mirror of
https://github.com/infiniflow/ragflow.git
synced 2026-02-06 02:25:05 +08:00
Feat: support verify to set llm key and boost bigrams. (#12980)
#12863 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
@ -13,6 +13,7 @@
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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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#
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import copy
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import inspect
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import json
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import logging
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@ -46,6 +47,7 @@ from rag.nlp import search
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from rag.utils.redis_conn import REDIS_CONN
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from common import settings
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from api.apps import login_required, current_user
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from api.db.services.canvas_service import completion as agent_completion
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@manager.route('/templates', methods=['GET']) # noqa: F821
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@ -184,6 +186,50 @@ async def run():
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return resp
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@manager.route("/<canvas_id>/completion", methods=["POST"]) # noqa: F821
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@login_required
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async def exp_agent_completion(canvas_id):
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tenant_id = current_user.id
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req = await get_request_json()
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return_trace = bool(req.get("return_trace", False))
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async def generate():
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trace_items = []
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async for answer in agent_completion(tenant_id=tenant_id, agent_id=canvas_id, **req):
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if isinstance(answer, str):
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try:
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ans = json.loads(answer[5:]) # remove "data:"
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except Exception:
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continue
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event = ans.get("event")
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if event == "node_finished":
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if return_trace:
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data = ans.get("data", {})
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trace_items.append(
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{
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"component_id": data.get("component_id"),
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"trace": [copy.deepcopy(data)],
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}
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)
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ans.setdefault("data", {})["trace"] = trace_items
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answer = "data:" + json.dumps(ans, ensure_ascii=False) + "\n\n"
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yield answer
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if event not in ["message", "message_end"]:
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continue
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yield answer
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yield "data:[DONE]\n\n"
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resp = Response(generate(), mimetype="text/event-stream")
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resp.headers.add_header("Cache-control", "no-cache")
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resp.headers.add_header("Connection", "keep-alive")
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resp.headers.add_header("X-Accel-Buffering", "no")
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resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
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return resp
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@manager.route('/rerun', methods=['POST']) # noqa: F821
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@validate_request("id", "dsl", "component_id")
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@login_required
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@ -532,20 +578,65 @@ def sessions(canvas_id):
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from_date = request.args.get("from_date")
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to_date = request.args.get("to_date")
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orderby = request.args.get("orderby", "update_time")
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exp_user_id = request.args.get("exp_user_id")
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if request.args.get("desc") == "False" or request.args.get("desc") == "false":
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desc = False
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else:
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desc = True
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if exp_user_id:
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sess = API4ConversationService.get_names(canvas_id, exp_user_id)
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return get_json_result(data={"total": len(sess), "sessions": sess})
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# dsl defaults to True in all cases except for False and false
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include_dsl = request.args.get("dsl") != "False" and request.args.get("dsl") != "false"
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total, sess = API4ConversationService.get_list(canvas_id, tenant_id, page_number, items_per_page, orderby, desc,
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None, user_id, include_dsl, keywords, from_date, to_date)
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None, user_id, include_dsl, keywords, from_date, to_date, exp_user_id=exp_user_id)
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try:
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return get_json_result(data={"total": total, "sessions": sess})
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except Exception as e:
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return server_error_response(e)
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@manager.route('/<canvas_id>/sessions', methods=['PUT']) # noqa: F821
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@login_required
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async def set_session(canvas_id):
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req = await get_request_json()
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tenant_id = current_user.id
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e, cvs = UserCanvasService.get_by_id(canvas_id)
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assert e, "Agent not found."
