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add inputs to display to every components (#3242)
### What problem does this PR solve? #3240 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
GitHub
parent
f3aaa0d453
commit
4097912d59
@ -37,6 +37,7 @@ class ComponentParamBase(ABC):
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self.output_var_name = "output"
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self.message_history_window_size = 22
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self.query = []
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self.inputs = []
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def set_name(self, name: str):
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self._name = name
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@ -444,8 +445,13 @@ class ComponentBase(ABC):
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if self._param.query:
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outs = []
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for q in self._param.query:
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if q["value"]: outs.append(pd.DataFrame([{"content": q["value"]}]))
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if q["component_id"]: outs.append(self._canvas.get_component(q["component_id"])["obj"].output(allow_partial=False)[1])
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if q["component_id"]:
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outs.append(self._canvas.get_component(q["component_id"])["obj"].output(allow_partial=False)[1])
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self._param.inputs.append({"component_id": q["component_id"],
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"content": "\n".join([str(d["content"]) for d in outs[-1].to_dict('records')])})
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elif q["value"]:
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self._param.inputs.append({"component_id": None, "content": q["value"]})
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outs.append(pd.DataFrame([{"content": q["value"]}]))
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if outs:
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df = pd.concat(outs, ignore_index=True)
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if "content" in df: df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
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@ -463,31 +469,38 @@ class ComponentBase(ABC):
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if self.component_name.lower() == "generate" and self.get_component_name(u) == "retrieval":
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o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
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if o is not None:
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o["component_id"] = u
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upstream_outs.append(o)
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continue
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if u not in self._canvas.get_component(self._id)["upstream"]: continue
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if self.component_name.lower()!="answer" and u not in self._canvas.get_component(self._id)["upstream"]: continue
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if self.component_name.lower().find("switch") < 0 \
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and self.get_component_name(u) in ["relevant", "categorize"]:
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continue
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if u.lower().find("answer") >= 0:
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for r, c in self._canvas.history[::-1]:
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if r == "user":
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upstream_outs.append(pd.DataFrame([{"content": c}]))
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upstream_outs.append(pd.DataFrame([{"content": c, "component_id": u}]))
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break
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break
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if self.component_name.lower().find("answer") >= 0 and self.get_component_name(u) in ["relevant"]:
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continue
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o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
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if o is not None:
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o["component_id"] = u
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upstream_outs.append(o)
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break
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if upstream_outs:
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df = pd.concat(upstream_outs, ignore_index=True)
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if "content" in df:
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df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
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return df
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return pd.DataFrame(self._canvas.get_history(3)[-1:])
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assert upstream_outs, "Can't inference the where the component input is."
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df = pd.concat(upstream_outs, ignore_index=True)
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if "content" in df:
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df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
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self._param.inputs = []
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for _,r in df.iterrows():
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self._param.inputs.append({"component_id": r["component_id"], "content": r["content"]})
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return df
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def get_stream_input(self):
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reversed_cpnts = []
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@ -1,75 +0,0 @@
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#
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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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#
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from abc import ABC
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import pandas as pd
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from api.db import LLMType
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.llm_service import LLMBundle
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from api.settings import retrievaler
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from agent.component.base import ComponentBase, ComponentParamBase
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class CiteParam(ComponentParamBase):
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"""
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Define the Retrieval component parameters.
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"""
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def __init__(self):
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super().__init__()
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self.cite_sources = []
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def check(self):
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self.check_empty(self.cite_source, "Please specify where you want to cite from.")
