Merge pull request #86 from parea-ai/update-sdk

update cookbook and examples, fix nits

README.md

@@ -82,61 +82,31 @@ async def main_async():
 
 ```python
 import os
-import time
 
 import openai
 from dotenv import load_dotenv
 
 from parea import Parea
-from parea.helpers import to_date_and_time_string, gen_trace_id
-from parea.parea_logger import parea_logger
-from parea.schemas.models import TraceLog, LLMInputs
 
 load_dotenv()
 
 openai.api_key = os.getenv("OPENAI_API_KEY")
+
 p = Parea(api_key=os.getenv("PAREA_API_KEY"))
 
-# define your OpenAI call as you would normally
 x = "Golang"
 y = "Fiber"
-inputs = {"x": x, "y": y}
-messages = [
-  {"role": "user", "content": f"Write a hello world program using {x} and the {y} framework."},
-]
+messages = [{
+  "role": "user",
+  "content": f"Write a hello world program using {x} and the {y} framework."
+}]
 model = "gpt-3.5-turbo"
-model_params = {
-  "temperature": 0.7,
-  "top_p": 1.0,
-}
-model_config = {"model": model, "messages": messages, "model_params": model_params}
-start_time = time.time()
-completion = openai.ChatCompletion.create(model=model, messages=messages, **model_params)
-output = completion.choices[0].message["content"]
-end_time = time.time()
-
-# the TraceLog schema
-log_request = TraceLog(
-  trace_id=gen_trace_id(),
-  start_timestamp=to_date_and_time_string(start_time),
-  end_timestamp=to_date_and_time_string(end_time),
-  status="success",
-  trace_name="Test Log",
-  inputs=inputs,
-  configuration=LLMInputs(**model_config),
-  output=output,
-  input_tokens=completion.usage["prompt_tokens"],
-  output_tokens=completion.usage["completion_tokens"],
-  total_tokens=completion.usage["total_tokens"],
-)
+temperature = 0.0
 
 
+# define your OpenAI call as you would normally and we'll automatically log the results
 def main():
-  parea_logger.record_log(data=log_request)
-
-
-async def main_async():
-  await parea_logger.arecord_log(data=log_request)
+  openai.ChatCompletion.create(model=model, temperature=temperature, messages=messages).choices[0].message["content"]
 ```
 
 ### Open source community features

parea/client.py

@@ -1,10 +1,10 @@
 import asyncio
 import time
-from uuid import uuid4
 
 from attrs import asdict, define, field
 
 from parea.api_client import HTTPClient
+from parea.helpers import gen_trace_id
 from parea.parea_logger import parea_logger
 from parea.schemas.models import Completion, CompletionResponse, FeedbackRequest, UseDeployedPrompt, UseDeployedPromptResponse
 from parea.utils.trace_utils import default_logger, get_current_trace_id, trace_data
@@ -24,7 +24,7 @@ def __attrs_post_init__(self):
         parea_logger.set_client(self._client)
 
     def completion(self, data: Completion) -> CompletionResponse:
-        inference_id = str(uuid4())
+        inference_id = gen_trace_id()
         data.inference_id = inference_id
         r = self._client.request(
             "POST",
@@ -37,7 +37,7 @@ def completion(self, data: Completion) -> CompletionResponse:
         return CompletionResponse(**r.json())
 
     async def acompletion(self, data: Completion) -> CompletionResponse:
-        inference_id = str(uuid4())
+        inference_id = gen_trace_id()
         data.inference_id = inference_id
         r = await self._client.request_async(
             "POST",

