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Add embedding_dim parameter support to embedding functions

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• Pass embedding_dim to jina_embed call
• Pass embedding_dim to openai_embed call
This commit is contained in:
yangdx 2025-11-07 21:23:59 +08:00
parent c14f25b7f8
commit ce28f30ca6
1 changed files with 5 additions and 3 deletions
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8
lightrag/api/lightrag_server.py
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@ -605,7 +605,7 @@ def create_app(args):
Uses lazy imports for all bindings and avoids repeated configuration parsing.
"""
async def optimized_embedding_function(texts):
async def optimized_embedding_function(texts, embedding_dim=None):
try:
if binding == "lollms":
from lightrag.llm.lollms import lollms_embed
@ -643,12 +643,14 @@ def create_app(args):
elif binding == "jina":
from lightrag.llm.jina import jina_embed
return await jina_embed(texts, base_url=host, api_key=api_key)
return await jina_embed(
texts, embedding_dim=embedding_dim, base_url=host, api_key=api_key
)
else: # openai and compatible
from lightrag.llm.openai import openai_embed
return await openai_embed(
texts, model=model, base_url=host, api_key=api_key
texts, model=model, base_url=host, api_key=api_key, embedding_dim=embedding_dim
)
except ImportError as e:
raise Exception(f"Failed to import {binding} embedding: {e}")