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feat:嵌入模型整改

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雷雨
2025-12-02 17:31:02 +08:00
parent a2309606bf
commit 721ea07652
1 changed files with 12 additions and 16 deletions
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28
service/cus_vanna_srevice.py
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@@ -492,8 +492,8 @@ class CustomQdrant_VectorStore(Qdrant_VectorStore):
request_body = {
"model": self.embedding_model_name,
"encoding_format": "float",
# "input": [data],
"sentences":[data]
"input": [data],
#"sentences":[data]
}
#硅基流动的beg3
# request_body = {
@@ -510,27 +510,23 @@ class CustomQdrant_VectorStore(Qdrant_VectorStore):
# logger.info(f"request_body:{request_body}")
request_body.update(kwargs)
#AI中台的beg3
response = requests.post(
url=f"{self.embedding_api_base}",
json=request_body,
headers={"Authorization": f"Bearer {self.embedding_api_key}", 'Content-Type': 'application/json'},
)
#硅基流动的
# response = requests.post(
# url=f"{self.embedding_api_base}/v1/embeddings",
# json=request_body,
# headers={"Authorization": f"Bearer {self.embedding_api_key}", 'Content-Type': 'application/json'},
# )
response = requests.post(
url=f"{self.embedding_api_base}/v1/embeddings",
json=request_body,
headers={"Authorization": f"Bearer {self.embedding_api_key}", 'Content-Type': 'application/json'},
)
if response.status_code != 200:
raise RuntimeError(
f"Failed to create the embeddings, detail: {_get_error_string(response)}"
)
result = response.json()
# logger.info("embedding result:{0}".format(result))
embeddings = result['embeddings']
# embeddings = result['data'][0]['embedding']
return embeddings[0]
# return embeddings
#embeddings = result['embeddings']
embeddings = result['data'][0]['embedding']
#return embeddings[0]
return embeddings
class CustomVanna(CustomQdrant_VectorStore, OpenAICompatibleLLM):
def __init__(self, llm_config=None, vector_store_config=None):