Merge pull request #250 from infosatheesh2020/main

Updated ch06 with chat completion methods for newer models

06-text-generation-apps/README.md

@@ -190,12 +190,13 @@ Now that we learned how to set up and configure openai, it's time to build your
 
     # add your completion code
     prompt = "Complete the following: Once upon a time there was a"
+    messages = [{"role": "user", "content": prompt}] 
 
     # make completion
-    completion = openai.Completion.create(engine= deployment_name, model="davinci-002", prompt=prompt)
+    completion = client.chat.completions.create(model=deployment, messages=messages)
 
     # print response
-    print(completion.choices[0].text)
+    print(completion.choices[0].message.content)
     ```
 
     > [!NOTE]
@@ -511,15 +512,16 @@ To further improve it, we want to add the following:
     Locate the part in the code that prints out the result from the first prompt and add the following code below:
 
     ```python
-    old_prompt_result = completion.choices[0].text
+    old_prompt_result = completion.choices[0].message.content
     prompt = "Produce a shopping list for the generated recipes and please don't include ingredients that I already have."
 
     new_prompt = f"{old_prompt_result} {prompt}"
-    completion = openai.Completion.create(engine=deployment_name, prompt=new_prompt, max_tokens=1200)
+    messages = [{"role": "user", "content": new_prompt}]  
+    completion = openai.Completion.create(engine=deployment_name, messages=messages, max_tokens=1200)
 
     # print response
     print("Shopping list:")
-    print(completion.choices[0].text)
+    print(completion.choices[0].message.content)
     ```
 
     Note the following:
@@ -585,15 +587,15 @@ What we have so far is code that works, but there are some tweaks we should be d
    To change the tokens used, you can use the `max_tokens` parameter. For example, if you want to use 100 tokens, you would do:
 
     ```python
-    completion = openai.Completion.create(model="davinci-002", prompt=prompt, max_tokens=100)
+    completion = client.chat.completions.create(model=deployment, messages=messages, max_tokens=100)
     ```
 
 - **Experimenting with temperature**. Temperature is something we haven't mentioned so far but is an important context for how our program performs. The higher the temperature value the more random the output will be. Conversely the lower the temperature value the more predictable the output will be. Consider whether you want variation in your output or not.
 
    To alter the temperature, you can use the `temperature` parameter. For example, if you want to use a temperature of 0.5, you would do:
 
     ```python
-    completion = openai.Completion.create(model="davinci-002", prompt=prompt, temperature=0.5)
+    completion = client.chat.completions.create(model=deployment, messages=messages, temperature=0.5)
     ```
 
    > Note, the closer to 1.0, the more varied the output.

06-text-generation-apps/app-recipe.py

@@ -22,22 +22,23 @@
 
 # interpolate the number of recipes into the prompt an ingredients
 prompt = f"Show me {no_recipes} recipes for a dish with the following ingredients: {ingredients}. Per recipe, list all the ingredients used, no {filter}: "
+messages = [{"role": "user", "content": prompt}]
 
-
-completion = client.completions.create(model=deployment, prompt=prompt, max_tokens=600, temperature = 0.1)
+completion = client.chat.completions.create(model=deployment, messages=messages, max_tokens=600, temperature = 0.1)
 
 
 # print response
 print("Recipes:")
-print(completion.choices[0].text)
+print(completion.choices[0].message.content)
 
-old_prompt_result = completion.choices[0].text
+old_prompt_result = completion.choices[0].message.content
 prompt_shopping = "Produce a shopping list, and please don't include ingredients that I already have at home: "
 
 new_prompt = f"Given ingredients at home {ingredients} and these generated recipes: {old_prompt_result}, {prompt_shopping}"
-completion = client.completions.create(model=deployment, prompt=prompt, max_tokens=600)
+messages = [{"role": "user", "content": new_prompt}]
+completion = client.chat.completions.create(model=deployment, messages=messages, max_tokens=600, temperature=0)
 
 # print response
 print("\n=====Shopping list ======= \n")
-print(completion.choices[0].text)
+print(completion.choices[0].message.content)
 

