调用模型对话实现

src/pgdb/chat/c_user_table.py

 from .c_db import UPostgresDB
-import json
 
 TABLE_USER = """
 DROP TABLE IF EXISTS "c_user";

src/pgdb/chat/chat_table.py

 from .c_db import UPostgresDB
-import json
 
 TABLE_CHAT = """
 DROP TABLE IF EXISTS "chat";

src/pgdb/chat/turn_qa_table.py

 from .c_db import UPostgresDB
-import json
 
 TABLE_CHAT = """
 DROP TABLE IF EXISTS "turn_qa";

src/pgdb/knowledge/callback.py

-import os, sys
-from os import path
+import sys
 
-sys.path.append("../")
 from abc import ABC, abstractmethod
 import json
-from typing import List, Any, Tuple, Dict
+from typing import List, Tuple
 from langchain.schema import Document
 from src.pgdb.knowledge.pgsqldocstore import PgSqlDocstore, str2hash_base64
 
+sys.path.append("../")
+
 
 class DocumentCallback(ABC):
     @abstractmethod  # 向量库储存前文档处理--

src/pgdb/knowledge/similarity.py

-import os, sys
-import re, time
+import os
+import sys
+import re
 from os import path
 
-sys.path.append("../")
 import copy
 from typing import List, OrderedDict, Any, Optional, Tuple, Dict
 from src.pgdb.knowledge.pgsqldocstore import InMemorySecondaryDocstore
@@ -23,6 +23,10 @@ from langchain.callbacks.manager import (
 from src.loader import load
 from langchain.embeddings.base import Embeddings
 from src.pgdb.knowledge.callback import DocumentCallback, DefaultDocumentCallback
+import operator
+from langchain.vectorstores.utils import DistanceStrategy
+import numpy as np
+sys.path.append("../")
 
 
 def singleton(cls):
@@ -38,22 +42,20 @@ def singleton(cls):
 
 @singleton
 class EmbeddingFactory:
-    def __init__(self, path: str):
-        self.path = path
-        self.embedding = HuggingFaceEmbeddings(model_name=path)
+    def __init__(self, _path: str):
+        self.path = _path
+        self.embedding = HuggingFaceEmbeddings(model_name=_path)
 
     def get_embedding(self):
         return self.embedding
 
 
-def GetEmbding(_path: str) -> Embeddings:
+def get_embding(_path: str) -> Embeddings:
     # return HuggingFaceEmbeddings(model_name=path)
     return EmbeddingFactory(_path).get_embedding()
 
 
-import operator
-from langchain.vectorstores.utils import DistanceStrategy
-import numpy as np
+
 
 
 class RE_FAISS(FAISS):
@@ -159,7 +161,7 @@ class RE_FAISS(FAISS):
 
 def getFAISS(embedding_model_name: str, store_path: str, info: dict = None, index_name: str = "index",
              is_pgsql: bool = True, reset: bool = False) -> RE_FAISS:
-    embeddings = GetEmbding(_path=embedding_model_name)
+    embeddings = get_embding(_path=embedding_model_name)
     docstore1: PgSqlDocstore = None
     if is_pgsql:
         if info and "host" in info and "dbname" in info and "username" in info and "password" in info:
@@ -298,7 +300,7 @@ class VectorStore_FAISS(FAISS):
 
         Examples:
 
-        .. code-block:: python
+        . code-block:: python
 
             # Retrieve more documents with higher diversity
             # Useful if your dataset has many similar documents

src/server/qa.py

+# -*- coding: utf-8 -*-
+import sys
+
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+from typing import Awaitable
+import asyncio
+from langchain.callbacks import AsyncIteratorCallbackHandler
+from src.llm.ernie_with_sdk import ChatERNIESerLLM
+from qianfan import ChatCompletion
+
+sys.path.append("../..")
+prompt1 = """'''
+{context}
+'''
+请你根据上述已知资料回答下面的问题，问题如下：
+{question}"""
+PROMPT1 = PromptTemplate(input_variables=["context", "question"], template=prompt1)
+
+
+def chat(context, question):
+    base_llm = ChatERNIESerLLM(
+        chat_completion=ChatCompletion(ak="pT7sV1smp4AeDl0LjyZuHBV9", sk="b3N0ibo1IKTLZlSs7weZc8jdR0oHjyMu"))
+    erniellm = LLMChain(llm=base_llm, prompt=PROMPT1, llm_kwargs={"temperature": 0.9})
+    if not context and not question:
+        return ""
+    result = erniellm.run({"context": context, "question": question})
+    return result
+
+
+async def async_chat_stc(context, question):
+    base_llm = ChatERNIESerLLM(
+        chat_completion=ChatCompletion(ak="pT7sV1smp4AeDl0LjyZuHBV9", sk="b3N0ibo1IKTLZlSs7weZc8jdR0oHjyMu"))
+    erniellm = LLMChain(llm=base_llm, prompt=PROMPT1, llm_kwargs={"temperature": 0.9})
+    callback = AsyncIteratorCallbackHandler()
+
+    async def wrap_done(fn: Awaitable, event: asyncio.Event):
+        try:
+            await fn
+        except Exception as e:
+            import traceback
+            traceback.print_exc()
+            print(f"Caught exception: {e}")
+        finally:
+            event.set()
+
+    task = asyncio.create_task(
+        wrap_done(erniellm.arun({"context": context, "question": question}, callbacks=[callback]), callback.done))
+    print("*" * 20)
+    text = ""
+    async for token in callback.aiter():
+        text = text + token
+        yield f"{text}"
+    await task
+
+
+if __name__ == "__main__":
+    print("main函数begin")
+    print(chat("当别人想你说你好的时候，你也应该说你好", "你好"))

test/chat_table_test.py

 import sys
 
-sys.path.append("../")
 from src.pgdb.chat.c_db import UPostgresDB
 from src.pgdb.chat.chat_table import Chat
 from src.pgdb.chat.c_user_table import CUser
 from src.pgdb.chat.turn_qa_table import TurnQa
 
+sys.path.append("../")
 """测试会话相关数据可的连接"""
 
 
@@ -29,5 +29,5 @@ def test():
     turn_qa.insert(["222", "1111", "nihao", "nihao", 1, 0])
 
 
-if __name__ == "main":
+if __name__ == "__main__":
     test()

test/k_store_test.py

 import sys
 
-sys.path.append("../")
 from src.loader.load import loads_path
 from src.pgdb.knowledge.similarity import VectorStore_FAISS
 from src.config.consts import (
@@ -17,9 +16,11 @@ from src.config.consts import (
 )
 from src.loader.callback import BaseCallback
 
+sys.path.append("../")
+
 
 # 当返回值中带有“思考题”字样的时候，默认将其忽略。
-class localCallback(BaseCallback):
+class LocalCallBack(BaseCallback):
     def filter(self, title: str, content: str) -> bool:
         if len(title + content) == 0:
             return True
@@ -38,7 +39,7 @@ def test_faiss_from_dir():
               "password": VEC_DB_PASSWORD},
         show_number=SIMILARITY_SHOW_NUMBER,
         reset=True)
-    docs = loads_path(KNOWLEDGE_PATH, mode="paged", sentence_size=512, callbacks=[localCallback()])
+    docs = loads_path(KNOWLEDGE_PATH, mode="paged", sentence_size=512, callbacks=[LocalCallBack()])
     print(len(docs))
     last_doc = None
     docs1 = []

test/qa_test.py

+from src.server.qa import chat
+
+print(chat("当别人想你说你好的时候，你也应该说你好", "你好"))