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class Cite(ComponentBase, ABC):
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component_name = "Cite"
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def _run(self, history, **kwargs):
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input = "\n- ".join(self.get_input()["content"])
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sources = [self._canvas.get_component(cpn_id).output()[1] for cpn_id in self._param.cite_source]
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query = []
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for role, cnt in history[::-1][:self._param.message_history_window_size]:
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if role != "user":continue
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query.append(cnt)
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query = "\n".join(query)
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kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
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if not kbs:
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raise ValueError("Can't find knowledgebases by {}".format(self._param.kb_ids))
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embd_nms = list(set([kb.embd_id for kb in kbs]))
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assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
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embd_mdl = LLMBundle(kbs[0].tenant_id, LLMType.EMBEDDING, embd_nms[0])
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rerank_mdl = None
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if self._param.rerank_id:
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rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
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kbinfos = retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
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1, self._param.top_n,
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self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
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aggs=False, rerank_mdl=rerank_mdl)
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if not kbinfos["chunks"]: return pd.DataFrame()
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df = pd.DataFrame(kbinfos["chunks"])
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df["content"] = df["content_with_weight"]
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del df["content_with_weight"]
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return df
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@ -101,8 +101,8 @@ class Generate(ComponentBase):
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chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
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prompt = self._param.prompt
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retrieval_res = self.get_input()
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input = (" - "+"\n - ".join([c for c in retrieval_res["content"] if isinstance(c, str)])) if "content" in retrieval_res else ""
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retrieval_res = []
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self._param.inputs = []
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for para in self._param.parameters:
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cpn = self._canvas.get_component(para["component_id"])["obj"]
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if cpn.component_name.lower() == "answer":
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@ -112,12 +112,24 @@ class Generate(ComponentBase):
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if "content" not in out.columns:
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kwargs[para["key"]] = "Nothing"
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else:
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if cpn.component_name.lower() == "retrieval":
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retrieval_res.append(out)
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kwargs[para["key"]] = " - "+"\n - ".join([o if isinstance(o, str) else str(o) for o in out["content"]])
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self._param.inputs.append({"component_id": para["component_id"], "content": kwargs[para["key"]]})
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if retrieval_res:
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retrieval_res = pd.concat(retrieval_res, ignore_index=True)
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else: retrieval_res = pd.DataFrame([])
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kwargs["input"] = input
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for n, v in kwargs.items():
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prompt = re.sub(r"\{%s\}" % re.escape(n), re.escape(str(v)), prompt)
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if not self._param.inputs and prompt.find("{input}") >= 0:
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retrieval_res = self.get_input()
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input = (" - " + "\n - ".join(
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[c for c in retrieval_res["content"] if isinstance(c, str)])) if "content" in retrieval_res else ""
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prompt = re.sub(r"\{input\}", re.escape(input), prompt)
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downstreams = self._canvas.get_component(self._id)["downstream"]
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if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
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"obj"].component_name.lower() == "answer":
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@ -50,14 +50,11 @@ class KeywordExtract(Generate, ABC):
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component_name = "KeywordExtract"
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def _run(self, history, **kwargs):
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q = ""
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for r, c in self._canvas.history[::-1]:
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if r == "user":
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q += c
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break
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query = self.get_input()
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query = str(query["content"][0]) if "content" in query else ""
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chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
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ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": q}],
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ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": query}],
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self._param.gen_conf())
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ans = re.sub(r".*keyword:", "", ans).strip()
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@ -396,6 +396,7 @@ def use_sql(question, field_map, tenant_id, chat_mdl, quota=True):
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rows = ["|" +
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"|".join([rmSpace(str(r[i])) for i in clmn_idx]).replace("None", " ") +
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"|" for r in tbl["rows"]]
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rows = [r for r in rows if re.sub(r"[ |]+", "", r)]
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if quota:
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rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
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else:
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@ -1299,7 +1299,7 @@
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"llm": []
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},
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{
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"name": "cohere",
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"name": "Cohere",
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"logo": "",
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"tags": "LLM,TEXT EMBEDDING, TEXT RE-RANK",
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"status": "1",
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@ -39,7 +39,7 @@ EmbeddingModel = {
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"NVIDIA": NvidiaEmbed,
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"LM-Studio": LmStudioEmbed,
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"OpenAI-API-Compatible": OpenAI_APIEmbed,
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"cohere": CoHereEmbed,
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"Cohere": CoHereEmbed,
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"TogetherAI": TogetherAIEmbed,
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"PerfXCloud": PerfXCloudEmbed,
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"Upstage": UpstageEmbed,
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@ -92,7 +92,7 @@ ChatModel = {
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"NVIDIA": NvidiaChat,
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"LM-Studio": LmStudioChat,
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"OpenAI-API-Compatible": OpenAI_APIChat,
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"cohere": CoHereChat,
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"Cohere": CoHereChat,
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"LeptonAI": LeptonAIChat,
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"TogetherAI": TogetherAIChat,
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"PerfXCloud": PerfXCloudChat,
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@ -117,7 +117,7 @@ RerankModel = {
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"NVIDIA": NvidiaRerank,
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"LM-Studio": LmStudioRerank,
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"OpenAI-API-Compatible": OpenAI_APIRerank,
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"cohere": CoHereRerank,
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"Cohere": CoHereRerank,
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"TogetherAI": TogetherAIRerank,
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"SILICONFLOW": SILICONFLOWRerank,
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"BaiduYiyan": BaiduYiyanRerank,
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@ -394,6 +394,7 @@ class VoyageRerank(Base):
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rank[r.index] = r.relevance_score
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return rank, res.total_tokens
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class QWenRerank(Base):
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def __init__(self, key, model_name='gte-rerank', base_url=None, **kwargs):
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import dashscope
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