Merge pull request #200 from openchatai/fix/nullability_issue

Modified slack truncated json, added prompts

_swaggers/slack_truncated.json

@@ -1233,7 +1233,7 @@
                 "tags": [
                     "chat"
                 ],
-                "description": "Sends a message to a channel.",
+                "description": "Post messages to public channels, private channels, direct messages, and multi-person direct messages.",
                 "externalDocs": {
                     "description": "API method documentation",
                     "url": "https://api.slack.com/methods/chat.postMessage"
@@ -2262,7 +2262,7 @@
                 "tags": [
                     "conversations"
                 ],
-                "description": "Initiates a public or private channel-based conversation",
+                "description": "Creates a new public or private channel.",
                 "externalDocs": {
                     "description": "API method documentation",
                     "url": "https://api.slack.com/methods/conversations.create"
@@ -3448,7 +3448,7 @@
                 "tags": [
                     "conversations"
                 ],
-                "description": "Opens or resumes a direct message or multi-person direct message.",
+                "description": "Opens or resumes an existing direct message or multi-person direct message.",
                 "externalDocs": {
                     "description": "API method documentation",
                     "url": "https://api.slack.com/methods/conversations.open"

llm-server/integrations/custom_prompts/slack.py

@@ -1,3 +1,3 @@
 flow_generation_prompts = ""
 
-api_generation_prompt = "Use encoded value for channel"
+api_generation_prompt = "Use encoded value for channel. When sending message, also identify whether the message is being sent to a channel or a user and generate body accordingly."

llm-server/integrations/slack.py

@@ -56,7 +56,7 @@ def process_state(headers: Dict[str, Any]) -> Dict[str, Any]:
     cache = mongo.app_cache.find_one({"app": "slack"}, {"_id": 0})
 
     # Check if cache exists and is less than 2 minutes old
-    if cache and now - cache["timestamp"] < 120:
+    if cache and now - cache["timestamp"] < 600:
         print("Returning cached data")
         return cache
 

llm-server/routes/root_service.py

@@ -51,7 +51,6 @@ def handle_request(data: Dict[str, Any]) -> Any:
     ) = extract_data(data)
 
     log_user_request(text)
-
     check_required_fields(base_prompt, text, swagger_url)
 
     swagger_doc = get_swagger_doc(swagger_url)
@@ -124,16 +123,17 @@ def check_required_fields(base_prompt: str, text: str, swagger_url: str) -> None
 
 
 def get_swagger_doc(swagger_url: str) -> ResolvingParser:
+    logging.info(f"Swagger url: {swagger_url}")
     swagger_doc: Optional[Dict[str, Any]] = mongo.swagger_files.find_one(
         {"meta.swagger_url": swagger_url}, {"meta": 0, "_id": 0}
     )
 
     if swagger_url.startswith("http:") or swagger_url.startswith("https:"):
         return ResolvingParser(url=swagger_url)
+    elif swagger_url.endswith(".json") or swagger_url.endswith(".yaml"):
+        return ResolvingParser(url=shared_folder + swagger_url)
     elif swagger_doc:
         return ResolvingParser(spec_string=swagger_doc)
-    else:
-        return ResolvingParser(url=shared_folder + swagger_url)
 
 
 def handle_existing_workflow(

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

@@ -25,7 +25,7 @@ def gen_body_from_schema(
         model="gpt-3.5-turbo-16k",
         temperature=0,
     )
-
+    api_generation_prompt = None
     if app:
         module_name = f"integrations.custom_prompts.{app}"
         module = importlib.import_module(module_name)
@@ -43,7 +43,7 @@ def gen_body_from_schema(
         HumanMessage(content="prev api responses: {}".format(prev_api_response)),
         HumanMessage(content="current_state: {}".format(current_state)),
         HumanMessage(
-            content="Given the provided information, generate the appropriate minified JSON payload to use as body for the API request. If a user doesn't provide a required parameter, use sensible defaults for required params, and leave optional params"
+            content="Given the provided information, generate the appropriate minified JSON payload to use as body for the API request. If a user doesn't provide a required parameter, use sensible defaults for required params, and leave optional params."
         ),
     ]
 

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

@@ -116,16 +116,16 @@ def generate_consolidated_requirement(
     conversation_str = join_conversations(history)
     messages = [
         SystemMessage(
-            content="You are an AI model designed to perform text substitution. You will receive user input. If the user's input contains references such as `the` or `this` etc... you should replace these words with the specific objects or concepts they refer to within the ongoing conversation, if applicable. However, if the user's input already contains all the necessary information, you should return the original user text."
+            content="You are an AI model designed to generate a standalone prompt. The user message may also contain instructions for you as a bot, like generating some content in this message. You should act accordingly"
+        ),
+        HumanMessage(
+            content="You will receive user input. Based on the conversation and the current user prompt, I want you to convert the user prompt into a standalone prompt if the user prompt references something in conversation history."
         ),
         HumanMessage(
             content="Conversation History: ({}), \n\n Current User input: ({}).".format(
                 conversation_str, user_input
             ),
         ),
-        HumanMessage(
-            content="Give me the user input after substituting the references."
-        ),
     ]
     content = chat(messages).content
     return content