Merge pull request #1010 from parea-ai/PAI-1402-add-history-via-p-com…

…pletion

feat(history): add history to completion

cookbook/parea_llm_proxy/deployments/tracing_with_deployed_prompt.py

@@ -8,7 +8,7 @@
 from dotenv import load_dotenv
 
 from parea import Parea, get_current_trace_id, trace
-from parea.schemas import Completion, CompletionResponse, FeedbackRequest
+from parea.schemas import Completion, CompletionResponse, LLMInputs, Message, Role
 
 load_dotenv()
 
@@ -18,60 +18,64 @@
 def deployed_argument_generator(query: str, additional_description: str = "") -> str:
     return p.completion(
         Completion(
-            deployment_id="p-RG8d9rfJc_0cctwfpb_n6",
+            deployment_id="p-XOh3kp8B0nIE82WgioPnr",
             llm_inputs={
                 "additional_description": additional_description,
                 "date": f"{datetime.now()}",
                 "query": query,
             },
+            llm_configuration=LLMInputs(history=[Message(role=Role.user, content="Some history")]),
         )
     ).content
 
 
 def deployed_critic(argument: str) -> str:
     return p.completion(
         Completion(
-            deployment_id="p-fXgZytT3dJjXD_71TDR4s",
+            deployment_id="p-PSOwRyIPaQRq4xQW3MbpV",
             llm_inputs={"argument": argument},
+            llm_configuration=LLMInputs(history=[Message(role=Role.user, content="Some history")]),
         )
     ).content
 
 
 def deployed_refiner(query: str, additional_description: str, current_arg: str, criticism: str) -> str:
     return p.completion(
         Completion(
-            deployment_id="p--G2s9okMTvBEh3d8YqLY2",
+            deployment_id="p-bJ3-UKh9-ixapZafaRBsj",
             llm_inputs={
                 "additional_description": additional_description,
                 "date": f"{datetime.now()}",
                 "query": query,
                 "argument": current_arg,
                 "criticism": criticism,
             },
+            llm_configuration=LLMInputs(history=[Message(role=Role.user, content="Some history")]),
         )
     ).content
 
 
 def deployed_refiner2(query: str, additional_description: str, current_arg: str, criticism: str) -> CompletionResponse:
     return p.completion(
         Completion(
-            deployment_id="p--G2s9okMTvBEh3d8YqLY2",
+            deployment_id="p-bJ3-UKh9-ixapZafaRBsj",
             llm_inputs={
                 "additional_description": additional_description,
                 "date": f"{datetime.now()}",
                 "query": query,
                 "argument": current_arg,
                 "criticism": criticism,
             },
+            llm_configuration=LLMInputs(history=[Message(role=Role.user, content="Some history")]),
         )
     )
 
 
-# @trace
-# def deployed_argument_chain(query: str, additional_description: str = "") -> str:
-#     argument = deployed_argument_generator(query, additional_description)
-#     criticism = deployed_critic(argument)
-#     return deployed_refiner(query, additional_description, argument, criticism)
+@trace
+def deployed_argument_chain(query: str, additional_description: str = "") -> str:
+    argument = deployed_argument_generator(query, additional_description)
+    criticism = deployed_critic(argument)
+    return deployed_refiner(query, additional_description, argument, criticism)
 
 
 @trace(
@@ -86,21 +90,21 @@ def deployed_argument_chain_tags_metadata(query: str, additional_description: st
 
 
 if __name__ == "__main__":
-    # result1 = deployed_argument_chain(
-    #     "Whether coffee is good for you.",
-    #     additional_description="Provide a concise, few sentence argument on why coffee is good for you.",
-    # )
-    # print(result1)
+    result1 = deployed_argument_chain(
+        "Whether coffee is good for you.",
+        additional_description="Provide a concise, few sentence argument on why coffee is good for you.",
+    )
+    print(result1)
 
     result2, trace_id = deployed_argument_chain_tags_metadata(
         "Whether coffee is good for you.",
         additional_description="Provide a concise, few sentence argument on why coffee is good for you.",
     )
     print(json.dumps(asdict(result2), indent=2))
-    p.record_feedback(
-        FeedbackRequest(
-            trace_id=trace_id,
-            score=0.7,  # 0.0 (bad) to 1.0 (good)
-            target="Coffee is wonderful. End of story.",
-        )
-    )
+    # p.record_feedback(
+    #     FeedbackRequest(
+    #         trace_id=trace_id,
+    #         score=0.7,  # 0.0 (bad) to 1.0 (good)
+    #         target="Coffee is wonderful. End of story.",
+    #     )
+    # )

cookbook/parea_llm_proxy/tracing_without_deployed_prompt.py

@@ -6,7 +6,7 @@
 from dotenv import load_dotenv
 
 from parea import Parea, get_current_trace_id, trace
-from parea.schemas import Completion, CompletionResponse, FeedbackRequest, LLMInputs, Message, ModelParams
+from parea.schemas import Completion, CompletionResponse, FeedbackRequest, LLMInputs, Message, ModelParams, Role
 
 load_dotenv()
 
@@ -21,9 +21,7 @@ def call_llm(
     return p.completion(
         data=Completion(
             llm_configuration=LLMInputs(
-                model=model,
-                model_params=ModelParams(temp=temperature),
-                messages=[Message(**d) for d in data],
+                model=model, model_params=ModelParams(temp=temperature), messages=[Message(**d) for d in data], history=[Message(role=Role.user, content="Some history")]
             )
         )
     )

parea/schemas/log.py

@@ -50,6 +50,7 @@ class LLMInputs:
     provider: Optional[str] = None
     model_params: Optional[ModelParams] = ModelParams()
     messages: Optional[List[Message]] = None
+    history: Optional[List[Message]] = None
     functions: Optional[List[Any]] = None
     function_call: Optional[Union[str, Dict[str, str]]] = None
 

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 [tool.poetry]
 name = "parea-ai"
 packages = [{ include = "parea" }]
-version = "0.2.188"
+version = "0.2.189"
 description = "Parea python sdk"
 readme = "README.md"
 authors = ["joel-parea-ai <[email protected]>"]