|
||||
|
||||
# storage
|
||||
key_string_value_json_storage_cls: Type[BaseKVStorage] = JsonKVStorage
|
||||
@@ -179,7 +180,11 @@ class LightRAG:
|
||||
)
|
||||
|
||||
self.llm_model_func = limit_async_func_call(self.llm_model_max_async)(
|
||||
partial(self.llm_model_func, hashing_kv=self.llm_response_cache)
|
||||
partial(
|
||||
self.llm_model_func,
|
||||
hashing_kv=self.llm_response_cache,
|
||||
**self.llm_model_kwargs,
|
||||
)
|
||||
)
|
||||
def _get_storage_class(self) -> Type[BaseGraphStorage]:
|
||||
return {
|
||||
@@ -239,7 +244,7 @@ class LightRAG:
|
||||
logger.info("[Entity Extraction]...")
|
||||
maybe_new_kg = await extract_entities(
|
||||
inserting_chunks,
|
||||
knwoledge_graph_inst=self.chunk_entity_relation_graph,
|
||||
knowledge_graph_inst=self.chunk_entity_relation_graph,
|
||||
entity_vdb=self.entities_vdb,
|
||||
relationships_vdb=self.relationships_vdb,
|
||||
global_config=asdict(self),
|
||||
|
||||
246
lightrag/llm.py
246
lightrag/llm.py
@@ -7,7 +7,13 @@ import aiohttp
|
||||
import numpy as np
|
||||
import ollama
|
||||
|
||||
from openai import AsyncOpenAI, APIConnectionError, RateLimitError, Timeout, AsyncAzureOpenAI
|
||||
from openai import (
|
||||
AsyncOpenAI,
|
||||
APIConnectionError,
|
||||
RateLimitError,
|
||||
Timeout,
|
||||
AsyncAzureOpenAI,
|
||||
)
|
||||
|
||||
import base64
|
||||
import struct
|
||||
@@ -70,26 +76,31 @@ async def openai_complete_if_cache(
|
||||
)
|
||||
return response.choices[0].message.content
|
||||
|
||||
|
||||
@retry(
|
||||
stop=stop_after_attempt(3),
|
||||
wait=wait_exponential(multiplier=1, min=4, max=10),
|
||||
retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
|
||||
)
|
||||
async def azure_openai_complete_if_cache(model,
|
||||
async def azure_openai_complete_if_cache(
|
||||
model,
|
||||
prompt,
|
||||
system_prompt=None,
|
||||
history_messages=[],
|
||||
base_url=None,
|
||||
api_key=None,
|
||||
**kwargs):
|
||||
**kwargs,
|
||||
):
|
||||
if api_key:
|
||||
os.environ["AZURE_OPENAI_API_KEY"] = api_key
|
||||
if base_url:
|
||||
os.environ["AZURE_OPENAI_ENDPOINT"] = base_url
|
||||
|
||||
openai_async_client = AsyncAzureOpenAI(azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
|
||||
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
|
||||
api_version=os.getenv("AZURE_OPENAI_API_VERSION"))
|
||||
openai_async_client = AsyncAzureOpenAI(
|
||||
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
|
||||
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
|
||||
api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
|
||||
)
|
||||
|
||||
hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
|
||||
messages = []
|
||||
@@ -114,6 +125,7 @@ async def azure_openai_complete_if_cache(model,
|
||||
)
|
||||
return response.choices[0].message.content
|
||||
|
||||
|
||||
class BedrockError(Exception):
|
||||
"""Generic error for issues related to Amazon Bedrock"""
|
||||
|
||||
@@ -205,8 +217,12 @@ async def bedrock_complete_if_cache(
|
||||
|
||||
@lru_cache(maxsize=1)
|
||||
def initialize_hf_model(model_name):
|
||||
hf_tokenizer = AutoTokenizer.from_pretrained(model_name, device_map="auto", trust_remote_code=True)
|
||||
hf_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True)
|
||||
hf_tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_name, device_map="auto", trust_remote_code=True
|
||||
)
|
||||
hf_model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name, device_map="auto", trust_remote_code=True
