Update embeddings_utils.py and related notebooks to API V1 (issue #855) (#857)

examples/Code_search_using_embeddings.ipynb

@@ -235,10 +235,10 @@
     }
    ],
    "source": [
-    "from openai.embeddings_utils import get_embedding\n",
+    "from utils.embeddings_utils import get_embedding\n",
     "\n",
     "df = pd.DataFrame(all_funcs)\n",
-    "df['code_embedding'] = df['code'].apply(lambda x: get_embedding(x, engine='text-embedding-ada-002'))\n",
+    "df['code_embedding'] = df['code'].apply(lambda x: get_embedding(x, model='text-embedding-ada-002'))\n",
     "df['filepath'] = df['filepath'].map(lambda x: Path(x).relative_to(code_root))\n",
     "df.to_csv(\"data/code_search_openai-python.csv\", index=False)\n",
     "df.head()"
@@ -266,10 +266,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from openai.embeddings_utils import cosine_similarity\n",
+    "from utils.embeddings_utils import cosine_similarity\n",
     "\n",
     "def search_functions(df, code_query, n=3, pprint=True, n_lines=7):\n",
-    "    embedding = get_embedding(code_query, engine='text-embedding-ada-002')\n",
+    "    embedding = get_embedding(code_query, model='text-embedding-ada-002')\n",
     "    df['similarities'] = df.code_embedding.apply(lambda x: cosine_similarity(x, embedding))\n",
     "\n",
     "    res = df.sort_values('similarities', ascending=False).head(n)\n",

examples/Customizing_embeddings.ipynb

@@ -51,7 +51,7 @@
         "from sklearn.model_selection import train_test_split  # for splitting train & test data\n",
         "import torch  # for matrix optimization\n",
         "\n",
-        "from openai.embeddings_utils import get_embedding, cosine_similarity  # for embeddings\n"
+        "from utils.embeddings_utils import get_embedding, cosine_similarity  # for embeddings\n"
       ]
     },
     {

examples/Multiclass_classification_for_transactions.ipynb

@@ -193,7 +193,7 @@
    "source": [
     "def request_completion(prompt):\n",
     "\n",
-    "    completion_response =   openai.Completion.create(\n",
+    "    completion_response =   openai.completions.create(\n",
     "                            prompt=prompt,\n",
     "                            temperature=0,\n",
     "                            max_tokens=5,\n",
@@ -211,7 +211,7 @@
     "    prompt = prompt.replace('DESCRIPTION_TEXT',transaction['Description'])\n",
     "    prompt = prompt.replace('TRANSACTION_VALUE',str(transaction['Transaction value (£)']))\n",
     "\n",
-    "    classification = request_completion(prompt)['choices'][0]['text'].replace('\\n','')\n",
+    "    classification = request_completion(prompt).choices[0].text.replace('\\n','')\n",
     "\n",
     "    return classification\n",
     "\n",
@@ -304,7 +304,7 @@
     "\n",
     "# Use our completion function to return a prediction\n",
     "completion_response = request_completion(prompt)\n",
-    "print(completion_response['choices'][0]['text'])\n"
+    "print(completion_response.choices[0].text)\n"
    ]
   },
   {
@@ -351,7 +351,7 @@
        " Building Improvement    14\n",
        " Could not classify       5\n",
        " Literature & Archive     3\n",
-       " Software/IT              2\n",
+" Software/IT              2\n",
        " Utility Bills            1\n",
        "Name: Classification, dtype: int64"
       ]
@@ -916,8 +916,8 @@
    "source": [
     "from utils.embeddings_utils import get_embedding\n",
     "\n",
-    "df['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x, engine='text-similarity-babbage-001'))\n",
-    "df['babbage_search'] = df.combined.apply(lambda x: get_embedding(x, engine='text-search-babbage-doc-001'))\n",
+    "df['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x, model='text-similarity-babbage-001'))\n",
+    "df['babbage_search'] = df.combined.apply(lambda x: get_embedding(x, model='text-search-babbage-doc-001'))\n",
     "df.to_csv(embedding_path)\n"
    ]
   },
@@ -2203,7 +2203,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

