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refine OpenAi Api (#159)

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KevinHuSh 2024-03-27 17:55:45 +08:00 committed by GitHub
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commit bf2e3d7fc1
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5 changed files with 16 additions and 12 deletions
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2
README.md
View File

@ -127,7 +127,7 @@ Open your browser, enter the IP address of your server, _**Hallelujah**_ again!
# System Architecture Diagram
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://github.com/infiniflow/ragflow/assets/12318111/39c8e546-51ca-4b50-a1da-83731b540cd0" width="1000"/>
<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
</div>
# Configuration

2
api/apps/llm_app.py
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@ -51,7 +51,7 @@ def set_api_key():
if len(arr[0]) == 0 or tc == 0:
raise Exception("Fail")
except Exception as e:
msg += f"\nFail to access embedding model({llm.llm_name}) using this api key."
msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
elif not chat_passed and llm.model_type == LLMType.CHAT.value:
mdl = ChatModel[factory](
req["api_key"], llm.llm_name)

2
rag/app/picture.py
View File

@ -29,7 +29,7 @@ def chunk(filename, binary, tenant_id, lang, callback=None, **kwargs):
except Exception as e:
callback(prog=-1, msg=str(e))
return []
img = Image.open(io.BytesIO(binary))
img = Image.open(io.BytesIO(binary)).convert('RGB')
doc = {
"docnm_kwd": filename,
"image": img

10
rag/llm/chat_model.py
View File

@ -43,8 +43,8 @@ class GptTurbo(Base):
model=self.model_name,
messages=history,
**gen_conf)
ans = response.output.choices[0]['message']['content'].strip()
if response.output.choices[0].get("finish_reason", "") == "length":
ans = response.choices[0].message.content.strip()
if response.choices[0].finish_reason == "length":
ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
return ans, response.usage.completion_tokens
@ -114,12 +114,12 @@ class ZhipuChat(Base):
history.insert(0, {"role": "system", "content": system})
try:
response = self.client.chat.completions.create(
self.model_name,
model=self.model_name,
messages=history,
**gen_conf
)
ans = response.output.choices[0]['message']['content'].strip()
if response.output.choices[0].get("finish_reason", "") == "length":
ans = response.choices[0].message.content.strip()
if response.choices[0].finish_reason == "length":
ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
return ans, response.usage.completion_tokens

12
rag/llm/embedding_model.py
View File

@ -139,12 +139,16 @@ class ZhipuEmbed(Base):
self.model_name = model_name
def encode(self, texts: list, batch_size=32):
res = self.client.embeddings.create(input=texts,
arr = []
tks_num = 0
for txt in texts:
res = self.client.embeddings.create(input=txt,
model=self.model_name)
return np.array([d.embedding for d in res.data]
), res.usage.total_tokens
arr.append(res.data[0].embedding)
tks_num += res.usage.total_tokens
return np.array(arr), tks_num
def encode_queries(self, text):
res = self.client.embeddings.create(input=text,
model=self.model_name)
return np.array(res["data"][0]["embedding"]), res.usage.total_tokens
return np.array(res.data[0].embedding), res.usage.total_tokens