|
||||
)
|
||||
if hf_tokenizer.pad_token is None:
|
||||
hf_tokenizer.pad_token = hf_tokenizer.eos_token
|
||||
|
||||
@@ -266,10 +282,13 @@ async def hf_model_if_cache(
|
||||
input_ids = hf_tokenizer(
|
||||
input_prompt, return_tensors="pt", padding=True, truncation=True
|
||||
).to("cuda")
|
||||
inputs = {k: v.to(hf_model.device) for k, v in input_ids.items()}
|
||||
output = hf_model.generate(
|
||||
**input_ids, max_new_tokens=200, num_return_sequences=1, early_stopping=True
|
||||
**input_ids, max_new_tokens=512, num_return_sequences=1, early_stopping=True
|
||||
)
|
||||
response_text = hf_tokenizer.decode(
|
||||
output[0][len(inputs["input_ids"][0]) :], skip_special_tokens=True
|
||||
)
|
||||
response_text = hf_tokenizer.decode(output[0], skip_special_tokens=True)
|
||||
if hashing_kv is not None:
|
||||
await hashing_kv.upsert({args_hash: {"return": response_text, "model": model}})
|
||||
return response_text
|
||||
@@ -280,8 +299,10 @@ async def ollama_model_if_cache(
|
||||
) -> str:
|
||||
kwargs.pop("max_tokens", None)
|
||||
kwargs.pop("response_format", None)
|
||||
host = kwargs.pop("host", None)
|
||||
timeout = kwargs.pop("timeout", None)
|
||||
|
||||
ollama_client = ollama.AsyncClient()
|
||||
ollama_client = ollama.AsyncClient(host=host, timeout=timeout)
|
||||
messages = []
|
||||
if system_prompt:
|
||||
messages.append({"role": "system", "content": system_prompt})
|
||||
@@ -305,6 +326,135 @@ async def ollama_model_if_cache(
|
||||
return result
|
||||
|
||||
|
||||
@lru_cache(maxsize=1)
|
||||
def initialize_lmdeploy_pipeline(
|
||||
model,
|
||||
tp=1,
|
||||
chat_template=None,
|
||||
log_level="WARNING",
|
||||
model_format="hf",
|
||||
quant_policy=0,
|
||||
):
|
||||
from lmdeploy import pipeline, ChatTemplateConfig, TurbomindEngineConfig
|
||||
|
||||
lmdeploy_pipe = pipeline(
|
||||
model_path=model,
|
||||
backend_config=TurbomindEngineConfig(
|
||||
tp=tp, model_format=model_format, quant_policy=quant_policy
|
||||
),
|
||||
chat_template_config=ChatTemplateConfig(model_name=chat_template)
|
||||
if chat_template
|
||||
else None,
|
||||
log_level="WARNING",
|
||||
)
|
||||
return lmdeploy_pipe
|
||||
|
||||
|
||||
async def lmdeploy_model_if_cache(
|
||||
model,
|
||||
prompt,
|
||||
system_prompt=None,
|
||||
history_messages=[],
|
||||
chat_template=None,
|
||||
model_format="hf",
|
||||
quant_policy=0,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
"""
|
||||
Args:
|
||||
model (str): The path to the model.
|
||||
It could be one of the following options:
|
||||
- i) A local directory path of a turbomind model which is
|
||||
converted by `lmdeploy convert` command or download
|
||||
from ii) and iii).
|
||||
- ii) The model_id of a lmdeploy-quantized model hosted
|
||||
inside a model repo on huggingface.co, such as
|
||||
"InternLM/internlm-chat-20b-4bit",
|
||||
"lmdeploy/llama2-chat-70b-4bit", etc.
|
||||
- iii) The model_id of a model hosted inside a model repo
|
||||
on huggingface.co, such as "internlm/internlm-chat-7b",
|
||||
"Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
|
||||
and so on.
|
||||
chat_template (str): needed when model is a pytorch model on
|
||||
huggingface.co, such as "internlm-chat-7b",
|
||||
"Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on,
|
||||
and when the model name of local path did not match the original model name in HF.
|
||||
tp (int): tensor parallel
|
||||
prompt (Union[str, List[str]]): input texts to be completed.
