From b7623fcd7104995021a213997c718c611e01e185 Mon Sep 17 00:00:00 2001
From: Joel Alexander <a.joeljr@hotmail.com>
Date: Sat, 9 Dec 2023 14:19:50 -0500
Subject: [PATCH 1/7] example cookbook based on Helicone example

---
 parea/cookbook/img/trace_log_view.png         | Bin 0 -> 208641 bytes
 parea/cookbook/tracing_with_Parea_sdk.ipynb   | 136 ++++----
 ...ing_with_function_calling_and_chains.ipynb | 305 ++++++++++++++++++
 pyproject.toml                                |   2 +-
 4 files changed, 374 insertions(+), 69 deletions(-)
 create mode 100644 parea/cookbook/img/trace_log_view.png
 create mode 100644 parea/cookbook/tracing_with_function_calling_and_chains.ipynb

diff --git a/parea/cookbook/img/trace_log_view.png b/parea/cookbook/img/trace_log_view.png
new file mode 100644
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HcmV?d00001

diff --git a/parea/cookbook/tracing_with_Parea_sdk.ipynb b/parea/cookbook/tracing_with_Parea_sdk.ipynb
index 0c42e97c..b34190fa 100644
--- a/parea/cookbook/tracing_with_Parea_sdk.ipynb
+++ b/parea/cookbook/tracing_with_Parea_sdk.ipynb
@@ -117,40 +117,40 @@
     "\n",
     "\n",
     "def argument_generator(query: str, additional_description: str = \"\") -> str:\n",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
-    "            llm_inputs={\n",
-    "                \"additional_description\": additional_description,\n",
-    "                \"date\": f\"{datetime.now()}\",\n",
-    "                \"query\": query,\n",
-    "            },\n",
-    "        )\n",
-    "    ).content\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
+    "      llm_inputs={\n",
+    "        \"additional_description\": additional_description,\n",
+    "        \"date\": f\"{datetime.now()}\",\n",
+    "        \"query\": query,\n",
+    "      },\n",
+    "    )\n",
+    "  ).content\n",
     "\n",
     "\n",
     "def critic(argument: str) -> str:\n",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\n",
-    "            llm_inputs={\"argument\": argument},\n",
-    "        )\n",
-    "    ).content\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\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",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "            llm_inputs={\n",
-    "                \"additional_description\": additional_description,\n",
-    "                \"date\": f\"{datetime.now()}\",\n",
-    "                \"query\": query,\n",
-    "                \"argument\": current_arg,\n",
-    "                \"criticism\": criticism,\n",
-    "            },\n",
-    "        )\n",
-    "    ).content\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "      llm_inputs={\n",
+    "        \"additional_description\": additional_description,\n",
+    "        \"date\": f\"{datetime.now()}\",\n",
+    "        \"query\": query,\n",
+    "        \"argument\": current_arg,\n",
+    "        \"criticism\": criticism,\n",
+    "      },\n",
+    "    )\n",
+    "  ).content\n",
     "\n",
     "\n",
     "# Non deployed version\n",
@@ -209,9 +209,9 @@
     "\n",
     "@trace\n",
     "def argument_chain(query: str, additional_description: str = \"\") -> str:\n",
-    "    argument = argument_generator(query, additional_description)\n",
-    "    criticism = critic(argument)\n",
-    "    return refiner(query, additional_description, argument, criticism)"
+    "  argument = argument_generator(query, additional_description)\n",
+    "  criticism = critic(argument)\n",
+    "  return refiner(query, additional_description, argument, criticism)"
    ]
   },
   {
@@ -251,8 +251,8 @@
    ],
    "source": [
     "result1 = argument_chain(\n",
-    "    \"Whether coffee is good for you.\",\n",
-    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(result1)"
    ]
@@ -302,10 +302,10 @@
     "\n",
     "@trace\n",
     "def argument_chain2(query: str, additional_description: str = \"\") -> tuple[str, 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 refiner(query, additional_description, argument, criticism), trace_id"
+    "  trace_id = get_current_trace_id()  # get parent's trace_id\n",
+    "  argument = argument_generator(query, additional_description)\n",
+    "  criticism = critic(argument)\n",
+    "  return refiner(query, additional_description, argument, criticism), trace_id"
    ]
   },
   {
@@ -331,8 +331,8 @@
    ],
    "source": [
     "result, trace_id = argument_chain2(\n",
-    "    \"Whether coffee is good for you.\",\n",
-    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(trace_id)"
    ]
@@ -424,30 +424,30 @@
     "\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",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "            llm_inputs={\n",
-    "                \"additional_description\": additional_description,\n",
-    "                \"date\": f\"{datetime.now()}\",\n",
-    "                \"query\": query,\n",
-    "                \"argument\": current_arg,\n",
-    "                \"criticism\": criticism,\n",
-    "            },\n",
-    "        )\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "      llm_inputs={\n",
+    "        \"additional_description\": additional_description,\n",
+    "        \"date\": f\"{datetime.now()}\",\n",
+    "        \"query\": query,\n",
+    "        \"argument\": current_arg,\n",
+    "        \"criticism\": criticism,\n",
+    "      },\n",
     "    )\n",
+    "  )\n",
     "\n",
     "\n",
     "@trace\n",
     "def argument_chain3(query: str, additional_description: str = \"\") -> CompletionResponse:\n",
-    "    argument = argument_generator(query, additional_description)\n",
-    "    criticism = critic(argument)\n",
-    "    return refiner2(query, additional_description, argument, criticism)\n",
+    "  argument = argument_generator(query, additional_description)\n",
+    "  criticism = critic(argument)\n",
+    "  return refiner2(query, additional_description, argument, criticism)\n",
     "\n",
     "\n",
     "result2 = argument_chain3(\n",
-    "    \"Whether coffee is good for you.\",\n",
-    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(json.dumps(asdict(result2), indent=2))"
    ],
@@ -498,14 +498,14 @@
     "\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",
+    "  tags=[\"cookbook-example-deployed\", \"feedback_tracked-deployed\"],\n",
+    "  metadata={\"source\": \"python-sdk\", \"deployed\": \"True\"},\n",
     ")\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), trace_id"
+    "  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), trace_id"
    ]
   },
   {
@@ -515,17 +515,17 @@
     "from attrs import asdict\n",
     "\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",
+    "  \"Whether coffee is good for you.\",\n",
+    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\n",
     ")\n",
     "print(json.dumps(asdict(result2), indent=2))\n",
     "\n",
     "p.record_feedback(\n",
-    "    FeedbackRequest(\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",
+    "  FeedbackRequest(\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",
     ")"
    ],
    "metadata": {
diff --git a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
new file mode 100644
index 00000000..93e55b96
--- /dev/null
+++ b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
@@ -0,0 +1,305 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "formattedRanges": [],
+    "cell_id": "d9114afabb54438ea6c7f68a68bf6fb5",
+    "deepnote_cell_type": "text-cell-h1"
+   },
+   "source": [
+    "# Tracing with Function Calls and Chains"
+   ],
+   "block_group": "53e88a688fb843c381543ca9b45c9d76"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "from typing import List, Dict, Any, Union\n",
+    "\n",
+    "from parea import Parea\n",
+    "from parea.schemas.models import Completion, LLMInputs, Message, ModelParams\n",
+    "from parea.utils.trace_utils import trace, trace_insert\n",
+    "\n",
+    "p = Parea(api_key=os.environ[\"PAREA_API_KEY\"])"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "LIMIT = 2  # limit any loops to 2 iterations for demo purposes\n",
+    "\n",
+    "COURSE_FUNCTIONS = [\n",
+    "  {\n",
+    "    \"name\": \"generate_course_outline\",\n",
+    "    \"description\": \"Generates a course outline\",\n",
+    "    \"parameters\": {\n",
+    "      \"type\": \"object\",\n",
+    "      \"properties\": {\n",
+    "        \"Description\": {\n",
+    "          \"type\": \"string\",\n",
+    "          \"description\": \"The description of the course\"\n",
+    "        },\n",
+    "        \"chapters\": {\n",
+    "          \"type\": \"array\",\n",
+    "          \"items\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "              \"name\": {\n",
+    "                \"type\": \"string\",\n",
+    "                \"description\": \"The title of the chapter\"\n",
+    "              },\n",
+    "              \"description\": {\n",
+    "                \"type\": \"string\",\n",
+    "                \"description\": \"The summary of the chapter\"\n",
+    "              },\n",
+    "            },\n",
+    "            \"required\": [\"name\", \"description\"]\n",
+    "          },\n",
+    "          \"description\": \"The chapters included in the course\",\n",
+    "          \"minItems\": 1,\n",
+    "          \"maxItems\": LIMIT\n",
+    "        }\n",
+    "      },\n",
+    "      \"required\": [\"Description\", \"chapters\"]\n",
+    "    },\n",
+    "\n",
+    "  }\n",
+    "]\n",
+    "\n",
+    "CHAPTER_FUNCTIONS = [\n",
+    "  {\n",
+    "    \"name\": \"generate_chapter_outline\",\n",
+    "    \"description\": \"Generates a chapter outline\",\n",
+    "    \"parameters\": {\n",
+    "      \"type\": \"object\",\n",
+    "      \"properties\": {\n",
+    "        \"name\": {\n",
+    "          \"type\": \"string\",\n",
+    "          \"description\": \"The title of the chapter\"\n",
+    "        },\n",
+    "        \"description\": {\n",
+    "          \"type\": \"string\",\n",
+    "          \"description\": \"The summary of the chapter\"\n",
+    "        },\n",
+    "        \"sections\": {\n",
+    "          \"type\": \"array\",\n",
+    "          \"items\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "              \"name\": {\n",
+    "                \"type\": \"string\",\n",
+    "                \"description\": \"The title of the section\"\n",
+    "              },\n",
+    "              \"description\": {\n",
+    "                \"type\": \"string\",\n",
+    "                \"description\": \"The summary of the section\"\n",
+    "              },\n",
+    "            },\n",
+    "            \"required\": [\"name\", \"description\"]\n",
+    "          },\n",
+    "          \"description\": \"The sections included in the chapter\",\n",
+    "          \"minItems\": 1,\n",
+    "          \"maxItems\": LIMIT\n",
+    "        }\n",
+    "      },\n",
+    "      \"required\": [\"name\", \"description\", \"sections\"]\n",
+    "    },\n",
+    "  }\n",
+    "]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-09T19:18:33.559895Z",
+     "start_time": "2023-12-09T19:18:33.291059Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "USER = \"alice@bob.com\"\n",
+    "\n",
+    "\n",
+    "# Create a reusable call lmm helper function \n",
+    "# Parea SDK is automatically traced\n",
+    "def call_llm(messages: List[Dict[str, str]], name: str = \"LLM Call\") -> str:\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      llm_configuration=LLMInputs(\n",
+    "        model=\"gpt-3.5-turbo-1106\",\n",
+    "        model_params=ModelParams(temp=0.0, max_length=512),\n",
+    "        messages=[Message(**m) for m in messages],\n",
+    "        functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
+    "      ),\n",
+    "      end_user_identifier=USER,\n",
+    "      trace_name=name,\n",
+    "    )\n",
+    "  ).content\n",
+    "\n",
+    "\n",
+    "# Helper function to get function call arguments if they exists\n",
+    "def get_function_call_or_content(response: str) -> Union[str, Dict[str, Any]]:\n",
+    "  # Function calls are returned in triple backticks\n",
+    "  parsed_response = response.replace(\"```\", \"\")\n",
+    "  # return function call or content string\n",
+    "  try:\n",
+    "    return json.loads(parsed_response).get(\"arguments\")\n",
+    "  except Exception as e:\n",
+    "    return parsed_response"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# PROMPTS\n",
+    "\n",
+    "def get_course(topic: str):\n",
+    "  _course_outline = call_llm(\n",
+    "    messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
+    "    name=f\"Create {topic} Course Outline\",\n",
+    "  )\n",
+    "  course_outline = get_function_call_or_content(_course_outline)\n",
+    "  print(json.dumps(course_outline, indent=4))\n",
+    "  return course_outline\n",
+    "\n",
+    "\n",
+    "# I want to group all llm calls into one trace, \n",
+    "# so I add the trace decorator to create a parent trace, \n",
+    "# each call_llm will be a child span\n",
+    "@trace(name=\"Get Chapters\")\n",
+    "def get_chapters(course_outline: Dict[str, str]):\n",
+    "  chapter_outlines = course_outline.get(\"chapters\", [])\n",
+    "  chapters = [\n",
+    "    get_function_call_or_content(\n",
+    "      call_llm(\n",
+    "        messages=[{\n",
+    "          \"role\": \"user\",\n",
+    "          \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"\n",
+    "        }],\n",
+    "        name=f\"Create Chapter {idx}\"\n",
+    "      )\n",
+    "    )\n",
+    "    for idx, chapter in enumerate(chapter_outlines, start=1)\n",
+    "  ]\n",
+    "  return chapters[:LIMIT]\n",
+    "\n",
+    "\n",
+    "@trace\n",
+    "def get_sections(chapter: Dict[str, str]):\n",
+    "  chapter_name = chapter.get('name', 'section')\n",
+    "  # I want the trace name to be dynamic based on the chapter param, \n",
+    "  # so I can use Parea trace_insert helper method to add dynamic data\n",
+    "  trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
+    "\n",
+    "  section_outlines = chapter.get(\"sections\", [])\n",
+    "  sections = [\n",
+    "    get_function_call_or_content(\n",
+    "      call_llm(\n",
+    "        messages=[{\n",
+    "          \"role\": \"user\",\n",
+    "          \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"\n",
+    "        }],\n",
+    "        name=f\"Create Section {idx}\"\n",
+    "      )\n",
+    "    )\n",
+    "    for idx, section in enumerate(section_outlines, start=1)\n",
+    "  ]\n",
+    "  return sections[:LIMIT]"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "@trace(name=\"Get Sections\")\n",
+    "def get_all_sections(chapters: List[Dict[str, str]]):\n",
+    "  return [get_sections(chapter) for chapter in chapters]\n",
+    "\n",
+    "\n",
+    "@trace\n",
+    "def run_creation(topic: str):\n",
+    "  trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
+    "  course_outline = get_course(topic)\n",
+    "  chapters = get_chapters(course_outline)\n",
+    "  sections = get_all_sections(chapters)\n",
+    "  return sections\n",
+    "\n",
+    "\n",
+    "@trace(name=\"Generate Courses for Topics\")\n",
+    "def main(topics):\n",
+    "  for topic in topics:\n",
+    "    print(f\"\\n New Topic: {topic} \\n\")\n",
+    "    run_creation(topic)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "main([\"Artificial Intelligence\", \"Machine Learning\", \"Deep Learning\"])"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Trace Log View in UI\n",
+    "\n",
+    "![TraceView](img/trace_log_view.png)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "deepnote": {},
+  "orig_nbformat": 2,
+  "deepnote_notebook_id": "27ae7c4c406747b5b5936b15081cdaec",
+  "deepnote_execution_queue": [],
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ }
+}
diff --git a/pyproject.toml b/pyproject.toml
index 1f9f1f3d..cbc7ba49 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 [tool.poetry]
 name = "parea-ai"
 packages = [{ include = "parea" }]
-version = "0.2.20"
+version = "0.2.21a0"
 description = "Parea python sdk"
 readme = "README.md"
 authors = ["joel-parea-ai <joel@parea.ai>"]

