diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ff3243764..dd09e9627 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -5,15 +5,12 @@ default_stages: [commit]
 default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
-  #  - repo: https://github.com/astral-sh/ruff-pre-commit
-  #    # Ruff version.
-  #    rev: v0.1.11
-  #    hooks:
-  #      # Run the linter.
-  #      - id: ruff
-  #        args: [--fix]
-  #      # Run the formatter.
-  #      - id: ruff-format
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.11
+    hooks:
+      - id: ruff
+        args: [--fix]
+      - id: ruff-format
 
   - repo: https://github.com/timothycrosley/isort
     rev: 5.12.0
@@ -50,14 +47,15 @@ repos:
         args:
           - "--autofix"
           - "--indent=2"
-  #  - repo: local
-  #    hooks:
-  #      - id: validate-commit-msg
-  #        name: Commit Message is Valid
-  #        language: pygrep
-  #        entry: ^(break|build|ci|docs|feat|fix|perf|refactor|style|test|ops|hotfix|release|maint|init|enh|revert)\([\w,\.,\-,\(,\),\/]+\)(!?)(:)\s{1}([\w,\W,:]+)
-  #        stages: [commit-msg]
-  #        args: [--negate]
+
+  - repo: local
+    hooks:
+      - id: validate-commit-msg
+        name: Commit Message is Valid
+        language: pygrep
+        entry: ^(break|build|ci|docs|feat|fix|perf|refactor|style|test|ops|hotfix|release|maint|init|enh|revert)\([\w,\.,\-,\(,\),\/]+\)(!?)(:)\s{1}([\w,\W,:]+)
+        stages: [commit-msg]
+        args: [--negate]
 
   - repo: https://github.com/pre-commit/mirrors-prettier
     rev: v3.0.3
diff --git a/dsp/modules/cache_utils.py b/dsp/modules/cache_utils.py
index e6c09fe71..a7e2bde2f 100644
--- a/dsp/modules/cache_utils.py
+++ b/dsp/modules/cache_utils.py
@@ -6,7 +6,7 @@
 
 from dsp.utils import dotdict
 
-cache_turn_on = True
+cache_turn_on = os.environ.get('DSP_CACHEBOOL', 'True').lower() != 'false'
 
 
 def noop_decorator(arg=None, *noop_args, **noop_kwargs):
diff --git a/dsp/modules/gpt3.py b/dsp/modules/gpt3.py
index cd9359bc1..02d3929b9 100644
--- a/dsp/modules/gpt3.py
+++ b/dsp/modules/gpt3.py
@@ -62,12 +62,15 @@ def __init__(
         api_provider: Literal["openai"] = "openai",
         api_base: Optional[str] = None,
         model_type: Literal["chat", "text"] = None,
+        system_prompt: Optional[str] = None,
         **kwargs,
     ):
         super().__init__(model)
         self.provider = "openai"
         openai.api_type = api_provider
 
+        self.system_prompt = system_prompt
+
         assert (
             api_provider != "azure"
         ), "Azure functionality with base OpenAI has been deprecated, please use dspy.AzureOpenAI instead."
@@ -118,6 +121,9 @@ def basic_request(self, prompt: str, **kwargs):
         kwargs = {**self.kwargs, **kwargs}
         if self.model_type == "chat":
             # caching mechanism requires hashable kwargs
+            messages = [{"role": "user", "content": prompt}]
+            if self.system_prompt:
+                messages.insert(0, {"role": "system", "content": self.system_prompt})
             kwargs["messages"] = [{"role": "user", "content": prompt}]
             kwargs = {"stringify_request": json.dumps(kwargs)}
             response = chat_request(**kwargs)
diff --git a/dsp/primitives/predict.py b/dsp/primitives/predict.py
index 41edf6c51..ffd4f8caa 100644
--- a/dsp/primitives/predict.py
+++ b/dsp/primitives/predict.py
@@ -98,11 +98,20 @@ def do_generate(
             completion[field_names[last_field_idx]] = ""
 
