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Remove depreced sample code

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yangdx 2025-11-07 16:15:20 +08:00
parent 924c8cb8a3
commit c580874a1a
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105
examples/lightrag_gemini_demo.py
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# pip install -q -U google-genai to use gemini as a client
import os
import numpy as np
from google import genai
from google.genai import types
from dotenv import load_dotenv
from lightrag.utils import EmbeddingFunc
from lightrag import LightRAG, QueryParam
from sentence_transformers import SentenceTransformer
from lightrag.kg.shared_storage import initialize_pipeline_status
import asyncio
import nest_asyncio
# Apply nest_asyncio to solve event loop issues
nest_asyncio.apply()
load_dotenv()
gemini_api_key = os.getenv("GEMINI_API_KEY")
WORKING_DIR = "./dickens"
if os.path.exists(WORKING_DIR):
import shutil
shutil.rmtree(WORKING_DIR)
os.mkdir(WORKING_DIR)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
# 1. Initialize the GenAI Client with your Gemini API Key
client = genai.Client(api_key=gemini_api_key)
# 2. Combine prompts: system prompt, history, and user prompt
if history_messages is None:
history_messages = []
combined_prompt = ""
if system_prompt:
combined_prompt += f"{system_prompt}\n"
for msg in history_messages:
# Each msg is expected to be a dict: {"role": "...", "content": "..."}
combined_prompt += f"{msg['role']}: {msg['content']}\n"
# Finally, add the new user prompt
combined_prompt += f"user: {prompt}"
# 3. Call the Gemini model
response = client.models.generate_content(
model="gemini-1.5-flash",
contents=[combined_prompt],
config=types.GenerateContentConfig(max_output_tokens=500, temperature=0.1),
)
# 4. Return the response text
return response.text
async def embedding_func(texts: list[str]) -> np.ndarray:
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(texts, convert_to_numpy=True)
return embeddings
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=384,
max_token_size=8192,
func=embedding_func,
),
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
file_path = "story.txt"
with open(file_path, "r") as file:
text = file.read()
rag.insert(text)
response = rag.query(
query="What is the main theme of the story?",
param=QueryParam(mode="hybrid", top_k=5, response_type="single line"),
)
print(response)
if __name__ == "__main__":
main()

230
examples/lightrag_gemini_demo_no_tiktoken.py
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# pip install -q -U google-genai to use gemini as a client
import os
from typing import Optional
import dataclasses
from pathlib import Path
import hashlib
import numpy as np
from google import genai
from google.genai import types
from dotenv import load_dotenv
from lightrag.utils import EmbeddingFunc, Tokenizer
from lightrag import LightRAG, QueryParam
from sentence_transformers import SentenceTransformer
from lightrag.kg.shared_storage import initialize_pipeline_status
import sentencepiece as spm
import requests
import asyncio
import nest_asyncio
# Apply nest_asyncio to solve event loop issues
nest_asyncio.apply()
load_dotenv()
gemini_api_key = os.getenv("GEMINI_API_KEY")
WORKING_DIR = "./dickens"
if os.path.exists(WORKING_DIR):
import shutil
shutil.rmtree(WORKING_DIR)
os.mkdir(WORKING_DIR)
class GemmaTokenizer(Tokenizer):
# adapted from google-cloud-aiplatform[tokenization]
@dataclasses.dataclass(frozen=True)