From f604638556f5e01f9ea80111912f84f781831853 Mon Sep 17 00:00:00 2001
From: Joel Alexander <a.joeljr@hotmail.com>
Date: Sat, 9 Dec 2023 14:33:43 -0500
Subject: [PATCH 2/7] add new models costs

---
 parea/cookbook/tracing_with_Parea_sdk.ipynb   | 136 +++++-----
 ...ing_with_function_calling_and_chains.ipynb | 247 ++++++++----------
 parea/wrapper/openai.py                       | 142 ++++++----
 3 files changed, 272 insertions(+), 253 deletions(-)

diff --git a/parea/cookbook/tracing_with_Parea_sdk.ipynb b/parea/cookbook/tracing_with_Parea_sdk.ipynb
index b34190fa..0c42e97c 100644
--- a/parea/cookbook/tracing_with_Parea_sdk.ipynb
+++ b/parea/cookbook/tracing_with_Parea_sdk.ipynb
@@ -117,40 +117,40 @@
     "\n",
     "\n",
     "def argument_generator(query: str, additional_description: str = \"\") -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "      },\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "            },\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "def critic(argument: str) -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\n",
-    "      llm_inputs={\"argument\": argument},\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\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",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "        \"argument\": current_arg,\n",
-    "        \"criticism\": criticism,\n",
-    "      },\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "                \"argument\": current_arg,\n",
+    "                \"criticism\": criticism,\n",
+    "            },\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "# Non deployed version\n",
@@ -209,9 +209,9 @@
     "\n",
     "@trace\n",
     "def argument_chain(query: str, additional_description: str = \"\") -> str:\n",
-    "  argument = argument_generator(query, additional_description)\n",
-    "  criticism = critic(argument)\n",
-    "  return refiner(query, additional_description, argument, criticism)"
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner(query, additional_description, argument, criticism)"
    ]
   },
   {
@@ -251,8 +251,8 @@
    ],
    "source": [
     "result1 = argument_chain(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(result1)"
    ]
@@ -302,10 +302,10 @@
     "\n",
     "@trace\n",
     "def argument_chain2(query: str, additional_description: str = \"\") -> tuple[str, 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 refiner(query, additional_description, argument, criticism), trace_id"
+    "    trace_id = get_current_trace_id()  # get parent's trace_id\n",
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner(query, additional_description, argument, criticism), trace_id"
    ]
   },
   {
@@ -331,8 +331,8 @@
    ],
    "source": [
     "result, trace_id = argument_chain2(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(trace_id)"
    ]
@@ -424,30 +424,30 @@
     "\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",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "        \"argument\": current_arg,\n",
-    "        \"criticism\": criticism,\n",
-    "      },\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "                \"argument\": current_arg,\n",
+    "                \"criticism\": criticism,\n",
+    "            },\n",
+    "        )\n",
     "    )\n",
-    "  )\n",
     "\n",
     "\n",
     "@trace\n",
     "def argument_chain3(query: str, additional_description: str = \"\") -> CompletionResponse:\n",
-    "  argument = argument_generator(query, additional_description)\n",
-    "  criticism = critic(argument)\n",
-    "  return refiner2(query, additional_description, argument, criticism)\n",
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner2(query, additional_description, argument, criticism)\n",
     "\n",
     "\n",
     "result2 = argument_chain3(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(json.dumps(asdict(result2), indent=2))"
    ],
@@ -498,14 +498,14 @@
     "\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",
+    "    tags=[\"cookbook-example-deployed\", \"feedback_tracked-deployed\"],\n",
+    "    metadata={\"source\": \"python-sdk\", \"deployed\": \"True\"},\n",
     ")\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), trace_id"
+    "    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), trace_id"
    ]
   },
   {
@@ -515,17 +515,17 @@
     "from attrs import asdict\n",
     "\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",
+    "    \"Whether coffee is good for you.\",\n",
+    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\n",
     ")\n",
     "print(json.dumps(asdict(result2), indent=2))\n",
     "\n",
     "p.record_feedback(\n",
-    "  FeedbackRequest(\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",
+    "    FeedbackRequest(\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",
     ")"
    ],
    "metadata": {
diff --git a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
index 93e55b96..b77e41cd 100644
--- a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
+++ b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
@@ -39,82 +39,60 @@
     "LIMIT = 2  # limit any loops to 2 iterations for demo purposes\n",
     "\n",
     "COURSE_FUNCTIONS = [\n",
-    "  {\n",
-    "    \"name\": \"generate_course_outline\",\n",
-    "    \"description\": \"Generates a course outline\",\n",
-    "    \"parameters\": {\n",
-    "      \"type\": \"object\",\n",
-    "      \"properties\": {\n",
-    "        \"Description\": {\n",
-    "          \"type\": \"string\",\n",
-    "          \"description\": \"The description of the course\"\n",
-    "        },\n",
-    "        \"chapters\": {\n",
-    "          \"type\": \"array\",\n",
-    "          \"items\": {\n",
+    "    {\n",
+    "        \"name\": \"generate_course_outline\",\n",
+    "        \"description\": \"Generates a course outline\",\n",
+    "        \"parameters\": {\n",
     "            \"type\": \"object\",\n",
     "            \"properties\": {\n",
-    "              \"name\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The title of the chapter\"\n",
-    "              },\n",
-    "              \"description\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The summary of the chapter\"\n",
-    "              },\n",
+    "                \"Description\": {\"type\": \"string\", \"description\": \"The description of the course\"},\n",
+    "                \"chapters\": {\n",
+    "                    \"type\": \"array\",\n",
+    "                    \"items\": {\n",
+    "                        \"type\": \"object\",\n",
+    "                        \"properties\": {\n",
+    "                            \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
+    "                            \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "                        },\n",
+    "                        \"required\": [\"name\", \"description\"],\n",
+    "                    },\n",
+    "                    \"description\": \"The chapters included in the course\",\n",
+    "                    \"minItems\": 1,\n",
+    "                    \"maxItems\": LIMIT,\n",
+    "                },\n",
     "            },\n",
-    "            \"required\": [\"name\", \"description\"]\n",
-    "          },\n",
-    "          \"description\": \"The chapters included in the course\",\n",
-    "          \"minItems\": 1,\n",
-    "          \"maxItems\": LIMIT\n",
-    "        }\n",
-    "      },\n",
-    "      \"required\": [\"Description\", \"chapters\"]\n",
-    "    },\n",
-    "\n",
-    "  }\n",
+    "            \"required\": [\"Description\", \"chapters\"],\n",
+    "        },\n",
+    "    }\n",
     "]\n",
     "\n",
     "CHAPTER_FUNCTIONS = [\n",
-    "  {\n",
-    "    \"name\": \"generate_chapter_outline\",\n",
-    "    \"description\": \"Generates a chapter outline\",\n",
-    "    \"parameters\": {\n",
-    "      \"type\": \"object\",\n",
-    "      \"properties\": {\n",
-    "        \"name\": {\n",
-    "          \"type\": \"string\",\n",
-    "          \"description\": \"The title of the chapter\"\n",
-    "        },\n",
-    "        \"description\": {\n",
-    "          \"type\": \"string\",\n",
-    "          \"description\": \"The summary of the chapter\"\n",
-    "        },\n",
-    "        \"sections\": {\n",
-    "          \"type\": \"array\",\n",
-    "          \"items\": {\n",
+    "    {\n",
+    "        \"name\": \"generate_chapter_outline\",\n",
+    "        \"description\": \"Generates a chapter outline\",\n",
+    "        \"parameters\": {\n",
     "            \"type\": \"object\",\n",
     "            \"properties\": {\n",
-    "              \"name\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The title of the section\"\n",
-    "              },\n",
-    "              \"description\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The summary of the section\"\n",
-    "              },\n",
+    "                \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
+    "                \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "                \"sections\": {\n",
+    "                    \"type\": \"array\",\n",
+    "                    \"items\": {\n",
+    "                        \"type\": \"object\",\n",
+    "                        \"properties\": {\n",
+    "                            \"name\": {\"type\": \"string\", \"description\": \"The title of the section\"},\n",
+    "                            \"description\": {\"type\": \"string\", \"description\": \"The summary of the section\"},\n",
+    "                        },\n",
+    "                        \"required\": [\"name\", \"description\"],\n",
+    "                    },\n",
+    "                    \"description\": \"The sections included in the chapter\",\n",
+    "                    \"minItems\": 1,\n",
+    "                    \"maxItems\": LIMIT,\n",
+    "                },\n",
     "            },\n",
-    "            \"required\": [\"name\", \"description\"]\n",
-    "          },\n",
-    "          \"description\": \"The sections included in the chapter\",\n",
-    "          \"minItems\": 1,\n",
-    "          \"maxItems\": LIMIT\n",
-    "        }\n",
-    "      },\n",
-    "      \"required\": [\"name\", \"description\", \"sections\"]\n",
-    "    },\n",
-    "  }\n",
+    "            \"required\": [\"name\", \"description\", \"sections\"],\n",
+    "        },\n",
+    "    }\n",
     "]"
    ],
    "metadata": {
@@ -133,32 +111,32 @@
     "USER = \"alice@bob.com\"\n",
     "\n",
     "\n",
-    "# Create a reusable call lmm helper function \n",
+    "# Create a reusable call lmm helper function\n",
     "# Parea SDK is automatically traced\n",
     "def call_llm(messages: List[Dict[str, str]], name: str = \"LLM Call\") -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      llm_configuration=LLMInputs(\n",
-    "        model=\"gpt-3.5-turbo-1106\",\n",
-    "        model_params=ModelParams(temp=0.0, max_length=512),\n",
-    "        messages=[Message(**m) for m in messages],\n",
-    "        functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
-    "      ),\n",
-    "      end_user_identifier=USER,\n",
-    "      trace_name=name,\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            llm_configuration=LLMInputs(\n",
+    "                model=\"gpt-3.5-turbo-1106\",\n",
+    "                model_params=ModelParams(temp=0.0, max_length=512),\n",
+    "                messages=[Message(**m) for m in messages],\n",
+    "                functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
+    "            ),\n",
+    "            end_user_identifier=USER,\n",
+    "            trace_name=name,\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "# Helper function to get function call arguments if they exists\n",
     "def get_function_call_or_content(response: str) -> Union[str, Dict[str, Any]]:\n",
-    "  # Function calls are returned in triple backticks\n",
-    "  parsed_response = response.replace(\"```\", \"\")\n",
-    "  # return function call or content string\n",
-    "  try:\n",
-    "    return json.loads(parsed_response).get(\"arguments\")\n",
-    "  except Exception as e:\n",
-    "    return parsed_response"
+    "    # Function calls are returned in triple backticks\n",
+    "    parsed_response = response.replace(\"```\", \"\")\n",
+    "    # return function call or content string\n",
+    "    try:\n",
+    "        return json.loads(parsed_response).get(\"arguments\")\n",
+    "    except Exception as e:\n",
+    "        return parsed_response"
    ],
    "metadata": {
     "collapsed": false
@@ -171,58 +149,53 @@
    "source": [
     "# PROMPTS\n",
     "\n",
+    "\n",
     "def get_course(topic: str):\n",
-    "  _course_outline = call_llm(\n",
-    "    messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
-    "    name=f\"Create {topic} Course Outline\",\n",
-    "  )\n",
-    "  course_outline = get_function_call_or_content(_course_outline)\n",
-    "  print(json.dumps(course_outline, indent=4))\n",
-    "  return course_outline\n",
+    "    _course_outline = call_llm(\n",
+    "        messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
+    "        name=f\"Create {topic} Course Outline\",\n",
+    "    )\n",
+    "    course_outline = get_function_call_or_content(_course_outline)\n",
+    "    print(json.dumps(course_outline, indent=4))\n",
+    "    return course_outline\n",
     "\n",
     "\n",
-    "# I want to group all llm calls into one trace, \n",
-    "# so I add the trace decorator to create a parent trace, \n",
+    "# I want to group all llm calls into one trace,\n",
+    "# so I add the trace decorator to create a parent trace,\n",
     "# each call_llm will be a child span\n",
     "@trace(name=\"Get Chapters\")\n",
     "def get_chapters(course_outline: Dict[str, str]):\n",
-    "  chapter_outlines = course_outline.get(\"chapters\", [])\n",
-    "  chapters = [\n",
-    "    get_function_call_or_content(\n",
-    "      call_llm(\n",
-    "        messages=[{\n",
-    "          \"role\": \"user\",\n",
-    "          \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"\n",
-    "        }],\n",
-    "        name=f\"Create Chapter {idx}\"\n",
-    "      )\n",
-    "    )\n",
-    "    for idx, chapter in enumerate(chapter_outlines, start=1)\n",
-    "  ]\n",
-    "  return chapters[:LIMIT]\n",
+    "    chapter_outlines = course_outline.get(\"chapters\", [])\n",
+    "    chapters = [\n",
+    "        get_function_call_or_content(\n",
+    "            call_llm(\n",
+    "                messages=[{\"role\": \"user\", \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"}],\n",
+    "                name=f\"Create Chapter {idx}\",\n",
+    "            )\n",
+    "        )\n",
+    "        for idx, chapter in enumerate(chapter_outlines, start=1)\n",
+    "    ]\n",
+    "    return chapters[:LIMIT]\n",
     "\n",
     "\n",
     "@trace\n",
     "def get_sections(chapter: Dict[str, str]):\n",
-    "  chapter_name = chapter.get('name', 'section')\n",
-    "  # I want the trace name to be dynamic based on the chapter param, \n",
-    "  # so I can use Parea trace_insert helper method to add dynamic data\n",
-    "  trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
+    "    chapter_name = chapter.get(\"name\", \"section\")\n",
+    "    # I want the trace name to be dynamic based on the chapter param,\n",
+    "    # so I can use Parea trace_insert helper method to add dynamic data\n",
+    "    trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
     "\n",
-    "  section_outlines = chapter.get(\"sections\", [])\n",
-    "  sections = [\n",
-    "    get_function_call_or_content(\n",
-    "      call_llm(\n",
-    "        messages=[{\n",
-    "          \"role\": \"user\",\n",
-    "          \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"\n",
-    "        }],\n",
-    "        name=f\"Create Section {idx}\"\n",
-    "      )\n",
-    "    )\n",
-    "    for idx, section in enumerate(section_outlines, start=1)\n",
-    "  ]\n",
-    "  return sections[:LIMIT]"
+    "    section_outlines = chapter.get(\"sections\", [])\n",
+    "    sections = [\n",
+    "        get_function_call_or_content(\n",
+    "            call_llm(\n",
+    "                messages=[{\"role\": \"user\", \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"}],\n",
+    "                name=f\"Create Section {idx}\",\n",
+    "            )\n",
+    "        )\n",
+    "        for idx, section in enumerate(section_outlines, start=1)\n",
+    "    ]\n",
+    "    return sections[:LIMIT]"
    ],
    "metadata": {
     "collapsed": false
@@ -235,23 +208,23 @@
    "source": [
     "@trace(name=\"Get Sections\")\n",
     "def get_all_sections(chapters: List[Dict[str, str]]):\n",
-    "  return [get_sections(chapter) for chapter in chapters]\n",
+    "    return [get_sections(chapter) for chapter in chapters]\n",
     "\n",
     "\n",
     "@trace\n",
     "def run_creation(topic: str):\n",
-    "  trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
-    "  course_outline = get_course(topic)\n",
-    "  chapters = get_chapters(course_outline)\n",
-    "  sections = get_all_sections(chapters)\n",
-    "  return sections\n",
+    "    trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
+    "    course_outline = get_course(topic)\n",
+    "    chapters = get_chapters(course_outline)\n",
+    "    sections = get_all_sections(chapters)\n",
+    "    return sections\n",
     "\n",
     "\n",
     "@trace(name=\"Generate Courses for Topics\")\n",
     "def main(topics):\n",
-    "  for topic in topics:\n",
-    "    print(f\"\\n New Topic: {topic} \\n\")\n",
-    "    run_creation(topic)"
+    "    for topic in topics:\n",
+    "        print(f\"\\n New Topic: {topic} \\n\")\n",
+    "        run_creation(topic)"
    ],
    "metadata": {
     "collapsed": false
diff --git a/parea/wrapper/openai.py b/parea/wrapper/openai.py
index ea482284..ff762516 100644
--- a/parea/wrapper/openai.py
+++ b/parea/wrapper/openai.py
@@ -1,7 +1,8 @@
-from typing import Any, AsyncIterator, Callable, Dict, Iterator, Optional, Sequence, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 import json
 from collections import defaultdict
+from collections.abc import AsyncIterator, Iterator, Sequence
 
