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add support for cohere (#1849)

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### What problem does this PR solve?

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: Zhedong Cen <cenzhedong2@126.com>
This commit is contained in:
黄腾 2024-08-07 18:40:51 +08:00 committed by GitHub
parent 60428c4ad2
commit e34817c2a9
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10 changed files with 260 additions and 6 deletions
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110
conf/llm_factories.json
View File

@ -2216,6 +2216,116 @@
"tags": "LLM,TEXT EMBEDDING,IMAGE2TEXT",
"status": "1",
"llm": []
},
{
"name": "cohere",
"logo": "",
"tags": "LLM,TEXT EMBEDDING, TEXT RE-RANK",
"status": "1",
"llm": [
{
"llm_name": "command-r-plus",
"tags": "LLM,CHAT,128k",
"max_tokens": 131072,
"model_type": "chat"
},
{
"llm_name": "command-r",
"tags": "LLM,CHAT,128k",
"max_tokens": 131072,
"model_type": "chat"
},
{
"llm_name": "command",
"tags": "LLM,CHAT,4k",
"max_tokens": 4096,
"model_type": "chat"
},
{
"llm_name": "command-nightly",
"tags": "LLM,CHAT,128k",
"max_tokens": 131072,
"model_type": "chat"
},
{
"llm_name": "command-light",
"tags": "LLM,CHAT,4k",
"max_tokens": 4096,
"model_type": "chat"
},
{
"llm_name": "command-light-nightly",
"tags": "LLM,CHAT,4k",
"max_tokens": 4096,
"model_type": "chat"
},
{
"llm_name": "embed-english-v3.0",
"tags": "TEXT EMBEDDING",
"max_tokens": 512,
"model_type": "embedding"
},
{
"llm_name": "embed-english-light-v3.0",
"tags": "TEXT EMBEDDING",
"max_tokens": 512,
"model_type": "embedding"
},
{
"llm_name": "embed-multilingual-v3.0",
"tags": "TEXT EMBEDDING",
"max_tokens": 512,
"model_type": "embedding"
},
{
"llm_name": "embed-multilingual-light-v3.0",
"tags": "TEXT EMBEDDING",
"max_tokens": 512,
"model_type": "embedding"
},
{
"llm_name": "embed-english-v2.0",
"tags": "TEXT EMBEDDING",
"max_tokens": 512,
"model_type": "embedding"
},
{
"llm_name": "embed-english-light-v2.0",
"tags": "TEXT EMBEDDING",
"max_tokens": 512,
"model_type": "embedding"
},
{
"llm_name": "embed-multilingual-v2.0",
"tags": "TEXT EMBEDDING",
"max_tokens": 256,
"model_type": "embedding"
},
{
"llm_name": "rerank-english-v3.0",
"tags": "RE-RANK,4k",
"max_tokens": 4096,
"model_type": "rerank"
},
{
"llm_name": "rerank-multilingual-v3.0",
"tags": "RE-RANK,4k",
"max_tokens": 4096,
"model_type": "rerank"
},
{
"llm_name": "rerank-english-v2.0",
"tags": "RE-RANK,512",
"max_tokens": 8196,
"model_type": "rerank"
},
{
"llm_name": "rerank-multilingual-v2.0",
"tags": "RE-RANK,512",
"max_tokens": 512,
"model_type": "rerank"
}
]
}
]
}

9
rag/llm/__init__.py
View File

@ -37,7 +37,8 @@ EmbeddingModel = {
"Gemini": GeminiEmbed,
"NVIDIA": NvidiaEmbed,
"LM-Studio": LmStudioEmbed,
"OpenAI-API-Compatible": OpenAI_APIEmbed
"OpenAI-API-Compatible": OpenAI_APIEmbed,
"cohere": CoHereEmbed
}
@ -81,7 +82,8 @@ ChatModel = {
"StepFun": StepFunChat,
"NVIDIA": NvidiaChat,
"LM-Studio": LmStudioChat,
"OpenAI-API-Compatible": OpenAI_APIChat
"OpenAI-API-Compatible": OpenAI_APIChat,
"cohere": CoHereChat
}
@ -92,7 +94,8 @@ RerankModel = {
"Xinference": XInferenceRerank,
"NVIDIA": NvidiaRerank,
"LM-Studio": LmStudioRerank,
"OpenAI-API-Compatible": OpenAI_APIRerank
"OpenAI-API-Compatible": OpenAI_APIRerank,
"cohere": CoHereRerank
}