|
||||
do_preprocess (bool): whether pre-process the messages. Default to
|
||||
True, which means chat_template will be applied.
|
||||
skip_special_tokens (bool): Whether or not to remove special tokens
|
||||
in the decoding. Default to be True.
|
||||
do_sample (bool): Whether or not to use sampling, use greedy decoding otherwise.
|
||||
Default to be False, which means greedy decoding will be applied.
|
||||
"""
|
||||
try:
|
||||
import lmdeploy
|
||||
from lmdeploy import version_info, GenerationConfig
|
||||
except Exception:
|
||||
raise ImportError("Please install lmdeploy before intialize lmdeploy backend.")
|
||||
|
||||
kwargs.pop("response_format", None)
|
||||
max_new_tokens = kwargs.pop("max_tokens", 512)
|
||||
tp = kwargs.pop("tp", 1)
|
||||
skip_special_tokens = kwargs.pop("skip_special_tokens", True)
|
||||
do_preprocess = kwargs.pop("do_preprocess", True)
|
||||
do_sample = kwargs.pop("do_sample", False)
|
||||
gen_params = kwargs
|
||||
|
||||
version = version_info
|
||||
if do_sample is not None and version < (0, 6, 0):
|
||||
raise RuntimeError(
|
||||
"`do_sample` parameter is not supported by lmdeploy until "
|
||||
f"v0.6.0, but currently using lmdeloy {lmdeploy.__version__}"
|
||||
)
|
||||
else:
|
||||
do_sample = True
|
||||
gen_params.update(do_sample=do_sample)
|
||||
|
||||
lmdeploy_pipe = initialize_lmdeploy_pipeline(
|
||||
model=model,
|
||||
tp=tp,
|
||||
chat_template=chat_template,
|
||||
model_format=model_format,
|
||||
quant_policy=quant_policy,
|
||||
log_level="WARNING",
|
||||
)
|
||||
|
||||
messages = []
|
||||
if system_prompt:
|
||||
messages.append({"role": "system", "content": system_prompt})
|
||||
|
||||
hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
|
||||
messages.extend(history_messages)
|
||||
messages.append({"role": "user", "content": prompt})
|
||||
if hashing_kv is not None:
|
||||
args_hash = compute_args_hash(model, messages)
|
||||
if_cache_return = await hashing_kv.get_by_id(args_hash)
|
||||
if if_cache_return is not None:
|
||||
return if_cache_return["return"]
|
||||
|
||||
gen_config = GenerationConfig(
|
||||
skip_special_tokens=skip_special_tokens,
|
||||
max_new_tokens=max_new_tokens,
|
||||
**gen_params,
|
||||
)
|
||||
|
||||
response = ""
|
||||
async for res in lmdeploy_pipe.generate(
|
||||
messages,
|
||||
gen_config=gen_config,
|
||||
do_preprocess=do_preprocess,
|
||||
stream_response=False,
|
||||
session_id=1,
|
||||
):
|
||||
response += res.response
|
||||
|
||||
if hashing_kv is not None:
|
||||
await hashing_kv.upsert({args_hash: {"return": response, "model": model}})
|
||||
return response
|
||||
|
||||
|
||||
async def gpt_4o_complete(
|
||||
prompt, system_prompt=None, history_messages=[], **kwargs
|
||||
) -> str:
|
||||
@@ -328,8 +478,9 @@ async def gpt_4o_mini_complete(
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
async def azure_openai_complete(
|
||||
prompt, system_prompt=None, history_messages=[], **kwargs
|
||||
prompt, system_prompt=None, history_messages=[], **kwargs
|
||||
) -> str:
|
||||
return await azure_openai_complete_if_cache(
|
||||
"conversation-4o-mini",
|
||||
@@ -339,6 +490,7 @@ async def azure_openai_complete(
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
async def bedrock_complete(
|
||||
prompt, system_prompt=None, history_messages=[], **kwargs
|
||||
) -> str:
|
||||
@@ -418,9 +570,11 @@ async def azure_openai_embedding(
|
||||
if base_url:
|
||||
os.environ["AZURE_OPENAI_ENDPOINT"] = base_url
|
||||
|
||||
openai_async_client = AsyncAzureOpenAI(azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
|
||||
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
|
||||
api_version=os.getenv("AZURE_OPENAI_API_VERSION"))
|
||||
openai_async_client = AsyncAzureOpenAI(
|
||||
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