 import openai
 from openai import __version__ as openai_version
@@ -21,38 +22,87 @@ def convert_to_openai_object(**kwargs):
 from ..utils.trace_utils import trace_data
 from .wrapper import Wrapper
 
-MODEL_COST_MAPPING: Dict[str, float] = {
-    "gpt-4": 0.03,
-    "gpt-4-0314": 0.03,
-    "gpt-4-0613": 0.03,
-    "gpt-4-completion": 0.06,
-    "gpt-4-0314-completion": 0.06,
-    "gpt-4-0613-completion": 0.06,
-    "gpt-4-32k": 0.06,
-    "gpt-4-32k-0314": 0.06,
-    "gpt-4-32k-0613": 0.06,
-    "gpt-4-32k-completion": 0.12,
-    "gpt-4-32k-0314-completion": 0.12,
-    "gpt-4-32k-0613-completion": 0.12,
-    "gpt-3.5-turbo": 0.0015,
-    "gpt-3.5-turbo-0301": 0.0015,
-    "gpt-3.5-turbo-0613": 0.0015,
-    "gpt-3.5-turbo-16k": 0.003,
-    "gpt-3.5-turbo-16k-0613": 0.003,
-    "gpt-3.5-turbo-completion": 0.002,
-    "gpt-3.5-turbo-0301-completion": 0.002,
-    "gpt-3.5-turbo-0613-completion": 0.004,
-    "gpt-3.5-turbo-16k-completion": 0.004,
-    "gpt-3.5-turbo-16k-0613-completion": 0.004,
-    "text-ada-001": 0.0004,
-    "ada": 0.0004,
-    "text-babbage-001": 0.0005,
-    "babbage": 0.0005,
-    "text-curie-001": 0.002,
-    "curie": 0.002,
-    "text-davinci-003": 0.02,
-    "text-davinci-002": 0.02,
-    "code-davinci-002": 0.02,
+OPENAI_MODEL_INFO: dict[str, dict[str, Union[float, int, dict[str, int]]]] = {
+    "gpt-3.5-turbo": {
+        "prompt": 1.5,
+        "completion": 2.0,
+        "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 4096},
+    },
+    "gpt-3.5-turbo-0301": {
+        "prompt": 1.5,
+        "completion": 4.0,
+        "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 4096},
+    },
+    "gpt-3.5-turbo-0613": {
+        "prompt": 1.5,
+        "completion": 4.0,
+        "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 4096},
+    },
+    "gpt-3.5-turbo-16k": {
+        "prompt": 3.0,
+        "completion": 4.0,
+        "token_limit": {"max_completion_tokens": 16385, "max_prompt_tokens": 16385},
+    },
+    "gpt-3.5-turbo-16k-0301": {
+        "prompt": 3.0,
+        "completion": 4.0,
+        "token_limit": {"max_completion_tokens": 16385, "max_prompt_tokens": 16385},
+    },
+    "gpt-3.5-turbo-16k-0613": {
+        "prompt": 3.0,
+        "completion": 4.0,
+        "token_limit": {"max_completion_tokens": 16385, "max_prompt_tokens": 16385},
+    },
+    "gpt-3.5-turbo-1106": {
+        "prompt": 1.0,
+        "completion": 2.0,
+        "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 4096},
+    },
+    "gpt-3.5-turbo-instruct": {
+        "prompt": 1.5,
+        "completion": 4.0,
+        "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 4096},
+    },
+    "gpt-4": {
+        "prompt": 30.0,
+        "completion": 60.0,
+        "token_limit": {"max_completion_tokens": 8192, "max_prompt_tokens": 8192},
+    },
+    "gpt-4-0314": {
+        "prompt": 30.0,
+        "completion": 60.0,
+        "token_limit": {"max_completion_tokens": 8192, "max_prompt_tokens": 8192},
+    },
+    "gpt-4-0613": {
+        "prompt": 30.0,
+        "completion": 60.0,
+        "token_limit": {"max_completion_tokens": 8192, "max_prompt_tokens": 8192},
+    },
+    "gpt-4-32k": {
+        "prompt": 60.0,
+        "completion": 120.0,
+        "token_limit": {"max_completion_tokens": 32768, "max_prompt_tokens": 32768},
+    },
+    "gpt-4-32k-0314": {
+        "prompt": 60.0,
+        "completion": 120.0,
+        "token_limit": {"max_completion_tokens": 32768, "max_prompt_tokens": 32768},
+    },
+    "gpt-4-32k-0613": {
+        "prompt": 60.0,
+        "completion": 120.0,
+        "token_limit": {"max_completion_tokens": 32768, "max_prompt_tokens": 32768},
+    },
+    "gpt-4-vision-preview": {
+        "prompt": 30.0,
+        "completion": 60.0,
+        "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 128000},
+    },
+    "gpt-4-1106-preview": {
+        "prompt": 10.0,
+        "completion": 30.0,
+        "token_limit": {"max_completion_tokens": 4096, "max_prompt_tokens": 128000},
+    },
 }
 
 
@@ -77,7 +127,7 @@ def init(self, log: Callable, cache: Cache = None):
         )
 
     @staticmethod
-    def resolver(trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any], response: Optional[Any]) -> Optional[Any]:
+    def resolver(trace_id: str, _args: Sequence[Any], kwargs: dict[str, Any], response: Optional[Any]) -> Optional[Any]:
         if response:
             output = OpenAIWrapper._get_output(response)
             if openai_version.startswith("0."):
@@ -101,8 +151,8 @@ def resolver(trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any], respon
 
         model_rate = OpenAIWrapper.get_model_cost(model)
         model_completion_rate = OpenAIWrapper.get_model_cost(model, is_completion=True)
-        prompt_cost = model_rate * (input_tokens / 1000)
-        completion_cost = model_completion_rate * (output_tokens / 1000)
+        prompt_cost = (model_rate / 1000) * (input_tokens / 1000)
+        completion_cost = (model_completion_rate / 1000) * (output_tokens / 1000)
         total_cost = sum([prompt_cost, completion_cost])
 
         trace_data.get()[trace_id].configuration = llm_configuration
@@ -114,7 +164,7 @@ def resolver(trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any], respon
         return response
 
     @staticmethod
-    def gen_resolver(trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any], response: Iterator[Any], final_log) -> Iterator[Any]:
+    def gen_resolver(trace_id: str, _args: Sequence[Any], kwargs: dict[str, Any], response: Iterator[Any], final_log) -> Iterator[Any]:
         llm_configuration = OpenAIWrapper._kwargs_to_llm_configuration(kwargs)
         trace_data.get()[trace_id].configuration = llm_configuration
 
@@ -131,7 +181,7 @@ def gen_resolver(trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any], re
         final_log()
 
     @staticmethod
-    async def agen_resolver(trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any], response: AsyncIterator[Any], final_log) -> AsyncIterator[Any]:
+    async def agen_resolver(trace_id: str, _args: Sequence[Any], kwargs: dict[str, Any], response: AsyncIterator[Any], final_log) -> AsyncIterator[Any]:
         llm_configuration = OpenAIWrapper._kwargs_to_llm_configuration(kwargs)
         trace_data.get()[trace_id].configuration = llm_configuration
 