             # Recurse with greedy decoding and a shorter length.
-            max_tokens = kwargs.get("max_tokens", dsp.settings.lm.kwargs["max_tokens"])
+            max_tokens = (kwargs.get("max_tokens") or 
+                        kwargs.get("max_output_tokens") or
+                        dsp.settings.lm.kwargs.get("max_tokens") or 
+                        dsp.settings.lm.kwargs.get('max_output_tokens'))
+
+
+            if max_tokens is None:
+                raise ValueError("Required 'max_tokens' or 'max_output_tokens' not specified in settings.")
             max_tokens = min(max(75, max_tokens // 2), max_tokens)
+            keys = list(kwargs.keys()) + list(dsp.settings.lm.kwargs.keys()) 
+            max_tokens_key = "max_tokens" if "max_tokens" in keys else "max_output_tokens"
             new_kwargs = {
                 **kwargs,
-                "max_tokens": max_tokens,
+                max_tokens_key: max_tokens,
                 "n": 1,
                 "temperature": 0.0,
             }
diff --git a/dsp/utils/settings.py b/dsp/utils/settings.py
index 1de0ada7e..0d0846800 100644
--- a/dsp/utils/settings.py
+++ b/dsp/utils/settings.py
@@ -36,7 +36,7 @@ def __new__(cls):
                 force_reuse_cached_compilation=False,
                 compiling=False,
                 skip_logprobs=False,
-                trace=None,
+                trace=[],
                 release=0,
                 log_openai_usage=False,
                 bypass_assert=False,
diff --git a/dspy/experimental/synthesizer/synthesizer.py b/dspy/experimental/synthesizer/synthesizer.py
index 0672a2808..bd70e6d7a 100644
--- a/dspy/experimental/synthesizer/synthesizer.py
+++ b/dspy/experimental/synthesizer/synthesizer.py
@@ -202,7 +202,9 @@ def generate(
             }
 
             if self.config.num_example_for_optim:
-                kwargs["ground_source"] = random.sample(ground_source, self.config.num_example_for_optim)
+                if not isinstance(ground_source, list):
+                    raise ValueError("Ground source must be a list of examples when `num_example_for_optim` is provided.")
+                kwargs["ground_source"] = random.sample(ground_source, k=self.config.num_example_for_optim)
             
             with dspy.context(lm=self.input_lm):
                 inputs = self.input_predictor(**kwargs)
diff --git a/dspy/retrieve/pgvector_rm.py b/dspy/retrieve/pgvector_rm.py
index cf1773f17..d12eb6b05 100644
--- a/dspy/retrieve/pgvector_rm.py
+++ b/dspy/retrieve/pgvector_rm.py
@@ -1,6 +1,5 @@
-from typing import List, Optional
-
-import openai
+import warnings
+from typing import Callable, Optional
 
 import dspy
 
@@ -12,6 +11,11 @@
     raise ImportError(
         "The 'pgvector' extra is required to use PgVectorRM. Install it with `pip install dspy-ai[pgvector]`",
     )
+try:
+    import openai
+except ImportError:
+    warnings.warn("`openai` is not installed. Install it with `pip install openai` to use OpenAI embedding models.",
+                  category=ImportWarning)
 
 
 class PgVectorRM(dspy.Retrieve):
@@ -26,7 +30,8 @@ class PgVectorRM(dspy.Retrieve):
     Args:
         db_url (str): A PostgreSQL database URL in psycopg2's DSN format
         pg_table_name (Optional[str]): name of the table containing passages
-        openai_client (openai.OpenAI): OpenAI client to use for computing query embeddings
+        openai_client (openai.OpenAI): OpenAI client to use for computing query embeddings. Either openai_client or embedding_func must be provided.
+        embedding_func (Callable): A function to use for computing query embeddings. Either openai_client or embedding_func must be provided.
         k (Optional[int]): Default number of top passages to retrieve. Defaults to 20
         embedding_field (str = "embedding"): Field containing passage embeddings. Defaults to "embedding"
         fields (List[str] = ['text']): Fields to retrieve from the table. Defaults to "text"
@@ -41,10 +46,10 @@ class PgVectorRM(dspy.Retrieve):
 