parea/cookbook/tracing_with_Parea_sdk.ipynb

@@ -114,7 +114,7 @@
     "def argument_generator(query: str, additional_description: str = \"\") -> str:\n",
     "    return p.completion(\n",
     "        Completion(\n",
-    "            deployment_id=\"p-RG8d9rfJc_0cctwfpb_n6\",\n",
+    "            deployment_id=\"p-XOh3kp8B0nIE82WgioPnr\",\n",
     "            llm_inputs={\n",
     "                \"additional_description\": additional_description,\n",
     "                \"date\": f\"{datetime.now()}\",\n",
@@ -127,21 +127,21 @@
     "def critic(argument: str) -> str:\n",
     "    return p.completion(\n",
     "        Completion(\n",
-    "            deployment_id=\"p-fXgZytT3dJjXD_71TDR4s\",\n",
+    "            deployment_id=\"p-PSOwRyIPaQRq4xQW3MbpV\",\n",
     "            llm_inputs={\"argument\": argument},\n",
     "        )\n",
     "    ).content\n",
     "\n",
     "\n",
-    "def refiner(query: str, additional_description: str, current_arg: str, criticism: str) -> str:\n",
+    "def refiner(query: str, additional_description: str, argument: str, criticism: str) -> str:\n",
     "    return p.completion(\n",
     "        Completion(\n",
-    "            deployment_id=\"p--G2s9okMTvBEh3d8YqLY2\",\n",
+    "            deployment_id=\"p-bJ3-UKh9-ixapZafaRBsj\",\n",
     "            llm_inputs={\n",
     "                \"additional_description\": additional_description,\n",
     "                \"date\": f\"{datetime.now()}\",\n",
     "                \"query\": query,\n",
-    "                \"current_arg\": current_arg,\n",
+    "                \"argument\": argument,\n",
     "                \"criticism\": criticism,\n",
     "            },\n",
     "        )\n",
@@ -285,15 +285,15 @@
     "\n",
     "\n",
     "# let's return the full CompletionResponse to see what other information is returned\n",
-    "def refiner2(query: str, additional_description: str, current_arg: str, criticism: str) -> CompletionResponse:\n",
+    "def refiner2(query: str, additional_description: str, argument: str, criticism: str) -> CompletionResponse:\n",
     "    return p.completion(\n",
     "        Completion(\n",
-    "            deployment_id=\"p-8Er1Xo0GDGF2xtpmMOpbn\",\n",
+    "            deployment_id=\"p-bJ3-UKh9-ixapZafaRBsj\",\n",
     "            llm_inputs={\n",
     "                \"additional_description\": additional_description,\n",
     "                \"date\": f\"{datetime.now()}\",\n",
     "                \"query\": query,\n",
-    "                \"current_arg\": current_arg,\n",
+    "                \"argument\": argument,\n",
     "                \"criticism\": criticism,\n",
     "            },\n",
     "        )\n",
@@ -337,35 +337,37 @@
    },
    "outputs": [],
    "source": [
+    "from typing import Tuple\n",
+    "\n",
+    "\n",
     "# you can also add metadata and tags via the decorator\n",
     "@trace(\n",
     "    tags=[\"cookbook-example-deployed\", \"feedback_tracked-deployed\"],\n",
-    "    metadata={\"source\": \"python-sdk\", \"deployed\": True},\n",
+    "    metadata={\"source\": \"python-sdk\", \"deployed\": \"True\"},\n",
     ")\n",
-    "def argument_chain3(query: str, additional_description: str = \"\") -> CompletionResponse:\n",
+    "def argument_chain_tags_metadata(query: str, additional_description: str = \"\") -> Tuple[CompletionResponse, str]:\n",
+    "    trace_id = get_current_trace_id()  # get parent's trace_id\n",
     "    argument = argument_generator(query, additional_description)\n",
     "    criticism = critic(argument)\n",
-    "    return refiner2(query, additional_description, argument, criticism)"
+    "    return refiner2(query, additional_description, argument, criticism), trace_id"
    ]
   },
   {
    "cell_type": "code",
    "source": [
     "import json, attrs\n",
     "\n",
-    "result = argument_chain3(\n",
+    "result2, trace_id = argument_chain_tags_metadata(\n",
     "    \"Whether coffee is good for you.\",\n",
     "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\n",
     ")\n",
-    "\n",
-    "print(json.dumps(attrs.asdict(result), indent=4))\n",
+    "print(json.dumps(attrs.asdict(result2), indent=2))\n",
     "\n",
     "p.record_feedback(\n",
     "    FeedbackRequest(\n",
-    "        # we can access the inference_id (same as trace_id) from the CompletionResponse object directly\n",
-    "        trace_id=result.inference_id,\n",
-    "        score=0.5,\n",
-    "        target=\"Coffee is nice. Full stop.\",\n",
+    "        trace_id=trace_id,\n",
+    "        score=0.7,  # 0.0 (bad) to 1.0 (good)\n",
+    "        target=\"Coffee is wonderful. End of story.\",\n",
     "    )\n",
     ")"
    ],

parea/cookbook/tracing_with_agent.py

@@ -1,15 +1,17 @@
 import os
 import random
+import time
 
 from dotenv import load_dotenv
 
 from parea import Parea
+from parea.helpers import to_date_and_time_string
 from parea.schemas.models import Completion, CompletionResponse, FeedbackRequest, LLMInputs, Message, ModelParams, Role
 from parea.utils.trace_utils import get_current_trace_id, trace
 
 load_dotenv()
 