@@ -196,25 +246,21 @@ def _format_function_call(response_message) -> str:
     @staticmethod
     def get_model_cost(model_name: str, is_completion: bool = False) -> float:
         model_name = model_name.lower()
-
-        if model_name.startswith("gpt-4") and is_completion:
-            model_name += "-completion"
-
-        cost = MODEL_COST_MAPPING.get(model_name, None)
+        cost = OPENAI_MODEL_INFO.get(model_name, {}).get("completion" if is_completion else "prompt", None)
         if cost is None:
-            msg = f"Unknown model: {model_name}. " f"Please provide a valid OpenAI model name. " f"Known models are: {', '.join(MODEL_COST_MAPPING.keys())}"
+            msg = f"Unknown model: {model_name}. " f"Please provide a valid OpenAI model name. " f"Known models are: {', '.join(OPENAI_MODEL_INFO.keys())}"
             raise ValueError(msg)
 
         return cost
 
     @staticmethod
-    def convert_kwargs_to_cache_request(_args: Sequence[Any], kwargs: Dict[str, Any]) -> CacheRequest:
+    def convert_kwargs_to_cache_request(_args: Sequence[Any], kwargs: dict[str, Any]) -> CacheRequest:
         return CacheRequest(
             configuration=OpenAIWrapper._kwargs_to_llm_configuration(kwargs),
         )
 
     @staticmethod
-    def _convert_cache_to_response(_args: Sequence[Any], kwargs: Dict[str, Any], cache_response: TraceLog) -> OpenAIObject:
+    def _convert_cache_to_response(_args: Sequence[Any], kwargs: dict[str, Any], cache_response: TraceLog) -> OpenAIObject:
         content = cache_response.output
         message = {"role": "assistant"}
         try:
@@ -248,7 +294,7 @@ def _convert_cache_to_response(_args: Sequence[Any], kwargs: Dict[str, Any], cac
         )
 
     @staticmethod
-    def convert_cache_to_response(_args: Sequence[Any], kwargs: Dict[str, Any], cache_response: TraceLog) -> Union[OpenAIObject, Iterator[OpenAIObject]]:
+    def convert_cache_to_response(_args: Sequence[Any], kwargs: dict[str, Any], cache_response: TraceLog) -> Union[OpenAIObject, Iterator[OpenAIObject]]:
         response = OpenAIWrapper._convert_cache_to_response(_args, kwargs, cache_response)
         if kwargs.get("stream", False):
             return iter([response])
@@ -256,7 +302,7 @@ def convert_cache_to_response(_args: Sequence[Any], kwargs: Dict[str, Any], cach
             return response
 
     @staticmethod
-    def aconvert_cache_to_response(_args: Sequence[Any], kwargs: Dict[str, Any], cache_response: TraceLog) -> Union[OpenAIObject, AsyncIterator[OpenAIObject]]:
+    def aconvert_cache_to_response(_args: Sequence[Any], kwargs: dict[str, Any], cache_response: TraceLog) -> Union[OpenAIObject, AsyncIterator[OpenAIObject]]:
         response = OpenAIWrapper._convert_cache_to_response(_args, kwargs, cache_response)
         if kwargs.get("stream", False):
 

From 76414b8b1fc52fe009d0b0bc6f0d5a91577e0528 Mon Sep 17 00:00:00 2001
From: Joel Alexander <a.joeljr@hotmail.com>
Date: Sat, 9 Dec 2023 15:07:01 -0500
Subject: [PATCH 3/7] update other cookbook examples

---
 parea/cookbook/tracing_with_Parea_sdk.ipynb   | 138 ++++++------
 ...ing_with_function_calling_and_chains.ipynb | 213 +++++++++---------
 .../tracing_with_open_ai_endpoint_directly.py |   6 +-
 .../tracing_with_openai_with_functions.py     |   2 +-
 parea/wrapper/openai.py                       |  13 +-
 5 files changed, 190 insertions(+), 182 deletions(-)

diff --git a/parea/cookbook/tracing_with_Parea_sdk.ipynb b/parea/cookbook/tracing_with_Parea_sdk.ipynb
index b34190fa..cf7ca43e 100644
--- a/parea/cookbook/tracing_with_Parea_sdk.ipynb
+++ b/parea/cookbook/tracing_with_Parea_sdk.ipynb
@@ -117,44 +117,44 @@
     "\n",
     "\n",
     "def argument_generator(query: str, additional_description: str = \"\") -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "      },\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "            },\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "def critic(argument: str) -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\n",
-    "      llm_inputs={\"argument\": argument},\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\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",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "        \"argument\": current_arg,\n",
-    "        \"criticism\": criticism,\n",
-    "      },\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "                \"argument\": current_arg,\n",
+    "                \"criticism\": criticism,\n",
+    "            },\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "# Non deployed version\n",
-    "# from parea.schemas.models import LLMInputs, Message, ModelParams, Role\n",
+    "# from parea.schemas.logs import LLMInputs, Message, ModelParams, Role\n",
     "\n",
     "\n",
     "# def argument_generator(query: str, additional_description: str = \"\") -> str:\n",
@@ -209,9 +209,9 @@
     "\n",
     "@trace\n",
     "def argument_chain(query: str, additional_description: str = \"\") -> str:\n",
-    "  argument = argument_generator(query, additional_description)\n",
-    "  criticism = critic(argument)\n",
-    "  return refiner(query, additional_description, argument, criticism)"
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner(query, additional_description, argument, criticism)"
    ]
   },
   {
@@ -251,8 +251,8 @@
    ],
    "source": [
     "result1 = argument_chain(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(result1)"
    ]
@@ -302,10 +302,10 @@
     "\n",
     "@trace\n",
     "def argument_chain2(query: str, additional_description: str = \"\") -> tuple[str, 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 refiner(query, additional_description, argument, criticism), trace_id"
+    "    trace_id = get_current_trace_id()  # get parent's trace_id\n",
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner(query, additional_description, argument, criticism), trace_id"
    ]
   },
   {
@@ -331,8 +331,8 @@
    ],
    "source": [
     "result, trace_id = argument_chain2(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(trace_id)"
    ]
@@ -424,30 +424,30 @@
     "\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",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "        \"argument\": current_arg,\n",
-    "        \"criticism\": criticism,\n",
-    "      },\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "                \"argument\": current_arg,\n",
+    "                \"criticism\": criticism,\n",
+    "            },\n",
+    "        )\n",
     "    )\n",
-    "  )\n",
     "\n",
     "\n",
     "@trace\n",
     "def argument_chain3(query: str, additional_description: str = \"\") -> CompletionResponse:\n",
-    "  argument = argument_generator(query, additional_description)\n",
-    "  criticism = critic(argument)\n",
-    "  return refiner2(query, additional_description, argument, criticism)\n",
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner2(query, additional_description, argument, criticism)\n",
     "\n",
     "\n",
     "result2 = argument_chain3(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(json.dumps(asdict(result2), indent=2))"
    ],
@@ -498,14 +498,14 @@
     "\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",
+    "    tags=[\"cookbook-example-deployed\", \"feedback_tracked-deployed\"],\n",
+    "    metadata={\"source\": \"python-sdk\", \"deployed\": \"True\"},\n",
     ")\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), trace_id"
+    "    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), trace_id"
    ]
   },
   {
@@ -515,17 +515,17 @@
     "from attrs import asdict\n",
     "\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",
+    "    \"Whether coffee is good for you.\",\n",
+    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\n",
     ")\n",
     "print(json.dumps(asdict(result2), indent=2))\n",
     "\n",
     "p.record_feedback(\n",
-    "  FeedbackRequest(\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",
+    "    FeedbackRequest(\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",
     ")"
    ],
    "metadata": {
diff --git a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
index 1da2fbec..126d201e 100644
--- a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
+++ b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
@@ -22,7 +22,8 @@
     "from typing import List, Dict, Any, Union\n",
     "\n",
     "from parea import Parea\n",
-    "from parea.schemas.models import Completion, LLMInputs, Message, ModelParams\n",
+    "from parea.schemas.log import LLMInputs, Message, ModelParams\n",
+    "from parea.schemas.models import Completion\n",
     "from parea.utils.trace_utils import trace, trace_insert\n",
     "\n",
     "p = Parea(api_key=os.environ[\"PAREA_API_KEY\"])"
@@ -39,60 +40,60 @@
     "LIMIT = 2  # limit any loops to 2 iterations for demo purposes\n",
     "\n",
     "COURSE_FUNCTIONS = [\n",
-    "  {\n",
-    "    \"name\": \"generate_course_outline\",\n",
-    "    \"description\": \"Generates a course outline\",\n",
-    "    \"parameters\": {\n",
-    "      \"type\": \"object\",\n",
-    "      \"properties\": {\n",
-    "        \"Description\": {\"type\": \"string\", \"description\": \"The description of the course\"},\n",
-    "        \"chapters\": {\n",
-    "          \"type\": \"array\",\n",
-    "          \"items\": {\n",
+    "    {\n",
+    "        \"name\": \"generate_course_outline\",\n",
+    "        \"description\": \"Generates a course outline\",\n",
+    "        \"parameters\": {\n",
     "            \"type\": \"object\",\n",
     "            \"properties\": {\n",
-    "              \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
-    "              \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "                \"Description\": {\"type\": \"string\", \"description\": \"The description of the course\"},\n",
+    "                \"chapters\": {\n",
+    "                    \"type\": \"array\",\n",
+    "                    \"items\": {\n",
+    "                        \"type\": \"object\",\n",
+    "                        \"properties\": {\n",
+    "                            \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
+    "                            \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "                        },\n",
+    "                        \"required\": [\"name\", \"description\"],\n",
+    "                    },\n",
+    "                    \"description\": \"The chapters included in the course\",\n",
+    "                    \"minItems\": 1,\n",
+    "                    \"maxItems\": LIMIT,\n",
+    "                },\n",
     "            },\n",
-    "            \"required\": [\"name\", \"description\"],\n",
-    "          },\n",
-    "          \"description\": \"The chapters included in the course\",\n",
-    "          \"minItems\": 1,\n",
-    "          \"maxItems\": LIMIT,\n",
+    "            \"required\": [\"Description\", \"chapters\"],\n",
     "        },\n",
-    "      },\n",
-    "      \"required\": [\"Description\", \"chapters\"],\n",
-    "    },\n",
-    "  }\n",
+    "    }\n",
     "]\n",
     "\n",
     "CHAPTER_FUNCTIONS = [\n",
-    "  {\n",
-    "    \"name\": \"generate_chapter_outline\",\n",
-    "    \"description\": \"Generates a chapter outline\",\n",
-    "    \"parameters\": {\n",
-    "      \"type\": \"object\",\n",
-    "      \"properties\": {\n",
-    "        \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
-    "        \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
-    "        \"sections\": {\n",
-    "          \"type\": \"array\",\n",
-    "          \"items\": {\n",
+    "    {\n",
+    "        \"name\": \"generate_chapter_outline\",\n",
+    "        \"description\": \"Generates a chapter outline\",\n",
+    "        \"parameters\": {\n",
     "            \"type\": \"object\",\n",
     "            \"properties\": {\n",
-    "              \"name\": {\"type\": \"string\", \"description\": \"The title of the section\"},\n",
-    "              \"description\": {\"type\": \"string\", \"description\": \"The summary of the section\"},\n",
+    "                \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
+    "                \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "                \"sections\": {\n",
+    "                    \"type\": \"array\",\n",
+    "                    \"items\": {\n",
+    "                        \"type\": \"object\",\n",
+    "                        \"properties\": {\n",
+    "                            \"name\": {\"type\": \"string\", \"description\": \"The title of the section\"},\n",
+    "                            \"description\": {\"type\": \"string\", \"description\": \"The summary of the section\"},\n",
+    "                        },\n",
+    "                        \"required\": [\"name\", \"description\"],\n",
+    "                    },\n",
+    "                    \"description\": \"The sections included in the chapter\",\n",
+    "                    \"minItems\": 1,\n",
+    "                    \"maxItems\": LIMIT,\n",
+    "                },\n",
     "            },\n",
-    "            \"required\": [\"name\", \"description\"],\n",
-    "          },\n",
-    "          \"description\": \"The sections included in the chapter\",\n",
-    "          \"minItems\": 1,\n",
-    "          \"maxItems\": LIMIT,\n",
+    "            \"required\": [\"name\", \"description\", \"sections\"],\n",
     "        },\n",
-    "      },\n",
-    "      \"required\": [\"name\", \"description\", \"sections\"],\n",
-    "    },\n",
-    "  }\n",
+    "    }\n",
     "]"
    ],
    "metadata": {
@@ -114,29 +115,29 @@
     "# Create a reusable call lmm helper function\n",
     "# Parea SDK is automatically traced\n",
     "def call_llm(messages: List[Dict[str, str]], name: str = \"LLM Call\") -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      llm_configuration=LLMInputs(\n",
-    "        model=\"gpt-3.5-turbo-1106\",\n",
-    "        model_params=ModelParams(temp=0.0, max_length=512),\n",
-    "        messages=[Message(**m) for m in messages],\n",
-    "        functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
-    "      ),\n",
-    "      end_user_identifier=USER,\n",
-    "      trace_name=name,\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            llm_configuration=LLMInputs(\n",
+    "                model=\"gpt-3.5-turbo-1106\",\n",
+    "                model_params=ModelParams(temp=0.0, max_length=512),\n",
+    "                messages=[Message(**m) for m in messages],\n",
+    "                functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
+    "            ),\n",
+    "            end_user_identifier=USER,\n",
+    "            trace_name=name,\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "# Helper function to get function call arguments if they exists\n",
     "def get_function_call_or_content(response: str) -> Union[str, Dict[str, Any]]:\n",
-    "  # Function calls are returned in triple backticks\n",
-    "  parsed_response = response.replace(\"```\", \"\")\n",
-    "  # return function call or content string\n",
-    "  try:\n",
-    "    return json.loads(parsed_response).get(\"arguments\")\n",
-    "  except Exception as e:\n",
-    "    return parsed_response"
+    "    # Function calls are returned in triple backticks\n",
+    "    parsed_response = response.replace(\"```\", \"\")\n",
+    "    # return function call or content string\n",
+    "    try:\n",
+    "        return json.loads(parsed_response).get(\"arguments\")\n",
+    "    except Exception as e:\n",
+    "        return parsed_response"
    ],
    "metadata": {
     "collapsed": false
@@ -151,13 +152,13 @@
     "\n",
     "\n",
     "def get_course(topic: str):\n",
-    "  _course_outline = call_llm(\n",
-    "    messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
-    "    name=f\"Create {topic} Course Outline\",\n",
-    "  )\n",
-    "  course_outline = get_function_call_or_content(_course_outline)\n",
-    "  print(json.dumps(course_outline, indent=4))\n",
-    "  return course_outline\n",
+    "    _course_outline = call_llm(\n",
+    "        messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
+    "        name=f\"Create {topic} Course Outline\",\n",
+    "    )\n",
+    "    course_outline = get_function_call_or_content(_course_outline)\n",
+    "    print(json.dumps(course_outline, indent=4))\n",
+    "    return course_outline\n",
     "\n",
     "\n",
     "# I want to group all llm calls into one trace,\n",
@@ -165,39 +166,37 @@
     "# each call_llm will be a child span\n",
     "@trace(name=\"Get Chapters\")\n",
     "def get_chapters(course_outline: Dict[str, str]):\n",
-    "  chapter_outlines = course_outline.get(\"chapters\", [])\n",
-    "  chapters = [\n",
-    "    get_function_call_or_content(\n",
-    "      call_llm(\n",
-    "        messages=[{\"role\": \"user\",\n",
-    "                   \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"}],\n",
-    "        name=f\"Create Chapter {idx}\",\n",
-    "      )\n",
-    "    )\n",
-    "    for idx, chapter in enumerate(chapter_outlines, start=1)\n",
-    "  ]\n",
-    "  return chapters[:LIMIT]\n",
+    "    chapter_outlines = course_outline.get(\"chapters\", [])\n",
+    "    chapters = [\n",
+    "        get_function_call_or_content(\n",
+    "            call_llm(\n",
+    "                messages=[{\"role\": \"user\", \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"}],\n",
+    "                name=f\"Create Chapter {idx}\",\n",
+    "            )\n",
+    "        )\n",
+    "        for idx, chapter in enumerate(chapter_outlines, start=1)\n",
+    "    ]\n",
+    "    return chapters[:LIMIT]\n",
     "\n",
     "\n",
     "@trace\n",
     "def get_sections(chapter: Dict[str, str]):\n",
-    "  chapter_name = chapter.get(\"name\", \"section\")\n",
-    "  # I want the trace name to be dynamic based on the chapter param,\n",
-    "  # so I can use Parea trace_insert helper method to add dynamic data\n",
-    "  trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
+    "    chapter_name = chapter.get(\"name\", \"section\")\n",
+    "    # I want the trace name to be dynamic based on the chapter param,\n",
+    "    # so I can use Parea trace_insert helper method to add dynamic data\n",
+    "    trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
     "\n",
-    "  section_outlines = chapter.get(\"sections\", [])\n",
-    "  sections = [\n",
-    "    get_function_call_or_content(\n",
-    "      call_llm(\n",
-    "        messages=[{\"role\": \"user\",\n",
-    "                   \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"}],\n",
-    "        name=f\"Create Section {idx}\",\n",
-    "      )\n",
-    "    )\n",
-    "    for idx, section in enumerate(section_outlines, start=1)\n",
-    "  ]\n",
-    "  return sections[:LIMIT]"
+    "    section_outlines = chapter.get(\"sections\", [])\n",
+    "    sections = [\n",
+    "        get_function_call_or_content(\n",
+    "            call_llm(\n",
+    "                messages=[{\"role\": \"user\", \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"}],\n",
+    "                name=f\"Create Section {idx}\",\n",
+    "            )\n",
+    "        )\n",
+    "        for idx, section in enumerate(section_outlines, start=1)\n",
+    "    ]\n",
+    "    return sections[:LIMIT]"
    ],
    "metadata": {
     "collapsed": false
@@ -210,23 +209,23 @@
    "source": [
     "@trace(name=\"Get Sections\")\n",
     "def get_all_sections(chapters: List[Dict[str, str]]):\n",
-    "  return [get_sections(chapter) for chapter in chapters]\n",
+    "    return [get_sections(chapter) for chapter in chapters]\n",
     "\n",
     "\n",
     "@trace\n",
     "def run_creation(topic: str):\n",
-    "  trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
-    "  course_outline = get_course(topic)\n",
-    "  chapters = get_chapters(course_outline)\n",
-    "  sections = get_all_sections(chapters)\n",
-    "  return sections\n",
+    "    trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
+    "    course_outline = get_course(topic)\n",
+    "    chapters = get_chapters(course_outline)\n",
+    "    sections = get_all_sections(chapters)\n",
+    "    return sections\n",
     "\n",
     "\n",
     "@trace(name=\"Generate Courses for Topics\")\n",
     "def main(topics):\n",
-    "  for topic in topics:\n",
-    "    print(f\"\\n New Topic: {topic} \\n\")\n",
-    "    run_creation(topic)"
+    "    for topic in topics:\n",
+    "        print(f\"\\n New Topic: {topic} \\n\")\n",
+    "        run_creation(topic)"
    ],
    "metadata": {
     "collapsed": false
diff --git a/parea/cookbook/tracing_with_open_ai_endpoint_directly.py b/parea/cookbook/tracing_with_open_ai_endpoint_directly.py
index 14cd4656..8274d5d8 100644
--- a/parea/cookbook/tracing_with_open_ai_endpoint_directly.py
+++ b/parea/cookbook/tracing_with_open_ai_endpoint_directly.py
@@ -15,14 +15,14 @@
 
