Merge branch 'fix/runtime-error' into deploy/dev

api/core/agent/agent/multi_dataset_router_agent.py

@@ -76,7 +76,7 @@ def plan(
             agent_decision = self.real_plan(intermediate_steps, callbacks, **kwargs)
             if isinstance(agent_decision, AgentAction):
                 tool_inputs = agent_decision.tool_input
-                if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
+                if isinstance(tool_inputs, dict) and 'query' in tool_inputs and 'chat_history' not in kwargs:
                     tool_inputs['query'] = kwargs['input']
                     agent_decision.tool_input = tool_inputs
             else:

api/core/agent/agent/structed_multi_dataset_router_agent.py

@@ -1,7 +1,7 @@
 import re
 from typing import List, Tuple, Any, Union, Sequence, Optional, cast
 
-from langchain import BasePromptTemplate
+from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
 from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
 from langchain.callbacks.base import BaseCallbackManager
@@ -12,6 +12,7 @@
 from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
 
 from core.chain.llm_chain import LLMChain
+from core.model_providers.models.entity.model_params import ModelMode
 from core.model_providers.models.llm.base import BaseLLM
 from core.tool.dataset_retriever_tool import DatasetRetrieverTool
 
@@ -92,6 +93,10 @@ def plan(
             rst = tool.run(tool_input={'query': kwargs['input']})
             return AgentFinish(return_values={"output": rst}, log=rst)
 
+        if intermediate_steps:
+            _, observation = intermediate_steps[-1]
+            return AgentFinish(return_values={"output": observation}, log=observation)
+
         full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
 
         try:
@@ -107,6 +112,8 @@ def plan(
                 if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
                     tool_inputs['query'] = kwargs['input']
                     agent_decision.tool_input = tool_inputs
+                elif isinstance(tool_inputs, str):
+                    agent_decision.tool_input = kwargs['input']
             else:
                 agent_decision.return_values['output'] = ''
             return agent_decision
@@ -143,6 +150,61 @@ def create_prompt(
         ]
         return ChatPromptTemplate(input_variables=input_variables, messages=messages)
 
+    @classmethod
+    def create_completion_prompt(
+            cls,
+            tools: Sequence[BaseTool],
+            prefix: str = PREFIX,
+            format_instructions: str = FORMAT_INSTRUCTIONS,
+            input_variables: Optional[List[str]] = None,
+    ) -> PromptTemplate:
+        """Create prompt in the style of the zero shot agent.
+
+        Args:
+            tools: List of tools the agent will have access to, used to format the
+                prompt.
+            prefix: String to put before the list of tools.
+            input_variables: List of input variables the final prompt will expect.
+
+        Returns:
+            A PromptTemplate with the template assembled from the pieces here.
+        """
+        suffix = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
+Question: {input}
+Thought: {agent_scratchpad}
+"""
+
+        tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
+        tool_names = ", ".join([tool.name for tool in tools])
+        format_instructions = format_instructions.format(tool_names=tool_names)
+        template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
+        if input_variables is None:
+            input_variables = ["input", "agent_scratchpad"]
+        return PromptTemplate(template=template, input_variables=input_variables)
+
+    def _construct_scratchpad(
+        self, intermediate_steps: List[Tuple[AgentAction, str]]
+    ) -> str:
+        agent_scratchpad = ""
+        for action, observation in intermediate_steps:
+            agent_scratchpad += action.log
+            agent_scratchpad += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
+
+        if not isinstance(agent_scratchpad, str):
+            raise ValueError("agent_scratchpad should be of type string.")
+        if agent_scratchpad:
+            llm_chain = cast(LLMChain, self.llm_chain)
+            if llm_chain.model_instance.model_mode == ModelMode.CHAT:
+                return (
+                    f"This was your previous work "
+                    f"(but I haven't seen any of it! I only see what "
+                    f"you return as final answer):\n{agent_scratchpad}"
+                )
+            else:
+                return agent_scratchpad
+        else:
+            return agent_scratchpad
+
     @classmethod
     def from_llm_and_tools(
             cls,
@@ -160,15 +222,23 @@ def from_llm_and_tools(
     ) -> Agent:
         """Construct an agent from an LLM and tools."""
         cls._validate_tools(tools)
-        prompt = cls.create_prompt(
-            tools,
-            prefix=prefix,
-            suffix=suffix,
-            human_message_template=human_message_template,
-            format_instructions=format_instructions,
-            input_variables=input_variables,
-            memory_prompts=memory_prompts,
-        )
+        if model_instance.model_mode == ModelMode.CHAT:
+            prompt = cls.create_prompt(
+                tools,
+                prefix=prefix,
+                suffix=suffix,
+                human_message_template=human_message_template,
+                format_instructions=format_instructions,
+                input_variables=input_variables,
+                memory_prompts=memory_prompts,
+            )
+        else:
+            prompt = cls.create_completion_prompt(
+                tools,
+                prefix=prefix,
+                format_instructions=format_instructions,
+                input_variables=input_variables
+            )
         llm_chain = LLMChain(
             model_instance=model_instance,
             prompt=prompt,

