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)

agent/component/base.py

@@ -37,6 +37,7 @@ class ComponentParamBase(ABC):
         self.output_var_name = "output"
         self.message_history_window_size = 22
         self.query = []
+        self.inputs = []
 
     def set_name(self, name: str):
         self._name = name
@@ -444,8 +445,13 @@ class ComponentBase(ABC):
         if self._param.query:
             outs = []
             for q in self._param.query:
-                if q["value"]: outs.append(pd.DataFrame([{"content": q["value"]}]))
-                if q["component_id"]: outs.append(self._canvas.get_component(q["component_id"])["obj"].output(allow_partial=False)[1])
+                if q["component_id"]:
+                    outs.append(self._canvas.get_component(q["component_id"])["obj"].output(allow_partial=False)[1])
+                    self._param.inputs.append({"component_id": q["component_id"],
+                                               "content": "\n".join([str(d["content"]) for d in outs[-1].to_dict('records')])})
+                elif q["value"]:
+                    self._param.inputs.append({"component_id": None, "content": q["value"]})
+                    outs.append(pd.DataFrame([{"content": q["value"]}]))
             if outs:
                 df = pd.concat(outs, ignore_index=True)
                 if "content" in df: df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
@@ -463,31 +469,38 @@ class ComponentBase(ABC):
             if self.component_name.lower() == "generate" and self.get_component_name(u) == "retrieval":
                 o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
                 if o is not None:
+                    o["component_id"] = u
                     upstream_outs.append(o)
                     continue
-            if u not in self._canvas.get_component(self._id)["upstream"]: continue
+            if self.component_name.lower()!="answer" and u not in self._canvas.get_component(self._id)["upstream"]: continue
             if self.component_name.lower().find("switch") < 0 \
                     and self.get_component_name(u) in ["relevant", "categorize"]:
                 continue
             if u.lower().find("answer") >= 0:
                 for r, c in self._canvas.history[::-1]:
                     if r == "user":
-                        upstream_outs.append(pd.DataFrame([{"content": c}]))
+                        upstream_outs.append(pd.DataFrame([{"content": c, "component_id": u}]))
                         break
                 break
             if self.component_name.lower().find("answer") >= 0 and self.get_component_name(u) in ["relevant"]:
                 continue
             o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
             if o is not None:
+                o["component_id"] = u
                 upstream_outs.append(o)
             break
 
-        if upstream_outs:
-            df = pd.concat(upstream_outs, ignore_index=True)
-            if "content" in df:
-                df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
-            return df
-        return pd.DataFrame(self._canvas.get_history(3)[-1:])
+        assert upstream_outs, "Can't inference the where the component input is."
+
+        df = pd.concat(upstream_outs, ignore_index=True)
+        if "content" in df:
+            df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
+
+        self._param.inputs = []
+        for _,r in df.iterrows():
+            self._param.inputs.append({"component_id": r["component_id"], "content": r["content"]})
+
+        return df
 
     def get_stream_input(self):
         reversed_cpnts = []

agent/component/cite.py

@@ -1,75 +0,0 @@
-#
-#  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 abc import ABC
-
-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 agent.component.base import ComponentBase, ComponentParamBase
-
-
-class CiteParam(ComponentParamBase):
-
-    """
-    Define the Retrieval component parameters.
-    """
-    def __init__(self):
-        super().__init__()
-        self.cite_sources = []
-
-    def check(self):
-        self.check_empty(self.cite_source, "Please specify where you want to cite from.")
-
-
-class Cite(ComponentBase, ABC):
-    component_name = "Cite"
-
-    def _run(self, history, **kwargs):
-        input = "\n- ".join(self.get_input()["content"])
-        sources = [self._canvas.get_component(cpn_id).output()[1] for cpn_id in self._param.cite_source]
-        query = []
-        for role, cnt in history[::-1][:self._param.message_history_window_size]:
-            if role != "user":continue
-            query.append(cnt)
-        query = "\n".join(query)
-
-        kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
-        if not kbs:
-            raise ValueError("Can't find knowledgebases by {}".format(self._param.kb_ids))
-        embd_nms = list(set([kb.embd_id for kb in kbs]))
-        assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
-
-        embd_mdl = LLMBundle(kbs[0].tenant_id, LLMType.EMBEDDING, embd_nms[0])
-
-        rerank_mdl = None
-        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,
-                                        1, self._param.top_n,
-                                        self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
-                                        aggs=False, rerank_mdl=rerank_mdl)
-
-        if not kbinfos["chunks"]: return pd.DataFrame()
-        df = pd.DataFrame(kbinfos["chunks"])
-        df["content"] = df["content_with_weight"]
-        del df["content_with_weight"]
-        return df
-
-

agent/component/generate.py

@@ -101,8 +101,8 @@ class Generate(ComponentBase):
         chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
         prompt = self._param.prompt
 