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
-p = Parea(api_key=os.getenv("PAREA_API_KEY"))
+p = Parea(api_key=os.getenv("DEV_API_KEY"))
 
 
 def call_llm(data: list[dict], model: str = "gpt-3.5-turbo", temperature: float = 0.0) -> str:
-    return openai.ChatCompletion.create(model=model, temperature=temperature, messages=data).choices[0].message["content"]
+    return openai.chat.completions.create(model=model, temperature=temperature, messages=data).choices[0].message.content
 
 
-def random_eval(inputs: Dict[str, str], output, target: Optional[str] = None) -> float:
+def random_eval(inputs: dict[str, str], output=None, target: Optional[str] = None) -> float:
     # return random number between 0 and 1
     return random.random()
 
diff --git a/parea/cookbook/tracing_with_openai_with_functions.py b/parea/cookbook/tracing_with_openai_with_functions.py
index 7702aaf4..bf7c3d0e 100644
--- a/parea/cookbook/tracing_with_openai_with_functions.py
+++ b/parea/cookbook/tracing_with_openai_with_functions.py
@@ -140,7 +140,7 @@ def provide_user_specific_recommendations(user_input, user_id, functions) -> tup
 
     food_preference = customer_profile.get("preferences", {}).get("food", [])[0] if customer_profile.get("preferences", {}).get("food") else None
 
-    response = openai.ChatCompletion.create(
+    response = openai.chat.completions.create(
         model="gpt-3.5-turbo",
         messages=[
             {
diff --git a/parea/wrapper/openai.py b/parea/wrapper/openai.py
index 629e6c23..10add123 100644
--- a/parea/wrapper/openai.py
+++ b/parea/wrapper/openai.py
@@ -1,6 +1,7 @@
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 import json
+import os
 from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 
@@ -17,12 +18,16 @@ def convert_to_openai_object(**kwargs):
         return OpenAIObject(**kwargs)
 
 
+from dotenv import load_dotenv
+
 from ..cache.cache import Cache
 from ..schemas.log import LLMInputs, ModelParams
 from ..schemas.models import CacheRequest, TraceLog
 from ..utils.trace_utils import trace_data
 from .wrapper import Wrapper
 
+load_dotenv()
+
 OPENAI_MODEL_INFO: dict[str, dict[str, Union[float, int, dict[str, int]]]] = {
     "gpt-3.5-turbo": {
         "prompt": 1.5,
@@ -108,10 +113,14 @@ def convert_to_openai_object(**kwargs):
 
 
 class OpenAIWrapper:
+    openai.api_key = os.getenv("OPENAI_API_KEY")
     if openai_version.startswith("0."):
         original_methods = {"ChatCompletion.create": openai.ChatCompletion.create, "ChatCompletion.acreate": openai.ChatCompletion.acreate}
     else:
-        original_methods = {"chat.completions.create": openai.chat.completions.create}
+        try:
+            original_methods = {"chat.completions.create": openai.chat.completions.create}
+        except openai.OpenAIError:
+            original_methods = {}
 
     def init(self, log: Callable, cache: Cache = None):
         Wrapper(

From d803734aadc455bdd0e280d7b4c14635a4eeacbf Mon Sep 17 00:00:00 2001
From: Joel Alexander <a.joeljr@hotmail.com>
Date: Sat, 9 Dec 2023 15:08:31 -0500
Subject: [PATCH 4/7] prepatch

---
 parea/cookbook/tracing_with_Parea_sdk.ipynb   | 136 ++++++------
 ...ing_with_function_calling_and_chains.ipynb | 210 +++++++++---------
 2 files changed, 174 insertions(+), 172 deletions(-)

