Merge pull request #193 from openchatai/feat/slack

adding prompts to customise behaviour of application

llm-server/integrations/custom_prompts/slack.py

@@ -1,3 +1,3 @@
 flow_generation_prompts = ""
 
-api_generation_prompt = ""
+api_generation_prompt = "Use encoded value for channel"

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

@@ -6,6 +6,7 @@
 from typing import Any, Optional
 from routes.workflow.extractors.extract_json import extract_json_payload
 from custom_types.t_json import JsonData
+import importlib
 import logging
 
 openai_api_key = os.getenv("OPENAI_API_KEY")
@@ -16,7 +17,7 @@ def gen_body_from_schema(
     body_schema: str,
     text: str,
     prev_api_response: str,
-    example: str,
+    app: Optional[str],
     current_state: Optional[str],
 ) -> Any:
     chat = ChatOpenAI(
@@ -25,6 +26,11 @@ def gen_body_from_schema(
         temperature=0,
     )
 
+    if app:
+        module_name = f"integrations.custom_prompts.{app}"
+        module = importlib.import_module(module_name)
+        api_generation_prompt = getattr(module, "api_generation_prompt")
+
     messages = [
         SystemMessage(
             content="You are an intelligent machine learning model that can produce REST API's body in json format"
@@ -37,10 +43,13 @@ 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. Avoid using fields that are not required, and user input doesnot require it."
+            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"
         ),
     ]
 
+    if api_generation_prompt is not None:
+        messages.append(HumanMessage(content="{}".format(api_generation_prompt)))
+
     result = chat(messages)
 
     logging.info("[OpenCopilot] LLM Body Response: {}".format(result.content))

llm-server/routes/workflow/generate_openapi_payload.py

@@ -85,6 +85,7 @@ def generate_openapi_payload(
     text: str,
     _operation_id: str,
     prev_api_response: str,
+    app: Optional[str],
     current_state: Optional[str],
 ) -> ApiInfo:
     (
@@ -119,12 +120,12 @@ def generate_openapi_payload(
     )
 
     if api_info.body_schema:
-        example = gen_ex_from_schema(api_info.body_schema)
+        # example = gen_ex_from_schema(api_info.body_schema)
         api_info.body_schema = gen_body_from_schema(
             json.dumps(api_info.body_schema, separators=(",", ":")),
             text,
             prev_api_response,
-            example,
+            app,
             current_state,
         )
 

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

@@ -32,7 +32,12 @@ def run_openapi_operations(
                 # refresh state after every api call, we can look into optimizing this later as well
                 operation_id = step.get("open_api_operation_id")
                 api_payload = generate_openapi_payload(
-                    swagger_json, text, operation_id, prev_api_response, current_state
+                    swagger_json,
+                    text,
+                    operation_id,
+                    prev_api_response,
+                    app,
+                    current_state,
                 )
 
                 api_response = make_api_request(headers=headers, **api_payload.__dict__)

llm-server/utils/make_api_call.py

@@ -35,6 +35,7 @@ def make_api_request(
 ) -> Response:
     try:
         endpoint = replace_url_placeholders(endpoint, path_params)
+        print(f"Endpoint: {endpoint}")
         url = servers[0] + endpoint
         # Create a session and configure it with headers
         session = requests.Session()