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fix demo

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zrguo
2025-03-03 18:33:42 +08:00
parent 887388c317
commit 1611400854
41 changed files with 1390 additions and 1301 deletions
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README.md
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@@ -102,33 +102,47 @@ Use the below Python snippet (in a script) to initialize LightRAG and perform qu
```python
import os
import asyncio
from lightrag import LightRAG, QueryParam
from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
from lightrag.kg.shared_storage import initialize_pipeline_status
rag = LightRAG(
working_dir="your/path",
embedding_func=openai_embed,
llm_model_func=gpt_4o_mini_complete
)
async def initialize_rag():
rag = LightRAG(
working_dir="your/path",
embedding_func=openai_embed,
llm_model_func=gpt_4o_mini_complete
)
# Insert text
rag.insert("Your text")
await rag.initialize_storages()
await initialize_pipeline_status()
# Perform naive search
mode="naive"
# Perform local search
mode="local"
# Perform global search
mode="global"
# Perform hybrid search
mode="hybrid"
# Mix mode Integrates knowledge graph and vector retrieval.
mode="mix"
return rag
rag.query(
"What are the top themes in this story?",
param=QueryParam(mode=mode)
)
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
# Insert text
rag.insert("Your text")
# Perform naive search
mode="naive"
# Perform local search
mode="local"
# Perform global search
mode="global"
# Perform hybrid search
mode="hybrid"
# Mix mode Integrates knowledge graph and vector retrieval.
mode="mix"
rag.query(
"What are the top themes in this story?",
param=QueryParam(mode=mode)
)
if __name__ == "__main__":
main()
```
### Query Param
@@ -190,15 +204,21 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
base_url="https://api.upstage.ai/v1/solar"
)
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 initialize_rag():
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
)
)
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
```
</details>
@@ -210,10 +230,6 @@ rag = LightRAG(
See `lightrag_hf_demo.py`
```python
from lightrag.llm import hf_model_complete, hf_embed
from transformers import AutoModel, AutoTokenizer
from lightrag.utils import EmbeddingFunc
# Initialize LightRAG with Hugging Face model
rag = LightRAG(
working_dir=WORKING_DIR,
@@ -242,9 +258,6 @@ If you want to use Ollama models, you need to pull model you plan to use and emb
Then you only need to set LightRAG as follows:
```python
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
from lightrag.utils import EmbeddingFunc
# Initialize LightRAG with Ollama model
rag = LightRAG(
working_dir=WORKING_DIR,
@@ -325,20 +338,58 @@ LightRAG supports integration with LlamaIndex.
```python
# Using LlamaIndex with direct OpenAI access
import asyncio
from lightrag import LightRAG
from lightrag.llm.llama_index_impl import llama_index_complete_if_cache, llama_index_embed
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from lightrag.kg.shared_storage import initialize_pipeline_status
rag = LightRAG(
working_dir="your/path",
llm_model_func=llama_index_complete_if_cache, # LlamaIndex-compatible completion function
embedding_func=EmbeddingFunc( # LlamaIndex-compatible embedding function
embedding_dim=1536,
max_token_size=8192,
func=lambda texts: llama_index_embed(texts, embed_model=embed_model)
),
)
async def initialize_rag():
rag = LightRAG(
working_dir="your/path",
llm_model_func=llama_index_complete_if_cache, # LlamaIndex-compatible completion function
embedding_func=EmbeddingFunc( # LlamaIndex-compatible embedding function
embedding_dim=1536,
max_token_size=8192,
func=lambda texts: llama_index_embed(texts, embed_model=embed_model)
),
)
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())
# 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__":
main()
```
#### For detailed documentation and examples, see:
@@ -353,11 +404,6 @@ rag = LightRAG(
LightRAG now supports multi-turn dialogue through the conversation history feature. Here's how to use it:
```python
from lightrag import LightRAG, QueryParam
# Initialize LightRAG
rag = LightRAG(working_dir=WORKING_DIR)
# Create conversation history
conversation_history = [
{"role": "user", "content": "What is the main character's attitude towards Christmas?"},
@@ -387,11 +433,6 @@ response = rag.query(
LightRAG now supports custom prompts for fine-tuned control over the system's behavior. Here's how to use it:
```python
from lightrag import LightRAG, QueryParam
# Initialize LightRAG
rag = LightRAG(working_dir=WORKING_DIR)
# Create query parameters
query_param = QueryParam(
mode="hybrid", # or other mode: "local", "global", "hybrid", "mix" and "naive"
@@ -456,16 +497,6 @@ rag.query_with_separate_keyword_extraction(
<summary> <b>Insert Custom KG</b> </summary>
```python
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,
),
)
custom_kg = {
"entities": [
{
@@ -534,6 +565,7 @@ rag = LightRAG(
"insert_batch_size": 20 # Process 20 documents per batch
}
)
rag.insert(["TEXT1", "TEXT2", "TEXT3", ...]) # Documents will be processed in batches of 20
```
@@ -560,27 +592,6 @@ rag.insert(["TEXT1", "TEXT2",...], ids=["ID_FOR_TEXT1", "ID_FOR_TEXT2"])
</details>
<details>
<summary><b>Incremental Insert</b></summary>
```python
# Incremental Insert: Insert new documents into an existing LightRAG instance
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())
```
</details>
<details>
<summary><b>Insert using Pipeline</b></summary>
@@ -592,6 +603,7 @@ And using a routine to process news documents.
```python
rag = LightRAG(..)
await rag.apipeline_enqueue_documents(input)
# Your routine in loop
await rag.apipeline_process_enqueue_documents(input)
@@ -633,8 +645,6 @@ export NEO4J_PASSWORD="password"
# Note: Default settings use NetworkX
# Initialize LightRAG with Neo4J implementation.
WORKING_DIR = "./local_neo4jWorkDir"
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=gpt_4o_mini_complete, # Use gpt_4o_mini_complete LLM model
@@ -706,26 +716,26 @@ You can also install `faiss-gpu` if you have GPU support.
- Here we are using `sentence-transformers` but you can also use `OpenAIEmbedding` model with `3072` dimensions.
```
```python
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
# Initialize LightRAG with the LLM model function and embedding function
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,
),
vector_storage="FaissVectorDBStorage",
vector_db_storage_cls_kwargs={
"cosine_better_than_threshold": 0.3 # Your desired threshold
}
)
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,
),
vector_storage="FaissVectorDBStorage",
vector_db_storage_cls_kwargs={
"cosine_better_than_threshold": 0.3 # Your desired threshold
}
)
```
</details>
@@ -733,17 +743,6 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
## Delete
```python
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,
),
)
# Delete Entity: Deleting entities by their names
rag.delete_by_entity("Project Gutenberg")