81
rag/llm/chat_model.py
View File

@ -900,3 +900,84 @@ class OpenAI_APIChat(Base):
base_url = os.path.join(base_url, "v1")
model_name = model_name.split("___")[0]
super().__init__(key, model_name, base_url)
class CoHereChat(Base):
def __init__(self, key, model_name, base_url=""):
from cohere import Client
self.client = Client(api_key=key)
self.model_name = model_name
def chat(self, system, history, gen_conf):
if system:
history.insert(0, {"role": "system", "content": system})
if "top_p" in gen_conf:
gen_conf["p"] = gen_conf.pop("top_p")
if "frequency_penalty" in gen_conf and "presence_penalty" in gen_conf:
gen_conf.pop("presence_penalty")
for item in history:
if "role" in item and item["role"] == "user":
item["role"] = "USER"
if "role" in item and item["role"] == "assistant":
item["role"] = "CHATBOT"
if "content" in item:
item["message"] = item.pop("content")
mes = history.pop()["message"]
ans = ""
try:
response = self.client.chat(
model=self.model_name, chat_history=history, message=mes, **gen_conf
)
ans = response.text
if response.finish_reason == "MAX_TOKENS":
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.meta.tokens.input_tokens + response.meta.tokens.output_tokens,
)
except Exception as e:
return ans + "\n**ERROR**: " + str(e), 0
def chat_streamly(self, system, history, gen_conf):
if system:
history.insert(0, {"role": "system", "content": system})
if "top_p" in gen_conf:
gen_conf["p"] = gen_conf.pop("top_p")
if "frequency_penalty" in gen_conf and "presence_penalty" in gen_conf:
gen_conf.pop("presence_penalty")
for item in history:
if "role" in item and item["role"] == "user":
item["role"] = "USER"
if "role" in item and item["role"] == "assistant":
item["role"] = "CHATBOT"
if "content" in item:
item["message"] = item.pop("content")
mes = history.pop()["message"]
ans = ""
total_tokens = 0
try:
response = self.client.chat_stream(
model=self.model_name, chat_history=history, message=mes, **gen_conf
)
for resp in response:
if resp.event_type == "text-generation":
ans += resp.text
total_tokens += num_tokens_from_string(resp.text)
elif resp.event_type == "stream-end":
if resp.finish_reason == "MAX_TOKENS":
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？"
)
yield ans
except Exception as e:
yield ans + "\n**ERROR**: " + str(e)
yield total_tokens

30
rag/llm/embedding_model.py
View File

@ -523,3 +523,33 @@ class OpenAI_APIEmbed(OpenAIEmbed):
base_url = os.path.join(base_url, "v1")
self.client = OpenAI(api_key=key, base_url=base_url)
self.model_name = model_name.split("___")[0]
class CoHereEmbed(Base):
def __init__(self, key, model_name, base_url=None):
from cohere import Client
self.client = Client(api_key=key)
self.model_name = model_name
def encode(self, texts: list, batch_size=32):
res = self.client.embed(
texts=texts,
model=self.model_name,
input_type="search_query",
embedding_types=["float"],
)
return np.array([d for d in res.embeddings.float]), int(
res.meta.billed_units.input_tokens
)
def encode_queries(self, text):
res = self.client.embed(
texts=[text],
model=self.model_name,
input_type="search_query",
embedding_types=["float"],
)
return np.array([d for d in res.embeddings.float]), int(
res.meta.billed_units.input_tokens
)

