API: retrieval api (#1763)

### What problem does this PR solve?

Add retrieval api on a specific knowledge base



https://github.com/infiniflow/ragflow/issues/1102

### Type of change

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

api/apps/api_app.py

@@ -20,7 +20,7 @@ from datetime import datetime, timedelta
 from flask import request, Response
 from flask_login import login_required, current_user
 
-from api.db import FileType, ParserType, FileSource
+from api.db import FileType, ParserType, FileSource, LLMType
 from api.db.db_models import APIToken, API4Conversation, Task, File
 from api.db.services import duplicate_name
 from api.db.services.api_service import APITokenService, API4ConversationService
@@ -29,6 +29,7 @@ from api.db.services.document_service import DocumentService
 from api.db.services.file2document_service import File2DocumentService
 from api.db.services.file_service import FileService
 from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import TenantLLMService
 from api.db.services.task_service import queue_tasks, TaskService
 from api.db.services.user_service import UserTenantService
 from api.settings import RetCode, retrievaler
@@ -37,6 +38,7 @@ from api.utils.api_utils import server_error_response, get_data_error_result, ge
 from itsdangerous import URLSafeTimedSerializer
 
 from api.utils.file_utils import filename_type, thumbnail
+from rag.nlp import keyword_extraction
 from rag.utils.minio_conn import MINIO
 
 
@@ -587,3 +589,55 @@ def completion_faq():
 
     except Exception as e:
         return server_error_response(e)
+
+
+@manager.route('/retrieval', methods=['POST'])
+@validate_request("kb_id", "question")
+def retrieval():
+    token = request.headers.get('Authorization').split()[1]
+    objs = APIToken.query(token=token)
+    if not objs:
+        return get_json_result(
+            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+
+    req = request.json
+    kb_id = req.get("kb_id")
+    doc_ids = req.get("doc_ids", [])
+    question = req.get("question")
+    page = int(req.get("page", 1))
+    size = int(req.get("size", 30))
+    similarity_threshold = float(req.get("similarity_threshold", 0.2))
+    vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
+    top = int(req.get("top_k", 1024))
+
+    try:
+        e, kb = KnowledgebaseService.get_by_id(kb_id)
+        if not e:
+            return get_data_error_result(retmsg="Knowledgebase not found!")
+
+        embd_mdl = TenantLLMService.model_instance(
+            kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+
+        rerank_mdl = None
+        if req.get("rerank_id"):
+            rerank_mdl = TenantLLMService.model_instance(
+                kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
+
+        if req.get("keyword", False):
+            chat_mdl = TenantLLMService.model_instance(kb.tenant_id, LLMType.CHAT)
+            question += keyword_extraction(chat_mdl, question)
+
+        ranks = retrievaler.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size,
+                                      similarity_threshold, vector_similarity_weight, top,
+                                      doc_ids, rerank_mdl=rerank_mdl)
+        for c in ranks["chunks"]:
+            if "vector" in c:
+                del c["vector"]
+
+        return get_json_result(data=ranks)
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
+                                   retcode=RetCode.DATA_ERROR)
+        return server_error_response(e)
+