class _TokenizerConfig:
tokenizer_model_url: str
tokenizer_model_hash: str
_TOKENIZERS = {
"google/gemma2": _TokenizerConfig(
tokenizer_model_url="https://raw.githubusercontent.com/google/gemma_pytorch/33b652c465537c6158f9a472ea5700e5e770ad3f/tokenizer/tokenizer.model",
tokenizer_model_hash="61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2",
),
"google/gemma3": _TokenizerConfig(
tokenizer_model_url="https://raw.githubusercontent.com/google/gemma_pytorch/cb7c0152a369e43908e769eb09e1ce6043afe084/tokenizer/gemma3_cleaned_262144_v2.spiece.model",
tokenizer_model_hash="1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c",
),
}
def __init__(
self, model_name: str = "gemini-2.0-flash", tokenizer_dir: Optional[str] = None
):
# https://github.com/google/gemma_pytorch/tree/main/tokenizer
if "1.5" in model_name or "1.0" in model_name:
# up to gemini 1.5 gemma2 is a comparable local tokenizer
# https://github.com/googleapis/python-aiplatform/blob/main/vertexai/tokenization/_tokenizer_loading.py
tokenizer_name = "google/gemma2"
else:
# for gemini > 2.0 gemma3 was used
tokenizer_name = "google/gemma3"
file_url = self._TOKENIZERS[tokenizer_name].tokenizer_model_url
tokenizer_model_name = file_url.rsplit("/", 1)[1]
expected_hash = self._TOKENIZERS[tokenizer_name].tokenizer_model_hash
tokenizer_dir = Path(tokenizer_dir)
if tokenizer_dir.is_dir():
file_path = tokenizer_dir / tokenizer_model_name
model_data = self._maybe_load_from_cache(
file_path=file_path, expected_hash=expected_hash
)
else:
model_data = None
if not model_data:
model_data = self._load_from_url(
file_url=file_url, expected_hash=expected_hash
)
self.save_tokenizer_to_cache(cache_path=file_path, model_data=model_data)
tokenizer = spm.SentencePieceProcessor()
tokenizer.LoadFromSerializedProto(model_data)
super().__init__(model_name=model_name, tokenizer=tokenizer)
def _is_valid_model(self, model_data: bytes, expected_hash: str) -> bool:
"""Returns true if the content is valid by checking the hash."""
return hashlib.sha256(model_data).hexdigest() == expected_hash
def _maybe_load_from_cache(self, file_path: Path, expected_hash: str) -> bytes:
"""Loads the model data from the cache path."""
if not file_path.is_file():
return
with open(file_path, "rb") as f:
content = f.read()
if self._is_valid_model(model_data=content, expected_hash=expected_hash):
return content
# Cached file corrupted.
self._maybe_remove_file(file_path)
def _load_from_url(self, file_url: str, expected_hash: str) -> bytes:
"""Loads model bytes from the given file url."""
resp = requests.get(file_url)
resp.raise_for_status()
content = resp.content
if not self._is_valid_model(model_data=content, expected_hash=expected_hash):
actual_hash = hashlib.sha256(content).hexdigest()
raise ValueError(
f"Downloaded model file is corrupted."
f" Expected hash {expected_hash}. Got file hash {actual_hash}."
)
return content
@staticmethod
def save_tokenizer_to_cache(cache_path: Path, model_data: bytes) -> None:
"""Saves the model data to the cache path."""
try:
if not cache_path.is_file():
cache_dir = cache_path.parent
cache_dir.mkdir(parents=True, exist_ok=True)
with open(cache_path, "wb") as f:
f.write(model_data)
except OSError:
# Don't raise if we cannot write file.
pass
@staticmethod
def _maybe_remove_file(file_path: Path) -> None:
"""Removes the file if exists."""
if not file_path.is_file():
return
try:
file_path.unlink()
except OSError:
# Don't raise if we cannot remove file.
pass
# def encode(self, content: str) -> list[int]:
# return self.tokenizer.encode(content)
# def decode(self, tokens: list[int]) -> str:
# return self.tokenizer.decode(tokens)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
# 1. Initialize the GenAI Client with your Gemini API Key
client = genai.Client(api_key=gemini_api_key)
# 2. Combine prompts: system prompt, history, and user prompt
if history_messages is None:
history_messages = []
combined_prompt = ""
if system_prompt:
combined_prompt += f"{system_prompt}\n"
for msg in history_messages:
# Each msg is expected to be a dict: {"role": "...", "content": "..."}
combined_prompt += f"{msg['role']}: {msg['content']}\n"
# Finally, add the new user prompt
combined_prompt += f"user: {prompt}"
# 3. Call the Gemini model
response = client.models.generate_content(
model="gemini-1.5-flash",
contents=[combined_prompt],
config=types.GenerateContentConfig(max_output_tokens=500, temperature=0.1),
)
# 4. Return the response text
return response.text
async def embedding_func(texts: list[str]) -> np.ndarray:
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(texts, convert_to_numpy=True)
return embeddings
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
# tiktoken_model_name="gpt-4o-mini",
tokenizer=GemmaTokenizer(
tokenizer_dir=(Path(WORKING_DIR) / "vertexai_tokenizer_model"),
model_name="gemini-2.0-flash",
),
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=384,
max_token_size=8192,
func=embedding_func,
),
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
file_path = "story.txt"
with open(file_path, "r") as file:
text = file.read()
rag.insert(text)
response = rag.query(
query="What is the main theme of the story?",
param=QueryParam(mode="hybrid", top_k=5, response_type="single line"),
)
print(response)
if __name__ == "__main__":
main()

151
examples/lightrag_gemini_track_token_demo.py
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# pip install -q -U google-genai to use gemini as a client
import os
import asyncio
import numpy as np
import nest_asyncio
from google import genai
from google.genai import types
from dotenv import load_dotenv
from lightrag.utils import EmbeddingFunc
from lightrag import LightRAG, QueryParam
from lightrag.kg.shared_storage import initialize_pipeline_status
from lightrag.llm.siliconcloud import siliconcloud_embedding
from lightrag.utils import setup_logger
from lightrag.utils import TokenTracker
setup_logger("lightrag", level="DEBUG")
# Apply nest_asyncio to solve event loop issues
nest_asyncio.apply()
load_dotenv()
gemini_api_key = os.getenv("GEMINI_API_KEY")
siliconflow_api_key = os.getenv("SILICONFLOW_API_KEY")
WORKING_DIR = "./dickens"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
token_tracker = TokenTracker()
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
# 1. Initialize the GenAI Client with your Gemini API Key
client = genai.Client(api_key=gemini_api_key)
# 2. Combine prompts: system prompt, history, and user prompt
if history_messages is None:
history_messages = []
combined_prompt = ""
if system_prompt:
combined_prompt += f"{system_prompt}\n"
for msg in history_messages:
# Each msg is expected to be a dict: {"role": "...", "content": "..."}
combined_prompt += f"{msg['role']}: {msg['content']}\n"
# Finally, add the new user prompt
combined_prompt += f"user: {prompt}"
# 3. Call the Gemini model
response = client.models.generate_content(
model="gemini-2.0-flash",
contents=[combined_prompt],
config=types.GenerateContentConfig(
max_output_tokens=5000, temperature=0, top_k=10
),
)
# 4. Get token counts with null safety
usage = getattr(response, "usage_metadata", None)
prompt_tokens = getattr(usage, "prompt_token_count", 0) or 0
completion_tokens = getattr(usage, "candidates_token_count", 0) or 0
total_tokens = getattr(usage, "total_token_count", 0) or (
prompt_tokens + completion_tokens
)
token_counts = {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": total_tokens,
}
token_tracker.add_usage(token_counts)
# 5. Return the response text
return response.text
async def embedding_func(texts: list[str]) -> np.ndarray:
return await siliconcloud_embedding(
texts,
model="BAAI/bge-m3",
api_key=siliconflow_api_key,
max_token_size=512,
)
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
entity_extract_max_gleaning=1,
enable_llm_cache=True,
enable_llm_cache_for_entity_extract=True,
embedding_cache_config={"enabled": True, "similarity_threshold": 0.90},
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=1024,
max_token_size=8192,
func=embedding_func,
),
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
with open("./book.txt", "r", encoding="utf-8") as f:
rag.insert(f.read())
# Context Manager Method
with token_tracker:
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="local")
)
)
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="hybrid"),
)
)
if __name__ == "__main__":
main()