|
||||
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
|
||||
api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
|
||||
)
|
||||
|
||||
response = await openai_async_client.embeddings.create(
|
||||
model=model, input=texts, encoding_format="float"
|
||||
@@ -440,35 +594,28 @@ async def siliconcloud_embedding(
|
||||
max_token_size: int = 512,
|
||||
api_key: str = None,
|
||||
) -> np.ndarray:
|
||||
if api_key and not api_key.startswith('Bearer '):
|
||||
api_key = 'Bearer ' + api_key
|
||||
if api_key and not api_key.startswith("Bearer "):
|
||||
api_key = "Bearer " + api_key
|
||||
|
||||
headers = {
|
||||
"Authorization": api_key,
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
headers = {"Authorization": api_key, "Content-Type": "application/json"}
|
||||
|
||||
truncate_texts = [text[0:max_token_size] for text in texts]
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"input": truncate_texts,
|
||||
"encoding_format": "base64"
|
||||
}
|
||||
payload = {"model": model, "input": truncate_texts, "encoding_format": "base64"}
|
||||
|
||||
base64_strings = []
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.post(base_url, headers=headers, json=payload) as response:
|
||||
content = await response.json()
|
||||
if 'code' in content:
|
||||
if "code" in content:
|
||||
raise ValueError(content)
|
||||
base64_strings = [item['embedding'] for item in content['data']]
|
||||
|
||||
base64_strings = [item["embedding"] for item in content["data"]]
|
||||
|
||||
embeddings = []
|
||||
for string in base64_strings:
|
||||
decode_bytes = base64.b64decode(string)
|
||||
n = len(decode_bytes) // 4
|
||||
float_array = struct.unpack('<' + 'f' * n, decode_bytes)
|
||||
float_array = struct.unpack("<" + "f" * n, decode_bytes)
|
||||
embeddings.append(float_array)
|
||||
return np.array(embeddings)
|
||||
|
||||
@@ -555,14 +702,16 @@ async def hf_embedding(texts: list[str], tokenizer, embed_model) -> np.ndarray:
|
||||
return embeddings.detach().numpy()
|
||||
|
||||
|
||||
async def ollama_embedding(texts: list[str], embed_model) -> np.ndarray:
|
||||
async def ollama_embedding(texts: list[str], embed_model, **kwargs) -> np.ndarray:
|
||||
embed_text = []
|
||||
ollama_client = ollama.Client(**kwargs)
|
||||
for text in texts:
|
||||
data = ollama.embeddings(model=embed_model, prompt=text)
|
||||
data = ollama_client.embeddings(model=embed_model, prompt=text)
|
||||
embed_text.append(data["embedding"])
|
||||
|
||||
return embed_text
|
||||
|
||||
|
||||
class Model(BaseModel):
|
||||
"""
|
||||
This is a Pydantic model class named 'Model' that is used to define a custom language model.
|
||||
@@ -580,14 +729,20 @@ class Model(BaseModel):
|
||||
The 'kwargs' dictionary contains the model name and API key to be passed to the function.
|
||||
"""
|
||||
|
||||
gen_func: Callable[[Any], str] = Field(..., description="A function that generates the response from the llm. The response must be a string")
|
||||
kwargs: Dict[str, Any] = Field(..., description="The arguments to pass to the callable function. Eg. the api key, model name, etc")
|
||||
gen_func: Callable[[Any], str] = Field(
|
||||
...,
|
||||
description="A function that generates the response from the llm. The response must be a string",
|
||||
)
|
||||
kwargs: Dict[str, Any] = Field(
|
||||
...,
|
||||
description="The arguments to pass to the callable function. Eg. the api key, model name, etc",
|
||||
)
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
|
||||
class MultiModel():
|
||||
class MultiModel:
|
||||
"""
|
||||
Distributes the load across multiple language models. Useful for circumventing low rate limits with certain api providers especially if you are on the free tier.
|
||||
Could also be used for spliting across diffrent models or providers.