api/core/agent/agent/structured_chat.py

@@ -1,7 +1,7 @@
 import re
-from typing import List, Tuple, Any, Union, Sequence, Optional
+from typing import List, Tuple, Any, Union, Sequence, Optional, cast
 
-from langchain import BasePromptTemplate
+from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
 from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
 from langchain.callbacks.base import BaseCallbackManager
@@ -15,6 +15,7 @@
 
 from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
 from core.chain.llm_chain import LLMChain
+from core.model_providers.models.entity.model_params import ModelMode
 from core.model_providers.models.llm.base import BaseLLM
 
 FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
@@ -184,6 +185,61 @@ def create_prompt(
         ]
         return ChatPromptTemplate(input_variables=input_variables, messages=messages)
 
+    @classmethod
+    def create_completion_prompt(
+            cls,
+            tools: Sequence[BaseTool],
+            prefix: str = PREFIX,
+            format_instructions: str = FORMAT_INSTRUCTIONS,
+            input_variables: Optional[List[str]] = None,
+    ) -> PromptTemplate:
+        """Create prompt in the style of the zero shot agent.
+
+        Args:
+            tools: List of tools the agent will have access to, used to format the
+                prompt.
+            prefix: String to put before the list of tools.
+            input_variables: List of input variables the final prompt will expect.
+
+        Returns:
+            A PromptTemplate with the template assembled from the pieces here.
+        """
+        suffix = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
+Question: {input}
+Thought: {agent_scratchpad}
+"""
+
+        tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
+        tool_names = ", ".join([tool.name for tool in tools])
+        format_instructions = format_instructions.format(tool_names=tool_names)
+        template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
+        if input_variables is None:
+            input_variables = ["input", "agent_scratchpad"]
+        return PromptTemplate(template=template, input_variables=input_variables)
+
+    def _construct_scratchpad(
+        self, intermediate_steps: List[Tuple[AgentAction, str]]
+    ) -> str:
+        agent_scratchpad = ""
+        for action, observation in intermediate_steps:
+            agent_scratchpad += action.log
+            agent_scratchpad += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
+
+        if not isinstance(agent_scratchpad, str):
+            raise ValueError("agent_scratchpad should be of type string.")
+        if agent_scratchpad:
+            llm_chain = cast(LLMChain, self.llm_chain)
+            if llm_chain.model_instance.model_mode == ModelMode.CHAT:
+                return (
+                    f"This was your previous work "
+                    f"(but I haven't seen any of it! I only see what "
+                    f"you return as final answer):\n{agent_scratchpad}"
+                )
+            else:
+                return agent_scratchpad
+        else:
+            return agent_scratchpad
+
     @classmethod
     def from_llm_and_tools(
             cls,
@@ -201,15 +257,23 @@ def from_llm_and_tools(
     ) -> Agent:
         """Construct an agent from an LLM and tools."""
         cls._validate_tools(tools)
-        prompt = cls.create_prompt(
-            tools,
-            prefix=prefix,
-            suffix=suffix,
-            human_message_template=human_message_template,
-            format_instructions=format_instructions,
-            input_variables=input_variables,
-            memory_prompts=memory_prompts,
-        )
+        if model_instance.model_mode == ModelMode.CHAT:
+            prompt = cls.create_prompt(
+                tools,
+                prefix=prefix,
+                suffix=suffix,
+                human_message_template=human_message_template,
+                format_instructions=format_instructions,
+                input_variables=input_variables,
+                memory_prompts=memory_prompts,
+            )
+        else:
+            prompt = cls.create_completion_prompt(
+                tools,
+                prefix=prefix,
+                format_instructions=format_instructions,
+                input_variables=input_variables,
+            )
         llm_chain = LLMChain(
             model_instance=model_instance,
             prompt=prompt,