         openai.api_key = os.environ.get("OPENAI_API_KEY", None)
         openai_client = openai.OpenAI()
-        
+
         llm = dspy.OpenAI(model="gpt-3.5-turbo")
-        
-        DATABASE_URL should be in the format postgresql://user:password@host/database 
+
+        DATABASE_URL should be in the format postgresql://user:password@host/database
         db_url=os.getenv("DATABASE_URL")
 
         retriever_model = PgVectorRM(conn, openai_client=openai_client, "paragraphs", fields=["text", "document_id"], k=20)
@@ -60,16 +65,19 @@ def __init__(
             self,
             db_url: str,
             pg_table_name: str,
-            openai_client: openai.OpenAI,
-            k: Optional[int]=20,
+            openai_client: Optional[openai.OpenAI] = None,
+            embedding_func: Optional[Callable] = None,
+            k: Optional[int] = 20,
             embedding_field: str = "embedding",
-            fields: List[str] = ['text'],
+            fields: list[str] = ['text'],
     ):
         """
         k = 20 is the number of paragraphs to retrieve
         """
+        assert openai_client or embedding_func, "Either openai_client or embedding_func must be provided."
         self.openai_client = openai_client
-        
+        self.embedding_func = embedding_func
+
         self.conn = psycopg2.connect(db_url)
         register_vector(self.conn)
         self.pg_table_name = pg_table_name
@@ -80,19 +88,15 @@ def __init__(
 
     def forward(self, query: str, k: Optional[int]=20):
         """Search with PgVector for self.k top passages for query
-        
+
         Args:
             query  (str): The query to search for
             k (Optional[int]): The number of top passages to retrieve. Defaults to self.k
-        Returns: 
+        Returns:
             dspy.Prediction: an object containing the retrieved passages.
         """
         # Embed query
-        query_embedding = self.openai_client.embeddings.create(
-            model="text-embedding-ada-002",
-            input=query,
-            encoding_format="float",
-        ).data[0].embedding
+        query_embedding = self._get_embeddings(query)
 
         related_paragraphs = []
 
