Feat:Compatible with Dify's External Knowledge API (#2848)

### What problem does this PR solve?

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._
Fixes #2731 
### Type of change

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

api/apps/sdk/dify_retrieval.py

@@ -0,0 +1,62 @@
+from flask import request, jsonify
+
+from db import LLMType, ParserType
+from db.services.knowledgebase_service import KnowledgebaseService
+from db.services.llm_service import LLMBundle
+from settings import retrievaler, kg_retrievaler, RetCode
+from utils.api_utils import validate_request, build_error_result, apikey_required
+
+
+@manager.route('/dify/retrieval', methods=['POST'])
+@apikey_required
+@validate_request("knowledge_id", "query")
+def retrieval(tenant_id):
+    req = request.json
+    question = req["query"]
+    kb_id = req["knowledge_id"]
+    retrieval_setting = req.get("retrieval_setting", {})
+    similarity_threshold = float(retrieval_setting.get("score_threshold", 0.0))
+    top = int(retrieval_setting.get("top_k", 1024))
+
+    try:
+
+        e, kb = KnowledgebaseService.get_by_id(kb_id)
+        if not e:
+            return build_error_result(error_msg="Knowledgebase not found!", retcode=RetCode.NOT_FOUND)
+
+        if kb.tenant_id != tenant_id:
+            return build_error_result(error_msg="Knowledgebase not found!", retcode=RetCode.NOT_FOUND)
+
+        embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+
+        retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
+        ranks = retr.retrieval(
+            question,
+            embd_mdl,
+            kb.tenant_id,
+            [kb_id],
+            page=1,
+            page_size=top,
+            similarity_threshold=similarity_threshold,
+            vector_similarity_weight=0.3,
+            top=top
+        )
+        records = []
+        for c in ranks["chunks"]:
+            if "vector" in c:
+                del c["vector"]
+            records.append({
+                "content": c["content_ltks"],
+                "score": c["similarity"],
+                "title": c["docnm_kwd"],
+                "metadata": ""
+            })
+
+        return jsonify({"records": records})
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return build_error_result(
+                error_msg=f'No chunk found! Check the chunk status please!',
+                retcode=RetCode.NOT_FOUND
+            )
+        return build_error_result(error_msg=str(e), retcode=RetCode.SERVER_ERROR)