add rerank model

api/apps/chunk_app.py

@@ -257,8 +257,15 @@ def retrieval_test():
 
         embd_mdl = TenantLLMService.model_instance(
             kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
-        ranks = retrievaler.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size, similarity_threshold,
-                                      vector_similarity_weight, top, doc_ids)
+
+        rerank_mdl = None
+        if req.get("rerank_id"):
+            rerank_mdl = TenantLLMService.model_instance(
+                kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
+
+        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"]

api/apps/dialog_app.py

@@ -33,6 +33,9 @@ def set_dialog():
     name = req.get("name", "New Dialog")
     description = req.get("description", "A helpful Dialog")
     top_n = req.get("top_n", 6)
+    top_k = req.get("top_k", 1024)
+    rerank_id = req.get("rerank_id", "")
+    if not rerank_id: req["rerank_id"] = ""
     similarity_threshold = req.get("similarity_threshold", 0.1)
     vector_similarity_weight = req.get("vector_similarity_weight", 0.3)
     llm_setting = req.get("llm_setting", {})
@@ -83,6 +86,8 @@ def set_dialog():
                 "llm_setting": llm_setting,
                 "prompt_config": prompt_config,
                 "top_n": top_n,
+                "top_k": top_k,
+                "rerank_id": rerank_id,
                 "similarity_threshold": similarity_threshold,
                 "vector_similarity_weight": vector_similarity_weight
             }

api/apps/llm_app.py

@@ -20,15 +20,15 @@
 from api.db import StatusEnum, LLMType
 from api.db.db_models import TenantLLM
 from api.utils.api_utils import get_json_result
-from rag.llm import EmbeddingModel, ChatModel
+from rag.llm import EmbeddingModel, ChatModel, RerankModel
 
 
 @manager.route('/factories', methods=['GET'])
 @login_required
 def factories():
     try:
         fac = LLMFactoriesService.get_all()
-        return get_json_result(data=[f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed"]])
+        return get_json_result(data=[f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]])
     except Exception as e:
         return server_error_response(e)
 
@@ -64,6 +64,16 @@ def set_api_key():
             except Exception as e:
                 msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
                     e)
+        elif llm.model_type == LLMType.RERANK:
+            mdl = RerankModel[factory](
+                req["api_key"], llm.llm_name, base_url=req.get("base_url"))
+            try:
+                m, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
+                if len(arr[0]) == 0 or tc == 0:
+                    raise Exception("Fail")
+            except Exception as e:
+                msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
+                    e)
 
     if msg:
         return get_data_error_result(retmsg=msg)
@@ -199,7 +209,7 @@ def list_app():
         llms = [m.to_dict()
                 for m in llms if m.status == StatusEnum.VALID.value]
         for m in llms:
-            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in ["Youdao","FastEmbed"]
+            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in ["Youdao","FastEmbed", "BAAI"]
 
         llm_set = set([m["llm_name"] for m in llms])
         for o in objs:

api/apps/user_app.py

@@ -26,8 +26,9 @@
 from api.utils.api_utils import server_error_response, validate_request
 from api.utils import get_uuid, get_format_time, decrypt, download_img, current_timestamp, datetime_format
 from api.db import UserTenantRole, LLMType, FileType
-from api.settings import RetCode, GITHUB_OAUTH, FEISHU_OAUTH, CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, API_KEY, \
-    LLM_FACTORY, LLM_BASE_URL
+from api.settings import RetCode, GITHUB_OAUTH, FEISHU_OAUTH, CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, \
+    API_KEY, \
+    LLM_FACTORY, LLM_BASE_URL, RERANK_MDL
 from api.db.services.user_service import UserService, TenantService, UserTenantService
 from api.db.services.file_service import FileService
 from api.settings import stat_logger
@@ -288,7 +289,8 @@ def user_register(user_id, user):
         "embd_id": EMBEDDING_MDL,
         "asr_id": ASR_MDL,
         "parser_ids": PARSERS,
-        "img2txt_id": IMAGE2TEXT_MDL
+        "img2txt_id": IMAGE2TEXT_MDL,
+        "rerank_id": RERANK_MDL
     }
     usr_tenant = {
         "tenant_id": user_id,

api/db/__init__.py

@@ -54,6 +54,7 @@ class LLMType(StrEnum):
     EMBEDDING = 'embedding'
     SPEECH2TEXT = 'speech2text'
     IMAGE2TEXT = 'image2text'
+    RERANK = 'rerank'
 
 
 class ChatStyle(StrEnum):

api/db/db_models.py

@@ -437,6 +437,10 @@ class Tenant(DataBaseModel):
         max_length=128,
         null=False,
         help_text="default image to text model ID")
+    rerank_id = CharField(
+        max_length=128,
+        null=False,
+        help_text="default rerank model ID")
     parser_ids = CharField(
         max_length=256,
         null=False,
@@ -771,11 +775,16 @@ class Dialog(DataBaseModel):
     similarity_threshold = FloatField(default=0.2)
     vector_similarity_weight = FloatField(default=0.3)
     top_n = IntegerField(default=6)
+    top_k = IntegerField(default=1024)
     do_refer = CharField(
         max_length=1,
         null=False,
         help_text="it needs to insert reference index into answer or not",
         default="1")
+    rerank_id = CharField(
+        max_length=128,
+        null=False,
+        help_text="default rerank model ID")
 
     kb_ids = JSONField(null=False, default=[])
     status = CharField(
@@ -825,11 +834,29 @@ class Meta:
 
