6329427ad5
### What problem does this PR solve? Refactor Document API ### Type of change - [x] Refactoring Co-authored-by: liuhua <10215101452@stu.ecun.edu.cn>
257 lines
11 KiB
Python
257 lines
11 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List
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import requests
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from .modules.chat import Chat
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from .modules.chunk import Chunk
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from .modules.dataset import DataSet
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from .modules.document import Document
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class RAGFlow:
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def __init__(self, user_key, base_url, version='v1'):
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"""
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api_url: http://<host_address>/api/v1
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"""
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self.user_key = user_key
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self.api_url = f"{base_url}/api/{version}"
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self.authorization_header = {"Authorization": "{} {}".format("Bearer", self.user_key)}
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def post(self, path, json=None, stream=False, files=None):
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res = requests.post(url=self.api_url + path, json=json, headers=self.authorization_header, stream=stream,files=files)
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return res
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def get(self, path, params=None, json=None):
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res = requests.get(url=self.api_url + path, params=params, headers=self.authorization_header,json=json)
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return res
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def delete(self, path, json):
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res = requests.delete(url=self.api_url + path, json=json, headers=self.authorization_header)
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return res
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def put(self, path, json):
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res = requests.put(url=self.api_url + path, json= json,headers=self.authorization_header)
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return res
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def create_dataset(self, name: str, avatar: str = "", description: str = "", language: str = "English",
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permission: str = "me",
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document_count: int = 0, chunk_count: int = 0, parse_method: str = "naive",
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parser_config: DataSet.ParserConfig = None) -> DataSet:
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if parser_config is None:
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parser_config = DataSet.ParserConfig(self, {"chunk_token_count": 128, "layout_recognize": True,
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"delimiter": "\n!?。；！？", "task_page_size": 12})
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parser_config = parser_config.to_json()
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res = self.post("/dataset",
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{"name": name, "avatar": avatar, "description": description, "language": language,
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"permission": permission,
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"document_count": document_count, "chunk_count": chunk_count, "parse_method": parse_method,
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"parser_config": parser_config
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}
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)
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res = res.json()
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if res.get("code") == 0:
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return DataSet(self, res["data"])
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raise Exception(res["message"])
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def delete_datasets(self, ids: List[str] = None, names: List[str] = None):
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res = self.delete("/dataset",{"ids": ids, "names": names})
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res=res.json()
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if res.get("code") != 0:
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raise Exception(res["message"])
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def list_datasets(self, page: int = 1, page_size: int = 1024, orderby: str = "create_time", desc: bool = True,
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id: str = None, name: str = None) -> \
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List[DataSet]:
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res = self.get("/dataset",
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{"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name})
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res = res.json()
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result_list = []
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if res.get("code") == 0:
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for data in res['data']:
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result_list.append(DataSet(self, data))
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return result_list
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raise Exception(res["message"])
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def create_chat(self, name: str = "assistant", avatar: str = "path", knowledgebases: List[DataSet] = [],
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llm: Chat.LLM = None, prompt: Chat.Prompt = None) -> Chat:
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datasets = []
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for dataset in knowledgebases:
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datasets.append(dataset.to_json())
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if llm is None:
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llm = Chat.LLM(self, {"model_name": None,
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"temperature": 0.1,
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"top_p": 0.3,
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"presence_penalty": 0.4,
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"frequency_penalty": 0.7,
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"max_tokens": 512, })
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if prompt is None:
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prompt = Chat.Prompt(self, {"similarity_threshold": 0.2,
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"keywords_similarity_weight": 0.7,
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"top_n": 8,
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"variables": [{
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"key": "knowledge",
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"optional": True
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}], "rerank_model": "",
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"empty_response": None,
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"opener": None,
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"show_quote": True,
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"prompt": None})
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if prompt.opener is None:
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prompt.opener = "Hi! I'm your assistant, what can I do for you?"
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if prompt.prompt is None:
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prompt.prompt = (
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"You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. "
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"Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, "
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"your answer must include the sentence 'The answer you are looking for is not found in the knowledge base!' "
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"Answers need to consider chat history.\nHere is the knowledge base:\n{knowledge}\nThe above is the knowledge base."
