Add tavily as web searh tool. (#5349)

### What problem does this PR solve?

#5198

### Type of change

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

rag/nlp/query.py

@@ -206,6 +206,8 @@ class FulltextQueryer:
 
         sims = CosineSimilarity([avec], bvecs)
         tksim = self.token_similarity(atks, btkss)
+        if np.sum(sims[0]) == 0:
+            return np.array(tksim), tksim, sims[0]
         return np.array(sims[0]) * vtweight + np.array(tksim) * tkweight, tksim, sims[0]
 
     def token_similarity(self, atks, btkss):

rag/utils/tavily_conn.py

@@ -0,0 +1,66 @@
+#
+#  Copyright 2025 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.
+#
+import logging
+from tavily import TavilyClient
+from api.utils import get_uuid
+from rag.nlp import rag_tokenizer
+
+
+class Tavily:
+    def __init__(self, api_key: str):
+        self.tavily_client = TavilyClient(api_key=api_key)
+
+    def search(self, query):
+        try:
+            response = self.tavily_client.search(
+                query=query,
+                search_depth="advanced"
+            )
+            return [{"url": res["url"], "title": res["title"], "content": res["content"], "score": res["score"]} for res in response["results"]]
+        except Exception as e:
+            logging.exception(e)
+
+        return []
+
+    def retrieve_chunks(self, question):
+        chunks = []
+        aggs = []
+        for r in self.search(question):
+            id = get_uuid()
+            chunks.append({
+                "chunk_id": id,
+                "content_ltks": rag_tokenizer.tokenize(r["content"]),
+                "content_with_weight": r["content"],
+                "doc_id": id,
+                "docnm_kwd": r["title"],
+                "kb_id": [],
+                "important_kwd": [],
+                "image_id": "",
+                "similarity": r["score"],
+                "vector_similarity": 1.,
+                "term_similarity": 0,
+                "vector": [],
+                "positions": [],
+                "url": r["url"]
+            })
+            aggs.append({
+                "doc_name": r["title"],
+                "doc_id": id,
+                "count": 1,
+                "url": r["url"]
+            })
+            logging.info("[Tavily]: "+r["content"][:128]+"...")
+        return {"chunks": chunks, "doc_aggs": aggs}