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if not isinstance(cvs.dsl, str):
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cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
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session_id=get_uuid()
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canvas = Canvas(cvs.dsl, tenant_id, canvas_id, canvas_id=cvs.id)
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canvas.reset()
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conv = {
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"id": session_id,
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"name": req.get("name", ""),
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"dialog_id": cvs.id,
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"user_id": tenant_id,
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"exp_user_id": tenant_id,
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"message": [],
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"source": "agent",
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"dsl": cvs.dsl,
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"reference": []
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}
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API4ConversationService.save(**conv)
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return get_json_result(data=conv)
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@manager.route('/<canvas_id>/sessions/<session_id>', methods=['GET']) # noqa: F821
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@login_required
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def get_session(canvas_id, session_id):
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tenant_id = current_user.id
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if not UserCanvasService.accessible(canvas_id, tenant_id):
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return get_json_result(
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data=False, message='Only owner of canvas authorized for this operation.',
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code=RetCode.OPERATING_ERROR)
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conv = API4ConversationService.get_by_id(session_id)
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return get_json_result(data=conv.to_dict())
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@manager.route('/prompts', methods=['GET']) # noqa: F821
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@login_required
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def prompts():
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@ -13,6 +13,7 @@
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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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#
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import asyncio
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import logging
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import json
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import os
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@ -64,13 +65,17 @@ async def set_api_key():
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chat_passed, embd_passed, rerank_passed = False, False, False
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factory = req["llm_factory"]
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extra = {"provider": factory}
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timeout_seconds = int(os.environ.get("LLM_TIMEOUT_SECONDS", 10))
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msg = ""
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for llm in LLMService.query(fid=factory):
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if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
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assert factory in EmbeddingModel, f"Embedding model from {factory} is not supported yet."
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mdl = EmbeddingModel[factory](req["api_key"], llm.llm_name, base_url=req.get("base_url"))
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try:
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arr, tc = mdl.encode(["Test if the api key is available"])
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arr, tc = await asyncio.wait_for(
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asyncio.to_thread(mdl.encode, ["Test if the api key is available"]),
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timeout=timeout_seconds,
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)
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if len(arr[0]) == 0:
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raise Exception("Fail")
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embd_passed = True
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@ -80,17 +85,27 @@ async def set_api_key():
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assert factory in ChatModel, f"Chat model from {factory} is not supported yet."
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mdl = ChatModel[factory](req["api_key"], llm.llm_name, base_url=req.get("base_url"), **extra)
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try:
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m, tc = await mdl.async_chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {"temperature": 0.9, "max_tokens": 50})
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m, tc = await asyncio.wait_for(
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mdl.async_chat(
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None,
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[{"role": "user", "content": "Hello! How are you doing!"}],
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{"temperature": 0.9, "max_tokens": 50},
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),
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timeout=timeout_seconds,
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)
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if m.find("**ERROR**") >= 0:
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raise Exception(m)
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chat_passed = True
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except Exception as e:
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msg += f"\nFail to access model({llm.fid}/{llm.llm_name}) using this api key." + str(e)
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elif not rerank_passed and llm.model_type == LLMType.RERANK:
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elif not rerank_passed and llm.model_type == LLMType.RERANK.value:
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assert factory in RerankModel, f"Re-rank model from {factory} is not supported yet."
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mdl = RerankModel[factory](req["api_key"], llm.llm_name, base_url=req.get("base_url"))
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try:
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arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
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arr, tc = await asyncio.wait_for(
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asyncio.to_thread(mdl.similarity, "What's the weather?", ["Is it sunny today?"]),
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timeout=timeout_seconds,
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)
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if len(arr) == 0 or tc == 0:
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raise Exception("Fail")
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rerank_passed = True
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@ -101,6 +116,9 @@ async def set_api_key():
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msg = ""
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break
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if req.get("verify", False):
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return get_json_result(data={"message": msg, "success": len(msg.strip())==0})
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if msg:
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return get_data_error_result(message=msg)
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@ -133,6 +151,7 @@ async def add_llm():
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factory = req["llm_factory"]
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api_key = req.get("api_key", "x")
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llm_name = req.get("llm_name")
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timeout_seconds = int(os.environ.get("LLM_TIMEOUT_SECONDS", 10))
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if factory not in [f.name for f in get_allowed_llm_factories()]:
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return get_data_error_result(message=f"LLM factory {factory} is not allowed")
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@ -215,7 +234,10 @@ async def add_llm():
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assert factory in EmbeddingModel, f"Embedding model from {factory} is not supported yet."