|
||||
@@ -611,26 +766,31 @@ class MultiModel():
|
||||
)
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, models: List[Model]):
|
||||
self._models = models
|
||||
self._current_model = 0
|
||||
|
||||
|
||||
def _next_model(self):
|
||||
self._current_model = (self._current_model + 1) % len(self._models)
|
||||
return self._models[self._current_model]
|
||||
|
||||
async def llm_model_func(
|
||||
self,
|
||||
prompt, system_prompt=None, history_messages=[], **kwargs
|
||||
self, prompt, system_prompt=None, history_messages=[], **kwargs
|
||||
) -> str:
|
||||
kwargs.pop("model", None) # stop from overwriting the custom model name
|
||||
kwargs.pop("model", None) # stop from overwriting the custom model name
|
||||
next_model = self._next_model()
|
||||
args = dict(prompt=prompt, system_prompt=system_prompt, history_messages=history_messages, **kwargs, **next_model.kwargs)
|
||||
|
||||
return await next_model.gen_func(
|
||||
**args
|
||||
args = dict(
|
||||
prompt=prompt,
|
||||
system_prompt=system_prompt,
|
||||
history_messages=history_messages,
|
||||
**kwargs,
|
||||
**next_model.kwargs,
|
||||
)
|
||||
|
||||
return await next_model.gen_func(**args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
|
||||
@@ -124,14 +124,14 @@ async def _handle_single_relationship_extraction(
|
||||
async def _merge_nodes_then_upsert(
|
||||
entity_name: str,
|
||||
nodes_data: list[dict],
|
||||
knwoledge_graph_inst: BaseGraphStorage,
|
||||
knowledge_graph_inst: BaseGraphStorage,
|
||||
global_config: dict,
|
||||
):
|
||||
already_entitiy_types = []
|
||||
already_source_ids = []
|
||||
already_description = []
|
||||
|
||||
already_node = await knwoledge_graph_inst.get_node(entity_name)
|
||||
already_node = await knowledge_graph_inst.get_node(entity_name)
|
||||
if already_node is not None:
|
||||
already_entitiy_types.append(already_node["entity_type"])
|
||||
already_source_ids.extend(
|
||||
@@ -160,7 +160,7 @@ async def _merge_nodes_then_upsert(
|
||||
description=description,
|
||||
source_id=source_id,
|
||||
)
|
||||
await knwoledge_graph_inst.upsert_node(
|
||||
await knowledge_graph_inst.upsert_node(
|
||||
entity_name,
|
||||
node_data=node_data,
|
||||
)
|
||||
@@ -172,7 +172,7 @@ async def _merge_edges_then_upsert(
|
||||
src_id: str,
|
||||
tgt_id: str,
|
||||
edges_data: list[dict],
|
||||
knwoledge_graph_inst: BaseGraphStorage,
|
||||
knowledge_graph_inst: BaseGraphStorage,
|
||||
global_config: dict,
|
||||
):
|
||||
already_weights = []
|
||||
@@ -180,8 +180,8 @@ async def _merge_edges_then_upsert(
|
||||
already_description = []
|
||||
already_keywords = []
|
||||
|
||||
if await knwoledge_graph_inst.has_edge(src_id, tgt_id):
|
||||
already_edge = await knwoledge_graph_inst.get_edge(src_id, tgt_id)
|
||||
if await knowledge_graph_inst.has_edge(src_id, tgt_id):
|
||||
already_edge = await knowledge_graph_inst.get_edge(src_id, tgt_id)
|
||||
already_weights.append(already_edge["weight"])
|
||||
already_source_ids.extend(
|
||||
split_string_by_multi_markers(already_edge["source_id"], [GRAPH_FIELD_SEP])
|
||||
@@ -202,8 +202,8 @@ async def _merge_edges_then_upsert(
|
||||
set([dp["source_id"] for dp in edges_data] + already_source_ids)
|
||||
)
|
||||
for need_insert_id in [src_id, tgt_id]:
|
||||
if not (await knwoledge_graph_inst.has_node(need_insert_id)):
|
||||
await knwoledge_graph_inst.upsert_node(
|
||||
if not (await knowledge_graph_inst.has_node(need_insert_id)):
|
||||
await knowledge_graph_inst.upsert_node(
|
||||
need_insert_id,
|
||||
node_data={
|
||||
"source_id": source_id,
|
||||
