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Separated llms from the main llm.py file and fixed some deprication bugs

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Saifeddine ALOUI
2025-01-25 00:11:00 +01:00
parent 7e1638525c
commit 34018cb1e0
55 changed files with 2144 additions and 1301 deletions
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10
README.md
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@@ -81,7 +81,7 @@ Use the below Python snippet (in a script) to initialize LightRAG and perform qu
```python
import os
from lightrag import LightRAG, QueryParam
from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
from lightrag.llm.openai 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()
@@ -177,7 +177,7 @@ async def llm_model_func(
)
async def embedding_func(texts: list[str]) -> np.ndarray:
return await openai_embedding(
return await openai_embed(
texts,
model="solar-embedding-1-large-query",
api_key=os.getenv("UPSTAGE_API_KEY"),
@@ -233,7 +233,7 @@ 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 import ollama_model_complete, ollama_embedding
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
from lightrag.utils import EmbeddingFunc
# Initialize LightRAG with Ollama model
@@ -245,7 +245,7 @@ rag = LightRAG(
embedding_func=EmbeddingFunc(
embedding_dim=768,
max_token_size=8192,
func=lambda texts: ollama_embedding(
func=lambda texts: ollama_embed(
texts,
embed_model="nomic-embed-text"
)
@@ -690,7 +690,7 @@ if __name__ == "__main__":
| **entity\_summary\_to\_max\_tokens** | `int` | Maximum token size for each entity summary | `500` |
| **node\_embedding\_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |
| **node2vec\_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` |
| **embedding\_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embedding` |
| **embedding\_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embed` |
| **embedding\_batch\_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` |
| **embedding\_func\_max\_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
| **llm\_model\_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` |