-        retrieval_res = self.get_input()
-        input = ("  - "+"\n  - ".join([c for c in retrieval_res["content"] if isinstance(c, str)])) if "content" in retrieval_res else ""
+        retrieval_res = []
+        self._param.inputs = []
         for para in self._param.parameters:
             cpn = self._canvas.get_component(para["component_id"])["obj"]
             if cpn.component_name.lower() == "answer":
@@ -112,12 +112,24 @@ class Generate(ComponentBase):
             if "content" not in out.columns:
                 kwargs[para["key"]] = "Nothing"
             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"]])
+            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([])
 
-        kwargs["input"] = input
         for n, v in kwargs.items():
             prompt = re.sub(r"\{%s\}" % re.escape(n), re.escape(str(v)), prompt)
 
+        if not self._param.inputs and prompt.find("{input}") >= 0:
+            retrieval_res = self.get_input()
+            input = ("  - " + "\n  - ".join(
+                [c for c in retrieval_res["content"] if isinstance(c, str)])) if "content" in retrieval_res else ""
+            prompt = re.sub(r"\{input\}", re.escape(input), prompt)
+
         downstreams = self._canvas.get_component(self._id)["downstream"]
         if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
             "obj"].component_name.lower() == "answer":

agent/component/keyword.py

@@ -50,14 +50,11 @@ class KeywordExtract(Generate, ABC):
     component_name = "KeywordExtract"
 
     def _run(self, history, **kwargs):
-        q = ""
-        for r, c in self._canvas.history[::-1]:
-            if r == "user":
-                q += c
-                break
+        query = self.get_input()
+        query = str(query["content"][0]) if "content" in query else ""
 
         chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
-        ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": q}],
+        ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": query}],
                             self._param.gen_conf())
 
         ans = re.sub(r".*keyword:", "", ans).strip()

api/db/services/dialog_service.py

@@ -396,6 +396,7 @@ def use_sql(question, field_map, tenant_id, chat_mdl, quota=True):
     rows = ["|" +
             "|".join([rmSpace(str(r[i])) for i in clmn_idx]).replace("None", " ") +
             "|" for r in tbl["rows"]]
+    rows = [r for r in rows if re.sub(r"[ |]+", "", r)]
     if quota:
         rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
     else:

conf/llm_factories.json

@@ -1299,7 +1299,7 @@
             "llm": []
         },
         {
-            "name": "cohere",
+            "name": "Cohere",
             "logo": "",
             "tags": "LLM,TEXT EMBEDDING, TEXT RE-RANK",
             "status": "1",

rag/llm/__init__.py

@@ -39,7 +39,7 @@ EmbeddingModel = {
     "NVIDIA": NvidiaEmbed,
     "LM-Studio": LmStudioEmbed,
     "OpenAI-API-Compatible": OpenAI_APIEmbed,
-    "cohere": CoHereEmbed,
+    "Cohere": CoHereEmbed,
     "TogetherAI": TogetherAIEmbed,
     "PerfXCloud": PerfXCloudEmbed,
     "Upstage": UpstageEmbed,
@@ -92,7 +92,7 @@ ChatModel = {
     "NVIDIA": NvidiaChat,
     "LM-Studio": LmStudioChat,
     "OpenAI-API-Compatible": OpenAI_APIChat,
-    "cohere": CoHereChat,
+    "Cohere": CoHereChat,
     "LeptonAI": LeptonAIChat,
     "TogetherAI": TogetherAIChat,
     "PerfXCloud": PerfXCloudChat,
@@ -117,7 +117,7 @@ RerankModel = {
     "NVIDIA": NvidiaRerank,
     "LM-Studio": LmStudioRerank,
     "OpenAI-API-Compatible": OpenAI_APIRerank,
-    "cohere": CoHereRerank,
+    "Cohere": CoHereRerank,
     "TogetherAI": TogetherAIRerank,
     "SILICONFLOW": SILICONFLOWRerank,
     "BaiduYiyan": BaiduYiyanRerank,

rag/llm/rerank_model.py

@@ -394,6 +394,7 @@ class VoyageRerank(Base):
             rank[r.index] = r.relevance_score
         return rank, res.total_tokens
 
+
 class QWenRerank(Base):
     def __init__(self, key, model_name='gte-rerank', base_url=None, **kwargs):
         import dashscope