fixing edge cases

llm-server/custom_types/bot_message.py

@@ -1,10 +1,11 @@
-from typing import List
+from typing import List, Optional
 from langchain.pydantic_v1 import BaseModel, Field
 from langchain.output_parsers import PydanticOutputParser
 
 class BotMessage(BaseModel):
     bot_message: str = Field(description="Message from the bot")
     ids: List[str] = Field(description="List of IDs")
+    missing_information: Optional[str] = Field(description="Incase of ambiguity ask user follow up question")
 
 
 # Set up a parser + inject instructions into the prompt template.

llm-server/routes/root_service.py

@@ -86,6 +86,12 @@ async def handle_request(
             bot_id=bot_id,
             base_prompt=base_prompt
         )
+
+        if step.missing_information is not None and len(step.missing_information) >= 10:
+            return {
+                "error": None,
+                "response": step.missing_information
+            }
 
         if len(step.ids) > 0:
             response = await handle_api_calls(

llm-server/routes/workflow/extractors/convert_json_to_text.py

@@ -22,7 +22,7 @@ def convert_json_to_text(
 
     api_summarizer_template = None
     system_message = SystemMessage(
-        content="You are a chatbot that can understand API responses"
+        content="You are an ai assistant that can summarize api responses"
     )
     prompt_templates = load_prompts(bot_id)
     api_summarizer_template = (
@@ -35,15 +35,15 @@ def convert_json_to_text(
     messages = [
         system_message,
         HumanMessage(
-            content="You'll receive user input and server responses obtained by making calls to various APIs. You will also recieve a dictionary that specifies, the body, param and query param used to make those api calls. Your task is to transform the JSON response into a response that is an answer to the user input. You should inform the user about the filters that were used to make these api calls. Try to respond in 3 sentences or less, unless there is too much to summarize."
+            content="You'll receive user input and server responses obtained by making calls to various APIs. Your task is to summarize the api response that is an answer to the user input. Try to be concise and accurate, and also include references if present."
         ),
-        HumanMessage(content="Here is the user input: {}.".format(user_input)),
+        HumanMessage(content=user_input),
         HumanMessage(
             content="Here is the response from the apis: {}".format(api_response)
         ),
-        HumanMessage(
-            content="Here is the api_request_data: {}".format(api_request_data)
-        ),
+        # HumanMessage(
+        #     content="Here is the api_request_data: {}".format(api_request_data)
+        # ),
     ]
 
     result = chat(messages)

llm-server/routes/workflow/utils/process_conversation_step.py

@@ -81,7 +81,8 @@ def process_conversation_step(
             content="""Based on the information provided to you I want you to answer the questions that follow. Your should respond with a json that looks like the following - 
     {{
         "ids": ["list", "of", "operationIds", "for apis to be called"],
-        "bot_message": "your response based on the instructions provided at the beginning"
+        "bot_message": "your response based on the instructions provided at the beginning",
+        "missing_information": "Optional Field; Incase of ambiguity where user input is not sufficient to make the api call, ask follow up questions. Followup question should only be asked once per user input"
     }}                
     """
         )
@@ -107,7 +108,7 @@ def process_conversation_step(
     except OutputParserException as e:
         logger.error("Failed to parse json", data=content)
         logger.error("Failed to parse json", err=str(e))
-        return BotMessage(bot_message=content, ids=[])
+        return BotMessage(bot_message=content, ids=[], missing_information=None)
     except Exception as e:
         logger.error("unexpected error occured", err=str(e))
-        return BotMessage(ids=[], bot_message=str(e))
+        return BotMessage(ids=[], bot_message=str(e), missing_information=None)

llm-server/routes/workflow/utils/run_openapi_ops.py

@@ -68,14 +68,19 @@ async def run_openapi_operations(
                 partial_json = load_json_config(app, operation_id)
                 if not partial_json:
                     logger.error(
-                        "Failed to find a config map. Consider adding a config map for this operation id",
+                        "Config map is not defined for this operationId",
                         incident="config_map_undefined",
                         operation_id=operation_id,
                         app=app
                     )
-                    record_info[operation_id] = transform_api_response_from_schema(
-                        api_payload.endpoint or "", api_response.text
-                    )
+                    record_info[operation_id] = api_response.text
+
+                    # Removed this because this slows down the bot response instead of speeding it
+                    # record_info[operation_id] = transform_api_response_from_schema(
+                    #     api_payload.endpoint or "", api_response.text
+                    # )
+
+                    pass
                 else:
                     logger.info(
                         "API Response",