cherry-pick 56e0365c
This commit is contained in:
Raphaël MANSUY
parent
49b0953ac1
commit
086191ae5a
2 changed files with 38 additions and 58 deletions
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@ -713,7 +713,6 @@ def create_app(args):
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)
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# Step 3: Create optimized embedding function (calls underlying function directly)
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# Note: When model is None, each binding will use its own default model
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async def optimized_embedding_function(texts, embedding_dim=None):
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try:
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if binding == "lollms":
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@ -725,9 +724,9 @@ def create_app(args):
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if isinstance(lollms_embed, EmbeddingFunc)
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else lollms_embed
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)
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# lollms embed_model is not used (server uses configured vectorizer)
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# Only pass base_url and api_key
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return await actual_func(texts, base_url=host, api_key=api_key)
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return await actual_func(
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texts, embed_model=model, host=host, api_key=api_key
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)
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elif binding == "ollama":
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from lightrag.llm.ollama import ollama_embed
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@ -746,16 +745,13 @@ def create_app(args):
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ollama_options = OllamaEmbeddingOptions.options_dict(args)
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# Pass embed_model only if provided, let function use its default (bge-m3:latest)
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kwargs = {
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"texts": texts,
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"host": host,
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"api_key": api_key,
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"options": ollama_options,
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}
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if model:
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kwargs["embed_model"] = model
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return await actual_func(**kwargs)
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return await actual_func(
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texts,
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embed_model=model,
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host=host,
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api_key=api_key,
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options=ollama_options,
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)
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elif binding == "azure_openai":
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from lightrag.llm.azure_openai import azure_openai_embed
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@ -764,11 +760,7 @@ def create_app(args):
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if isinstance(azure_openai_embed, EmbeddingFunc)
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else azure_openai_embed
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)
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# Pass model only if provided, let function use its default otherwise
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kwargs = {"texts": texts, "api_key": api_key}
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if model:
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kwargs["model"] = model
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return await actual_func(**kwargs)
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return await actual_func(texts, model=model, api_key=api_key)
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elif binding == "aws_bedrock":
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from lightrag.llm.bedrock import bedrock_embed
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@ -777,11 +769,7 @@ def create_app(args):
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if isinstance(bedrock_embed, EmbeddingFunc)
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else bedrock_embed
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)
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# Pass model only if provided, let function use its default otherwise
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kwargs = {"texts": texts}
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if model:
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kwargs["model"] = model
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return await actual_func(**kwargs)
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return await actual_func(texts, model=model)
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elif binding == "jina":
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from lightrag.llm.jina import jina_embed
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@ -790,16 +778,13 @@ def create_app(args):
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if isinstance(jina_embed, EmbeddingFunc)
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else jina_embed
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)
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# Pass model only if provided, let function use its default (jina-embeddings-v4)
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kwargs = {
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"texts": texts,
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"embedding_dim": embedding_dim,
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"base_url": host,
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"api_key": api_key,
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}
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if model:
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kwargs["model"] = model
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return await actual_func(**kwargs)
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return await actual_func(
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texts,
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model=model,
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embedding_dim=embedding_dim,
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base_url=host,
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api_key=api_key,
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)
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elif binding == "gemini":
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from lightrag.llm.gemini import gemini_embed
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@ -817,19 +802,14 @@ def create_app(args):
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gemini_options = GeminiEmbeddingOptions.options_dict(args)
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# Pass model only if provided, let function use its default (gemini-embedding-001)
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kwargs = {
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"texts": texts,
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"base_url": host,
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"api_key": api_key,
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"embedding_dim": embedding_dim,
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"task_type": gemini_options.get(
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"task_type", "RETRIEVAL_DOCUMENT"
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),
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}
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if model:
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kwargs["model"] = model
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return await actual_func(**kwargs)
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return await actual_func(
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texts,
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model=model,
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base_url=host,
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api_key=api_key,
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embedding_dim=embedding_dim,
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task_type=gemini_options.get("task_type", "RETRIEVAL_DOCUMENT"),
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)
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else: # openai and compatible
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from lightrag.llm.openai import openai_embed
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@ -838,16 +818,13 @@ def create_app(args):
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if isinstance(openai_embed, EmbeddingFunc)
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else openai_embed
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)
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# Pass model only if provided, let function use its default (text-embedding-3-small)
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kwargs = {
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"texts": texts,
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"base_url": host,
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"api_key": api_key,
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"embedding_dim": embedding_dim,
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}
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if model:
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kwargs["model"] = model
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return await actual_func(**kwargs)
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return await actual_func(
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texts,
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model=model,
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base_url=host,
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api_key=api_key,
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embedding_dim=embedding_dim,
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)
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except ImportError as e:
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raise Exception(f"Failed to import {binding} embedding: {e}")
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@ -69,6 +69,7 @@ async def fetch_data(url, headers, data):
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)
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async def jina_embed(
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texts: list[str],
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model: str = "jina-embeddings-v4",
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embedding_dim: int = 2048,
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late_chunking: bool = False,
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base_url: str = None,
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@ -78,6 +79,8 @@ async def jina_embed(
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Args:
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texts: List of texts to embed.
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model: The Jina embedding model to use (default: jina-embeddings-v4).
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Supported models: jina-embeddings-v3, jina-embeddings-v4, etc.
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embedding_dim: The embedding dimensions (default: 2048 for jina-embeddings-v4).
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**IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper.
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Do NOT manually pass this parameter when calling the function directly.
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@ -107,7 +110,7 @@ async def jina_embed(
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"Authorization": f"Bearer {os.environ['JINA_API_KEY']}",
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}
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data = {
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"model": "jina-embeddings-v4",
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"model": model,
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"task": "text-matching",
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"dimensions": embedding_dim,
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"embedding_type": "base64",
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