Merge pull request cheshire-cat-ai#781 from Pingdred/develop

Develop

core/cat/looking_glass/agent_manager.py

@@ -128,7 +128,7 @@ async def execute_procedures_agent(self, agent_input, stray):
         if "form" in out.keys():
             FormClass = allowed_procedures.get(out["form"], None)
             f = FormClass(stray)
-            stray.working_memory["forms"] = f
+            stray.working_memory["active_form"] = f
             # let the form reply directly
             out = f.next()
             out["return_direct"] = True
@@ -137,12 +137,12 @@ async def execute_procedures_agent(self, agent_input, stray):
 
     async def execute_form_agent(self, stray):
 
-        active_form = stray.working_memory.get("forms", None)
+        active_form = stray.working_memory.get("active_form", None)
         if active_form:
             log.warning(active_form._state)
             # closing form if state is closed
             if active_form._state == CatFormState.CLOSED:
-                del stray.working_memory["forms"]
+                del stray.working_memory["active_form"]
             else:
                 # continue form
                 return active_form.next()
@@ -165,7 +165,7 @@ async def execute_memory_chain(self, agent_input, prompt_prefix, prompt_suffix,
             output_key="output"
         )
 
-        return await memory_chain.ainvoke(agent_input, config=RunnableConfig(callbacks=[NewTokenHandler(stray)]))
+        return await memory_chain.ainvoke({**agent_input, "stop":"Human:"}, config=RunnableConfig(callbacks=[NewTokenHandler(stray)]))
 
     async def execute_agent(self, stray):
         """Instantiate the Agent with tools.
@@ -231,8 +231,14 @@ async def execute_agent(self, stray):
         # - no procedures where recalled or selected or
         # - procedures have all return_direct=False or
         # - procedures agent crashed big time
-        if "tools_output" not in agent_input:
-            agent_input["tools_output"] = ""
+
+        # Save tools output 
+        tools_output = agent_input.get("tools_output", "")
+        # Update agent input from working memory
+        agent_input = self.format_agent_input(stray)
+        # Add eventuals tools output
+        agent_input["tools_output"] = tools_output
+
         memory_chain_output = await self.execute_memory_chain(agent_input, prompt_prefix, prompt_suffix, stray)
         memory_chain_output["intermediate_steps"] = intermediate_steps
 
@@ -280,6 +286,7 @@ def format_agent_input(self, stray):
             "episodic_memory": episodic_memory_formatted_content,
             "declarative_memory": declarative_memory_formatted_content,
             "chat_history": conversation_history_formatted_content,
+            "tools_output": ""
         }
 
     def agent_prompt_episodic_memories(self, memory_docs: List[Document]) -> str:

core/cat/looking_glass/output_parser.py

@@ -22,14 +22,20 @@ def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
             parsed_output_log = json.dumps(parsed_output, indent=4)
         except Exception as e:
             log.error(e)
-            raise OutputParserException(f"Could not parse LLM output: `{llm_output}`")
+            return AgentFinish(
+                # Return values is generally always a dictionary with a single `output` key
+                # It is not recommended to try anything else at the moment :)
+                return_values={"output": None},
+                log=""
+            )
 
         # Extract action
         action = parsed_output["action"]
-        action_input = str(parsed_output["action_input"])
 
-        if action_input:
-            action_input = action_input.strip(" ").strip('"')
+        if isinstance(parsed_output["action_input"], str):
+            action_input = parsed_output["action_input"]
+        elif isinstance(parsed_output["action_input"], dict):
+            action_input = json.dumps(parsed_output["action_input"])
         else:
             action_input = ""