search between multiple indiices for team function (#3079)

### What problem does this PR solve?

#2834 
### Type of change

- [x] New Feature (non-breaking change which adds functionality)

agent/component/__init__.py

 from .tushare import TuShare, TuShareParam
 from .akshare import AkShare, AkShareParam
 from .crawler import Crawler, CrawlerParam
+from .invoke import Invoke, InvokeParam
 
 
 def component_class(class_name):

agent/component/generate.py

 from functools import partial
 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 agent.component.base import ComponentBase, ComponentParamBase
 
         kwargs["input"] = input
         for n, v in kwargs.items():
-            prompt = re.sub(r"\{%s\}" % re.escape(n), str(v), prompt)
+            prompt = re.sub(r"\{%s\}" % re.escape(n), re.escape(str(v)), prompt)
 
         downstreams = self._canvas.get_component(self._id)["downstream"]
         if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
                 retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
             return pd.DataFrame([res])
 
-        ans = chat_mdl.chat(prompt, self._canvas.get_history(self._param.message_history_window_size),
-                            self._param.gen_conf())
+        msg = self._canvas.get_history(self._param.message_history_window_size)
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
+        ans = chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf())
+
         if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
             res = self.set_cite(retrieval_res, ans)
             return pd.DataFrame([res])
             self.set_output(res)
             return
 
+        msg = self._canvas.get_history(self._param.message_history_window_size)
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
         answer = ""
-        for ans in chat_mdl.chat_streamly(prompt, self._canvas.get_history(self._param.message_history_window_size),
-                                          self._param.gen_conf()):
+        for ans in chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf()):
             res = {"content": ans, "reference": []}
             answer = ans
             yield res

agent/component/invoke.py

 #  limitations under the License.
 #
 import json
+import re
 from abc import ABC
-
 import requests
-
+from deepdoc.parser import HtmlParser
 from agent.component.base import ComponentBase, ComponentParamBase
 
 
         self.variables = []
         self.url = ""
         self.timeout = 60
+        self.clean_html = False
 
     def check(self):
         self.check_valid_value(self.method.lower(), "Type of content from the crawler", ['get', 'post', 'put'])
         self.check_empty(self.url, "End point URL")
         self.check_positive_integer(self.timeout, "Timeout time in second")
+        self.check_boolean(self.clean_html, "Clean HTML")
 
 
 class Invoke(ComponentBase, ABC):
         if self._param.headers:
             headers = json.loads(self._param.headers)
         proxies = None
-        if self._param.proxy:
+        if re.sub(r"https?:?/?/?", "", self._param.proxy):
             proxies = {"http": self._param.proxy, "https": self._param.proxy}
 
         if method == 'get':
                                     headers=headers,
                                     proxies=proxies,
                                     timeout=self._param.timeout)
+            if self._param.clean_html:
+                sections = HtmlParser()(None, response.content)
+                return Invoke.be_output("\n".join(sections))
+
             return Invoke.be_output(response.text)
 
         if method == 'put':
                                     headers=headers,
                                     proxies=proxies,
                                     timeout=self._param.timeout)
+            if self._param.clean_html:
+                sections = HtmlParser()(None, response.content)
+                return Invoke.be_output("\n".join(sections))
+            return Invoke.be_output(response.text)
 
+        if method == 'post':
+            response = requests.post(url=url,
+                                    json=args,
+                                    headers=headers,
+                                    proxies=proxies,
+                                    timeout=self._param.timeout)
+            if self._param.clean_html:
+                sections = HtmlParser()(None, response.content)
+                return Invoke.be_output("\n".join(sections))
             return Invoke.be_output(response.text)

api/db/services/dialog_service.py

     else:
         if prompt_config.get("keyword", False):
             questions[-1] += keyword_extraction(chat_mdl, questions[-1])
-        kbinfos = retr.retrieval(" ".join(questions), embd_mdl, dialog.tenant_id, dialog.kb_ids, 1, dialog.top_n,
+
+        tenant_ids = list(set([kb.tenant_id for kb in kbs]))
+        kbinfos = retr.retrieval(" ".join(questions), embd_mdl, tenant_ids, dialog.kb_ids, 1, dialog.top_n,
                                         dialog.similarity_threshold,
                                         dialog.vector_similarity_weight,
                                         doc_ids=attachments,

deepdoc/parser/html_parser.py

 import html_text
 import chardet
 
+
 def get_encoding(file):
     with open(file,'rb') as f:
         tmp = chardet.detect(f.read())
         return tmp['encoding']
 
