Adding custom query planners

llm-server/readme.md

@@ -63,6 +63,8 @@ To install Mypy, which is a static type checker for Python, follow these steps:
    MONGODB_URL=mongodb://localhost:27017/opencopilot
    QDRANT_URL=http://localhost:6333  
    STORE=QDRANT
+   QDRANT_API_KEY= # When using cloud hosted version
+   SCORE_THRESHOLD=0.95 # When using pre defined workflows, the confidence score at which the opencopilot should select your workflow. If the score falls below this, the planner will design it's own workflow
    ```
 
    Ensure you replace the placeholders with your actual API keys and configuration settings.

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

@@ -28,7 +28,7 @@ def gen_params_from_schema(
         HumanMessage(content="User input: {}".format(text)),
         HumanMessage(content="prev api responses: {}".format(prev_resp)),
         HumanMessage(
-            content="Given the provided information, generate the appropriate JSON payload to use as parameters for the API request"
+            content="Based on the information provided,  construct a valid parameter object to be used with python requests library. In cases where user input doesnot contain information for a query, DO NOT add that specific query parameter to the output. "
         ),
     ]
     result = chat(messages)

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

@@ -1,7 +1,5 @@
 import os, logging
 from langchain.chat_models import ChatOpenAI
-from custom_types.t_json import JsonData
-from typing import Optional
 from dotenv import load_dotenv
 from langchain.schema import HumanMessage, SystemMessage
 from typing import Any
@@ -12,9 +10,7 @@
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
 
-def transform_api_response_from_schema(
-    server_url: str, api_response: str
-) -> Optional[JsonData]:
+def transform_api_response_from_schema(server_url: str, api_response: str) -> str:
     chat = ChatOpenAI(
         openai_api_key=os.getenv("OPENAI_API_KEY"),
         model="gpt-3.5-turbo-16k",
@@ -23,26 +19,19 @@ def transform_api_response_from_schema(
 
     messages = [
         SystemMessage(
-            content="You are an intelligent AI assistant that can identify important fields from a REST API response."
+            content="You are a bot capable of comprehending API responses."
         ),
         HumanMessage(
-            content="Here is the response from a REST API call: {} for endpoint: {}".format(
+            content="Here is the response from current REST API: {} for endpoint: {}".format(
                 api_response, server_url
             )
         ),
         HumanMessage(
-            content="Please examine the given API response and return only the fields that are important when making API calls. Ignore any unimportant fields. Structure your response as a JSON object with self-descriptive keys mapped to the corresponding values from the API response."
+            content="Analyze the provided API responses and extract only the essential fields required for subsequent API interactions. Disregard any non-essential attributes such as CSS or color-related data. If there are generic fields like 'id,' provide them with more descriptive names in your response. Format your response as a JSON object with clear and meaningful keys that map to their respective values from the API response."
         ),
     ]
 
     result = chat(messages)
-    logging.info("[OpenCopilot] LLM Body Response: {}".format(result.content))
+    logging.info("[OpenCopilot] Transformed Response: {}".format(result.content))
 
-    d = extract_json_payload(result.content)
-    logging.info(
-        "[OpenCopilot] Parsed the json payload: {}, context: {}".format(
-            d, "gen_body_from_schema"
-        )
-    )
-
-    return d
+    return result.content

llm-server/routes/workflow/generate_openapi_payload.py

@@ -1,9 +1,6 @@
 import re
 import os
 import json
-from routes.workflow.extractors.transform_api_response import (
-    transform_api_response_from_schema,
-)
 from utils.get_llm import get_llm
 from dotenv import load_dotenv
 from .extractors.example_generator import gen_ex_from_schema
@@ -113,13 +110,7 @@ def generate_openapi_payload(
         api_info.body_schema = gen_body_from_schema(
             json.dumps(api_info.body_schema), text, prev_api_response, example
         )
-        # when you come back, clear the trello board and
-        # extract api info and set it up for next call
-        transformed_response = transform_api_response_from_schema(
-            api_info.endpoint or "", api_info.body_schema
-        )
 
-        prev_api_response = prev_api_response + json.loads(transformed_response)
     else:
         api_info.body_schema = {}
 

llm-server/routes/workflow/hierarchical_planner.py

@@ -11,7 +11,7 @@
 
 
 def create_and_run_openapi_agent(
-        swagger_json: Any, user_query: str, headers: Dict[str, str] = {}
+    swagger_json: Any, user_query: str, headers: Dict[str, str] = {}
 ) -> Any:
     # Load OpenAPI spec
     # raw_spec = json.loads(swagger_json)

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

@@ -4,6 +4,9 @@
 import traceback
 import logging
 from typing import Any
+from routes.workflow.extractors.transform_api_response import (
+    transform_api_response_from_schema,
+)
 
 
 def run_openapi_operations(
@@ -13,10 +16,9 @@ def run_openapi_operations(
     headers: Any,
     server_base_url: str,
 ) -> str:
+    prev_api_response = ""
     record_info = {"Workflow Name": record.get("name")}
     for flow in record.get("flows", []):
-        prev_api_response = ""
-
         for step in flow.get("steps"):
             try:
                 operation_id = step.get("open_api_operation_id")
@@ -25,8 +27,13 @@ def run_openapi_operations(
                 )
 
                 api_response = make_api_request(headers=headers, **api_payload.__dict__)
+
+                transformed_response = transform_api_response_from_schema(
+                    api_payload.endpoint or "", api_response.text
+                )
+
+                prev_api_response = prev_api_response + transformed_response
                 record_info[operation_id] = json.loads(api_response.text)
-                prev_api_response = api_response.text
 
             except Exception as e:
                 logging.error("Error making API call", exc_info=True)
@@ -36,8 +43,8 @@ def run_openapi_operations(
                     "error": str(e),
                     "traceback": traceback.format_exc(),
                 }
-
                 record_info[operation_id] = error_info
 
-        prev_api_response = ""
+                # At this point we will retry the operation with hierarchical planner
+                raise e
     return json.dumps(record_info)

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

@@ -2,6 +2,8 @@
 from routes.workflow.typings.run_workflow_input import WorkflowData
 from routes.workflow.utils.run_openapi_ops import run_openapi_operations
 from opencopilot_types.workflow_type import WorkflowDataType
+from routes.workflow.hierarchical_planner import create_and_run_openapi_agent
+import logging, json
 
 
 def run_workflow(
@@ -10,8 +12,26 @@ def run_workflow(
     headers = data.headers or {}
     server_base_url = data.server_base_url
 
-    result = run_openapi_operations(
-        workflow_doc, swagger_json, data.text, headers, server_base_url
-    )
+    result = ""
+    error = None
 
-    return {"response": result}
+    try:
+        result = run_openapi_operations(
+            workflow_doc, swagger_json, data.text, headers, server_base_url
+        )
+    except Exception as e:
+        logging.error("[OpenCopilot] Custom planner failed: %s", e)
+        error = str(e)
+
+        try:
+            result = create_and_run_openapi_agent(swagger_json, data.text, headers)
+        except Exception as e:
+            logging.error("[OpenCopilot] Hierarchical planner failed: %s", e)
+            error = str(e)
+            raise
+
+    output = {"response": result if not error else "", "error": error}
+
+    logging.info("[OpenCopilot] Workflow output %s", json.dumps(output))
+
+    return output