diff --git a/parea/cookbook/tracing_with_Parea_sdk.ipynb b/parea/cookbook/tracing_with_Parea_sdk.ipynb
index cf7ca43e..5556186c 100644
--- a/parea/cookbook/tracing_with_Parea_sdk.ipynb
+++ b/parea/cookbook/tracing_with_Parea_sdk.ipynb
@@ -117,40 +117,40 @@
     "\n",
     "\n",
     "def argument_generator(query: str, additional_description: str = \"\") -> str:\n",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
-    "            llm_inputs={\n",
-    "                \"additional_description\": additional_description,\n",
-    "                \"date\": f\"{datetime.now()}\",\n",
-    "                \"query\": query,\n",
-    "            },\n",
-    "        )\n",
-    "    ).content\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
+    "      llm_inputs={\n",
+    "        \"additional_description\": additional_description,\n",
+    "        \"date\": f\"{datetime.now()}\",\n",
+    "        \"query\": query,\n",
+    "      },\n",
+    "    )\n",
+    "  ).content\n",
     "\n",
     "\n",
     "def critic(argument: str) -> str:\n",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\n",
-    "            llm_inputs={\"argument\": argument},\n",
-    "        )\n",
-    "    ).content\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\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",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "            llm_inputs={\n",
-    "                \"additional_description\": additional_description,\n",
-    "                \"date\": f\"{datetime.now()}\",\n",
-    "                \"query\": query,\n",
-    "                \"argument\": current_arg,\n",
-    "                \"criticism\": criticism,\n",
-    "            },\n",
-    "        )\n",
-    "    ).content\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "      llm_inputs={\n",
+    "        \"additional_description\": additional_description,\n",
+    "        \"date\": f\"{datetime.now()}\",\n",
+    "        \"query\": query,\n",
+    "        \"argument\": current_arg,\n",
+    "        \"criticism\": criticism,\n",
+    "      },\n",
+    "    )\n",
+    "  ).content\n",
     "\n",
     "\n",
     "# Non deployed version\n",
@@ -209,9 +209,9 @@
     "\n",
     "@trace\n",
     "def argument_chain(query: str, additional_description: str = \"\") -> str:\n",
-    "    argument = argument_generator(query, additional_description)\n",
-    "    criticism = critic(argument)\n",
-    "    return refiner(query, additional_description, argument, criticism)"
+    "  argument = argument_generator(query, additional_description)\n",
+    "  criticism = critic(argument)\n",
+    "  return refiner(query, additional_description, argument, criticism)"
    ]
   },
   {
@@ -251,8 +251,8 @@
    ],
    "source": [
     "result1 = argument_chain(\n",
-    "    \"Whether coffee is good for you.\",\n",
-    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(result1)"
    ]
@@ -302,10 +302,10 @@
     "\n",
     "@trace\n",
     "def argument_chain2(query: str, additional_description: str = \"\") -> tuple[str, 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 refiner(query, additional_description, argument, criticism), trace_id"
+    "  trace_id = get_current_trace_id()  # get parent's trace_id\n",
+    "  argument = argument_generator(query, additional_description)\n",
+    "  criticism = critic(argument)\n",
+    "  return refiner(query, additional_description, argument, criticism), trace_id"
    ]
   },
   {
@@ -331,8 +331,8 @@
    ],
    "source": [
     "result, trace_id = argument_chain2(\n",
-    "    \"Whether coffee is good for you.\",\n",
-    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(trace_id)"
    ]
@@ -424,30 +424,30 @@
     "\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",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "            llm_inputs={\n",
-    "                \"additional_description\": additional_description,\n",
-    "                \"date\": f\"{datetime.now()}\",\n",
-    "                \"query\": query,\n",
-    "                \"argument\": current_arg,\n",
-    "                \"criticism\": criticism,\n",
-    "            },\n",
-    "        )\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "      llm_inputs={\n",
+    "        \"additional_description\": additional_description,\n",
+    "        \"date\": f\"{datetime.now()}\",\n",
+    "        \"query\": query,\n",
+    "        \"argument\": current_arg,\n",
+    "        \"criticism\": criticism,\n",
+    "      },\n",
     "    )\n",
+    "  )\n",
     "\n",
     "\n",
     "@trace\n",
     "def argument_chain3(query: str, additional_description: str = \"\") -> CompletionResponse:\n",
-    "    argument = argument_generator(query, additional_description)\n",
-    "    criticism = critic(argument)\n",
-    "    return refiner2(query, additional_description, argument, criticism)\n",
+    "  argument = argument_generator(query, additional_description)\n",
+    "  criticism = critic(argument)\n",
+    "  return refiner2(query, additional_description, argument, criticism)\n",
     "\n",
     "\n",
     "result2 = argument_chain3(\n",
-    "    \"Whether coffee is good for you.\",\n",
-    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(json.dumps(asdict(result2), indent=2))"
    ],
@@ -498,14 +498,14 @@
     "\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",
+    "  tags=[\"cookbook-example-deployed\", \"feedback_tracked-deployed\"],\n",
+    "  metadata={\"source\": \"python-sdk\", \"deployed\": \"True\"},\n",
     ")\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), trace_id"
+    "  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), trace_id"
    ]
   },
   {
@@ -515,17 +515,17 @@
     "from attrs import asdict\n",
     "\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",
+    "  \"Whether coffee is good for you.\",\n",
+    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\n",
     ")\n",
     "print(json.dumps(asdict(result2), indent=2))\n",
     "\n",
     "p.record_feedback(\n",
-    "    FeedbackRequest(\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",
+    "  FeedbackRequest(\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",
     ")"
    ],
    "metadata": {
diff --git a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
index 126d201e..a3156a66 100644
--- a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
+++ b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
@@ -40,60 +40,60 @@
     "LIMIT = 2  # limit any loops to 2 iterations for demo purposes\n",
     "\n",
     "COURSE_FUNCTIONS = [\n",
-    "    {\n",
-    "        \"name\": \"generate_course_outline\",\n",
-    "        \"description\": \"Generates a course outline\",\n",
-    "        \"parameters\": {\n",
+    "  {\n",
+    "    \"name\": \"generate_course_outline\",\n",
+    "    \"description\": \"Generates a course outline\",\n",
+    "    \"parameters\": {\n",
+    "      \"type\": \"object\",\n",
+    "      \"properties\": {\n",
+    "        \"Description\": {\"type\": \"string\", \"description\": \"The description of the course\"},\n",
+    "        \"chapters\": {\n",
+    "          \"type\": \"array\",\n",
+    "          \"items\": {\n",
     "            \"type\": \"object\",\n",
     "            \"properties\": {\n",
-    "                \"Description\": {\"type\": \"string\", \"description\": \"The description of the course\"},\n",
-    "                \"chapters\": {\n",
-    "                    \"type\": \"array\",\n",
-    "                    \"items\": {\n",
-    "                        \"type\": \"object\",\n",
-    "                        \"properties\": {\n",
-    "                            \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
-    "                            \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
-    "                        },\n",
-    "                        \"required\": [\"name\", \"description\"],\n",
-    "                    },\n",
-    "                    \"description\": \"The chapters included in the course\",\n",
-    "                    \"minItems\": 1,\n",
-    "                    \"maxItems\": LIMIT,\n",
-    "                },\n",
+    "              \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
+    "              \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
     "            },\n",
-    "            \"required\": [\"Description\", \"chapters\"],\n",
+    "            \"required\": [\"name\", \"description\"],\n",
+    "          },\n",
+    "          \"description\": \"The chapters included in the course\",\n",
+    "          \"minItems\": 1,\n",
+    "          \"maxItems\": LIMIT,\n",
     "        },\n",
-    "    }\n",
+    "      },\n",
+    "      \"required\": [\"Description\", \"chapters\"],\n",
+    "    },\n",
+    "  }\n",
     "]\n",
     "\n",
     "CHAPTER_FUNCTIONS = [\n",
-    "    {\n",
-    "        \"name\": \"generate_chapter_outline\",\n",
-    "        \"description\": \"Generates a chapter outline\",\n",
-    "        \"parameters\": {\n",
+    "  {\n",
+    "    \"name\": \"generate_chapter_outline\",\n",
+    "    \"description\": \"Generates a chapter outline\",\n",
+    "    \"parameters\": {\n",
+    "      \"type\": \"object\",\n",
+    "      \"properties\": {\n",
+    "        \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
+    "        \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "        \"sections\": {\n",
+    "          \"type\": \"array\",\n",
+    "          \"items\": {\n",
     "            \"type\": \"object\",\n",
     "            \"properties\": {\n",
-    "                \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
-    "                \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
-    "                \"sections\": {\n",
-    "                    \"type\": \"array\",\n",
-    "                    \"items\": {\n",
-    "                        \"type\": \"object\",\n",
-    "                        \"properties\": {\n",
-    "                            \"name\": {\"type\": \"string\", \"description\": \"The title of the section\"},\n",
-    "                            \"description\": {\"type\": \"string\", \"description\": \"The summary of the section\"},\n",
-    "                        },\n",
-    "                        \"required\": [\"name\", \"description\"],\n",
-    "                    },\n",
-    "                    \"description\": \"The sections included in the chapter\",\n",
-    "                    \"minItems\": 1,\n",
-    "                    \"maxItems\": LIMIT,\n",
-    "                },\n",
+    "              \"name\": {\"type\": \"string\", \"description\": \"The title of the section\"},\n",
+    "              \"description\": {\"type\": \"string\", \"description\": \"The summary of the section\"},\n",
     "            },\n",
-    "            \"required\": [\"name\", \"description\", \"sections\"],\n",
+    "            \"required\": [\"name\", \"description\"],\n",
+    "          },\n",
+    "          \"description\": \"The sections included in the chapter\",\n",
+    "          \"minItems\": 1,\n",
+    "          \"maxItems\": LIMIT,\n",
     "        },\n",
-    "    }\n",
+    "      },\n",
+    "      \"required\": [\"name\", \"description\", \"sections\"],\n",
+    "    },\n",
+    "  }\n",
     "]"
    ],
    "metadata": {
@@ -115,29 +115,29 @@
     "# Create a reusable call lmm helper function\n",
     "# Parea SDK is automatically traced\n",
     "def call_llm(messages: List[Dict[str, str]], name: str = \"LLM Call\") -> str:\n",
-    "    return p.completion(\n",
-    "        Completion(\n",
-    "            llm_configuration=LLMInputs(\n",
-    "                model=\"gpt-3.5-turbo-1106\",\n",
-    "                model_params=ModelParams(temp=0.0, max_length=512),\n",
-    "                messages=[Message(**m) for m in messages],\n",
-    "                functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
-    "            ),\n",
-    "            end_user_identifier=USER,\n",
-    "            trace_name=name,\n",
-    "        )\n",
-    "    ).content\n",
+    "  return p.completion(\n",
+    "    Completion(\n",
+    "      llm_configuration=LLMInputs(\n",
+    "        model=\"gpt-3.5-turbo-1106\",\n",
+    "        model_params=ModelParams(temp=0.0, max_length=512),\n",
+    "        messages=[Message(**m) for m in messages],\n",
+    "        functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
+    "      ),\n",
+    "      end_user_identifier=USER,\n",
+    "      trace_name=name,\n",
+    "    )\n",
+    "  ).content\n",
     "\n",
     "\n",
     "# Helper function to get function call arguments if they exists\n",
     "def get_function_call_or_content(response: str) -> Union[str, Dict[str, Any]]:\n",
-    "    # Function calls are returned in triple backticks\n",
-    "    parsed_response = response.replace(\"```\", \"\")\n",
-    "    # return function call or content string\n",
-    "    try:\n",
-    "        return json.loads(parsed_response).get(\"arguments\")\n",
-    "    except Exception as e:\n",
-    "        return parsed_response"
+    "  # Function calls are returned in triple backticks\n",
+    "  parsed_response = response.replace(\"```\", \"\")\n",
+    "  # return function call or content string\n",
+    "  try:\n",
+    "    return json.loads(parsed_response).get(\"arguments\")\n",
+    "  except Exception as e:\n",
+    "    return parsed_response"
    ],
    "metadata": {
     "collapsed": false
@@ -152,13 +152,13 @@
     "\n",
     "\n",
     "def get_course(topic: str):\n",
-    "    _course_outline = call_llm(\n",
-    "        messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
-    "        name=f\"Create {topic} Course Outline\",\n",
-    "    )\n",
-    "    course_outline = get_function_call_or_content(_course_outline)\n",
-    "    print(json.dumps(course_outline, indent=4))\n",
-    "    return course_outline\n",
+    "  _course_outline = call_llm(\n",
+    "    messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
+    "    name=f\"Create {topic} Course Outline\",\n",
+    "  )\n",
+    "  course_outline = get_function_call_or_content(_course_outline)\n",
+    "  print(json.dumps(course_outline, indent=4))\n",
+    "  return course_outline\n",
     "\n",
     "\n",
     "# I want to group all llm calls into one trace,\n",
@@ -166,37 +166,39 @@
     "# each call_llm will be a child span\n",
     "@trace(name=\"Get Chapters\")\n",
     "def get_chapters(course_outline: Dict[str, str]):\n",
-    "    chapter_outlines = course_outline.get(\"chapters\", [])\n",
-    "    chapters = [\n",
-    "        get_function_call_or_content(\n",
-    "            call_llm(\n",
-    "                messages=[{\"role\": \"user\", \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"}],\n",
-    "                name=f\"Create Chapter {idx}\",\n",
-    "            )\n",
-    "        )\n",
-    "        for idx, chapter in enumerate(chapter_outlines, start=1)\n",
-    "    ]\n",
-    "    return chapters[:LIMIT]\n",
+    "  chapter_outlines = course_outline.get(\"chapters\", [])\n",
+    "  chapters = [\n",
+    "    get_function_call_or_content(\n",
+    "      call_llm(\n",
+    "        messages=[{\"role\": \"user\",\n",
+    "                   \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"}],\n",
+    "        name=f\"Create Chapter {idx}\",\n",
+    "      )\n",
+    "    )\n",
+    "    for idx, chapter in enumerate(chapter_outlines, start=1)\n",
+    "  ]\n",
+    "  return chapters[:LIMIT]\n",
     "\n",
     "\n",
     "@trace\n",
     "def get_sections(chapter: Dict[str, str]):\n",
-    "    chapter_name = chapter.get(\"name\", \"section\")\n",
-    "    # I want the trace name to be dynamic based on the chapter param,\n",
-    "    # so I can use Parea trace_insert helper method to add dynamic data\n",
-    "    trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
+    "  chapter_name = chapter.get(\"name\", \"section\")\n",
+    "  # I want the trace name to be dynamic based on the chapter param,\n",
+    "  # so I can use Parea trace_insert helper method to add dynamic data\n",
+    "  trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
     "\n",
-    "    section_outlines = chapter.get(\"sections\", [])\n",
-    "    sections = [\n",
-    "        get_function_call_or_content(\n",
-    "            call_llm(\n",
-    "                messages=[{\"role\": \"user\", \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"}],\n",
-    "                name=f\"Create Section {idx}\",\n",
-    "            )\n",
-    "        )\n",
-    "        for idx, section in enumerate(section_outlines, start=1)\n",
-    "    ]\n",
-    "    return sections[:LIMIT]"
+    "  section_outlines = chapter.get(\"sections\", [])\n",
+    "  sections = [\n",
+    "    get_function_call_or_content(\n",
+    "      call_llm(\n",
+    "        messages=[{\"role\": \"user\",\n",
+    "                   \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"}],\n",
+    "        name=f\"Create Section {idx}\",\n",
+    "      )\n",
+    "    )\n",
+    "    for idx, section in enumerate(section_outlines, start=1)\n",
+    "  ]\n",
+    "  return sections[:LIMIT]"
    ],
    "metadata": {
     "collapsed": false
@@ -209,23 +211,23 @@
    "source": [
     "@trace(name=\"Get Sections\")\n",
     "def get_all_sections(chapters: List[Dict[str, str]]):\n",
-    "    return [get_sections(chapter) for chapter in chapters]\n",
+    "  return [get_sections(chapter) for chapter in chapters]\n",
     "\n",
     "\n",
     "@trace\n",
     "def run_creation(topic: str):\n",
-    "    trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
-    "    course_outline = get_course(topic)\n",
-    "    chapters = get_chapters(course_outline)\n",
-    "    sections = get_all_sections(chapters)\n",
-    "    return sections\n",
+    "  trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
+    "  course_outline = get_course(topic)\n",
+    "  chapters = get_chapters(course_outline)\n",
+    "  sections = get_all_sections(chapters)\n",
+    "  return sections\n",
     "\n",
     "\n",
     "@trace(name=\"Generate Courses for Topics\")\n",
     "def main(topics):\n",
-    "    for topic in topics:\n",
-    "        print(f\"\\n New Topic: {topic} \\n\")\n",
-    "        run_creation(topic)"
+    "  for topic in topics:\n",
+    "    print(f\"\\n New Topic: {topic} \\n\")\n",
+    "    run_creation(topic)"
    ],
    "metadata": {
     "collapsed": false

