Merge pull request #373 from openchatai/fix/base_prompts

removing unused code

llm-server/routes/root_service.py

@@ -196,39 +196,6 @@ def get_swagger_doc(swagger_url: str) -> ResolvingParser:
     else:
         return ResolvingParser(spec_string=swagger_doc)
 
-
-def handle_existing_workflow(
-    document: Document,  # lagnchaing
-    text: str,
-    headers: Headers,
-    server_base_url: str,
-    swagger_url: Optional[str],
-    app: Optional[str],
-    swagger_doc: ResolvingParser,
-    session_id: str,
-    bot_id: str,
-) -> ResponseDict:
-    # use user defined workflows if exists, if not use auto_gen_workflow
-    _workflow = mongo.workflows.find_one(
-        {"_id": ObjectId(document.metadata["workflow_id"])}
-    )
-
-    if _workflow is None:
-        _workflow = mongo.auto_gen_workflows.find_one(
-            {"_id": ObjectId(document.metadata["workflow_id"])}
-        )
-
-    output = run_workflow(
-        _workflow,
-        swagger_doc,
-        WorkflowData(text, headers, server_base_url, swagger_url, app),
-        app,
-        bot_id=bot_id,
-    )
-
-    return output
-
-
 async def handle_api_calls(
     ids: List[str],
     swagger_doc: ResolvingParser,
@@ -250,8 +217,6 @@ async def handle_api_calls(
     )
 
     _workflow["swagger_url"] = swagger_url
-    # m_workflow = mongo.auto_gen_workflows.insert_one(_workflow)
-    # add_workflow_data_to_qdrant(m_workflow.inserted_id, _workflow, swagger_url)
 
     return output
 

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

@@ -1,4 +1,3 @@
-from .check_workflow_in_store import *
 from .create_workflow_from_operation_ids import *
 from .fetch_swagger_text import *
 from .get_swagger_op_by_id import *

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

@@ -1,15 +1,15 @@
-import os
 from custom_types.api_operation import ApiOperation_vs
 from opencopilot_types.workflow_type import WorkflowFlowType
 
 # push it to the library
 from shared.utils.opencopilot_utils.get_vector_store import get_vector_store
 from shared.utils.opencopilot_utils import StoreOptions
 from utils.chat_models import CHAT_MODELS
-from utils import get_chat_model
+from utils.get_chat_model import get_chat_model
 from typing import Optional, List
 from langchain.vectorstores.base import VectorStore
 from utils.get_logger import CustomLogger
+from utils.llm_consts import vs_thresholds
 
 logger = CustomLogger(module_name=__name__)
 chat = get_chat_model(CHAT_MODELS.gpt_3_5_turbo_16k)
@@ -21,12 +21,10 @@
 
 async def get_relevant_docs(text: str, bot_id: str) -> Optional[str]:
     try:
-        score_threshold = float(os.getenv("SCORE_THRESHOLD_KB", "0.65"))
-
         kb_retriever = knowledgebase.as_retriever(
             search_kwargs={
                 "k": 3,
-                "score_threshold": score_threshold,
+                "score_threshold": vs_thresholds.get("kb_score_threshold"),
                 "filter": {"bot_id": bot_id},
             },
         )
@@ -53,12 +51,11 @@ async def get_relevant_docs(text: str, bot_id: str) -> Optional[str]:
 
 async def get_relevant_flows(text: str, bot_id: str) -> List[WorkflowFlowType]:
     try:
-        score_threshold = float(os.getenv("SCORE_THRESHOLD_KB", "0.80"))
 
         flow_retriever = flows.as_retriever(
             search_kwargs={
                 "k": 3,
-                "score_threshold": score_threshold,
+                "score_threshold": vs_thresholds.get("flows_score_threshold"),
                 "filter": {"bot_id": bot_id},
             },
         )
@@ -83,12 +80,10 @@ async def get_relevant_flows(text: str, bot_id: str) -> List[WorkflowFlowType]:
 
 async def get_relevant_apis_summaries(text: str, bot_id: str) -> List[ApiOperation_vs]:
     try:
-        score_threshold = float(os.getenv("SCORE_THRESHOLD_KB", "0.75"))
-
         apis_retriever = apis.as_retriever(
             search_kwargs={
                 "k": 3,
-                "score_threshold": score_threshold,
+                "score_threshold": vs_thresholds.get("api_score_threshold"),
                 "filter": {"bot_id": bot_id},
             },
         )

llm-server/shared/utils/opencopilot_utils/get_vector_store.py

@@ -1,10 +1,8 @@
 import os
-from langchain.vectorstores.pinecone import Pinecone
 
 from langchain.vectorstores.qdrant import Qdrant
 from langchain.vectorstores.base import VectorStore
 from .store_type import StoreType
-from .config import VECTOR_STORE_INDEX_NAME, PINECONE_TEXT_KEY
 from .interfaces import StoreOptions
 from .get_embeddings import get_embeddings
 import qdrant_client

llm-server/utils/llm_consts.py

@@ -1,7 +1,18 @@
 import os
-
+from typing import TypedDict
 
 EXPERIMENTAL_FEATURES_ENABLED = os.getenv("EXPERIMENTAL_FEATURES_ENABLED", "NO")
 SYSTEM_MESSAGE_PROMPT = "system_message_prompt"
 SUMMARIZATION_PROMPT = "summarization_prompt"
 X_App_Name = "X-App-Name"
+
+class VsThresholds(TypedDict):
+    api_score_threshold: float
+    flows_score_threshold: float
+    kb_score_threshold: float
+
+vs_thresholds: VsThresholds = {
+    "api_score_threshold": float(os.getenv("API_SCORE_THRESHOLD", "0.75")),
+    "flows_score_threshold": float(os.getenv("FLOWS_SCORE_THRESHOLD", "0.75")),
+    "kb_score_threshold": float(os.getenv("KB_SCORE_THRESHOLD", "0.75"))    
+}