+
 class RAGFlowHtmlParser:
     def __call__(self, fnm, binary=None):
         txt = ""

rag/nlp/search.py

                     Q("bool", must_not=Q("range", available_int={"lt": 1})))
         return bqry
 
-    def search(self, req, idxnm, emb_mdl=None, highlight=False):
+    def search(self, req, idxnms, emb_mdl=None, highlight=False):
         qst = req.get("question", "")
         bqry, keywords = self.qryr.question(qst, min_match="30%")
         bqry = self._add_filters(bqry, req)
                 del s["highlight"]
             q_vec = s["knn"]["query_vector"]
         es_logger.info("【Q】: {}".format(json.dumps(s)))
-        res = self.es.search(deepcopy(s), idxnm=idxnm, timeout="600s", src=src)
+        res = self.es.search(deepcopy(s), idxnms=idxnms, timeout="600s", src=src)
         es_logger.info("TOTAL: {}".format(self.es.getTotal(res)))
         if self.es.getTotal(res) == 0 and "knn" in s:
             bqry, _ = self.qryr.question(qst, min_match="10%")
             s["query"] = bqry.to_dict()
             s["knn"]["filter"] = bqry.to_dict()
             s["knn"]["similarity"] = 0.17
-            res = self.es.search(s, idxnm=idxnm, timeout="600s", src=src)
+            res = self.es.search(s, idxnms=idxnms, timeout="600s", src=src)
             es_logger.info("【Q】: {}".format(json.dumps(s)))
 
         kwds = set([])
                                            rag_tokenizer.tokenize(ans).split(" "),
                                            rag_tokenizer.tokenize(inst).split(" "))
 
-    def retrieval(self, question, embd_mdl, tenant_id, kb_ids, page, page_size, similarity_threshold=0.2,
+    def retrieval(self, question, embd_mdl, tenant_ids, kb_ids, page, page_size, similarity_threshold=0.2,
                   vector_similarity_weight=0.3, top=1024, doc_ids=None, aggs=True, rerank_mdl=None, highlight=False):
         ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
         if not question:
             return ranks
+
         RERANK_PAGE_LIMIT = 3
         req = {"kb_ids": kb_ids, "doc_ids": doc_ids, "size": max(page_size*RERANK_PAGE_LIMIT, 128),
                "question": question, "vector": True, "topk": top,
                "similarity": similarity_threshold,
                "available_int": 1}
+
         if page > RERANK_PAGE_LIMIT:
             req["page"] = page
             req["size"] = page_size
-        sres = self.search(req, index_name(tenant_id), embd_mdl, highlight)
+
+        if isinstance(tenant_ids, str):
+            tenant_ids = tenant_ids.split(",")
+
+        sres = self.search(req, [index_name(tid) for tid in tenant_ids], embd_mdl, highlight)
         ranks["total"] = sres.total
 
         if page <= RERANK_PAGE_LIMIT:
         s = Search()
         s = s.query(Q("match", doc_id=doc_id))[0:max_count]
         s = s.to_dict()
-        es_res = self.es.search(s, idxnm=index_name(tenant_id), timeout="600s", src=fields)
+        es_res = self.es.search(s, idxnms=index_name(tenant_id), timeout="600s", src=fields)
         res = []
         for index, chunk in enumerate(es_res['hits']['hits']):
             res.append({fld: chunk['_source'].get(fld) for fld in fields})

rag/utils/es_conn.py

 
         return False
 
-    def search(self, q, idxnm=None, src=False, timeout="2s"):
+    def search(self, q, idxnms=None, src=False, timeout="2s"):
         if not isinstance(q, dict):
             q = Search().query(q).to_dict()
+        if isinstance(idxnms, str):
+            idxnms = idxnms.split(",")
         for i in range(3):
             try:
-                res = self.es.search(index=(self.idxnm if not idxnm else idxnm),
+                res = self.es.search(index=(self.idxnm if not idxnms else idxnms),
                                      body=q,
                                      timeout=timeout,
                                      # search_type="dfs_query_then_fetch",