gmakstutis/LightRAG
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Allow embedding models to use provider defaults when unspecified

Browse source 
- Set EMBEDDING_MODEL default to None
- Pass model param only when provided
- Let providers use their own defaults
- Fix lollms embed function params
- Add ollama embed_model default param
This commit is contained in:
yangdx 2025-11-28 16:57:33 +08:00
parent 881b8d3a50
commit 4ab4a7ac94
3 changed files with 66 additions and 37 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
lightrag/api/config.py
View file

@ -365,8 +365,12 @@ def parse_args() -> argparse.Namespace:
# Inject model configuration
args.llm_model = get_env_value("LLM_MODEL", "mistral-nemo:latest")
args.embedding_model = get_env_value("EMBEDDING_MODEL", "bge-m3:latest")
args.embedding_dim = get_env_value("EMBEDDING_DIM", 1024, int)
# EMBEDDING_MODEL defaults to None - each binding will use its own default model
# e.g., OpenAI uses "text-embedding-3-small", Jina uses "jina-embeddings-v4"
args.embedding_model = get_env_value("EMBEDDING_MODEL", None, special_none=True)
# EMBEDDING_DIM defaults to None - each binding will use its own default dimension
# Value is inherited from provider defaults via wrap_embedding_func_with_attrs decorator
args.embedding_dim = get_env_value("EMBEDDING_DIM", None, int, special_none=True)
args.embedding_send_dim = get_env_value("EMBEDDING_SEND_DIM", False, bool)
# Inject chunk configuration

91
lightrag/api/lightrag_server.py
View file

@ -713,6 +713,7 @@ def create_app(args):
)
# Step 3: Create optimized embedding function (calls underlying function directly)
# Note: When model is None, each binding will use its own default model
async def optimized_embedding_function(texts, embedding_dim=None):
try:
if binding == "lollms":
@ -724,9 +725,9 @@ def create_app(args):
if isinstance(lollms_embed, EmbeddingFunc)
else lollms_embed
)
return await actual_func(
texts, embed_model=model, host=host, api_key=api_key
)
# lollms embed_model is not used (server uses configured vectorizer)
# Only pass base_url and api_key
return await actual_func(texts, base_url=host, api_key=api_key)
elif binding == "ollama":
from lightrag.llm.ollama import ollama_embed
@ -745,13 +746,16 @@ def create_app(args):
ollama_options = OllamaEmbeddingOptions.options_dict(args)
return await actual_func(
texts,
embed_model=model,
host=host,
api_key=api_key,
options=ollama_options,
)
# Pass embed_model only if provided, let function use its default (bge-m3:latest)
kwargs = {
"texts": texts,
"host": host,
"api_key": api_key,
"options": ollama_options,
}
if model:
kwargs["embed_model"] = model
return await actual_func(**kwargs)
elif binding == "azure_openai":
from lightrag.llm.azure_openai import azure_openai_embed
@ -760,7 +764,11 @@ def create_app(args):
if isinstance(azure_openai_embed, EmbeddingFunc)
else azure_openai_embed
)
return await actual_func(texts, model=model, api_key=api_key)
# Pass model only if provided, let function use its default otherwise
kwargs = {"texts": texts, "api_key": api_key}
if model:
kwargs["model"] = model
return await actual_func(**kwargs)
elif binding == "aws_bedrock":
from lightrag.llm.bedrock import bedrock_embed
@ -769,7 +777,11 @@ def create_app(args):
if isinstance(bedrock_embed, EmbeddingFunc)
else bedrock_embed
)
return await actual_func(texts, model=model)
# Pass model only if provided, let function use its default otherwise
kwargs = {"texts": texts}
if model:
kwargs["model"] = model
return await actual_func(**kwargs)
elif binding == "jina":
from lightrag.llm.jina import jina_embed
@ -778,13 +790,16 @@ def create_app(args):
if isinstance(jina_embed, EmbeddingFunc)
else jina_embed
)
return await actual_func(
texts,
model=model,
embedding_dim=embedding_dim,
base_url=host,
api_key=api_key,
)
# Pass model only if provided, let function use its default (jina-embeddings-v4)
kwargs = {
"texts": texts,
"embedding_dim": embedding_dim,
"base_url": host,
"api_key": api_key,
}
if model:
kwargs["model"] = model
return await actual_func(**kwargs)
elif binding == "gemini":
from lightrag.llm.gemini import gemini_embed
@ -802,14 +817,19 @@ def create_app(args):
gemini_options = GeminiEmbeddingOptions.options_dict(args)
return await actual_func(
texts,
model=model,
base_url=host,
api_key=api_key,
embedding_dim=embedding_dim,
task_type=gemini_options.get("task_type", "RETRIEVAL_DOCUMENT"),
)
# Pass model only if provided, let function use its default (gemini-embedding-001)
kwargs = {
"texts": texts,
"base_url": host,
"api_key": api_key,
"embedding_dim": embedding_dim,
"task_type": gemini_options.get(
"task_type", "RETRIEVAL_DOCUMENT"
),
}
if model:
kwargs["model"] = model
return await actual_func(**kwargs)
else: # openai and compatible
from lightrag.llm.openai import openai_embed
@ -818,13 +838,16 @@ def create_app(args):
if isinstance(openai_embed, EmbeddingFunc)
else openai_embed
)
return await actual_func(
texts,
model=model,
base_url=host,
api_key=api_key,
embedding_dim=embedding_dim,
)
# Pass model only if provided, let function use its default (text-embedding-3-small)
kwargs = {
"texts": texts,
"base_url": host,
"api_key": api_key,
"embedding_dim": embedding_dim,
}
if model:
kwargs["model"] = model
return await actual_func(**kwargs)
except ImportError as e:
raise Exception(f"Failed to import {binding} embedding: {e}")

4
lightrag/llm/ollama.py
View file

@ -173,7 +173,9 @@ async def ollama_model_complete(
@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray:
async def ollama_embed(
texts: list[str], embed_model: str = "bge-m3:latest", **kwargs
) -> np.ndarray:
api_key = kwargs.pop("api_key", None)
if not api_key:
api_key = os.getenv("OLLAMA_API_KEY")