From 357d3646b1802d8f2c1728151d82924c06f3da2f Mon Sep 17 00:00:00 2001
From: Joel Alexander <a.joeljr@hotmail.com>
Date: Sat, 9 Dec 2023 15:12:31 -0500
Subject: [PATCH 5/7] move eval from example

---
 .../tracing_with_open_ai_endpoint_directly.py      | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/parea/cookbook/tracing_with_open_ai_endpoint_directly.py b/parea/cookbook/tracing_with_open_ai_endpoint_directly.py
index 8274d5d8..5cae2819 100644
--- a/parea/cookbook/tracing_with_open_ai_endpoint_directly.py
+++ b/parea/cookbook/tracing_with_open_ai_endpoint_directly.py
@@ -1,7 +1,4 @@
-from typing import Dict, Optional
-
 import os
-import random
 from datetime import datetime
 
 import openai
@@ -22,12 +19,7 @@ def call_llm(data: list[dict], model: str = "gpt-3.5-turbo", temperature: float
     return openai.chat.completions.create(model=model, temperature=temperature, messages=data).choices[0].message.content
 
 
-def random_eval(inputs: dict[str, str], output=None, target: Optional[str] = None) -> float:
-    # return random number between 0 and 1
-    return random.random()
-
-
-@trace(eval_funcs=[random_eval])
+@trace
 def argumentor(query: str, additional_description: str = "") -> str:
     return call_llm(
         [
@@ -56,7 +48,7 @@ def critic(argument: str) -> str:
     )
 
 
-@trace(eval_funcs=[random_eval])
+@trace
 def refiner(query: str, additional_description: str, argument: str, criticism: str) -> str:
     return call_llm(
         [
@@ -76,7 +68,7 @@ def refiner(query: str, additional_description: str, argument: str, criticism: s
     )
 
 
-@trace(eval_funcs=[random_eval], access_output_of_func=lambda x: x[0])
+@trace
 def argument_chain(query: str, additional_description: str = "") -> tuple[str, str]:
     trace_id = get_current_trace_id()
     argument = argumentor(query, additional_description)

From 3a08a1a18a9928d05caa68371575898ba8462c54 Mon Sep 17 00:00:00 2001
From: Joel Alexander <a.joeljr@hotmail.com>
Date: Sat, 9 Dec 2023 15:13:47 -0500
Subject: [PATCH 6/7] codestyle

---
 parea/cookbook/tracing_with_Parea_sdk.ipynb   | 136 ++++++------
 ...ing_with_function_calling_and_chains.ipynb | 210 +++++++++---------
 2 files changed, 172 insertions(+), 174 deletions(-)

diff --git a/parea/cookbook/tracing_with_Parea_sdk.ipynb b/parea/cookbook/tracing_with_Parea_sdk.ipynb
index 5556186c..cf7ca43e 100644
--- a/parea/cookbook/tracing_with_Parea_sdk.ipynb
+++ b/parea/cookbook/tracing_with_Parea_sdk.ipynb
@@ -117,40 +117,40 @@
     "\n",
     "\n",
     "def argument_generator(query: str, additional_description: str = \"\") -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "      },\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-tbFUZ5rRaXshj8o5Opfyr\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "            },\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "def critic(argument: str) -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\n",
-    "      llm_inputs={\"argument\": argument},\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-iAuVLFHy6VypfGZxwAWW0\",\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",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "        \"argument\": current_arg,\n",
-    "        \"criticism\": criticism,\n",
-    "      },\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "                \"argument\": current_arg,\n",
+    "                \"criticism\": criticism,\n",
+    "            },\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "# Non deployed version\n",
@@ -209,9 +209,9 @@
     "\n",
     "@trace\n",
     "def argument_chain(query: str, additional_description: str = \"\") -> str:\n",
-    "  argument = argument_generator(query, additional_description)\n",
-    "  criticism = critic(argument)\n",
-    "  return refiner(query, additional_description, argument, criticism)"
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner(query, additional_description, argument, criticism)"
    ]
   },
   {
@@ -251,8 +251,8 @@
    ],
    "source": [
     "result1 = argument_chain(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(result1)"
    ]
@@ -302,10 +302,10 @@
     "\n",
     "@trace\n",
     "def argument_chain2(query: str, additional_description: str = \"\") -> tuple[str, 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 refiner(query, additional_description, argument, criticism), trace_id"
+    "    trace_id = get_current_trace_id()  # get parent's trace_id\n",
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner(query, additional_description, argument, criticism), trace_id"
    ]
   },
   {
@@ -331,8 +331,8 @@
    ],
    "source": [
     "result, trace_id = argument_chain2(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(trace_id)"
    ]
@@ -424,30 +424,30 @@
     "\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",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
-    "      llm_inputs={\n",
-    "        \"additional_description\": additional_description,\n",
-    "        \"date\": f\"{datetime.now()}\",\n",
-    "        \"query\": query,\n",
-    "        \"argument\": current_arg,\n",
-    "        \"criticism\": criticism,\n",
-    "      },\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            deployment_id=\"p-rEjM4X10rJomOD8Rj9gzJ\",\n",
+    "            llm_inputs={\n",
+    "                \"additional_description\": additional_description,\n",
+    "                \"date\": f\"{datetime.now()}\",\n",
+    "                \"query\": query,\n",
+    "                \"argument\": current_arg,\n",
+    "                \"criticism\": criticism,\n",
+    "            },\n",
+    "        )\n",
     "    )\n",
-    "  )\n",
     "\n",
     "\n",
     "@trace\n",
     "def argument_chain3(query: str, additional_description: str = \"\") -> CompletionResponse:\n",
-    "  argument = argument_generator(query, additional_description)\n",
-    "  criticism = critic(argument)\n",
-    "  return refiner2(query, additional_description, argument, criticism)\n",
+    "    argument = argument_generator(query, additional_description)\n",
+    "    criticism = critic(argument)\n",
+    "    return refiner2(query, additional_description, argument, criticism)\n",
     "\n",
     "\n",
     "result2 = argument_chain3(\n",
-    "  \"Whether coffee is good for you.\",\n",
-    "  additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\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",
     "print(json.dumps(asdict(result2), indent=2))"
    ],
@@ -498,14 +498,14 @@
     "\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",
+    "    tags=[\"cookbook-example-deployed\", \"feedback_tracked-deployed\"],\n",
+    "    metadata={\"source\": \"python-sdk\", \"deployed\": \"True\"},\n",
     ")\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), trace_id"
+    "    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), trace_id"
    ]
   },
   {
@@ -515,17 +515,17 @@
     "from attrs import asdict\n",
     "\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",
+    "    \"Whether coffee is good for you.\",\n",
+    "    additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\n",
     ")\n",
     "print(json.dumps(asdict(result2), indent=2))\n",
     "\n",
     "p.record_feedback(\n",
-    "  FeedbackRequest(\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",
+    "    FeedbackRequest(\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",
     ")"
    ],
    "metadata": {
diff --git a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
index a3156a66..126d201e 100644
--- a/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
+++ b/parea/cookbook/tracing_with_function_calling_and_chains.ipynb
@@ -40,60 +40,60 @@
     "LIMIT = 2  # limit any loops to 2 iterations for demo purposes\n",
     "\n",
     "COURSE_FUNCTIONS = [\n",
-    "  {\n",
-    "    \"name\": \"generate_course_outline\",\n",
-    "    \"description\": \"Generates a course outline\",\n",
-    "    \"parameters\": {\n",
-    "      \"type\": \"object\",\n",
-    "      \"properties\": {\n",
-    "        \"Description\": {\"type\": \"string\", \"description\": \"The description of the course\"},\n",
-    "        \"chapters\": {\n",
-    "          \"type\": \"array\",\n",
-    "          \"items\": {\n",
+    "    {\n",
+    "        \"name\": \"generate_course_outline\",\n",
+    "        \"description\": \"Generates a course outline\",\n",
+    "        \"parameters\": {\n",
     "            \"type\": \"object\",\n",
     "            \"properties\": {\n",
-    "              \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
-    "              \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "                \"Description\": {\"type\": \"string\", \"description\": \"The description of the course\"},\n",
+    "                \"chapters\": {\n",
+    "                    \"type\": \"array\",\n",
+    "                    \"items\": {\n",
+    "                        \"type\": \"object\",\n",
+    "                        \"properties\": {\n",
+    "                            \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
+    "                            \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "                        },\n",
+    "                        \"required\": [\"name\", \"description\"],\n",
+    "                    },\n",
+    "                    \"description\": \"The chapters included in the course\",\n",
+    "                    \"minItems\": 1,\n",
+    "                    \"maxItems\": LIMIT,\n",
+    "                },\n",
     "            },\n",
-    "            \"required\": [\"name\", \"description\"],\n",
-    "          },\n",
-    "          \"description\": \"The chapters included in the course\",\n",
-    "          \"minItems\": 1,\n",
-    "          \"maxItems\": LIMIT,\n",
+    "            \"required\": [\"Description\", \"chapters\"],\n",
     "        },\n",
-    "      },\n",
-    "      \"required\": [\"Description\", \"chapters\"],\n",
-    "    },\n",
-    "  }\n",
+    "    }\n",
     "]\n",
     "\n",
     "CHAPTER_FUNCTIONS = [\n",
-    "  {\n",
-    "    \"name\": \"generate_chapter_outline\",\n",
-    "    \"description\": \"Generates a chapter outline\",\n",
-    "    \"parameters\": {\n",
-    "      \"type\": \"object\",\n",
-    "      \"properties\": {\n",
-    "        \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
-    "        \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
-    "        \"sections\": {\n",
-    "          \"type\": \"array\",\n",
-    "          \"items\": {\n",
+    "    {\n",
+    "        \"name\": \"generate_chapter_outline\",\n",
+    "        \"description\": \"Generates a chapter outline\",\n",
+    "        \"parameters\": {\n",
     "            \"type\": \"object\",\n",
     "            \"properties\": {\n",
-    "              \"name\": {\"type\": \"string\", \"description\": \"The title of the section\"},\n",
-    "              \"description\": {\"type\": \"string\", \"description\": \"The summary of the section\"},\n",
+    "                \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n",
+    "                \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n",
+    "                \"sections\": {\n",
+    "                    \"type\": \"array\",\n",
+    "                    \"items\": {\n",
+    "                        \"type\": \"object\",\n",
+    "                        \"properties\": {\n",
+    "                            \"name\": {\"type\": \"string\", \"description\": \"The title of the section\"},\n",
+    "                            \"description\": {\"type\": \"string\", \"description\": \"The summary of the section\"},\n",
+    "                        },\n",
+    "                        \"required\": [\"name\", \"description\"],\n",
+    "                    },\n",
+    "                    \"description\": \"The sections included in the chapter\",\n",
+    "                    \"minItems\": 1,\n",
+    "                    \"maxItems\": LIMIT,\n",
+    "                },\n",
     "            },\n",
-    "            \"required\": [\"name\", \"description\"],\n",
-    "          },\n",
-    "          \"description\": \"The sections included in the chapter\",\n",
-    "          \"minItems\": 1,\n",
-    "          \"maxItems\": LIMIT,\n",
+    "            \"required\": [\"name\", \"description\", \"sections\"],\n",
     "        },\n",
-    "      },\n",
-    "      \"required\": [\"name\", \"description\", \"sections\"],\n",
-    "    },\n",
-    "  }\n",
+    "    }\n",
     "]"
    ],
    "metadata": {
@@ -115,29 +115,29 @@
     "# Create a reusable call lmm helper function\n",
     "# Parea SDK is automatically traced\n",
     "def call_llm(messages: List[Dict[str, str]], name: str = \"LLM Call\") -> str:\n",
-    "  return p.completion(\n",
-    "    Completion(\n",
-    "      llm_configuration=LLMInputs(\n",
-    "        model=\"gpt-3.5-turbo-1106\",\n",
-    "        model_params=ModelParams(temp=0.0, max_length=512),\n",
-    "        messages=[Message(**m) for m in messages],\n",
-    "        functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
-    "      ),\n",
-    "      end_user_identifier=USER,\n",
-    "      trace_name=name,\n",
-    "    )\n",
-    "  ).content\n",
+    "    return p.completion(\n",
+    "        Completion(\n",
+    "            llm_configuration=LLMInputs(\n",
+    "                model=\"gpt-3.5-turbo-1106\",\n",
+    "                model_params=ModelParams(temp=0.0, max_length=512),\n",
+    "                messages=[Message(**m) for m in messages],\n",
+    "                functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n",
+    "            ),\n",
+    "            end_user_identifier=USER,\n",
+    "            trace_name=name,\n",
+    "        )\n",
+    "    ).content\n",
     "\n",
     "\n",
     "# Helper function to get function call arguments if they exists\n",
     "def get_function_call_or_content(response: str) -> Union[str, Dict[str, Any]]:\n",
-    "  # Function calls are returned in triple backticks\n",
-    "  parsed_response = response.replace(\"```\", \"\")\n",
-    "  # return function call or content string\n",
-    "  try:\n",
-    "    return json.loads(parsed_response).get(\"arguments\")\n",
-    "  except Exception as e:\n",
-    "    return parsed_response"
+    "    # Function calls are returned in triple backticks\n",
+    "    parsed_response = response.replace(\"```\", \"\")\n",
+    "    # return function call or content string\n",
+    "    try:\n",
+    "        return json.loads(parsed_response).get(\"arguments\")\n",
+    "    except Exception as e:\n",
+    "        return parsed_response"
    ],
    "metadata": {
     "collapsed": false
@@ -152,13 +152,13 @@
     "\n",
     "\n",
     "def get_course(topic: str):\n",
-    "  _course_outline = call_llm(\n",
-    "    messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
-    "    name=f\"Create {topic} Course Outline\",\n",
-    "  )\n",
-    "  course_outline = get_function_call_or_content(_course_outline)\n",
-    "  print(json.dumps(course_outline, indent=4))\n",
-    "  return course_outline\n",
+    "    _course_outline = call_llm(\n",
+    "        messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n",
+    "        name=f\"Create {topic} Course Outline\",\n",
+    "    )\n",
+    "    course_outline = get_function_call_or_content(_course_outline)\n",
+    "    print(json.dumps(course_outline, indent=4))\n",
+    "    return course_outline\n",
     "\n",
     "\n",
     "# I want to group all llm calls into one trace,\n",
@@ -166,39 +166,37 @@
     "# each call_llm will be a child span\n",
     "@trace(name=\"Get Chapters\")\n",
     "def get_chapters(course_outline: Dict[str, str]):\n",
-    "  chapter_outlines = course_outline.get(\"chapters\", [])\n",
-    "  chapters = [\n",
-    "    get_function_call_or_content(\n",
-    "      call_llm(\n",
-    "        messages=[{\"role\": \"user\",\n",
-    "                   \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"}],\n",
-    "        name=f\"Create Chapter {idx}\",\n",
-    "      )\n",
-    "    )\n",
-    "    for idx, chapter in enumerate(chapter_outlines, start=1)\n",
-    "  ]\n",
-    "  return chapters[:LIMIT]\n",
+    "    chapter_outlines = course_outline.get(\"chapters\", [])\n",
+    "    chapters = [\n",
+    "        get_function_call_or_content(\n",
+    "            call_llm(\n",
+    "                messages=[{\"role\": \"user\", \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"}],\n",
+    "                name=f\"Create Chapter {idx}\",\n",
+    "            )\n",
+    "        )\n",
+    "        for idx, chapter in enumerate(chapter_outlines, start=1)\n",
+    "    ]\n",
+    "    return chapters[:LIMIT]\n",
     "\n",
     "\n",
     "@trace\n",
     "def get_sections(chapter: Dict[str, str]):\n",
-    "  chapter_name = chapter.get(\"name\", \"section\")\n",
-    "  # I want the trace name to be dynamic based on the chapter param,\n",
-    "  # so I can use Parea trace_insert helper method to add dynamic data\n",
-    "  trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
+    "    chapter_name = chapter.get(\"name\", \"section\")\n",
+    "    # I want the trace name to be dynamic based on the chapter param,\n",
+    "    # so I can use Parea trace_insert helper method to add dynamic data\n",
+    "    trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n",
     "\n",
-    "  section_outlines = chapter.get(\"sections\", [])\n",
-    "  sections = [\n",
-    "    get_function_call_or_content(\n",
-    "      call_llm(\n",
-    "        messages=[{\"role\": \"user\",\n",
-    "                   \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"}],\n",
-    "        name=f\"Create Section {idx}\",\n",
-    "      )\n",
-    "    )\n",
-    "    for idx, section in enumerate(section_outlines, start=1)\n",
-    "  ]\n",
-    "  return sections[:LIMIT]"
+    "    section_outlines = chapter.get(\"sections\", [])\n",
+    "    sections = [\n",
+    "        get_function_call_or_content(\n",
+    "            call_llm(\n",
+    "                messages=[{\"role\": \"user\", \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"}],\n",
+    "                name=f\"Create Section {idx}\",\n",
+    "            )\n",
+    "        )\n",
+    "        for idx, section in enumerate(section_outlines, start=1)\n",
+    "    ]\n",
+    "    return sections[:LIMIT]"
    ],
    "metadata": {
     "collapsed": false
@@ -211,23 +209,23 @@
    "source": [
     "@trace(name=\"Get Sections\")\n",
     "def get_all_sections(chapters: List[Dict[str, str]]):\n",
-    "  return [get_sections(chapter) for chapter in chapters]\n",
+    "    return [get_sections(chapter) for chapter in chapters]\n",
     "\n",
     "\n",
     "@trace\n",
     "def run_creation(topic: str):\n",
-    "  trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
-    "  course_outline = get_course(topic)\n",
-    "  chapters = get_chapters(course_outline)\n",
-    "  sections = get_all_sections(chapters)\n",
-    "  return sections\n",
+    "    trace_insert({\"trace_name\": f\"Course on {topic}\"})\n",
+    "    course_outline = get_course(topic)\n",
+    "    chapters = get_chapters(course_outline)\n",
+    "    sections = get_all_sections(chapters)\n",
+    "    return sections\n",
     "\n",
     "\n",
     "@trace(name=\"Generate Courses for Topics\")\n",
     "def main(topics):\n",
-    "  for topic in topics:\n",
-    "    print(f\"\\n New Topic: {topic} \\n\")\n",
-    "    run_creation(topic)"
+    "    for topic in topics:\n",
+    "        print(f\"\\n New Topic: {topic} \\n\")\n",
+    "        run_creation(topic)"
    ],
    "metadata": {
     "collapsed": false