@@ -115,4 +119,14 @@ def forward(self, query: str, k: Optional[int]=20):
                 for row in rows:
                     related_paragraphs.append(dspy.Example(long_text=row[0], document_id=row[1]))
         # Return Prediction
-        return related_paragraphs
\ No newline at end of file
+        return related_paragraphs
+
+    def _get_embeddings(self, query: str) -> list[float]:
+        if self.openai_client is not None:
+            return self.openai_client.embeddings.create(
+                model="text-embedding-ada-002",
+                input=query,
+                encoding_format="float",
+            ).data[0].embedding
+        else:
+            return self.embedding_func(query)
diff --git a/intro.ipynb b/intro.ipynb
index c6db5871a..1dc2cf6d9 100644
--- a/intro.ipynb
+++ b/intro.ipynb
@@ -35,20 +35,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/qz/yy2p38hj2m9c7bfp30yq99340000gn/T/ipykernel_40349/1846046422.py:20: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n",
-      "  import pkg_resources # Install the package if it's not installed\n",
-      "/opt/homebrew/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -94,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -157,7 +146,7 @@
        "(20, 50)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -188,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -215,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -244,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -306,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,39 +319,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "ename": "OpenAIError",
-     "evalue": "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOpenAIError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[9], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m generate_answer \u001b[38;5;241m=\u001b[39m dspy\u001b[38;5;241m.\u001b[39mPredict(BasicQA)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Call the predictor on a particular input.\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m pred \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquestion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdev_example\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquestion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Print the input and the prediction.\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuestion: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdev_example\u001b[38;5;241m.\u001b[39mquestion\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/repos/dspy/dspy/predict/predict.py:49\u001b[0m, in \u001b[0;36mPredict.__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/repos/dspy/dspy/predict/predict.py:91\u001b[0m, in \u001b[0;36mPredict.forward\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     88\u001b[0m template \u001b[38;5;241m=\u001b[39m signature_to_template(signature)\n\u001b[1;32m     90\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlm \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 91\u001b[0m     x, C \u001b[38;5;241m=\u001b[39m \u001b[43mdsp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtemplate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     93\u001b[0m     \u001b[38;5;66;03m# Note: query_only=True means the instructions and examples are not included.\u001b[39;00m\n\u001b[1;32m     94\u001b[0m     \u001b[38;5;66;03m# I'm not really sure why we'd want to do that, but it's there.\u001b[39;00m\n\u001b[1;32m     95\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m dsp\u001b[38;5;241m.\u001b[39msettings\u001b[38;5;241m.\u001b[39mcontext(lm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlm, query_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n",
-      "File \u001b[0;32m~/repos/dspy/dsp/primitives/predict.py:77\u001b[0m, in \u001b[0;36m_generate.<locals>.do_generate\u001b[0;34m(example, stage, max_depth, original_example)\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;66;03m# Generate and extract the fields.\u001b[39;00m\n\u001b[1;32m     76\u001b[0m prompt \u001b[38;5;241m=\u001b[39m template(example)\n\u001b[0;32m---> 77\u001b[0m completions: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m \u001b[43mgenerator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     78\u001b[0m completions: \u001b[38;5;28mlist\u001b[39m[Example] \u001b[38;5;241m=\u001b[39m [template\u001b[38;5;241m.\u001b[39mextract(example, p) \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m completions]\n\u001b[1;32m     80\u001b[0m \u001b[38;5;66;03m# Find the completions that are most complete.\u001b[39;00m\n",
-      "File \u001b[0;32m~/repos/dspy/dsp/modules/gpt3.py:186\u001b[0m, in \u001b[0;36mGPT3.__call__\u001b[0;34m(self, prompt, only_completed, return_sorted, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m return_sorted \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor now\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    180\u001b[0m \u001b[38;5;66;03m# if kwargs.get(\"n\", 1) > 1:\u001b[39;00m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;66;03m#     if self.model_type == \"chat\":\u001b[39;00m\n\u001b[1;32m    182\u001b[0m \u001b[38;5;66;03m#         kwargs = {**kwargs}\u001b[39;00m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;66;03m#     else:\u001b[39;00m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;66;03m#         kwargs = {**kwargs, \"logprobs\": 5}\u001b[39;00m\n\u001b[0;32m--> 186\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dsp\u001b[38;5;241m.\u001b[39msettings\u001b[38;5;241m.\u001b[39mlog_openai_usage:\n\u001b[1;32m    189\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_usage(response)\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/backoff/_sync.py:105\u001b[0m, in \u001b[0;36mretry_exception.<locals>.retry\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     96\u001b[0m details \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m\"\u001b[39m: target,\n\u001b[1;32m     98\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m: args,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124melapsed\u001b[39m\u001b[38;5;124m\"\u001b[39m: elapsed,\n\u001b[1;32m    102\u001b[0m }\n\u001b[1;32m    104\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 105\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mtarget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    106\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m exception \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    107\u001b[0m     max_tries_exceeded \u001b[38;5;241m=\u001b[39m (tries \u001b[38;5;241m==\u001b[39m max_tries_value)\n",