examples/Semantic_text_search_using_embeddings.ipynb

@@ -59,7 +59,7 @@
     "def search_reviews(df, product_description, n=3, pprint=True):\n",
     "    product_embedding = get_embedding(\n",
     "        product_description,\n",
-    "        engine=\"text-embedding-ada-002\"\n",
+    "        model=\"text-embedding-ada-002\"\n",
     "    )\n",
     "    df[\"similarity\"] = df.embedding.apply(lambda x: cosine_similarity(x, product_embedding))\n",
     "\n",

examples/Visualizing_embeddings_in_3D.ipynb

@@ -138,7 +138,7 @@
    "source": [
     "from utils.embeddings_utils import get_embeddings\n",
     "# NOTE: The following code will send a query of batch size 200 to /embeddings\n",
-    "matrix = get_embeddings(samples[\"text\"].to_list(), engine=\"text-embedding-ada-002\")\n"
+    "matrix = get_embeddings(samples[\"text\"].to_list(), model=\"text-embedding-ada-002\")\n"
    ]
   },
   {

examples/Zero-shot_classification_with_embeddings.ipynb

@@ -93,7 +93,7 @@
     "    labels = ['negative', 'positive'],\n",
     "    model = EMBEDDING_MODEL,\n",
     "):\n",
-    "    label_embeddings = [get_embedding(label, engine=model) for label in labels]\n",
+    "    label_embeddings = [get_embedding(label, model=model) for label in labels]\n",
     "\n",
     "    def label_score(review_embedding, label_embeddings):\n",
     "        return cosine_similarity(review_embedding, label_embeddings[1]) - cosine_similarity(review_embedding, label_embeddings[0])\n",

examples/utils/embeddings_utils.py

@@ -15,51 +15,53 @@ import pandas as pd
 
 
 @retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
-def get_embedding(text: str, engine="text-similarity-davinci-001", **kwargs) -> List[float]:
+def get_embedding(text: str, model="text-similarity-davinci-001", **kwargs) -> List[float]:
 
     # replace newlines, which can negatively affect performance.
     text = text.replace("\n", " ")
 
-    return openai.Embedding.create(input=[text], engine=engine, **kwargs)["data"][0]["embedding"]
+    response = openai.embeddings.create(input=[text], model=model, **kwargs)
+
+    return response.data[0].embedding
 
 
 @retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
 async def aget_embedding(
-    text: str, engine="text-similarity-davinci-001", **kwargs
+    text: str, model="text-similarity-davinci-001", **kwargs
 ) -> List[float]:
 
     # replace newlines, which can negatively affect performance.
     text = text.replace("\n", " ")
 
-    return (await openai.Embedding.acreate(input=[text], engine=engine, **kwargs))["data"][0][
+    return (await openai.embeddings.create(input=[text], model=model, **kwargs))["data"][0][
         "embedding"
     ]
 
 
 @retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
 def get_embeddings(
-    list_of_text: List[str], engine="text-similarity-babbage-001", **kwargs
+    list_of_text: List[str], model="text-similarity-babbage-001", **kwargs
 ) -> List[List[float]]:
     assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
 
     # replace newlines, which can negatively affect performance.
     list_of_text = [text.replace("\n", " ") for text in list_of_text]
 
-    data = openai.Embedding.create(input=list_of_text, engine=engine, **kwargs).data
-    return [d["embedding"] for d in data]
+    data = openai.embeddings.create(input=list_of_text, model=model, **kwargs).data
+    return [d.embedding for d in data]
 
 
 @retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
 async def aget_embeddings(
-    list_of_text: List[str], engine="text-similarity-babbage-001", **kwargs
+    list_of_text: List[str], model="text-similarity-babbage-001", **kwargs
 ) -> List[List[float]]:
     assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
 
     # replace newlines, which can negatively affect performance.
     list_of_text = [text.replace("\n", " ") for text in list_of_text]
 
-    data = (await openai.Embedding.acreate(input=list_of_text, engine=engine, **kwargs)).data
-    return [d["embedding"] for d in data]
+    data = (await openai.embeddings.create(input=list_of_text, model=model, **kwargs)).data
+    return [d.embedding for d in data]
 
 
 def cosine_similarity(a, b):