cherry-pick 1d07ff7f
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Raphaël MANSUY
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2 changed files with 52 additions and 42 deletions
47
README-zh.md
47
README-zh.md
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@ -410,6 +410,11 @@ LightRAG 需要利用LLM和Embeding模型来完成文档索引和知识库查询
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* LightRAG还支持类OpenAI的聊天/嵌入API：
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```python
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import os
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import numpy as np
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from lightrag.utils import wrap_embedding_func_with_attrs
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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@ -423,8 +428,9 @@ async def llm_model_func(
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**kwargs
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)
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@wrap_embedding_func_with_attrs(embedding_dim=4096, max_token_size=8192)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await openai_embed(
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return await openai_embed.func(
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texts,
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model="solar-embedding-1-large-query",
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api_key=os.getenv("UPSTAGE_API_KEY"),
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@ -435,10 +441,7 @@ async def initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=4096,
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func=embedding_func
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)
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embedding_func=embedding_func # 直接传入装饰后的函数
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)
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await rag.initialize_storages()
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@ -481,19 +484,20 @@ rag = LightRAG(
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然后您只需要按如下方式设置LightRAG：
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```python
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import numpy as np
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from lightrag.utils import wrap_embedding_func_with_attrs
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from lightrag.llm.ollama import ollama_model_complete, ollama_embed
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@wrap_embedding_func_with_attrs(embedding_dim=768, max_token_size=8192)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await ollama_embed.func(texts, embed_model="nomic-embed-text")
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# 使用Ollama模型初始化LightRAG
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=ollama_model_complete, # 使用Ollama模型进行文本生成
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llm_model_name='your_model_name', # 您的模型名称
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# 使用Ollama嵌入函数
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embedding_func=EmbeddingFunc(
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embedding_dim=768,
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func=lambda texts: ollama_embed(
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texts,
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embed_model="nomic-embed-text"
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)
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),
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embedding_func=embedding_func, # 直接传入装饰后的函数
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)
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```
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@ -532,19 +536,20 @@ ollama create -f Modelfile qwen2m
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您可以使用`llm_model_kwargs`参数配置ollama：
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```python
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import numpy as np
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from lightrag.utils import wrap_embedding_func_with_attrs
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from lightrag.llm.ollama import ollama_model_complete, ollama_embed
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@wrap_embedding_func_with_attrs(embedding_dim=768, max_token_size=8192)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await ollama_embed.func(texts, embed_model="nomic-embed-text")
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=ollama_model_complete, # 使用Ollama模型进行文本生成
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llm_model_name='your_model_name', # 您的模型名称
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llm_model_kwargs={"options": {"num_ctx": 32768}},
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# 使用Ollama嵌入函数
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embedding_func=EmbeddingFunc(
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embedding_dim=768,
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func=lambda texts: ollama_embed(
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texts,
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embed_model="nomic-embed-text"
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)
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),
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embedding_func=embedding_func, # 直接传入装饰后的函数
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)
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```
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47
README.md
47
README.md
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@ -406,6 +406,11 @@ LightRAG requires the utilization of LLM and Embedding models to accomplish docu
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* LightRAG also supports Open AI-like chat/embeddings APIs:
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```python
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import os
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import numpy as np
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from lightrag.utils import wrap_embedding_func_with_attrs
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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@ -419,8 +424,9 @@ async def llm_model_func(
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**kwargs
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)
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@wrap_embedding_func_with_attrs(embedding_dim=4096, max_token_size=8192)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await openai_embed(
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return await openai_embed.func(
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texts,
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model="solar-embedding-1-large-query",
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api_key=os.getenv("UPSTAGE_API_KEY"),
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@ -431,10 +437,7 @@ async def initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=4096,
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func=embedding_func
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)
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embedding_func=embedding_func # Pass the decorated function directly
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)
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await rag.initialize_storages()
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@ -479,19 +482,20 @@ If you want to use Ollama models, you need to pull model you plan to use and emb
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Then you only need to set LightRAG as follows:
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```python
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import numpy as np
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from lightrag.utils import wrap_embedding_func_with_attrs
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from lightrag.llm.ollama import ollama_model_complete, ollama_embed
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@wrap_embedding_func_with_attrs(embedding_dim=768, max_token_size=8192)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await ollama_embed.func(texts, embed_model="nomic-embed-text")
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# Initialize LightRAG with Ollama model
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=ollama_model_complete, # Use Ollama model for text generation
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llm_model_name='your_model_name', # Your model name
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# Use Ollama embedding function
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embedding_func=EmbeddingFunc(
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embedding_dim=768,
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func=lambda texts: ollama_embed(
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texts,
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embed_model="nomic-embed-text"
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)
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),
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embedding_func=embedding_func, # Pass the decorated function directly
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)
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```
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@ -530,19 +534,20 @@ ollama create -f Modelfile qwen2m
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Tiy can use `llm_model_kwargs` param to configure ollama:
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```python
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import numpy as np
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from lightrag.utils import wrap_embedding_func_with_attrs
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from lightrag.llm.ollama import ollama_model_complete, ollama_embed
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@wrap_embedding_func_with_attrs(embedding_dim=768, max_token_size=8192)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await ollama_embed.func(texts, embed_model="nomic-embed-text")
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=ollama_model_complete, # Use Ollama model for text generation
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llm_model_name='your_model_name', # Your model name
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llm_model_kwargs={"options": {"num_ctx": 32768}},
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# Use Ollama embedding function
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embedding_func=EmbeddingFunc(
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embedding_dim=768,
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func=lambda texts: ollama_embed(
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texts,
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embed_model="nomic-embed-text"
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)
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),
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embedding_func=embedding_func, # Pass the decorated function directly
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)
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```
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