-p = Parea(api_key=os.getenv("PAREA_API_KEY"))
+p = Parea(api_key=os.getenv("DEV_API_KEY"))
 
 # Parea SDK makes it easy to use different LLMs with the same apis structure and standardized request/response schemas.
 LLM_OPTIONS = [("gpt-3.5-turbo", "openai"), ("gpt-4", "openai"), ("claude-instant-1", "anthropic"), ("claude-2", "anthropic")]
@@ -84,7 +86,7 @@ def generate_tasks(main_objective: str, expounded_initial_task: list[dict[str, s
     return new_tasks_list
 
 
-@trace
+@trace(name=f"run_agent-{to_date_and_time_string(time.time())}")  # You can provide a custom name other than the function name
 def run_agent(main_objective: str, initial_task: str = "") -> tuple[list[dict[str, str]], str]:
     trace_id = get_current_trace_id()
     generated_tasks = []

parea/cookbook/tracing_with_deployed_prompt.py

@@ -1,8 +1,10 @@
 from typing import Tuple
 
+import json
 import os
 from datetime import datetime
 
+from attrs import asdict
 from dotenv import load_dotenv
 
 from parea import Parea
@@ -11,13 +13,13 @@
 
 load_dotenv()
 
-p = Parea(api_key=os.getenv("PAREA_API_KEY"))
+p = Parea(api_key=os.getenv("DEV_API_KEY"))
 
 
 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()}",
@@ -30,7 +32,7 @@ def deployed_argument_generator(query: str, additional_description: str = "") ->
 def deployed_critic(argument: str) -> str:
     return p.completion(
         Completion(
-            deployment_id="p-fXgZytT3dJjXD_71TDR4s",
+            deployment_id="p-PSOwRyIPaQRq4xQW3MbpV",
             llm_inputs={"argument": argument},
         )
     ).content
@@ -39,12 +41,12 @@ def deployed_critic(argument: str) -> str:
 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,
-                "current_arg": current_arg,
+                "argument": current_arg,
                 "criticism": criticism,
             },
         )
@@ -54,12 +56,12 @@ def deployed_refiner(query: str, additional_description: str, current_arg: str,
 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,
-                "current_arg": current_arg,
+                "argument": current_arg,
                 "criticism": criticism,
             },
         )
@@ -73,22 +75,15 @@ def deployed_argument_chain(query: str, additional_description: str = "") -> str
     return deployed_refiner(query, additional_description, argument, criticism)
 
 
-@trace
-def deployed_argument_chain2(query: str, additional_description: str = "") -> Tuple[str, str]:
-    trace_id = get_current_trace_id()
-    argument = deployed_argument_generator(query, additional_description)
-    criticism = deployed_critic(argument)
-    return deployed_refiner(query, additional_description, argument, criticism), trace_id
-
-
 @trace(
     tags=["cookbook-example-deployed", "feedback_tracked-deployed"],
-    metadata={"source": "python-sdk", "deployed": True},
+    metadata={"source": "python-sdk", "deployed": "True"},
 )
-def deployed_argument_chain_tags_metadata(query: str, additional_description: str = "") -> CompletionResponse:
+def deployed_argument_chain_tags_metadata(query: str, additional_description: str = "") -> tuple[CompletionResponse, str]:
+    trace_id = get_current_trace_id()  # get parent's trace_id
     argument = deployed_argument_generator(query, additional_description)
     criticism = deployed_critic(argument)
-    return deployed_refiner2(query, additional_description, argument, criticism)
+    return deployed_refiner2(query, additional_description, argument, criticism), trace_id
 
 
 if __name__ == "__main__":
@@ -98,27 +93,14 @@ def deployed_argument_chain_tags_metadata(query: str, additional_description: st
     )
     print(result1)
 
-    result2, trace_id2 = deployed_argument_chain2(
-        "Whether wine is good for you.",
-        additional_description="Provide a concise, few sentence argument on why wine is good for you.",
-    )
-    print(result2)
-    p.record_feedback(
-        FeedbackRequest(
-            trace_id=trace_id2,
-            score=0.0,  # 0.0 (bad) to 1.0 (good)
-            target="Moonshine is wonderful.",
-        )
-    )
-
-    result3 = deployed_argument_chain_tags_metadata(
+    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(result3.content)
+    print(json.dumps(asdict(result2), indent=2))
     p.record_feedback(
         FeedbackRequest(
-            trace_id=result3.inference_id,
+            trace_id=trace_id,
             score=0.7,  # 0.0 (bad) to 1.0 (good)
             target="Coffee is wonderful. End of story.",
         )