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fix pre commit

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This commit is contained in:
jin
2024-11-12 13:32:40 +08:00
parent 90790747ee
commit 33caba3e12
7 changed files with 345 additions and 310 deletions
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79
examples/lightrag_api_oracle_demo..py
View File

@@ -1,10 +1,10 @@
from fastapi import FastAPI, HTTPException, File, UploadFile
from contextlib import asynccontextmanager
from pydantic import BaseModel
from typing import Optional
import sys, os
import sys
import os
from pathlib import Path
import asyncio
@@ -13,7 +13,6 @@ 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 datetime import datetime
from lightrag.kg.oracle_impl import OracleDB
@@ -24,8 +23,6 @@ script_directory = Path(__file__).resolve().parent.parent
sys.path.append(os.path.abspath(script_directory))
# Apply nest_asyncio to solve event loop issues
nest_asyncio.apply()
@@ -51,6 +48,7 @@ print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], **kwargs
) -> str:
@@ -80,8 +78,8 @@ async def get_embedding_dim():
embedding_dim = embedding.shape[1]
return embedding_dim
async def init():
# Detect embedding dimension
embedding_dimension = await get_embedding_dim()
print(f"Detected embedding dimension: {embedding_dimension}")
@@ -91,36 +89,36 @@ async def init():
# We storage data in unified tables, so we need to set a `workspace` parameter to specify which docs we want to store and query
# Below is an example of how to connect to Oracle Autonomous Database on Oracle Cloud
oracle_db = OracleDB(
config={
"user": "",
"password": "",
"dsn": "",
"config_dir": "",
"wallet_location": "",
"wallet_password": "",
"workspace": "",
} # specify which docs you want to store and query
)
oracle_db = OracleDB(config={
"user":"",
"password":"",
"dsn":"",
"config_dir":"",
"wallet_location":"",
"wallet_password":"",
"workspace":""
} # specify which docs you want to store and query
)
# Check if Oracle DB tables exist, if not, tables will be created
await oracle_db.check_tables()
# Initialize LightRAG
# We use Oracle DB as the KV/vector/graph storage
# We use Oracle DB as the KV/vector/graph storage
rag = LightRAG(
enable_llm_cache=False,
working_dir=WORKING_DIR,
chunk_token_size=512,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=512,
func=embedding_func,
),
graph_storage = "OracleGraphStorage",
kv_storage="OracleKVStorage",
vector_storage="OracleVectorDBStorage"
)
enable_llm_cache=False,
working_dir=WORKING_DIR,
chunk_token_size=512,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=512,
func=embedding_func,
),
graph_storage="OracleGraphStorage",
kv_storage="OracleKVStorage",
vector_storage="OracleVectorDBStorage",
)
# Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
rag.graph_storage_cls.db = oracle_db
@@ -129,6 +127,7 @@ async def init():
return rag
# Data models
@@ -152,6 +151,7 @@ class Response(BaseModel):
rag = None # 定义为全局对象
@asynccontextmanager
async def lifespan(app: FastAPI):
global rag
@@ -160,18 +160,21 @@ async def lifespan(app: FastAPI):
yield
app = FastAPI(title="LightRAG API", description="API for RAG operations",lifespan=lifespan)
app = FastAPI(
title="LightRAG API", description="API for RAG operations", lifespan=lifespan
)
@app.post("/query", response_model=Response)
async def query_endpoint(request: QueryRequest):
try:
# loop = asyncio.get_event_loop()
result = await rag.aquery(
request.query,
param=QueryParam(
mode=request.mode, only_need_context=request.only_need_context
),
)
request.query,
param=QueryParam(
mode=request.mode, only_need_context=request.only_need_context
),
)
return Response(status="success", data=result)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@@ -234,4 +237,4 @@ if __name__ == "__main__":
# 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"
# curl -X GET "http://127.0.0.1:8020/health"

49
examples/lightrag_oracle_demo.py
View File

@@ -1,11 +1,11 @@
import sys, os
import sys
import os
from pathlib import Path
import asyncio
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 datetime import datetime
from lightrag.kg.oracle_impl import OracleDB
print(os.getcwd())
@@ -25,6 +25,7 @@ EMBEDMODEL = "cohere.embed-multilingual-v3.0"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], **kwargs
) -> str:
@@ -66,22 +67,21 @@ async def main():
# More docs here https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html
# We storage data in unified tables, so we need to set a `workspace` parameter to specify which docs we want to store and query
# Below is an example of how to connect to Oracle Autonomous Database on Oracle Cloud
oracle_db = OracleDB(config={
"user":"username",
"password":"xxxxxxxxx",
"dsn":"xxxxxxx_medium",
"config_dir":"dir/path/to/oracle/config",
"wallet_location":"dir/path/to/oracle/wallet",
"wallet_password":"xxxxxxxxx",
"workspace":"company" # specify which docs you want to store and query
oracle_db = OracleDB(
config={
"user": "username",
"password": "xxxxxxxxx",
"dsn": "xxxxxxx_medium",
"config_dir": "dir/path/to/oracle/config",
"wallet_location": "dir/path/to/oracle/wallet",
"wallet_password": "xxxxxxxxx",
"workspace": "company", # specify which docs you want to store and query
}
)
)
# Check if Oracle DB tables exist, if not, tables will be created
await oracle_db.check_tables()
# Initialize LightRAG
# We use Oracle DB as the KV/vector/graph storage
rag = LightRAG(
@@ -93,10 +93,10 @@ async def main():
embedding_dim=embedding_dimension,
max_token_size=512,
func=embedding_func,
),
graph_storage = "OracleGraphStorage",
kv_storage="OracleKVStorage",
vector_storage="OracleVectorDBStorage"
),
graph_storage="OracleGraphStorage",
kv_storage="OracleKVStorage",
vector_storage="OracleVectorDBStorage",
)
# Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
@@ -106,18 +106,23 @@ async def main():
# Extract and Insert into LightRAG storage
with open("./dickens/demo.txt", "r", encoding="utf-8") as f:
await rag.ainsert(f.read())
await rag.ainsert(f.read())
# Perform search in different modes
modes = ["naive", "local", "global", "hybrid"]
for mode in modes:
print("="*20, mode, "="*20)
print(await rag.aquery("What are the top themes in this story?", param=QueryParam(mode=mode)))
print("-"*100, "\n")
print("=" * 20, mode, "=" * 20)
print(
await rag.aquery(
"What are the top themes in this story?",
param=QueryParam(mode=mode),
)
)
print("-" * 100, "\n")
except Exception as e:
print(f"An error occurred: {e}")
if __name__ == "__main__":
asyncio.run(main())
asyncio.run(main())