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mdl = EmbeddingModel[factory](key=model_api_key, model_name=mdl_nm, base_url=model_base_url)
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try:
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arr, tc = mdl.encode(["Test if the api key is available"])
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arr, tc = await asyncio.wait_for(
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asyncio.to_thread(mdl.encode, ["Test if the api key is available"]),
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timeout=timeout_seconds,
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)
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if len(arr[0]) == 0:
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raise Exception("Fail")
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except Exception as e:
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@ -229,7 +251,14 @@ async def add_llm():
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**extra,
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)
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try:
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m, tc = await mdl.async_chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {"temperature": 0.9})
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m, tc = await asyncio.wait_for(
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mdl.async_chat(
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None,
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[{"role": "user", "content": "Hello! How are you doing!"}],
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{"temperature": 0.9},
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),
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timeout=timeout_seconds,
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)
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if not tc and m.find("**ERROR**:") >= 0:
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raise Exception(m)
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except Exception as e:
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@ -239,7 +268,10 @@ async def add_llm():
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assert factory in RerankModel, f"RE-rank model from {factory} is not supported yet."
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try:
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mdl = RerankModel[factory](key=model_api_key, model_name=mdl_nm, base_url=model_base_url)
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arr, tc = mdl.similarity("Hello~ RAGFlower!", ["Hi, there!", "Ohh, my friend!"])
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arr, tc = await asyncio.wait_for(
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asyncio.to_thread(mdl.similarity, "Hello~ RAGFlower!", ["Hi, there!", "Ohh, my friend!"]),
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timeout=timeout_seconds,
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)
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if len(arr) == 0:
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raise Exception("Not known.")
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except KeyError:
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@ -252,7 +284,10 @@ async def add_llm():
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mdl = CvModel[factory](key=model_api_key, model_name=mdl_nm, base_url=model_base_url)
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try:
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image_data = test_image
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m, tc = mdl.describe(image_data)
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m, tc = await asyncio.wait_for(
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asyncio.to_thread(mdl.describe, image_data),
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timeout=timeout_seconds,
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)
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if not tc and m.find("**ERROR**:") >= 0:
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raise Exception(m)
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except Exception as e:
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@ -261,20 +296,29 @@ async def add_llm():
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assert factory in TTSModel, f"TTS model from {factory} is not supported yet."
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mdl = TTSModel[factory](key=model_api_key, model_name=mdl_nm, base_url=model_base_url)
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try:
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for resp in mdl.tts("Hello~ RAGFlower!"):
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pass
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def drain_tts():
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for _ in mdl.tts("Hello~ RAGFlower!"):
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pass
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await asyncio.wait_for(
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asyncio.to_thread(drain_tts),
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timeout=timeout_seconds,
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)
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except RuntimeError as e:
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msg += f"\nFail to access model({factory}/{mdl_nm})." + str(e)
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case LLMType.OCR.value:
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assert factory in OcrModel, f"OCR model from {factory} is not supported yet."
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try:
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mdl = OcrModel[factory](key=model_api_key, model_name=mdl_nm, base_url=model_base_url)
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ok, reason = mdl.check_available()
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ok, reason = await asyncio.wait_for(
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asyncio.to_thread(mdl.check_available),
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timeout=timeout_seconds,
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)
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if not ok:
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raise RuntimeError(reason or "Model not available")
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except Exception as e:
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msg += f"\nFail to access model({factory}/{mdl_nm})." + str(e)
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case LLMType.SPEECH2TEXT:
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case LLMType.SPEECH2TEXT.value:
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assert factory in Seq2txtModel, f"Speech model from {factory} is not supported yet."
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try:
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mdl = Seq2txtModel[factory](key=model_api_key, model_name=mdl_nm, base_url=model_base_url)
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@ -284,6 +328,9 @@ async def add_llm():
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case _:
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raise RuntimeError(f"Unknown model type: {model_type}")
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if req.get("verify", False):
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return get_json_result(data={"message": msg, "success": len(msg.strip()) == 0})
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if msg:
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return get_data_error_result(message=msg)
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@ -991,8 +991,10 @@ class APIToken(DataBaseModel):
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class API4Conversation(DataBaseModel):
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id = CharField(max_length=32, primary_key=True)
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name = CharField(max_length=255, null=True, help_text="conversation name", index=False)
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dialog_id = CharField(max_length=32, null=False, index=True)
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user_id = CharField(max_length=255, null=False, help_text="user_id", index=True)
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exp_user_id = CharField(max_length=255, null=True, help_text="exp_user_id", index=True)
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message = JSONField(null=True)
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reference = JSONField(null=True, default=[])
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tokens = IntegerField(default=0)
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@ -1376,6 +1378,8 @@ def migrate_db():
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alter_db_add_column(migrator, "tenant_llm", "status", CharField(max_length=1, null=False, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True))
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alter_db_add_column(migrator, "connector2kb", "auto_parse", CharField(max_length=1, null=False, default="1", index=False))
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alter_db_add_column(migrator, "llm_factories", "rank", IntegerField(default=0, index=False))
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alter_db_add_column(migrator, "api_4_conversation", "name", CharField(max_length=255, null=True, help_text="conversation name", index=False))
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alter_db_add_column(migrator, "api_4_conversation", "exp_user_id", CharField(max_length=255, null=True, help_text="exp_user_id", index=True))
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# Migrate system_settings.value from CharField to TextField for longer sandbox configs
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alter_db_column_type(migrator, "system_settings", "value", TextField(null=False, help_text="Configuration value (JSON, string, etc.)"))