06-text-generation-apps/app.py

@@ -16,12 +16,12 @@
 
 # add your completion code
 prompt = "Complete the following: Once upon a time there was a"
-
+messages = [{"role": "user", "content": prompt}]  
 # make completion
-completion = client.completions.create(model=deployment, prompt=prompt)
+completion = client.chat.completions.create(model=deployment, messages=messages)
 
 # print response
-print(completion.choices[0].text)
+print(completion.choices[0].message.content)
 
 #  very unhappy _____.
 

06-text-generation-apps/history-bot.py

@@ -0,0 +1,36 @@
+from openai import AzureOpenAI
+import os
+import dotenv
+
+# import dotenv
+dotenv.load_dotenv()
+
+# configure Azure OpenAI service client 
+client = AzureOpenAI(
+  azure_endpoint = os.environ["AZURE_OPENAI_ENDPOINT"], 
+  api_key=os.environ['AZURE_OPENAI_KEY'],  
+  api_version = "2023-10-01-preview"
+  )
+
+deployment=os.environ['AZURE_OPENAI_DEPLOYMENT']
+
+# add your completion code
+persona = input("Tell me the historical character I want to be: ")
+question = input("Ask your question about the historical character: ")
+prompt = f"""
+You are going to play as a historical character {persona}. 
+
+Whenever certain questions are asked, you need to remember facts about the timelines and incidents and respond the accurate answer only. Don't create content yourself. If you don't know something, tell that you don't remember.
+
+Provide answer for the question: {question}
+"""
+messages = [{"role": "user", "content": prompt}]  
+# make completion
+completion = client.chat.completions.create(model=deployment, messages=messages, temperature=0)
+
+# print response
+print(completion.choices[0].message.content)
+
+#  very unhappy _____.
+
+# Once upon a time there was a very unhappy mermaid.

