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Fix: jina embedding issue (#11628)

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### What problem does this PR solve?

Fix: jina embedding issue #11614 
Feat: Add jina embedding v4

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Billy Bao
2025-12-01 14:24:35 +08:00
committed by GitHub
parent b6c4722687
commit 41cff3e09e
2 changed files with 25 additions and 31 deletions
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6
conf/llm_factories.json
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@ -1194,6 +1194,12 @@
"tags": "TEXT EMBEDDING",
"max_tokens": 8196,
"model_type": "embedding"
},
{
"llm_name": "jina-embeddings-v4",
"tags": "TEXT EMBEDDING",
"max_tokens": 32768,
"model_type": "embedding"
}
]
},

50
rag/llm/embedding_model.py
View File

@ -349,35 +349,6 @@ class YoudaoEmbed(Base):
return np.array(embds[0]), num_tokens_from_string(text)
class JinaEmbed(Base):
_FACTORY_NAME = "Jina"
def __init__(self, key, model_name="jina-embeddings-v3", base_url="https://api.jina.ai/v1/embeddings"):
self.base_url = "https://api.jina.ai/v1/embeddings"
self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"}
self.model_name = model_name
def encode(self, texts: list):
texts = [truncate(t, 8196) for t in texts]
batch_size = 16
ress = []
token_count = 0
for i in range(0, len(texts), batch_size):
data = {"model": self.model_name, "input": texts[i : i + batch_size], "encoding_type": "float"}
response = requests.post(self.base_url, headers=self.headers, json=data)
try:
res = response.json()
ress.extend([d["embedding"] for d in res["data"]])
token_count += self.total_token_count(res)
except Exception as _e:
log_exception(_e, response)
return np.array(ress), token_count
def encode_queries(self, text):
embds, cnt = self.encode([text])
return np.array(embds[0]), cnt
class JinaMultiVecEmbed(Base):
_FACTORY_NAME = "Jina"
@ -403,11 +374,28 @@ class JinaMultiVecEmbed(Base):
img_b64s = base64.b64encode(text).decode('utf8')
input.append({"image": img_b64s}) # base64 encoded image
for i in range(0, len(texts), batch_size):
data = {"model": self.model_name, "task": task, "truncate": True, "return_multivector": True, "input": input[i : i + batch_size]}
data = {"model": self.model_name, "input": input[i : i + batch_size]}
if "v4" in self.model_name:
data["return_multivector"] = True
if "v3" in self.model_name or "v4" in self.model_name:
data['task'] = task
data['truncate'] = True
response = requests.post(self.base_url, headers=self.headers, json=data)
try:
res = response.json()
ress.extend([d["embeddings"] for d in res["data"]])
for d in res['data']:
if data.get("return_multivector", False): # v4
token_embs = np.asarray(d['embeddings'], dtype=np.float32)
chunk_emb = token_embs.mean(axis=0)
else:
# v2/v3
chunk_emb = np.asarray(d['embedding'], dtype=np.float32)
ress.append(chunk_emb)
token_count += self.total_token_count(res)
except Exception as _e:
log_exception(_e, response)