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add base url for OpenAI (#166)

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KevinHuSh 2024-03-28 19:15:16 +08:00 committed by GitHub
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8 changed files with 39 additions and 27 deletions
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7
README.md
View File

@ -20,7 +20,7 @@
<img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
</a>
</p>
[RagFlow](http://demo.ragflow.io) is a knowledge management platform built on custom-build document understanding engine and LLM, with reasoned and well-founded answers to your question. Clone this repository, you can deploy your own knowledge management platform to empower your business with AI.
[RagFlow](https://demo.ragflow.io) is a knowledge management platform built on custom-build document understanding engine and LLM, with reasoned and well-founded answers to your question. Clone this repository, you can deploy your own knowledge management platform to empower your business with AI.
<div align="center" style="margin-top:20px;margin-bottom:20px;">
@ -56,12 +56,12 @@
Then, you need to check the following command:
```bash
121:/ragflow# sysctl vm.max_map_count
$ sysctl vm.max_map_count
vm.max_map_count = 262144
```
If **vm.max_map_count** is not greater than 65535:
```bash
121:/ragflow# sudo sysctl -w vm.max_map_count=262144
$ sudo sysctl -w vm.max_map_count=262144
```
Note that this change is reset after a system reboot. To render your change permanent, add or update the following line in **/etc/sysctl.conf**:
@ -126,6 +126,7 @@ Open your browser, enter the IP address of your server, _**Hallelujah**_ again!
<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
</div>
## 🔧 Configurations
If you need to change the default setting of the system when you deploy it. There several ways to configure it.

8
api/apps/llm_app.py
View File

@ -45,7 +45,7 @@ def set_api_key():
for llm in LLMService.query(fid=factory):
if llm.model_type == LLMType.EMBEDDING.value:
mdl = EmbeddingModel[factory](
req["api_key"], llm.llm_name)
req["api_key"], llm.llm_name, req.get("base_url"))
try:
arr, tc = mdl.encode(["Test if the api key is available"])
if len(arr[0]) == 0 or tc == 0:
@ -54,7 +54,7 @@ def set_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)
req["api_key"], llm.llm_name, req.get("base_url"))
try:
m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
"temperature": 0.9})
@ -83,7 +83,9 @@ def set_api_key():
llm_factory=factory,
llm_name=llm.llm_name,
model_type=llm.model_type,
api_key=req["api_key"])
api_key=req["api_key"],
api_base=req.get("base_url", "")
)
return get_json_result(data=True)

8
api/db/services/llm_service.py
View File

@ -84,19 +84,21 @@ class TenantLLMService(CommonService):
if model_config["llm_factory"] not in EmbeddingModel:
return
return EmbeddingModel[model_config["llm_factory"]](
model_config["api_key"], model_config["llm_name"])
model_config["api_key"], model_config["llm_name"], model_config["api_base"])
if llm_type == LLMType.IMAGE2TEXT.value:
if model_config["llm_factory"] not in CvModel:
return
return CvModel[model_config["llm_factory"]](
model_config["api_key"], model_config["llm_name"], lang)
model_config["api_key"], model_config["llm_name"], lang,
base_url=model_config["api_base"]
)
if llm_type == LLMType.CHAT.value:
if model_config["llm_factory"] not in ChatModel:
return
return ChatModel[model_config["llm_factory"]](
model_config["api_key"], model_config["llm_name"])
model_config["api_key"], model_config["llm_name"], model_config["api_base"])
@classmethod
@DB.connection_context()

2
deepdoc/vision/recognizer.py
View File

@ -43,6 +43,8 @@ class Recognizer(object):
if not os.path.exists(model_file_path):
model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc")
model_file_path = os.path.join(model_dir, task_name + ".onnx")
else:
model_file_path = os.path.join(model_dir, task_name + ".onnx")
if not os.path.exists(model_file_path):
raise ValueError("not find model file path {}".format(

