add: history on /api/ask

CHANGELOG.md

@@ -5,6 +5,11 @@
     *Andrea Sponziello* 
 ### **Copyrigth**: *Tiledesk SRL*
 
+## [2024-07-27]
+### 0.2.8
+- add: history on /api/ask 
+
+
 ## [2024-07-26]
 ### 0.2.7
 - add: scrape_type=3|4 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "tilellm"
-version = "0.2.7"
+version = "0.2.8"
 description = "tiledesk for RAG"
 authors = ["Gianluca Lorenzo <gianluca.lorenzo@gmail.com>"]
 repository = "https://github.com/Tiledesk/tiledesk-llm"

tilellm/controller/controller.py

@@ -186,30 +186,64 @@ async def ask_with_memory1(question_answer, repo=None):
 async def ask_to_llm(question, chat_model=None):
     try:
         logger.info(question)
-        if question.llm == "cohere":
-            quest = question.system_context+" "+question.question
-            messages = [
-                HumanMessage(content=quest)
-            ]
-        else:
-            messages = [
-                SystemMessage(content=question.system_context),
-                HumanMessage(content=question.question)
+        chat_history_list = []
+
+        if question.chat_history_dict is not None:
+            for key, entry in question.chat_history_dict.items():
+                chat_history_list.append(HumanMessage(content=entry.question))  # ('human', entry.question))
+                chat_history_list.append(AIMessage(content=entry.answer))
+
+        # from pprint import pprint
+        # pprint(chat_history_list)
+
+        qa_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", question.system_context),
+                MessagesPlaceholder("chat_history_a"),
+                ("human", "{input}"),
             ]
+        )
+
+        store = {}
+
+        def get_session_history(session_id: str) -> BaseChatMessageHistory:
+            if session_id not in store:
+                store[session_id] = ChatMessageHistory()
+            return store[session_id]
+
+        runnable = qa_prompt | chat_model
 
-        a = chat_model.invoke(messages)
-        return SimpleAnswer(content=a.content)
+        runnable_with_history = RunnableWithMessageHistory(
+            runnable,
+            get_session_history,
+            input_messages_key="input",
+
+        )
+
+        result = await runnable_with_history.ainvoke(
+            {"input": question.question, 'chat_history_a': chat_history_list},
+            config={"configurable": {"session_id": uuid.uuid4().hex}
+                    },
+        )
+
+        if not question.chat_history_dict:
+            question.chat_history_dict = {}
+
+        num = len(question.chat_history_dict.keys())
+        question.chat_history_dict[str(num)] = {"question": question.question, "answer": result.content}
+
+        return SimpleAnswer(answer=result.content, chat_history_dict=question.chat_history_dict)
 
     except Exception as e:
         import traceback
         traceback.print_exc()
         question_answer_list = []
 
-        result_to_return = SimpleAnswer(
-            content=repr(e)
-        )
+        result_to_return = SimpleAnswer(answer=repr(e),
+                                        chat_history_dict={})
         raise fastapi.exceptions.HTTPException(status_code=400, detail=result_to_return.model_dump())
 
+
 @inject_repo
 async def ask_with_memory(question_answer, repo=None) -> RetrievalResult:
     try:

tilellm/models/item_model.py

@@ -123,6 +123,7 @@ class QuestionToLLM(BaseModel):
     max_tokens: int = Field(default=128)
     debug: bool = Field(default_factory=lambda: False)
     system_context: str = Field(default="You are a helpful AI bot. Always reply in the same language of the question.")
+    chat_history_dict: Optional[Dict[str, ChatEntry]] = None
 
     @field_validator("temperature")
     def temperature_range(cls, v):
@@ -140,7 +141,8 @@ def max_tokens_range(cls, v):
 
 
 class SimpleAnswer(BaseModel):
-    content: str
+    answer: str = Field(default="No answer")
+    chat_history_dict: Optional[Dict[str, ChatEntry]]
 
 
 class RetrievalResult(BaseModel):

tilellm/shared/utility.py

@@ -87,7 +87,7 @@ async def wrapper(self, item, *args, **kwargs):
 def inject_llm(func):
     @wraps(func)
     async def wrapper(question, *args, **kwargs):
-        print(question)
+        logger.debug(question)
         if question.llm == "openai":
             chat_model = ChatOpenAI(api_key=question.llm_key,
                                     model=question.model,
@@ -131,16 +131,21 @@ async def wrapper(question, *args, **kwargs):
             #                         region_name="eu-central-1"
             #                         )
 
-            import boto3
-            #session = boto3.Session(
-            #                        aws_access_key_id=question.llm_key.aws_secret_access_key,
+            # import boto3
+
+            # client_br = boto3.client('bedrock-runtime',
+            #                         aws_access_key_id=question.llm_key.aws_secret_access_key,
+            #                         aws_secret_access_key=question.llm_key.aws_secret_access_key,
+            #                         region_name=question.llm_key.region_name
+            #                         )
+            # session = boto3.Session(aws_access_key_id=question.llm_key.aws_secret_access_key,
             #                        aws_secret_access_key=question.llm_key.aws_secret_access_key,
             #                        region_name=question.llm_key.region_name
             #                        )
-
-
+            # client_ss = session.client("bedrock-runtime")
 
             chat_model = ChatBedrockConverse(
+                # client=client_br,
                 model=question.model,
                 temperature=question.temperature,
                 max_tokens=question.max_tokens,
@@ -150,7 +155,8 @@ async def wrapper(question, *args, **kwargs):
 
             )  # model_kwargs={"temperature": 0.001},
 
-            #print(chat_model.session)
+            # print(chat_model.client._get_credentials().access_key)
+
 
         else:
             chat_model = ChatOpenAI(api_key=question.llm_key,