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fix(llm): Fix when use custom embedding providers. #339

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fix(llm): Fix when use custom embedding providers. #339

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d4d6a62
fix(llm): Fix when use custom embedding providers.
lx200916 Feb 9, 2026
54d8528
fix validate.
lx200916 Feb 10, 2026
85b8135
fix validate.
lx200916 Feb 10, 2026
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25 changes: 13 additions & 12 deletions opencontext/llm/llm_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -266,7 +266,7 @@ async def _openai_chat_completion_stream_async(self, messages: List[Dict[str, An

def _request_embedding(self, text: str, **kwargs) -> List[float]:
try:
if self.provider == LLMProvider.OPENAI.value:
if self.provider != LLMProvider.DOUBAO.value:
response = self.client.embeddings.create(model=self.model, input=[text])
embedding = response.data[0].embedding
else:
Expand Down Expand Up @@ -313,14 +313,15 @@ def _request_embedding(self, text: str, **kwargs) -> List[float]:

async def _request_embedding_async(self, text: str, **kwargs) -> List[float]:
try:
if self.provider == LLMProvider.OPENAI.value:
response = await self.async_client.embeddings.create(model=self.model, input=[text])
embedding = response.data[0].embedding
else:
if self.provider == LLMProvider.DOUBAO.value:
# Only ark has multimodal_embeddings
response = self.client.multimodal_embeddings.create(
model=self.model, input=[{"type": "text", "text": text}]
)
embedding = response.data.embedding
else:
response = await self.async_client.embeddings.create(model=self.model, input=[text])
embedding = response.data[0].embedding

# Record token usage
if hasattr(response, "usage") and response.usage:
Expand Down Expand Up @@ -476,20 +477,20 @@ def _extract_error_summary(error: Any) -> str:

elif self.llm_type == LLMType.EMBEDDING:
# Test with a simple text
if self.provider == LLMProvider.OPENAI.value:
response = self.client.embeddings.create(model=self.model, input=["test"])
if response.data and len(response.data) > 0 and response.data[0].embedding:
return True, "Embedding model validation successful"
else:
return False, "Embedding model returned empty response"
else:
if self.provider == LLMProvider.DOUBAO.value:
response = self.client.multimodal_embeddings.create(
model=self.model, input=[{"type": "text", "text": "test"}]
)
if response.data and response.data.embedding:
return True, "Embedding model validation successful"
else:
return False, "Embedding model returned empty response"
else:
response = self.client.embeddings.create(model=self.model, input=["test"])
if response.data and len(response.data) > 0 and response.data[0].embedding:
return True, "Embedding model validation successful"
else:
return False, "Embedding model returned empty response"
else:
return False, f"Unsupported LLM type: {self.llm_type}"

Expand Down