From 74840e62e12224cb40e7d79491151a40e1d64cb8 Mon Sep 17 00:00:00 2001
From: Joel Alexander <a.joeljr@hotmail.com>
Date: Sat, 9 Dec 2023 15:16:40 -0500
Subject: [PATCH 7/7] fix readme

---
 README.md | 42 +++++++++++++++++++++++++-----------------
 1 file changed, 25 insertions(+), 17 deletions(-)

diff --git a/README.md b/README.md
index a33c2def..38c9307d 100644
--- a/README.md
+++ b/README.md
@@ -28,17 +28,21 @@ poetry add parea-ai
 
 ## Evaluating Your LLM App
 
-You can evaluate any step of your LLM app by wrapping it with a decorator, called `trace`, and specifying the evaluation function(s).
-The scores associated with the traces will be logged to the Parea [dashboard](https://app.parea.ai/logs) and/or in a local CSV file if you don't have a Parea API key.
-
-Evaluation functions receive an argument `log` (of type [Log](parea/schemas/models.py)) and should return a 
-float between 0 (bad) and 1 (good) inclusive. You don't need to start from scratch, there are pre-defined evaluation functions for [general purpose](parea/evals/general.py), 
+You can evaluate any step of your LLM app by wrapping it with a decorator, called `trace`, and specifying the evaluation
+function(s).
+The scores associated with the traces will be logged to the Parea [dashboard](https://app.parea.ai/logs) and/or in a
+local CSV file if you don't have a Parea API key.
+
+Evaluation functions receive an argument `log` (of type [Log](parea/schemas/models.py)) and should return a
+float between 0 (bad) and 1 (good) inclusive. You don't need to start from scratch, there are pre-defined evaluation
+functions for [general purpose](parea/evals/general.py),
 [chat](parea/evals/chat.py), [RAG](parea/evals/rag.py), and [summarization](parea/evals/summary.py) apps :)
 
-You can define evaluation functions locally or use the ones you have deployed to Parea's [Test Hub](https://app.parea.ai/test-hub).
+You can define evaluation functions locally or use the ones you have deployed to
+Parea's [Test Hub](https://app.parea.ai/test-hub).
 If you choose the latter option, the evaluation happens asynchronously and non-blocking.
 
-A fully locally working cookbook can be found [here](parea/cookbook/tracing_and_evaluating_openai_endpoint.py). 
+A fully locally working cookbook can be found [here](parea/cookbook/tracing_and_evaluating_openai_endpoint.py).
 Alternatively, you can add the following code to your codebase to get started:
 
 ```python
@@ -62,7 +66,8 @@ def function_to_evaluate(*args, **kwargs) -> ...:
 ## Debugging Chains & Agents
 
 You can iterate on your chains & agents much faster by using a local cache. This will allow you to make changes to your
-code & prompts without waiting for all previous, valid LLM responses. Simply add these two lines to the beginning your code and start
+code & prompts without waiting for all previous, valid LLM responses. Simply add these two lines to the beginning your
+code and start
 [a local redis cache](https://redis.io/docs/getting-started/install-stack/):
 
 ```python
@@ -71,14 +76,15 @@ from parea import init, RedisCache
 init(cache=RedisCache())
 ```
 
-Above will use the default redis cache at `localhost:6379` with no password. You can also specify your redis database by:
+Above will use the default redis cache at `localhost:6379` with no password. You can also specify your redis database
+by:
 
 ```python
 from parea import init, RedisCache
 
 cache = RedisCache(
   host=os.getenv("REDIS_HOST", "localhost"),  # default value
-  port=int(os.getenv("REDIS_PORT", 6379)),    # default value
+  port=int(os.getenv("REDIS_PORT", 6379)),  # default value
   password=os.getenv("REDIS_PASSWORT", None)  # default value
 )
 init(cache=cache)
@@ -88,21 +94,24 @@ If you set `cache = None` for `init`, no cache will be used.
 
 ### Benchmark your LLM app across many inputs
 
-You can benchmark your LLM app across many inputs by using the `benchmark` command. This will run your the entry point 
+You can benchmark your LLM app across many inputs by using the `benchmark` command. This will run your the entry point
 of your app with the specified inputs and create a report with the results.
 
 ```bash
 parea benchmark --func app:main --csv_path benchmark.csv
 ```
 
-The CSV file will be used to fill in the arguments to your function. The report will be a CSV file of all the traces. If you
-set your Parea API key, the traces will also be logged to the Parea dashboard. Note, for this feature you need to have a 
+The CSV file will be used to fill in the arguments to your function. The report will be a CSV file of all the traces. If
+you
+set your Parea API key, the traces will also be logged to the Parea dashboard. Note, for this feature you need to have a
 redis cache running. Please, raise a GitHub issue if you would like to use this feature without a redis cache.
 
 ### Automatically log all your LLM call traces
 
-You can automatically log all your LLM traces to the Parea dashboard by setting the `PAREA_API_KEY` environment variable or specifying it in the `init` function.
-This will help you debug issues your customers are facing by stepping through the LLM call traces and recreating the issue
+You can automatically log all your LLM traces to the Parea dashboard by setting the `PAREA_API_KEY` environment variable
+or specifying it in the `init` function.
+This will help you debug issues your customers are facing by stepping through the LLM call traces and recreating the
+issue
 in your local setup & code.
 
 ```python
@@ -114,7 +123,6 @@ init(
 )
 ```
 
-
 ## Use a deployed prompt
 
 ```python
@@ -195,7 +203,7 @@ temperature = 0.0
 
 # define your OpenAI call as you would normally and we'll automatically log the results
 def main():
-  openai.ChatCompletion.create(model=model, temperature=temperature, messages=messages).choices[0].message["content"]
+  openai.chat.completions.create(model=model, temperature=temperature, messages=messages).choices[0].message.content
 ```
 
 ### Open source community features