add self-rag (#1070)

### What problem does this PR solve?

#1069 

### Type of change

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

api/db/services/canvas_service.py

@@ -0,0 +1,26 @@
+#
+#  Copyright 2024 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.
+#
+from datetime import datetime
+import peewee
+from api.db.db_models import DB, API4Conversation, APIToken, Dialog, CanvasTemplate, UserCanvas
+from api.db.services.common_service import CommonService
+
+
+class CanvasTemplateService(CommonService):
+    model = CanvasTemplate
+
+class UserCanvasService(CommonService):
+    model = UserCanvas

api/db/services/dialog_service.py

@@ -23,6 +23,7 @@ from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import LLMService, TenantLLMService, LLMBundle
 from api.settings import chat_logger, retrievaler
 from rag.app.resume import forbidden_select_fields4resume
+from rag.nlp.rag_tokenizer import is_chinese
 from rag.nlp.search import index_name
 from rag.utils import rmSpace, num_tokens_from_string, encoder
 
@@ -80,7 +81,8 @@ def chat(dialog, messages, stream=True, **kwargs):
         if not llm:
             raise LookupError("LLM(%s) not found" % dialog.llm_id)
         max_tokens = 1024
-    else: max_tokens = llm[0].max_tokens
+    else:
+        max_tokens = llm[0].max_tokens
     kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids)
     embd_nms = list(set([kb.embd_id for kb in kbs]))
     if len(embd_nms) != 1:
@@ -124,6 +126,16 @@ def chat(dialog, messages, stream=True, **kwargs):
                                         doc_ids=kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else None,
                                         top=1024, aggs=False, rerank_mdl=rerank_mdl)
     knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
+    #self-rag
+    if dialog.prompt_config.get("self_rag") and not relevant(dialog.tenant_id, dialog.llm_id, questions[-1], knowledges):
+        questions[-1] = rewrite(dialog.tenant_id, dialog.llm_id, questions[-1])
+        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, rerank_mdl=rerank_mdl)
+        knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
+
     chat_logger.info(
         "{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
 
@@ -136,7 +148,7 @@ def chat(dialog, messages, stream=True, **kwargs):
 
     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"])
+                for m in messages if m["role"] != "system"])
     used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.97))
     assert len(msg) >= 2, f"message_fit_in has bug: {msg}"
 
@@ -150,9 +162,9 @@ def chat(dialog, messages, stream=True, **kwargs):
         if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
             answer, idx = retrievaler.insert_citations(answer,
                                                        [ck["content_ltks"]
-                                                           for ck in kbinfos["chunks"]],
+                                                        for ck in kbinfos["chunks"]],
                                                        [ck["vector"]
-                                                           for ck in kbinfos["chunks"]],
+                                                        for ck in kbinfos["chunks"]],
                                                        embd_mdl,
                                                        tkweight=1 - dialog.vector_similarity_weight,
                                                        vtweight=dialog.vector_similarity_weight)
@@ -166,7 +178,7 @@ def chat(dialog, messages, stream=True, **kwargs):
         for c in refs["chunks"]:
             if c.get("vector"):
                 del c["vector"]
-        if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api")>=0:
+        if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
             answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
         return {"answer": answer, "reference": refs}
 
@@ -204,7 +216,7 @@ def use_sql(question, field_map, tenant_id, chat_mdl, quota=True):
     def get_table():
         nonlocal sys_prompt, user_promt, question, tried_times
         sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_promt}], {
-                            "temperature": 0.06})
+            "temperature": 0.06})
         print(user_promt, sql)
         chat_logger.info(f"“{question}”==>{user_promt} get SQL: {sql}")
         sql = re.sub(r"[\r\n]+", " ", sql.lower())
@@ -273,17 +285,19 @@ def use_sql(question, field_map, tenant_id, chat_mdl, quota=True):
 
     # compose markdown table
     clmns = "|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"],
-                           tbl["columns"][i]["name"])) for i in clmn_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
+                                                                        tbl["columns"][i]["name"])) for i in
+                            clmn_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
 
     line = "|" + "|".join(["------" for _ in range(len(clmn_idx))]) + \
-        ("|------|" if docid_idx and docid_idx else "")
+           ("|------|" if docid_idx and docid_idx else "")
 
     rows = ["|" +
             "|".join([rmSpace(str(r[i])) for i in clmn_idx]).replace("None", " ") +
             "|" for r in tbl["rows"]]
     if quota:
         rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
-    else: rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
+    else:
+        rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
     rows = re.sub(r"T[0-9]{2}:[0-9]{2}:[0-9]{2}(\.[0-9]+Z)?\|", "|", rows)
 
     if not docid_idx or not docnm_idx:
@@ -303,5 +317,40 @@ def use_sql(question, field_map, tenant_id, chat_mdl, quota=True):
     return {
         "answer": "\n".join([clmns, line, rows]),
         "reference": {"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[docnm_idx]} for r in tbl["rows"]],
-                      "doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in doc_aggs.items()]}
+                      "doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in
+                                   doc_aggs.items()]}
     }
+
+
+def relevant(tenant_id, llm_id, question, contents: list):
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
+    prompt = """
+        You are a grader assessing relevance of a retrieved document to a user question. 
+        It does not need to be a stringent test. The goal is to filter out erroneous retrievals.
+        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. 
+        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.
+        No other words needed except 'yes' or 'no'.
+    """
+    if not contents:return False
+    contents = "Documents: \n" + "   - ".join(contents)
+    contents = f"Question: {question}\n" + contents
+    if num_tokens_from_string(contents) >= chat_mdl.max_length - 4:
+        contents = encoder.decode(encoder.encode(contents)[:chat_mdl.max_length - 4])
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": contents}], {"temperature": 0.01})
+    if ans.lower().find("yes") >= 0: return True
+    return False
+
+
+def rewrite(tenant_id, llm_id, question):
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
+    prompt = """
+        You are an expert at query expansion to generate a paraphrasing of a question.
+        I can't retrieval relevant information from the knowledge base by using user's question directly.     
+        You need to expand or paraphrase user's question by multiple ways such as using synonyms words/phrase, 
+        writing the abbreviation in its entirety, adding some extra descriptions or explanations, 
+        changing the way of expression, translating the original question into another language (English/Chinese), etc. 
+        And return 5 versions of question and one is from translation.
+        Just list the question. No other words are needed.
+    """
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": question}], {"temperature": 0.8})
+    return ans