make typing into list

lumen/ai/agents.py

@@ -113,7 +113,7 @@ def __panel__(self):
         return self.interface
 
     async def _system_prompt_with_context(
-        self, messages: str, context: str = ""
+        self, messages: list, context: str = ""
     ) -> str:
         system_prompt = self.system_prompt
         if self.embeddings:
@@ -178,10 +178,10 @@ async def _select_table(self, tables):
         self.interface.pop(-1)
         return tables
 
-    async def requirements(self, messages: str):
+    async def requirements(self, messages: list):
         return self.requires
 
-    async def answer(self, messages: str):
+    async def answer(self, messages: list):
         system_prompt = await self._system_prompt_with_context(messages)
 
         message = None
@@ -192,7 +192,7 @@ async def answer(self, messages: str):
                 output, replace=True, message=message, user=self.user, max_width=self._max_width
             )
 
-    async def invoke(self, messages: str):
+    async def invoke(self, messages: list):
         await self.answer(messages)
 
 
@@ -245,7 +245,7 @@ class ChatAgent(Agent):
     requires = param.List(default=["current_source"], readonly=True)
 
     @retry_llm_output()
-    async def requirements(self, messages: str, errors=None):
+    async def requirements(self, messages: list, errors=None):
         if 'current_data' in memory:
             return self.requires
 
@@ -270,7 +270,7 @@ async def requirements(self, messages: str, errors=None):
         return self.requires
 
     async def _system_prompt_with_context(
-        self, messages: str, context: str = ""
+        self, messages: list, context: str = ""
     ) -> str:
         source = memory.get("current_source")
         if not source:
@@ -321,7 +321,7 @@ class ChatDetailsAgent(ChatAgent):
     )
 
     async def _system_prompt_with_context(
-        self, messages: str, context: str = ""
+        self, messages: list, context: str = ""
     ) -> str:
         system_prompt = self.system_prompt
         topic = (await self.llm.invoke(
@@ -384,7 +384,7 @@ def _use_table(self, event):
         table = self._df.iloc[event.row, 0]
         self.interface.send(f"Show the table: {table!r}")
 
-    async def answer(self, messages: str):
+    async def answer(self, messages: list):
         tables = []
         for source in memory['available_sources']:
             tables += source.get_tables()
@@ -407,7 +407,7 @@ async def answer(self, messages: str):
         self.interface.stream(table_list, user="Lumen")
         return tables
 
-    async def invoke(self, messages: str):
+    async def invoke(self, messages: list):
         await self.answer(messages)
 
 
@@ -640,7 +640,7 @@ async def find_join_tables(self, messages: list):
             tables_to_source[a_table] = a_source
         return tables_to_source
 
-    async def answer(self, messages: str):
+    async def answer(self, messages: list):
         """
         Steps:
         1. Retrieve the current source and table from memory.
@@ -702,7 +702,7 @@ async def answer(self, messages: str):
         print(sql_query)
         return sql_query
 
-    async def invoke(self, messages: str):
+    async def invoke(self, messages: list):
         sql_query = await self.answer(messages)
         self._render_sql(sql_query)
 
@@ -716,7 +716,7 @@ class BaseViewAgent(LumenBaseAgent):
     async def _extract_spec(self, model: BaseModel):
         return dict(model)
 
-    async def answer(self, messages: str) -> hvPlotUIView:
+    async def answer(self, messages: list) -> hvPlotUIView:
         pipeline = memory["current_pipeline"]
 
         # Write prompts
@@ -746,7 +746,7 @@ async def answer(self, messages: str) -> hvPlotUIView:
         memory["current_view"] = dict(spec, type=self.view_type)
         return self.view_type(pipeline=pipeline, **spec)
 
-    async def invoke(self, messages: str):
+    async def invoke(self, messages: list):
         view = await self.answer(messages)
         self._render_lumen(view)
 
@@ -861,7 +861,7 @@ class AnalysisAgent(LumenBaseAgent):
     _output_type = AnalysisOutput
 
     async def _system_prompt_with_context(
-        self, messages: str, context: str = "", analyses: list[Analysis] = []
+        self, messages: list, context: str = "", analyses: list[Analysis] = []
     ) -> str:
         system_prompt = self.system_prompt
         for name, analysis in analyses.items():
@@ -880,7 +880,7 @@ async def _system_prompt_with_context(
             system_prompt += f"\n### CONTEXT: {context}".strip()
         return system_prompt
 
-    async def answer(self, messages: str, agents: list[Agent] | None = None):
+    async def answer(self, messages: list, agents: list[Agent] | None = None):
         pipeline = memory['current_pipeline']
         analyses = {a.name: a for a in self.analyses if await a.applies(pipeline)}
         if not analyses:
@@ -945,7 +945,7 @@ async def answer(self, messages: str, agents: list[Agent] | None = None):
                 view = None
         return view
 
-    async def invoke(self, messages: str, agents=None):
+    async def invoke(self, messages: list, agents=None):
         view = await self.answer(messages, agents=agents)
         analysis = memory["current_analysis"]
         if view is None and analysis.autorun:

lumen/ai/assistant.py

@@ -339,7 +339,7 @@ async def _fill_model(self, messages, system, agent_model, errors=None):
         )
         return out
 
-    async def _choose_agent(self, messages: str, agents: list[Agent] | None = None, primary: bool = False, unmet_dependencies: tuple[str] | None = None):
+    async def _choose_agent(self, messages: list, agents: list[Agent] | None = None, primary: bool = False, unmet_dependencies: tuple[str] | None = None):
         if agents is None:
             agents = self.agents
         agents = [agent for agent in agents if await agent.applies()]
@@ -390,7 +390,7 @@ async def _resolve_dependencies(self, messages, agents: dict[str, Agent]) -> lis
                 step.success_title = f"Solved a dependency with {output.agent}"
         return agent_chain[::-1]+[(agent, (), None)]
 
-    async def _get_agent(self, messages: str):
+    async def _get_agent(self, messages: list):
         if len(self.agents) == 1:
             return self.agents[0]
 
@@ -448,7 +448,7 @@ def _serialize(self, obj, exclude_passwords=True):
             obj = obj.value
         return str(obj)
 
-    async def invoke(self, messages: str) -> str:
+    async def invoke(self, messages: list) -> str:
         messages = self.interface.serialize(custom_serializer=self._serialize)[-4:]
         invalidation_assessment = await self._invalidate_memory(messages[-2:])
         context_length = 3

lumen/ai/llm.py

@@ -58,7 +58,7 @@ def _add_system_message(self, messages, system, input_kwargs):
 
     async def invoke(
         self,
-        messages: str,
+        messages: list,
         system: str = "",
         response_model: BaseModel | None = None,
         allow_partial: bool = False,
@@ -89,7 +89,7 @@ def _get_delta(cls, chunk):
 
     async def stream(
         self,
-        messages: str,
+        messages: list,
         system: str = "",
         response_model: BaseModel | None = None,
         field: str | None = None,
@@ -355,7 +355,7 @@ def _get_delta(cls, chunk):
 
     async def invoke(
         self,
-        messages: str,
+        messages: list,
         system: str = "",
         response_model: BaseModel | None = None,
         allow_partial: bool = False,
@@ -465,7 +465,7 @@ def _add_system_message(self, messages, system, input_kwargs):
 
     async def invoke(
         self,
-        messages: str,
+        messages: list,
         system: str = "",
         response_model: BaseModel | None = None,
         allow_partial: bool = False,