16
rag/llm/chat_model.py
View File

@ -31,8 +31,9 @@ class Base(ABC):
class GptTurbo(Base):
def __init__(self, key, model_name="gpt-3.5-turbo"):
self.client = OpenAI(api_key=key)
def __init__(self, key, model_name="gpt-3.5-turbo", base_url="https://api.openai.com/v1"):
if not base_url: base_url="https://api.openai.com/v1"
self.client = OpenAI(api_key=key, base_url=base_url)
self.model_name = model_name
def chat(self, system, history, gen_conf):
@ -53,9 +54,10 @@ class GptTurbo(Base):
class MoonshotChat(GptTurbo):
def __init__(self, key, model_name="moonshot-v1-8k"):
def __init__(self, key, model_name="moonshot-v1-8k", base_url="https://api.moonshot.cn/v1"):
if not base_url: base_url="https://api.moonshot.cn/v1"
self.client = OpenAI(
api_key=key, base_url="https://api.moonshot.cn/v1",)
api_key=key, base_url=base_url)
self.model_name = model_name
def chat(self, system, history, gen_conf):
@ -76,7 +78,7 @@ class MoonshotChat(GptTurbo):
class QWenChat(Base):
def __init__(self, key, model_name=Generation.Models.qwen_turbo):
def __init__(self, key, model_name=Generation.Models.qwen_turbo, **kwargs):
import dashscope
dashscope.api_key = key
self.model_name = model_name
@ -105,7 +107,7 @@ class QWenChat(Base):
class ZhipuChat(Base):
def __init__(self, key, model_name="glm-3-turbo"):
def __init__(self, key, model_name="glm-3-turbo", **kwargs):
self.client = ZhipuAI(api_key=key)
self.model_name = model_name
@ -154,7 +156,7 @@ class LocalLLM(Base):
return do_rpc
def __init__(self, key, model_name="glm-3-turbo"):
def __init__(self, **kwargs):
self.client = LocalLLM.RPCProxy("127.0.0.1", 7860)
def chat(self, system, history, gen_conf):

11
rag/llm/cv_model.py
View File

@ -67,8 +67,9 @@ class Base(ABC):
class GptV4(Base):
def __init__(self, key, model_name="gpt-4-vision-preview", lang="Chinese"):
self.client = OpenAI(api_key=key)
def __init__(self, key, model_name="gpt-4-vision-preview", lang="Chinese", base_url="https://api.openai.com/v1"):
if not base_url: base_url="https://api.openai.com/v1"
self.client = OpenAI(api_key=key, base_url=base_url)
self.model_name = model_name
self.lang = lang
@ -84,7 +85,7 @@ class GptV4(Base):
class QWenCV(Base):
def __init__(self, key, model_name="qwen-vl-chat-v1", lang="Chinese"):
def __init__(self, key, model_name="qwen-vl-chat-v1", lang="Chinese", **kwargs):
import dashscope
dashscope.api_key = key
self.model_name = model_name
@ -123,7 +124,7 @@ class QWenCV(Base):
class Zhipu4V(Base):
def __init__(self, key, model_name="glm-4v", lang="Chinese"):
def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
self.client = ZhipuAI(api_key=key)
self.model_name = model_name
self.lang = lang
@ -140,7 +141,7 @@ class Zhipu4V(Base):
class LocalCV(Base):
def __init__(self, key, model_name="glm-4v", lang="Chinese"):
def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
pass
def describe(self, image, max_tokens=1024):

11
rag/llm/embedding_model.py
View File

@ -51,7 +51,7 @@ class Base(ABC):
class HuEmbedding(Base):
def __init__(self, key="", model_name=""):
def __init__(self, **kwargs):
"""
If you have trouble downloading HuggingFace models, -_^ this might help!!
@ -81,8 +81,9 @@ class HuEmbedding(Base):
class OpenAIEmbed(Base):
def __init__(self, key, model_name="text-embedding-ada-002"):
self.client = OpenAI(api_key=key)
def __init__(self, key, model_name="text-embedding-ada-002", base_url="https://api.openai.com/v1"):
if not base_url: base_url="https://api.openai.com/v1"
self.client = OpenAI(api_key=key, base_url=base_url)
self.model_name = model_name
def encode(self, texts: list, batch_size=32):
@ -98,7 +99,7 @@ class OpenAIEmbed(Base):
class QWenEmbed(Base):
def __init__(self, key, model_name="text_embedding_v2"):
def __init__(self, key, model_name="text_embedding_v2", **kwargs):
dashscope.api_key = key
self.model_name = model_name
@ -131,7 +132,7 @@ class QWenEmbed(Base):
class ZhipuEmbed(Base):
def __init__(self, key, model_name="embedding-2"):
def __init__(self, key, model_name="embedding-2", **kwargs):
self.client = ZhipuAI(api_key=key)
self.model_name = model_name

3
rag/svr/task_executor.py
View File

@ -280,4 +280,5 @@ if __name__ == "__main__":
from mpi4py import MPI
comm = MPI.COMM_WORLD
main(int(sys.argv[2]), int(sys.argv[1]))
while True:
main(int(sys.argv[2]), int(sys.argv[1]))