 
 def migrate_db():
-    try:
         with DB.transaction():
             migrator = MySQLMigrator(DB)
-            migrate(
-                migrator.add_column('file', 'source_type', CharField(max_length=128, null=False, default="", help_text="where dose this document come from"))
-            )
-    except Exception as e:
-        pass
+            try:
+                migrate(
+                    migrator.add_column('file', 'source_type', CharField(max_length=128, null=False, default="", help_text="where dose this document come from"))
+                )
+            except Exception as e:
+                pass
+            try:
+                migrate(
+                    migrator.add_column('tenant', 'rerank_id', CharField(max_length=128, null=False, default="BAAI/bge-reranker-v2-m3", help_text="default rerank model ID"))
+                )
+            except Exception as e:
+                pass
+            try:
+                migrate(
+                    migrator.add_column('dialog', 'rerank_id', CharField(max_length=128, null=False, default="", help_text="default rerank model ID"))
+                )
+            except Exception as e:
+                pass
+            try:
+                migrate(
+                    migrator.add_column('dialog', 'top_k', IntegerField(default=1024))
+                )
+            except Exception as e:
+                pass

api/db/init_data.py

@@ -142,7 +142,17 @@ def init_superuser():
     "logo": "",
     "tags": "LLM,TEXT EMBEDDING",
     "status": "1",
-},
+},{
+    "name": "Jina",
+    "logo": "",
+    "tags": "TEXT EMBEDDING, TEXT RE-RANK",
+    "status": "1",
+},{
+    "name": "BAAI",
+    "logo": "",
+    "tags": "TEXT EMBEDDING, TEXT RE-RANK",
+    "status": "1",
+}
     # {
     #     "name": "文心一言",
     #     "logo": "",
@@ -367,6 +377,13 @@ def init_llm_factory():
             "max_tokens": 512,
             "model_type": LLMType.EMBEDDING.value
         },
+        {
+            "fid": factory_infos[7]["name"],
+            "llm_name": "maidalun1020/bce-reranker-base_v1",
+            "tags": "RE-RANK, 8K",
+            "max_tokens": 8196,
+            "model_type": LLMType.RERANK.value
+        },
         # ------------------------ DeepSeek -----------------------
         {
             "fid": factory_infos[8]["name"],
@@ -440,6 +457,85 @@ def init_llm_factory():
             "max_tokens": 512,
             "model_type": LLMType.EMBEDDING.value
         },
+        # ------------------------ Jina -----------------------
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-reranker-v1-base-en",
+            "tags": "RE-RANK,8k",
+            "max_tokens": 8196,
+            "model_type": LLMType.RERANK.value
+        },
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-reranker-v1-turbo-en",
+            "tags": "RE-RANK,8k",
+            "max_tokens": 8196,
+            "model_type": LLMType.RERANK.value
+        },
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-reranker-v1-tiny-en",
+            "tags": "RE-RANK,8k",
+            "max_tokens": 8196,
+            "model_type": LLMType.RERANK.value
+        },
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-colbert-v1-en",
+            "tags": "RE-RANK,8k",
+            "max_tokens": 8196,
+            "model_type": LLMType.RERANK.value
+        },
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-embeddings-v2-base-en",
+            "tags": "TEXT EMBEDDING",
+            "max_tokens": 8196,
+            "model_type": LLMType.EMBEDDING.value
+        },
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-embeddings-v2-base-de",
+            "tags": "TEXT EMBEDDING",
+            "max_tokens": 8196,
+            "model_type": LLMType.EMBEDDING.value
+        },
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-embeddings-v2-base-es",
+            "tags": "TEXT EMBEDDING",
+            "max_tokens": 8196,
+            "model_type": LLMType.EMBEDDING.value
+        },
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-embeddings-v2-base-code",
+            "tags": "TEXT EMBEDDING",
+            "max_tokens": 8196,
+            "model_type": LLMType.EMBEDDING.value
+        },
+        {
+            "fid": factory_infos[11]["name"],
+            "llm_name": "jina-embeddings-v2-base-zh",
+            "tags": "TEXT EMBEDDING",
+            "max_tokens": 8196,
+            "model_type": LLMType.EMBEDDING.value
+        },
+        # ------------------------ BAAI -----------------------
+        {
+            "fid": factory_infos[12]["name"],
+            "llm_name": "BAAI/bge-large-zh-v1.5",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 1024,
+            "model_type": LLMType.EMBEDDING.value
+        },
+        {
+            "fid": factory_infos[12]["name"],
+            "llm_name": "BAAI/bge-reranker-v2-m3",
+            "tags": "LLM,CHAT,",
+            "max_tokens": 16385,
+            "model_type": LLMType.RERANK.value
+        },
     ]
     for info in factory_infos:
         try:

api/db/services/dialog_service.py

@@ -115,11 +115,14 @@ def chat(dialog, messages, stream=True, **kwargs):
     if "knowledge" not in [p["key"] for p in prompt_config["parameters"]]:
         kbinfos = {"total": 0, "chunks": [], "doc_aggs": []}
     else:
+        rerank_mdl = None
+        if dialog.rerank_id:
+            rerank_mdl = LLMBundle(dialog.tenant_id, LLMType.RERANK, dialog.rerank_id)
         kbinfos = retrievaler.retrieval(" ".join(questions), embd_mdl, dialog.tenant_id, dialog.kb_ids, 1, dialog.top_n,
                                         dialog.similarity_threshold,
                                         dialog.vector_similarity_weight,
                                         doc_ids=kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else None,
-                                        top=1024, aggs=False)
+                                        top=1024, aggs=False, rerank_mdl=rerank_mdl)
     knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
     chat_logger.info(
         "{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
@@ -130,7 +133,7 @@ def chat(dialog, messages, stream=True, **kwargs):
 
     kwargs["knowledge"] = "\n".join(knowledges)
     gen_conf = dialog.llm_setting
-    
+
     msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}]
     msg.extend([{"role": m["role"], "content": m["content"]}
            for m in messages if m["role"] != "system"])