@@ -214,7 +214,7 @@ async def _merge_edges_then_upsert(
|
||||
description = await _handle_entity_relation_summary(
|
||||
(src_id, tgt_id), description, global_config
|
||||
)
|
||||
await knwoledge_graph_inst.upsert_edge(
|
||||
await knowledge_graph_inst.upsert_edge(
|
||||
src_id,
|
||||
tgt_id,
|
||||
edge_data=dict(
|
||||
@@ -237,7 +237,7 @@ async def _merge_edges_then_upsert(
|
||||
|
||||
async def extract_entities(
|
||||
chunks: dict[str, TextChunkSchema],
|
||||
knwoledge_graph_inst: BaseGraphStorage,
|
||||
knowledge_graph_inst: BaseGraphStorage,
|
||||
entity_vdb: BaseVectorStorage,
|
||||
relationships_vdb: BaseVectorStorage,
|
||||
global_config: dict,
|
||||
@@ -341,13 +341,13 @@ async def extract_entities(
|
||||
maybe_edges[tuple(sorted(k))].extend(v)
|
||||
all_entities_data = await asyncio.gather(
|
||||
*[
|
||||
_merge_nodes_then_upsert(k, v, knwoledge_graph_inst, global_config)
|
||||
_merge_nodes_then_upsert(k, v, knowledge_graph_inst, global_config)
|
||||
for k, v in maybe_nodes.items()
|
||||
]
|
||||
)
|
||||
all_relationships_data = await asyncio.gather(
|
||||
*[
|
||||
_merge_edges_then_upsert(k[0], k[1], v, knwoledge_graph_inst, global_config)
|
||||
_merge_edges_then_upsert(k[0], k[1], v, knowledge_graph_inst, global_config)
|
||||
for k, v in maybe_edges.items()
|
||||
]
|
||||
)
|
||||
@@ -384,7 +384,7 @@ async def extract_entities(
|
||||
}
|
||||
await relationships_vdb.upsert(data_for_vdb)
|
||||
|
||||
return knwoledge_graph_inst
|
||||
return knowledge_graph_inst
|
||||
|
||||
|
||||
async def local_query(
|
||||
|
||||
@@ -185,6 +185,7 @@ def save_data_to_file(data, file_name):
|
||||
with open(file_name, "w", encoding="utf-8") as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=4)
|
||||
|
||||
|
||||
def xml_to_json(xml_file):
|
||||
try:
|
||||
tree = ET.parse(xml_file)
|
||||
@@ -194,31 +195,42 @@ def xml_to_json(xml_file):
|
||||
print(f"Root element: {root.tag}")
|
||||
print(f"Root attributes: {root.attrib}")
|
||||
|
||||
data = {
|
||||
"nodes": [],
|
||||
"edges": []
|
||||
}
|
||||
data = {"nodes": [], "edges": []}
|
||||
|
||||
# Use namespace
|
||||
namespace = {'': 'http://graphml.graphdrawing.org/xmlns'}
|
||||
namespace = {"": "http://graphml.graphdrawing.org/xmlns"}
|
||||
|
||||
for node in root.findall('.//node', namespace):
|
||||
for node in root.findall(".//node", namespace):
|
||||
node_data = {
|
||||
"id": node.get('id').strip('"'),
|
||||
"entity_type": node.find("./data[@key='d0']", namespace).text.strip('"') if node.find("./data[@key='d0']", namespace) is not None else "",
|
||||
"description": node.find("./data[@key='d1']", namespace).text if node.find("./data[@key='d1']", namespace) is not None else "",
|
||||
"source_id": node.find("./data[@key='d2']", namespace).text if node.find("./data[@key='d2']", namespace) is not None else ""
|
||||
"id": node.get("id").strip('"'),
|
||||
"entity_type": node.find("./data[@key='d0']", namespace).text.strip('"')
|
||||
if node.find("./data[@key='d0']", namespace) is not None
|
||||
else "",
|
||||
"description": node.find("./data[@key='d1']", namespace).text
|
||||
if node.find("./data[@key='d1']", namespace) is not None
|
||||
else "",
|
||||
"source_id": node.find("./data[@key='d2']", namespace).text
|
||||
if node.find("./data[@key='d2']", namespace) is not None
|
||||
else "",
|
||||
}
|
||||
data["nodes"].append(node_data)
|
||||
|
||||
for edge in root.findall('.//edge', namespace):
|
||||
for edge in root.findall(".//edge", namespace):
|
||||
edge_data = {
|
||||
"source": edge.get('source').strip('"'),
|
||||
