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#
# 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.
from typing import List
import requests
from .modules.assistant import Assistant
from .modules.dataset import DataSet
from .modules.document import Document
from .modules.chunk import Chunk
class RAGFlow:
def __init__(self, user_key, base_url, version='v1'):
"""
api_url: http://<host_address>/api/v1
"""
self.user_key = user_key
self.api_url = f"{base_url}/api/{version}"
self.authorization_header = {"Authorization": "{} {}".format("Bearer", self.user_key)}
def post(self, path, param, stream=False):
res = requests.post(url=self.api_url + path, json=param, headers=self.authorization_header, stream=stream)
return res
def get(self, path, params=None):
res = requests.get(url=self.api_url + path, params=params, headers=self.authorization_header)
return res
def delete(self, path, params):
res = requests.delete(url=self.api_url + path, params=params, headers=self.authorization_header)
return res
def create_dataset(self, name: str, avatar: str = "", description: str = "", language: str = "English",
permission: str = "me",
document_count: int = 0, chunk_count: int = 0, parse_method: str = "naive",
parser_config: DataSet.ParserConfig = None) -> DataSet:
if parser_config is None:
parser_config = DataSet.ParserConfig(self, {"chunk_token_count": 128, "layout_recognize": True,
"delimiter": "\n!?。；！？", "task_page_size": 12})
parser_config = parser_config.to_json()
res = self.post("/dataset/save",
{"name": name, "avatar": avatar, "description": description, "language": language,
"permission": permission,
"document_count": document_count, "chunk_count": chunk_count, "parse_method": parse_method,
"parser_config": parser_config
}
)
res = res.json()
if res.get("retmsg") == "success":
return DataSet(self, res["data"])
raise Exception(res["retmsg"])
def list_datasets(self, page: int = 1, page_size: int = 1024, orderby: str = "create_time", desc: bool = True) -> \
List[DataSet]:
res = self.get("/dataset/list", {"page": page, "page_size": page_size, "orderby": orderby, "desc": desc})
res = res.json()
result_list = []
if res.get("retmsg") == "success":
for data in res['data']:
result_list.append(DataSet(self, data))
return result_list
raise Exception(res["retmsg"])
def get_dataset(self, id: str = None, name: str = None) -> DataSet:
res = self.get("/dataset/detail", {"id": id, "name": name})
res = res.json()
if res.get("retmsg") == "success":
return DataSet(self, res['data'])
raise Exception(res["retmsg"])
def create_assistant(self, name: str = "assistant", avatar: str = "path", knowledgebases: List[DataSet] = [],
llm: Assistant.LLM = None, prompt: Assistant.Prompt = None) -> Assistant:
datasets = []
for dataset in knowledgebases:
datasets.append(dataset.to_json())
if llm is None:
llm = Assistant.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 = Assistant.Prompt(self, {"similarity_threshold": 0.2,
"keywords_similarity_weight": 0.7,
"top_n": 8,
"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,
"knowledgebases": datasets,
"llm": llm.to_json(),
"prompt": prompt.to_json()}
res = self.post("/assistant/save", temp_dict)
res = res.json()
if res.get("retmsg") == "success":
return Assistant(self, res["data"])
raise Exception(res["retmsg"])
def get_assistant(self, id: str = None, name: str = None) -> Assistant:
res = self.get("/assistant/get", {"id": id, "name": name})
res = res.json()
if res.get("retmsg") == "success":
return Assistant(self, res['data'])
raise Exception(res["retmsg"])
def list_assistants(self) -> List[Assistant]:
res = self.get("/assistant/list")
res = res.json()
result_list = []
if res.get("retmsg") == "success":
for data in res['data']:
result_list.append(Assistant(self, data))
return result_list
raise Exception(res["retmsg"])
def create_document(self, ds: DataSet, name: str, blob: bytes) -> bool:
url = f"/doc/dataset/{ds.id}/documents/upload"
files = {
'file': (name, blob)
}
headers = {
'Authorization': f"Bearer {ds.rag.user_key}"
}
response = requests.post(self.api_url + url, files=files,
headers=headers)
if response.status_code == 200 and response.json().get('retmsg') == 'success':
return True
else:
raise Exception(f"Upload failed: {response.json().get('retmsg')}")
return False
def get_document(self, id: str = None, name: str = None) -> Document:
res = self.get("/doc/infos", {"id": id, "name": name})
res = res.json()
if res.get("retmsg") == "success":
return Document(self, res['data'])
raise Exception(res["retmsg"])
def async_parse_documents(self, doc_ids):
"""
Asynchronously start parsing multiple documents without waiting for completion.
:param doc_ids: A list containing multiple document IDs.
"""
try:
if not doc_ids or not isinstance(doc_ids, list):
raise ValueError("doc_ids must be a non-empty list of document IDs")
data = {"document_ids": doc_ids, "run": 1}
res = self.post(f'/doc/run', data)
if res.status_code != 200:
raise Exception(f"Failed to start async parsing for documents: {res.text}")
print(f"Async parsing started successfully for documents: {doc_ids}")
except Exception as e:
print(f"Error occurred during async parsing for documents: {str(e)}")
raise
def async_cancel_parse_documents(self, doc_ids):
"""
Cancel the asynchronous parsing of multiple documents.
:param doc_ids: A list containing multiple document IDs.
"""
try:
if not doc_ids or not isinstance(doc_ids, list):
raise ValueError("doc_ids must be a non-empty list of document IDs")
data = {"document_ids": doc_ids, "run": 2}
res = self.post(f'/doc/run', data)
if res.status_code != 200:
raise Exception(f"Failed to cancel async parsing for documents: {res.text}")
print(f"Async parsing canceled successfully for documents: {doc_ids}")
except Exception as e:
print(f"Error occurred during canceling parsing for documents: {str(e)}")
raise
def retrieval(self,
question,
datasets=None,
documents=None,
offset=0,
limit=6,
similarity_threshold=0.1,
vector_similarity_weight=0.3,
top_k=1024):
"""
Perform document retrieval based on the given parameters.
:param question: The query question.
:param datasets: A list of datasets (optional, as documents may be provided directly).
:param documents: A list of documents (if specific documents are provided).
:param offset: Offset for the retrieval results.
:param limit: Maximum number of retrieval results.
:param similarity_threshold: Similarity threshold.
:param vector_similarity_weight: Weight of vector similarity.
:param top_k: Number of top most similar documents to consider (for pre-filtering or ranking).
Note: This is a hypothetical implementation and may need adjustments based on the actual backend service API.
"""
try:
data = {
"question": question,
"datasets": datasets if datasets is not None else [],
"documents": [doc.id if hasattr(doc, 'id') else doc for doc in
documents] if documents is not None else [],
"offset": offset,
"limit": limit,
"similarity_threshold": similarity_threshold,
"vector_similarity_weight": vector_similarity_weight,
"top_k": top_k,
"knowledgebase_id": datasets,
}
# Send a POST request to the backend service (using requests library as an example, actual implementation may vary)
res = self.post(f'/doc/retrieval_test', data)
# Check the response status code
if res.status_code == 200:
res_data = res.json()
if res_data.get("retmsg") == "success":
chunks = []
for chunk_data in res_data["data"].get("chunks", []):
chunk = Chunk(self, chunk_data)
chunks.append(chunk)
return chunks
else:
raise Exception(f"Error fetching chunks: {res_data.get('retmsg')}")
else:
raise Exception(f"API request failed with status code {res.status_code}")
except Exception as e:
print(f"An error occurred during retrieval: {e}")
raise
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