api/services/completion_service.py

@@ -142,7 +142,7 @@ def completion(cls, app_model: App, user: Union[Account | EndUser], args: Any,
             'flask_app': current_app._get_current_object(),
             'generate_task_id': generate_task_id,
             'detached_app_model': app_model,
-            'app_model_config': app_model_config,
+            'app_model_config': app_model_config.copy(),
             'query': query,
             'inputs': inputs,
             'detached_user': user,
@@ -284,7 +284,7 @@ def generate_more_like_this(cls, app_model: App, user: Union[Account | EndUser],
             'flask_app': current_app._get_current_object(),
             'generate_task_id': generate_task_id,
             'detached_app_model': app_model,
-            'app_model_config': app_model_config,
+            'app_model_config': app_model_config.copy(),
             'query': message.query,
             'inputs': message.inputs,
             'detached_user': user,

docker/docker-compose.yaml

@@ -98,7 +98,7 @@ services:
       # The milvus password.
       MILVUS_PASSWORD: Milvus
       # The milvus tls switch.
-      MILVUS_SECURE: false
+      MILVUS_SECURE: 'false'
       # Mail configuration, support: resend
       MAIL_TYPE: ''
       # default send from email address, if not specified
@@ -176,7 +176,7 @@ services:
       # The milvus password.
       MILVUS_PASSWORD: Milvus
       # The milvus tls switch.
-      MILVUS_SECURE: false
+      MILVUS_SECURE: 'false'
       # Mail configuration, support: resend
       MAIL_TYPE: ''
       # default send from email address, if not specified

web/app/components/i18n-server.tsx

@@ -4,12 +4,10 @@ import { ToastProvider } from './base/toast'
 import { getDictionary, getLocaleOnServer } from '@/i18n/server'
 
 export type II18NServerProps = {
-  // locale: Locale
   children: React.ReactNode
 }
 
 const I18NServer = async ({
-  // locale,
   children,
 }: II18NServerProps) => {
   const locale = getLocaleOnServer()

web/app/components/i18n.tsx

@@ -1,23 +1,26 @@
 'use client'
 
 import type { FC } from 'react'
-import React from 'react'
-import '@/i18n/i18next-config'
+import React, { useEffect } from 'react'
+import { changeLanguage } from '@/i18n/i18next-config'
 import I18NContext from '@/context/i18n'
 import type { Locale } from '@/i18n'
-import { getLocaleOnClient, setLocaleOnClient } from '@/i18n/client'
+import { setLocaleOnClient } from '@/i18n/client'
 
 export type II18nProps = {
   locale: Locale
   dictionary: Record<string, any>
   children: React.ReactNode
-  setLocaleOnClient: (locale: Locale) => void
 }
 const I18n: FC<II18nProps> = ({
+  locale,
   dictionary,
   children,
 }) => {
-  const locale = getLocaleOnClient()
+  useEffect(() => {
+    changeLanguage(locale)
+  }, [locale])
+
   return (
     <I18NContext.Provider value={{
       locale,

web/i18n/i18next-config.ts

@@ -33,9 +33,6 @@ import datasetCreationEn from './lang/dataset-creation.en'
 import datasetCreationZh from './lang/dataset-creation.zh'
 import exploreEn from './lang/explore.en'
 import exploreZh from './lang/explore.zh'
-import { getLocaleOnClient } from '@/i18n/client'
-
-const localLng = getLocaleOnClient()
 
 const resources = {
   'en': {
@@ -88,7 +85,7 @@ i18n.use(initReactI18next)
   // init i18next
   // for all options read: https://www.i18next.com/overview/configuration-options
   .init({
-    lng: localLng,
+    lng: undefined,
     fallbackLng: 'en',
     // debug: true,
     resources,