"target": edge.get('target').strip('"'),
|
||||
"weight": float(edge.find("./data[@key='d3']", namespace).text) if edge.find("./data[@key='d3']", namespace) is not None else 0.0,
|
||||
"description": edge.find("./data[@key='d4']", namespace).text if edge.find("./data[@key='d4']", namespace) is not None else "",
|
||||
"keywords": edge.find("./data[@key='d5']", namespace).text if edge.find("./data[@key='d5']", namespace) is not None else "",
|
||||
"source_id": edge.find("./data[@key='d6']", namespace).text if edge.find("./data[@key='d6']", namespace) is not None else ""
|
||||
"source": edge.get("source").strip('"'),
|
||||
"target": edge.get("target").strip('"'),
|
||||
"weight": float(edge.find("./data[@key='d3']", namespace).text)
|
||||
if edge.find("./data[@key='d3']", namespace) is not None
|
||||
else 0.0,
|
||||
"description": edge.find("./data[@key='d4']", namespace).text
|
||||
if edge.find("./data[@key='d4']", namespace) is not None
|
||||
else "",
|
||||
"keywords": edge.find("./data[@key='d5']", namespace).text
|
||||
if edge.find("./data[@key='d5']", namespace) is not None
|
||||
else "",
|
||||
"source_id": edge.find("./data[@key='d6']", namespace).text
|
||||
if edge.find("./data[@key='d6']", namespace) is not None
|
||||
else "",
|
||||
}
|
||||
data["edges"].append(edge_data)
|
||||
|
||||
|
||||
@@ -18,8 +18,8 @@ def extract_queries(file_path):
|
||||
|
||||
async def process_query(query_text, rag_instance, query_param):
|
||||
try:
|
||||
result, context = await rag_instance.aquery(query_text, param=query_param)
|
||||
return {"query": query_text, "result": result, "context": context}, None
|
||||
result = await rag_instance.aquery(query_text, param=query_param)
|
||||
return {"query": query_text, "result": result}, None
|
||||
except Exception as e:
|
||||
return None, {"query": query_text, "error": str(e)}
|
||||
|
||||
|
||||
@@ -50,8 +50,8 @@ def extract_queries(file_path):
|
||||
|
||||
async def process_query(query_text, rag_instance, query_param):
|
||||
try:
|
||||
result, context = await rag_instance.aquery(query_text, param=query_param)
|
||||
return {"query": query_text, "result": result, "context": context}, None
|
||||
result = await rag_instance.aquery(query_text, param=query_param)
|
||||
return {"query": query_text, "result": result}, None
|
||||
except Exception as e:
|
||||
return None, {"query": query_text, "error": str(e)}
|
||||
|
||||
|
||||
@@ -1,16 +1,17 @@
|
||||
accelerate
|
||||
aioboto3
|
||||
aiohttp
|
||||
graspologic
|
||||
hnswlib
|
||||
nano-vectordb
|
||||
neo4j
|
||||
networkx
|
||||
ollama
|
||||
openai
|
||||
pyvis
|
||||
tenacity
|
||||
tiktoken
|
||||
torch
|
||||
transformers
|
||||
xxhash
|
||||
pyvis
|
||||
aiohttp
|
||||
neo4j
|
||||
# lmdeploy[all]
|
||||
|
||||
93
setup.py
93
setup.py
@@ -1,39 +1,88 @@
|
||||
import setuptools
|
||||
|
||||
with open("README.md", "r", encoding="utf-8") as fh:
|
||||
long_description = fh.read()
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
vars2find = ["__author__", "__version__", "__url__"]
|
||||
vars2readme = {}
|
||||
with open("./lightrag/__init__.py") as f:
|
||||
for line in f.readlines():
|
||||
for v in vars2find:
|
||||
if line.startswith(v):
|
||||
line = line.replace(" ", "").replace('"', "").replace("'", "").strip()
|
||||
vars2readme[v] = line.split("=")[1]
|
||||
# Reading the long description from README.md
|
||||
def read_long_description():
|
||||
try:
|
||||
return Path("README.md").read_text(encoding="utf-8")
|
||||
except FileNotFoundError:
|
||||
return "A description of LightRAG is currently unavailable."