-      "File \u001b[0;32m~/repos/dspy/dsp/modules/gpt3.py:152\u001b[0m, in \u001b[0;36mGPT3.request\u001b[0;34m(self, prompt, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbasic_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/repos/dspy/dsp/modules/gpt3.py:125\u001b[0m, in \u001b[0;36mGPT3.basic_request\u001b[0;34m(self, prompt, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: prompt}]\n\u001b[1;32m    124\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstringify_request\u001b[39m\u001b[38;5;124m\"\u001b[39m: json\u001b[38;5;241m.\u001b[39mdumps(kwargs)}\n\u001b[0;32m--> 125\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mchat_request\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    128\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m prompt\n",
-      "File \u001b[0;32m~/repos/dspy/dsp/modules/gpt3.py:273\u001b[0m, in \u001b[0;36mchat_request\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m OPENAI_LEGACY:\n\u001b[1;32m    271\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _cached_gpt3_turbo_request_v2_wrapped(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mv1_cached_gpt3_turbo_request_v2_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mmodel_dump()\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/joblib/memory.py:655\u001b[0m, in \u001b[0;36mMemorizedFunc.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    654\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 655\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cached_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/joblib/memory.py:598\u001b[0m, in \u001b[0;36mMemorizedFunc._cached_call\u001b[0;34m(self, args, kwargs, shelving)\u001b[0m\n\u001b[1;32m    595\u001b[0m     must_call \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    597\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m must_call:\n\u001b[0;32m--> 598\u001b[0m     out, metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    599\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmmap_mode \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    600\u001b[0m         \u001b[38;5;66;03m# Memmap the output at the first call to be consistent with\u001b[39;00m\n\u001b[1;32m    601\u001b[0m         \u001b[38;5;66;03m# later calls\u001b[39;00m\n\u001b[1;32m    602\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verbose:\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/joblib/memory.py:856\u001b[0m, in \u001b[0;36mMemorizedFunc.call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verbose \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    855\u001b[0m     \u001b[38;5;28mprint\u001b[39m(format_call(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc, args, kwargs))\n\u001b[0;32m--> 856\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    857\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstore_backend\u001b[38;5;241m.\u001b[39mdump_item(\n\u001b[1;32m    858\u001b[0m     [func_id, args_id], output, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verbose)\n\u001b[1;32m    860\u001b[0m duration \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time\n",
-      "File \u001b[0;32m~/repos/dspy/dsp/modules/gpt3.py:266\u001b[0m, in \u001b[0;36mv1_cached_gpt3_turbo_request_v2_wrapped\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mlru_cache(maxsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m cache_turn_on \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    264\u001b[0m \u001b[38;5;129m@NotebookCacheMemory\u001b[39m\u001b[38;5;241m.\u001b[39mcache\n\u001b[1;32m    265\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mv1_cached_gpt3_turbo_request_v2_wrapped\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 266\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mv1_cached_gpt3_turbo_request_v2\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/joblib/memory.py:655\u001b[0m, in \u001b[0;36mMemorizedFunc.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    654\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 655\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cached_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/joblib/memory.py:598\u001b[0m, in \u001b[0;36mMemorizedFunc._cached_call\u001b[0;34m(self, args, kwargs, shelving)\u001b[0m\n\u001b[1;32m    595\u001b[0m     must_call \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    597\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m must_call:\n\u001b[0;32m--> 598\u001b[0m     out, metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    599\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmmap_mode \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    600\u001b[0m         \u001b[38;5;66;03m# Memmap the output at the first call to be consistent with\u001b[39;00m\n\u001b[1;32m    601\u001b[0m         \u001b[38;5;66;03m# later calls\u001b[39;00m\n\u001b[1;32m    602\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verbose:\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/joblib/memory.py:856\u001b[0m, in \u001b[0;36mMemorizedFunc.call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verbose \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    855\u001b[0m     \u001b[38;5;28mprint\u001b[39m(format_call(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc, args, kwargs))\n\u001b[0;32m--> 856\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    857\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstore_backend\u001b[38;5;241m.\u001b[39mdump_item(\n\u001b[1;32m    858\u001b[0m     [func_id, args_id], output, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verbose)\n\u001b[1;32m    860\u001b[0m duration \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time\n",
