#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import requests
from .modules.chat import Chat
from .modules.chunk import Chunk
from .modules.dataset import DataSet
from .modules.agent import Agent
class RAGFlow:
def __init__(self, api_key, base_url, version='v1'):
"""
api_url: http://<host_address>/api/v1
"""
self.user_key = api_key
self.api_url = f"{base_url}/api/{version}"
self.authorization_header = {"Authorization": "{} {}".format("Bearer", self.user_key)}
def post(self, path, json=None, stream=False, files=None):
res = requests.post(url=self.api_url + path, json=json, headers=self.authorization_header, stream=stream,files=files)
return res
def get(self, path, params=None, json=None):
res = requests.get(url=self.api_url + path, params=params, headers=self.authorization_header,json=json)
return res
def delete(self, path, json):
res = requests.delete(url=self.api_url + path, json=json, headers=self.authorization_header)
return res
def put(self, path, json):
res = requests.put(url=self.api_url + path, json= json,headers=self.authorization_header)
return res
def create_dataset(self, name: str, avatar: str = "", description: str = "", embedding_model:str = "BAAI/bge-large-zh-v1.5",
language: str = "English",
permission: str = "me",chunk_method: str = "naive",
parser_config: DataSet.ParserConfig = None) -> DataSet:
if parser_config:
parser_config = parser_config.to_json()
res = self.post("/datasets",
{"name": name, "avatar": avatar, "description": description,"embedding_model":embedding_model,
"language": language,
"permission": permission, "chunk_method": chunk_method,
"parser_config": parser_config
}
)
res = res.json()
if res.get("code") == 0:
return DataSet(self, res["data"])
raise Exception(res["message"])
def delete_datasets(self, ids: list[str] | None = None):
res = self.delete("/datasets",{"ids": ids})
res=res.json()
if res.get("code") != 0:
raise Exception(res["message"])
def get_dataset(self,name: str):
_list = self.list_datasets(name=name)
if len(_list) > 0:
return _list[0]
raise Exception("Dataset %s not found" % name)
def list_datasets(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True,
id: str | None = None, name: str | None = None) -> \
list[DataSet]:
res = self.get("/datasets",
{"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name})
res = res.json()
result_list = []
if res.get("code") == 0:
for data in res['data']:
result_list.append(DataSet(self, data))
return result_list
raise Exception(res["message"])
def create_chat(self, name: str, avatar: str = "", dataset_ids=None,
llm: Chat.LLM | None = None, prompt: Chat.Prompt | None = None) -> Chat:
if dataset_ids is None:
dataset_ids = []
dataset_list = []
for id in dataset_ids:
dataset_list.append(id)
if llm is None:
llm = Chat.LLM(self, {"model_name": None,
"temperature": 0.1,
"top_p": 0.3,
"presence_penalty": 0.4,
"frequency_penalty": 0.7,
"max_tokens": 512, })
if prompt is None:
prompt = Chat.Prompt(self, {"similarity_threshold": 0.2,
"keywords_similarity_weight": 0.7,
"top_n": 8,
"top_k": 1024,
"variables": [{
"key": "knowledge",
"optional": True
}], "rerank_model": "",
"empty_response": None,
"opener": None,
"show_quote": True,
"prompt": None})
if prompt.opener is None:
prompt.opener = "Hi! I'm your assistant, what can I do for you?"
if prompt.prompt is None:
prompt.prompt = (
"You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. "
"Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, "
"your answer must include the sentence 'The answer you are looking for is not found in the knowledge base!' "
"Answers need to consider chat history.\nHere is the knowledge base:\n{knowledge}\nThe above is the knowledge base."
)
temp_dict = {"name": name,
"avatar": avatar,
"dataset_ids": dataset_list,
"llm": llm.to_json(),
"prompt": prompt.to_json()}
res = self.post("/chats", temp_dict)
res = res.json()
if res.get("code") == 0:
return Chat(self, res["data"])
raise Exception(res["message"])
def delete_chats(self,ids: list[str] | None = None):
res = self.delete('/chats',
{"ids":ids})
res = res.json()
if res.get("code") != 0:
raise Exception(res["message"])
def list_chats(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True,
id: str | None = None, name: str | None = None) -> list[Chat]:
res = self.get("/chats",{"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name})
res = res.json()
result_list = []
if res.get("code") == 0:
for data in res['data']:
result_list.append(Chat(self, data))
return result_list
raise Exception(res["message"])
def retrieve(self, dataset_ids, document_ids=None, question="", page=1, page_size=30, similarity_threshold=0.2, vector_similarity_weight=0.3, top_k=1024, rerank_id: str | None = None, keyword:bool=False, ):
if document_ids is None:
document_ids = []
data_json ={
"page": page,
"page_size": page_size,
"similarity_threshold": similarity_threshold,
"vector_similarity_weight": vector_similarity_weight,
"top_k": top_k,
"rerank_id": rerank_id,
"keyword": keyword,
"question": question,
"dataset_ids": dataset_ids,
"documents": document_ids
}
# Send a POST request to the backend service (using requests library as an example, actual implementation may vary)
res = self.post('/retrieval',json=data_json)
res = res.json()
if res.get("code") ==0:
chunks=[]
for chunk_data in res["data"].get("chunks"):
chunk=Chunk(self,chunk_data)
chunks.append(chunk)
return chunks
raise Exception(res.get("message"))
def list_agents(self, page: int = 1, page_size: int = 30, orderby: str = "update_time", desc: bool = True,
id: str | None = None, title: str | None = None) -> list[Agent]:
res = self.get("/agents",{"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "title": title})
res = res.json()
result_list = []
if res.get("code") == 0:
for data in res['data']:
result_list.append(Agent(self, data))
return result_list
raise Exception(res["message"])