|
||||
|
||||
deps = []
|
||||
with open("./requirements.txt") as f:
|
||||
for line in f.readlines():
|
||||
if not line.strip():
|
||||
continue
|
||||
deps.append(line.strip())
|
||||
|
||||
# Retrieving metadata from __init__.py
|
||||
def retrieve_metadata():
|
||||
vars2find = ["__author__", "__version__", "__url__"]
|
||||
vars2readme = {}
|
||||
try:
|
||||
with open("./lightrag/__init__.py") as f:
|
||||
for line in f.readlines():
|
||||
for v in vars2find:
|
||||
if line.startswith(v):
|
||||
line = (
|
||||
line.replace(" ", "")
|
||||
.replace('"', "")
|
||||
.replace("'", "")
|
||||
.strip()
|
||||
)
|
||||
vars2readme[v] = line.split("=")[1]
|
||||
except FileNotFoundError:
|
||||
raise FileNotFoundError("Metadata file './lightrag/__init__.py' not found.")
|
||||
|
||||
# Checking if all required variables are found
|
||||
missing_vars = [v for v in vars2find if v not in vars2readme]
|
||||
if missing_vars:
|
||||
raise ValueError(
|
||||
f"Missing required metadata variables in __init__.py: {missing_vars}"
|
||||
)
|
||||
|
||||
return vars2readme
|
||||
|
||||
|
||||
# Reading dependencies from requirements.txt
|
||||
def read_requirements():
|
||||
deps = []
|
||||
try:
|
||||
with open("./requirements.txt") as f:
|
||||
deps = [line.strip() for line in f if line.strip()]
|
||||
except FileNotFoundError:
|
||||
print(
|
||||
"Warning: 'requirements.txt' not found. No dependencies will be installed."
|
||||
)
|
||||
return deps
|
||||
|
||||
|
||||
metadata = retrieve_metadata()
|
||||
long_description = read_long_description()
|
||||
requirements = read_requirements()
|
||||
|
||||
setuptools.setup(
|
||||
name="lightrag-hku",
|
||||
url=vars2readme["__url__"],
|
||||
version=vars2readme["__version__"],
|
||||
author=vars2readme["__author__"],
|
||||
url=metadata["__url__"],
|
||||
version=metadata["__version__"],
|
||||
author=metadata["__author__"],
|
||||
description="LightRAG: Simple and Fast Retrieval-Augmented Generation",
|
||||
long_description=long_description,
|
||||
long_description_content_type="text/markdown",
|
||||
packages=["lightrag"],
|
||||
packages=setuptools.find_packages(
|
||||
exclude=("tests*", "docs*")
|
||||
), # Automatically find packages
|
||||
classifiers=[
|
||||
"Development Status :: 4 - Beta",
|
||||
"Programming Language :: Python :: 3",
|
||||
"License :: OSI Approved :: MIT License",
|
||||
"Operating System :: OS Independent",
|
||||
"Intended Audience :: Developers",
|
||||
"Topic :: Software Development :: Libraries :: Python Modules",
|
||||
],
|
||||
python_requires=">=3.9",
|
||||
install_requires=deps,
|
||||
install_requires=requirements,
|
||||
include_package_data=True, # Includes non-code files from MANIFEST.in
|
||||
project_urls={ # Additional project metadata
|
||||
"Documentation": metadata.get("__url__", ""),
|
||||
"Source": metadata.get("__url__", ""),
|
||||
"Tracker": f"{metadata.get('__url__', '')}/issues"
|
||||
if metadata.get("__url__")
|
||||
else "",
|
||||
},
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user