-      "File \u001b[0;32m~/repos/dspy/dsp/modules/gpt3.py:260\u001b[0m, in \u001b[0;36mv1_cached_gpt3_turbo_request_v2\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstringify_request\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[1;32m    259\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstringify_request\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m--> 260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopenai\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241m.\u001b[39mcreate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/openai/_utils/_proxy.py:20\u001b[0m, in \u001b[0;36mLazyProxy.__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m, attr: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m---> 20\u001b[0m     proxied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_proxied__\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(proxied, LazyProxy):\n\u001b[1;32m     22\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m proxied  \u001b[38;5;66;03m# pyright: ignore\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/openai/_utils/_proxy.py:55\u001b[0m, in \u001b[0;36mLazyProxy.__get_proxied__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_proxied__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[0;32m---> 55\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__load__\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/openai/_module_client.py:12\u001b[0m, in \u001b[0;36mChatProxy.__load__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;129m@override\u001b[39m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__load__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m resources\u001b[38;5;241m.\u001b[39mChat:\n\u001b[0;32m---> 12\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load_client\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mchat\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/openai/__init__.py:297\u001b[0m, in \u001b[0;36m_load_client\u001b[0;34m()\u001b[0m\n\u001b[1;32m    281\u001b[0m         _client \u001b[38;5;241m=\u001b[39m _AzureModuleClient(  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m    282\u001b[0m             api_version\u001b[38;5;241m=\u001b[39mapi_version,\n\u001b[1;32m    283\u001b[0m             azure_endpoint\u001b[38;5;241m=\u001b[39mazure_endpoint,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    293\u001b[0m             http_client\u001b[38;5;241m=\u001b[39mhttp_client,\n\u001b[1;32m    294\u001b[0m         )\n\u001b[1;32m    295\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m _client\n\u001b[0;32m--> 297\u001b[0m     _client \u001b[38;5;241m=\u001b[39m \u001b[43m_ModuleClient\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m        \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m        \u001b[49m\u001b[43morganization\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morganization\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    300\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbase_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_url\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    301\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    302\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    303\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdefault_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdefault_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhttp_client\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhttp_client\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    306\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    307\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _client\n\u001b[1;32m    309\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _client\n",
-      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/openai/_client.py:98\u001b[0m, in \u001b[0;36mOpenAI.__init__\u001b[0;34m(self, api_key, organization, base_url, timeout, max_retries, default_headers, default_query, http_client, _strict_response_validation)\u001b[0m\n\u001b[1;32m     96\u001b[0m     api_key \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39menviron\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOPENAI_API_KEY\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     97\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m api_key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 98\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m OpenAIError(\n\u001b[1;32m     99\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    100\u001b[0m     )\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapi_key \u001b[38;5;241m=\u001b[39m api_key\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m organization \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "\u001b[0;31mOpenAIError\u001b[0m: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n",
+      "Predicted Answer: American\n"
      ]
     }
    ],
@@ -389,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -482,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -520,7 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -553,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -579,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -625,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -676,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -711,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -861,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -894,7 +859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1052,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1218,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1241,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1305,7 +1270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1341,7 +1306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1472,7 +1437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1497,7 +1462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1528,28 +1493,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'evaluate_on_hotpotqa' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m uncompiled_baleen_retrieval_score \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate_on_hotpotqa\u001b[49m(uncompiled_baleen, metric\u001b[38;5;241m=\u001b[39mgold_passages_retrieved, display\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'evaluate_on_hotpotqa' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "uncompiled_baleen_retrieval_score = evaluate_on_hotpotqa(uncompiled_baleen, metric=gold_passages_retrieved, display=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1679,7 +1632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1718,7 +1671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1998,7 +1951,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.9.17"
   },
   "orig_nbformat": 4
  },