06-text-generation-apps/notebook-azure-openai.ipynb

@@ -88,7 +88,7 @@
     "pip install openai\n",
     "```\n",
     "\n",
-    "If you aren't running this notebook in a Codespaces or a Dev Container, you also need to install [Python](https://www.python.org/) on your machine.",
+    "If you aren't running this notebook in a Codespaces or a Dev Container, you also need to install [Python](https://www.python.org/) on your machine.\n",
     "\n",
     "### Create a resource\n",
     "\n",
@@ -141,13 +141,13 @@
     "\n",
     "## Generate text\n",
     "\n",
-    "The way to generate text is to use the `Completion` class. Here's an example:\n",
+    "The way to generate text is to use the `chat.completion` class. Here's an example:\n",
     "\n",
     "```python\n",
     "prompt = \"Complete the following: Once upon a time there was a\"\n",
     "\n",
-    "completion = client.completions.create(model=deployment, prompt=prompt)\n",
-    "print(completion.choices[0].text)\n",
+    "completion = client.chat.completions.create(model=deployment, messages=[{\"role\": \"user\", \"content\": prompt}])\n",
+    "print(completion.choices[0].message.content)\n",
     "```\n",
     "\n",
     "In the above code, we create a completion object and pass in the model we want to use and the prompt. Then we print the generated text.\n",
@@ -240,12 +240,13 @@
     "\n",
     "# add your completion code\n",
     "prompt = \"Complete the following: Once upon a time there was a\"\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]  \n",
     "\n",
     "# make completion\n",
-    "completion = client.completions.create(model=deployment, prompt=prompt)\n",
+    "completion = client.chat.completions.create(model=deployment, messages=messages)\n",
     "\n",
     "# print response\n",
-    "print(completion.choices[0].text)"
+    "print(completion.choices[0].message.content)"
    ]
   },
   {
@@ -457,12 +458,13 @@
     "deployment=os.environ['AZURE_OPENAI_DEPLOYMENT']\n",
     "\n",
     "prompt = \"Show me 5 recipes for a dish with the following ingredients: chicken, potatoes, and carrots. Per recipe, list all the ingredients used\"\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]  \n",
     "\n",
     "# make completion\n",
-    "completion = client.completions.create(model=deployment, prompt=prompt, max_tokens=600)\n",
+    "completion = client.chat.completions.create(model=deployment, messages=messages, max_tokens=600)\n",
     "\n",
     "# print response\n",
-    "print(completion.choices[0].text)"
+    "print(completion.choices[0].message.content)"
    ]
   },
   {
@@ -516,12 +518,13 @@
     "\n",
     "# interpolate the number of recipes into the prompt an ingredients\n",
     "prompt = f\"Show me {no_recipes} recipes for a dish with the following ingredients: {ingredients}. Per recipe, list all the ingredients used\"\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]  \n",
     "\n",
     "# make completion\n",
-    "completion = client.completions.create(model=deployment, prompt=prompt, max_tokens=600)\n",
+    "completion = client.chat.completions.create(model=deployment, messages=messages, max_tokens=600)\n",
     "\n",
     "# print response\n",
-    "print(completion.choices[0].text)"
+    "print(completion.choices[0].message.content)"
    ]
   },
   {
@@ -638,11 +641,12 @@
     "    prompt = \"Produce a shopping list for the generated recipes and please don't include ingredients that I already have.\"\n",
     "    \n",
     "    new_prompt = f\"{old_prompt_result} {prompt}\"\n",
-    "    completion = client.completion.create(model=deployment, prompt=new_prompt, max_tokens=1200)\n",
+    "    messages = [{\"role\": \"user\", \"content\": new_prompt}]\n",
+    "    completion = client.chat.completion.create(model=deployment, messages=messages, max_tokens=1200)\n",
     "    \n",
     "    # print response\n",
     "    print(\"Shopping list:\")\n",
-    "    print(completion.choices[0].text)\n",
+    "    print(completion.choices[0].message.content)\n",
     "    ```\n",
     "\n",
     "    Note the following:\n",
@@ -651,12 +655,13 @@
     "    \n",
     "        ```python\n",
     "        new_prompt = f\"{old_prompt_result} {prompt}\"\n",
+    "        messages = [{\"role\": \"user\", \"content\": new_prompt}]\n",
     "        ```\n",
     "\n",
     "    - We make a new request, but also considering the number of tokens we asked for in the first prompt, so this time we say `max_tokens` is 1200. \n",
     "\n",
     "        ```python\n",
-    "        completion = client.completion.create(model=deployment, prompt=new_prompt, max_tokens=1200)\n",
+    "        completion = client.chat.completion.create(model=deployment, messages=messages, max_tokens=1200)\n",
     "        ```  \n",
     "\n",
     "        Taking this code for a spin, we now arrive at the following output:\n",
@@ -679,15 +684,15 @@
     "   To change tokens used, you can use the `max_tokens` parameter. For example, if you want to use 100 tokens, you would do:\n",
     "\n",
     "    ```python\n",
-    "    completion = client.completions.create(model=deployment, prompt=prompt, max_tokens=100)\n",
+    "    completion = client.chat.completion.create(model=deployment, messages=messages, max_tokens=100)\n",
     "    ```\n",
     "\n",
     "- **Experimenting with temperature**. Temperature is something we haven't mentioned so far but is an important context for how our program performs. The higher the temperature value the more random the output will be. Conversely the lower the temperature value the more predictable the output will be. Consider whether you want variation in your output or not.\n",
     "\n",
     "   To alter the temperature, you can use the `temperature` parameter. For example, if you want to use a temperature of 0.5, you would do:\n",
     "\n",
     "    ```python\n",
-    "    completion = client.completions.create(model=deployment, prompt=prompt, temperature=0.5)\n",
+    "    completion = client.chat.completion.create(model=deployment, messages=messages, temperature=0.5)\n",
     "    ```\n",
     "\n",
     "   > Note, the closer to 1.0, the more varied the output.\n",