27
rag/llm/rerank_model.py
View File

@ -203,7 +203,9 @@ class NvidiaRerank(Base):
"top_n": len(texts),
}
res = requests.post(self.base_url, headers=self.headers, json=data).json()
return (np.array([d["logit"] for d in res["rankings"]]), token_count)
rank = np.array([d["logit"] for d in res["rankings"]])
indexs = [d["index"] for d in res["rankings"]]
return rank[indexs], token_count
class LmStudioRerank(Base):
@ -220,3 +222,26 @@ class OpenAI_APIRerank(Base):
def similarity(self, query: str, texts: list):
raise NotImplementedError("The api has not been implement")
class CoHereRerank(Base):
def __init__(self, key, model_name, base_url=None):
from cohere import Client
self.client = Client(api_key=key)
self.model_name = model_name
def similarity(self, query: str, texts: list):
token_count = num_tokens_from_string(query) + sum(
[num_tokens_from_string(t) for t in texts]
)
res = self.client.rerank(
model=self.model_name,
query=query,
documents=texts,
top_n=len(texts),
return_documents=False,
)
rank = np.array([d.relevance_score for d in res.results])
indexs = [d.index for d in res.results]
return rank[indexs], token_count

1
requirements.txt
View File

@ -7,6 +7,7 @@ botocore==1.34.140
cachetools==5.3.3
chardet==5.2.0
cn2an==0.5.22
cohere==5.6.2
dashscope==1.14.1
datrie==0.8.2
demjson3==3.0.6

1
requirements_arm.txt
View File

@ -14,6 +14,7 @@ certifi==2024.7.4
cffi==1.16.0
charset-normalizer==3.3.2
click==8.1.7
cohere==5.6.2
coloredlogs==15.0.1
cryptography==42.0.5
dashscope==1.14.1

1
requirements_dev.txt
View File

@ -14,6 +14,7 @@ certifi==2024.7.4
cffi==1.16.0
charset-normalizer==3.3.2
click==8.1.7
cohere==5.6.2
coloredlogs==15.0.1
cryptography==42.0.5
dashscope==1.14.1

1
web/src/assets/svg/llm/cohere.svg Normal file
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@ -0,0 +1 @@
<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" xml:space="preserve" style="enable-background:new 0 0 75 75" viewBox="0 0 75 75" width="75" height="75" ><path d="M24.3 44.7c2 0 6-.1 11.6-2.4 6.5-2.7 19.3-7.5 28.6-12.5 6.5-3.5 9.3-8.1 9.3-14.3C73.8 7 66.9 0 58.3 0h-36C10 0 0 10 0 22.3s9.4 22.4 24.3 22.4z" style="fill-rule:evenodd;clip-rule:evenodd;fill:#39594d"/><path d="M30.4 60c0-6 3.6-11.5 9.2-13.8l11.3-4.7C62.4 36.8 75 45.2 75 57.6 75 67.2 67.2 75 57.6 75H45.3c-8.2 0-14.9-6.7-14.9-15z" style="fill-rule:evenodd;clip-rule:evenodd;fill:#d18ee2"/><path d="M12.9 47.6C5.8 47.6 0 53.4 0 60.5v1.7C0 69.2 5.8 75 12.9 75c7.1 0 12.9-5.8 12.9-12.9v-1.7c-.1-7-5.8-12.8-12.9-12.8z" style="fill:#ff7759"/></svg>
Side by Side

After

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3
web/src/pages/user-setting/setting-model/constant.ts
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@ -22,7 +22,8 @@ export const IconMap = {
StepFun: 'stepfun',
NVIDIA:'nvidia',
'LM-Studio':'lm-studio',
'OpenAI-API-Compatible':'openai-api'
'OpenAI-API-Compatible':'openai-api',
'cohere':'cohere'
};
export const BedrockRegionList = [