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Code Issues Packages Projects Releases Wiki Activity

Move settings initialization after module init phase (#3438)

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### What problem does this PR solve?

1. Module init won't connect database any more.
2. Config in settings need to be used with settings.CONFIG_NAME

### Type of change

- [x] Refactoring

Signed-off-by: jinhai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
2024-11-15 17:30:56 +08:00
committed by GitHub
parent ac033b62cf
commit 1e90a1bf36
33 changed files with 452 additions and 411 deletions
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22
agent/component/generate.py
View File

@ -19,7 +19,7 @@ import pandas as pd
from api.db import LLMType
from api.db.services.dialog_service import message_fit_in
from api.db.services.llm_service import LLMBundle
from api.settings import retrievaler
from api import settings
from agent.component.base import ComponentBase, ComponentParamBase
@ -63,18 +63,20 @@ class Generate(ComponentBase):
component_name = "Generate"
def get_dependent_components(self):
cpnts = [para["component_id"] for para in self._param.parameters if para.get("component_id") and para["component_id"].lower().find("answer") < 0]
cpnts = [para["component_id"] for para in self._param.parameters if
para.get("component_id") and para["component_id"].lower().find("answer") < 0]
return cpnts
def set_cite(self, retrieval_res, answer):
retrieval_res = retrieval_res.dropna(subset=["vector", "content_ltks"]).reset_index(drop=True)
if "empty_response" in retrieval_res.columns:
retrieval_res["empty_response"].fillna("", inplace=True)
answer, idx = retrievaler.insert_citations(answer, [ck["content_ltks"] for _, ck in retrieval_res.iterrows()],
[ck["vector"] for _, ck in retrieval_res.iterrows()],
LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING,
self._canvas.get_embedding_model()), tkweight=0.7,
vtweight=0.3)
answer, idx = settings.retrievaler.insert_citations(answer,
[ck["content_ltks"] for _, ck in retrieval_res.iterrows()],
[ck["vector"] for _, ck in retrieval_res.iterrows()],
LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING,
self._canvas.get_embedding_model()), tkweight=0.7,
vtweight=0.3)
doc_ids = set([])
recall_docs = []
for i in idx:
@ -127,12 +129,14 @@ class Generate(ComponentBase):
else:
if cpn.component_name.lower() == "retrieval":
retrieval_res.append(out)
kwargs[para["key"]] = " - "+"\n - ".join([o if isinstance(o, str) else str(o) for o in out["content"]])
kwargs[para["key"]] = " - " + "\n - ".join(
[o if isinstance(o, str) else str(o) for o in out["content"]])
self._param.inputs.append({"component_id": para["component_id"], "content": kwargs[para["key"]]})
if retrieval_res:
retrieval_res = pd.concat(retrieval_res, ignore_index=True)
else: retrieval_res = pd.DataFrame([])
else:
retrieval_res = pd.DataFrame([])
for n, v in kwargs.items():
prompt = re.sub(r"\{%s\}" % re.escape(n), re.escape(str(v)), prompt)

4
agent/component/retrieval.py
View File

@ -21,7 +21,7 @@ import pandas as pd
from api.db import LLMType
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import LLMBundle
from api.settings import retrievaler
from api import settings
from agent.component.base import ComponentBase, ComponentParamBase
@ -67,7 +67,7 @@ class Retrieval(ComponentBase, ABC):
if self._param.rerank_id:
rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
kbinfos = retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
kbinfos = settings.retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
1, self._param.top_n,
self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
aggs=False, rerank_mdl=rerank_mdl)