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logging.disable(logging.NOTSET)
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@ -48,8 +48,8 @@ class API4ConversationService(CommonService):
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@DB.connection_context()
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def get_list(cls, dialog_id, tenant_id,
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page_number, items_per_page,
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orderby, desc, id, user_id=None, include_dsl=True, keywords="",
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from_date=None, to_date=None
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orderby, desc, id=None, user_id=None, include_dsl=True, keywords="",
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from_date=None, to_date=None, exp_user_id=None
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):
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if include_dsl:
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sessions = cls.model.select().where(cls.model.dialog_id == dialog_id)
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@ -66,6 +66,8 @@ class API4ConversationService(CommonService):
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sessions = sessions.where(cls.model.create_date >= from_date)
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if to_date:
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sessions = sessions.where(cls.model.create_date <= to_date)
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if exp_user_id:
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sessions = sessions.where(cls.model.exp_user_id == exp_user_id)
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if desc:
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sessions = sessions.order_by(cls.model.getter_by(orderby).desc())
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else:
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@ -75,6 +77,17 @@ class API4ConversationService(CommonService):
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return count, list(sessions.dicts())
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@classmethod
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@DB.connection_context()
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def get_names(cls, dialog_id, exp_user_id):
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fields = [cls.model.id, cls.model.name,]
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sessions = cls.model.select(*fields).where(
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cls.model.dialog_id == dialog_id,
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cls.model.exp_user_id == exp_user_id
|
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).order_by(cls.model.getter_by("create_date").desc())
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|
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return list(sessions.dicts())
|
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|
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@classmethod
|
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@DB.connection_context()
|
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def append_message(cls, id, conversation):
|
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|
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@ -55,13 +55,11 @@ class FulltextQueryer(QueryBase):
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keywords = [t for t in tks if t]
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tks_w = self.tw.weights(tks, preprocess=False)
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tks_w = [(re.sub(r"[ \\\"'^]", "", tk), w) for tk, w in tks_w]
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tks_w = [(re.sub(r"^[a-z0-9]$", "", tk), w) for tk, w in tks_w if tk]
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tks_w = [(re.sub(r"^[\+-]", "", tk), w) for tk, w in tks_w if tk]
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tks_w = [(tk.strip(), w) for tk, w in tks_w if tk.strip()]
|
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syns = []
|
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for tk, w in tks_w[:256]:
|
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syn = self.syn.lookup(tk)
|
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syn = rag_tokenizer.tokenize(" ".join(syn)).split()
|
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syn = [rag_tokenizer.tokenize(s) for s in self.syn.lookup(tk)]
|
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keywords.extend(syn)
|
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syn = ["\"{}\"^{:.4f}".format(s, w / 4.) for s in syn if s.strip()]
|
||||
syns.append(" ".join(syn))
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@ -190,7 +188,10 @@ class FulltextQueryer(QueryBase):
|
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d = defaultdict(int)
|
||||
wts = self.tw.weights(tks, preprocess=False)
|
||||
for i, (t, c) in enumerate(wts):
|
||||
d[t] += c
|
||||
d[t] += c * 0.4
|
||||
if i+1 < len(wts):
|
||||
_t, _c = wts[i+1]
|
||||
d[t+_t] += max(c, _c) * 0.6
|
||||
return d
|
||||
|
||||
atks = to_dict(atks)
|
||||
|
||||
Reference in New Issue
Block a user