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)
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temp_dict = {"name": name,
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"avatar": avatar,
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"knowledgebases": datasets,
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"llm": llm.to_json(),
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"prompt": prompt.to_json()}
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res = self.post("/chat", temp_dict)
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res = res.json()
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if res.get("code") == 0:
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return Chat(self, res["data"])
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raise Exception(res["message"])
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def delete_chats(self,ids: List[str] = None,names: List[str] = None ) -> bool:
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res = self.delete('/chat',
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{"ids":ids, "names":names})
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res = res.json()
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if res.get("code") != 0:
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raise Exception(res["message"])
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def list_chats(self, page: int = 1, page_size: int = 1024, orderby: str = "create_time", desc: bool = True,
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id: str = None, name: str = None) -> List[Chat]:
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res = self.get("/chat",{"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name})
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res = res.json()
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result_list = []
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if res.get("code") == 0:
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for data in res['data']:
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result_list.append(Chat(self, data))
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return result_list
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raise Exception(res["message"])
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def async_parse_documents(self, doc_ids):
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"""
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Asynchronously start parsing multiple documents without waiting for completion.
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:param doc_ids: A list containing multiple document IDs.
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"""
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try:
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if not doc_ids or not isinstance(doc_ids, list):
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raise ValueError("doc_ids must be a non-empty list of document IDs")
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data = {"document_ids": doc_ids, "run": 1}
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res = self.post(f'/doc/run', data)
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if res.status_code != 200:
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raise Exception(f"Failed to start async parsing for documents: {res.text}")
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print(f"Async parsing started successfully for documents: {doc_ids}")
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except Exception as e:
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print(f"Error occurred during async parsing for documents: {str(e)}")
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raise
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def async_cancel_parse_documents(self, doc_ids):
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"""
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Cancel the asynchronous parsing of multiple documents.
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:param doc_ids: A list containing multiple document IDs.
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"""
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try:
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if not doc_ids or not isinstance(doc_ids, list):
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raise ValueError("doc_ids must be a non-empty list of document IDs")
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data = {"document_ids": doc_ids, "run": 2}
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res = self.post(f'/doc/run', data)
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if res.status_code != 200:
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raise Exception(f"Failed to cancel async parsing for documents: {res.text}")
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print(f"Async parsing canceled successfully for documents: {doc_ids}")
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except Exception as e:
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print(f"Error occurred during canceling parsing for documents: {str(e)}")
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raise
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def retrieval(self,
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question,
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datasets=None,
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documents=None,
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offset=0,
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limit=6,
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similarity_threshold=0.1,
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vector_similarity_weight=0.3,
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top_k=1024):
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"""
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Perform document retrieval based on the given parameters.
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:param question: The query question.
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:param datasets: A list of datasets (optional, as documents may be provided directly).
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:param documents: A list of documents (if specific documents are provided).
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:param offset: Offset for the retrieval results.
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:param limit: Maximum number of retrieval results.
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:param similarity_threshold: Similarity threshold.
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:param vector_similarity_weight: Weight of vector similarity.
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:param top_k: Number of top most similar documents to consider (for pre-filtering or ranking).
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Note: This is a hypothetical implementation and may need adjustments based on the actual backend service API.
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"""
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try:
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data = {
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"question": question,
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"datasets": datasets if datasets is not None else [],
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"documents": [doc.id if hasattr(doc, 'id') else doc for doc in
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documents] if documents is not None else [],
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"offset": offset,
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"limit": limit,
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"similarity_threshold": similarity_threshold,
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"vector_similarity_weight": vector_similarity_weight,
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"top_k": top_k,
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"knowledgebase_id": datasets,
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}
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# Send a POST request to the backend service (using requests library as an example, actual implementation may vary)
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res = self.post(f'/doc/retrieval_test', data)
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# Check the response status code
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if res.status_code == 200:
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res_data = res.json()
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if res_data.get("retmsg") == "success":
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chunks = []
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for chunk_data in res_data["data"].get("chunks", []):
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chunk = Chunk(self, chunk_data)
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chunks.append(chunk)
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return chunks
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else:
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raise Exception(f"Error fetching chunks: {res_data.get('retmsg')}")
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else:
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raise Exception(f"API request failed with status code {res.status_code}")
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except Exception as e:
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print(f"An error occurred during retrieval: {e}")
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raise
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