06-text-generation-apps/study-buddy.py

@@ -0,0 +1,39 @@
+from openai import AzureOpenAI
+import os
+import dotenv
+
+# import dotenv
+dotenv.load_dotenv()
+
+# configure Azure OpenAI service client 
+client = AzureOpenAI(
+  azure_endpoint = os.environ["AZURE_OPENAI_ENDPOINT"], 
+  api_key=os.environ['AZURE_OPENAI_KEY'],  
+  api_version = "2023-10-01-preview"
+  )
+
+deployment=os.environ['AZURE_OPENAI_DEPLOYMENT']
+
+# add your completion code
+question = input("Ask your questions on python language to your study buddy: ")
+prompt = f"""
+You are an expert on the python language.
+
+Whenever certain questions are asked, you need to provide response in below format.
+
+- Concept
+- Example code showing the concept implementation
+- explanation of the example and how the concept is done for the user to understand better.
+
+Provide answer for the question: {question}
+"""
+messages = [{"role": "user", "content": prompt}]  
+# make completion
+completion = client.chat.completions.create(model=deployment, messages=messages)
+
+# print response
+print(completion.choices[0].message.content)
+
+#  very unhappy _____.
+
+# Once upon a time there was a very unhappy mermaid.

08-building-search-applications/notebook-azure-openai-simple.ipynb

@@ -16,21 +16,16 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "import openai\n",
+    "from openai import AzureOpenAI\n",
+    "import numpy as np\n",
     "from dotenv import load_dotenv\n",
     "load_dotenv()\n",
     "\n",
-    "openai.api_type = \"azure\"\n",
-    "openai.api_version = os.getenv(\"AZURE_OPENAI_API_VERSION\",\"\").strip()\n",
-    "\n",
-    "API_KEY = os.getenv(\"AZURE_OPENAI_API_KEY\",\"\").strip()\n",
-    "assert API_KEY, \"ERROR: Azure OpenAI Key is missing\"\n",
-    "openai.api_key = API_KEY\n",
-    "\n",
-    "RESOURCE_ENDPOINT = os.getenv(\"OPENAI_API_BASE\",\"\").strip()\n",
-    "assert RESOURCE_ENDPOINT, \"ERROR: Azure OpenAI Endpoint is missing\"\n",
-    "assert \"openai.azure.com\" in RESOURCE_ENDPOINT.lower(), \"ERROR: Azure OpenAI Endpoint should be in the form: \\n\\n\\t<your unique endpoint identifier>.openai.azure.com\"\n",
-    "openai.api_base = RESOURCE_ENDPOINT"
+    "client = AzureOpenAI(\n",
+    "  api_key = os.getenv(\"AZURE_OPENAI_KEY\"),  \n",
+    "  api_version = \"2023-05-15\",\n",
+    "  azure_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n",
+    ")"
    ]
   },
   {
@@ -49,7 +44,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from openai.embeddings_utils import cosine_similarity"
+    "def cosine_similarity(a, b):\n",
+    "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))"
    ]
   },
   {
@@ -60,8 +56,8 @@
    "source": [
     "text = 'the quick brown fox jumped over the lazy dog'\n",
     "model = 'text-embedding-ada-002'\n",
-    "openai.Embedding()\\\n",
-    "            .create(input=[text], engine='text-embedding-ada-002')[\"data\"][0][\"embedding\"]"
+    "\n",
+    "client.embeddings.create(input = [text], model=model).data[0].embedding"
    ]
   },
   {
@@ -71,10 +67,10 @@
    "outputs": [],
    "source": [
     "# compare several words\n",
-    "automobile_embedding    = openai.Embedding.create(input='automobile', engine=model)[\"data\"][0][\"embedding\"]\n",
-    "vehicle_embedding       = openai.Embedding.create(input='vehicle', engine=model)[\"data\"][0][\"embedding\"]\n",
-    "dinosaur_embedding      = openai.Embedding.create(input='dinosaur', engine=model)[\"data\"][0][\"embedding\"]\n",
-    "stick_embedding         = openai.Embedding.create(input='stick', engine=model)[\"data\"][0][\"embedding\"]\n",
+    "automobile_embedding    = client.embeddings.create(input = 'automobile', model=model).data[0].embedding\n",
+    "vehicle_embedding       = client.embeddings.create(input = 'vehicle', model=model).data[0].embedding\n",
+    "dinosaur_embedding      = client.embeddings.create(input = 'dinosaur', model=model).data[0].embedding\n",
+    "stick_embedding         = client.embeddings.create(input = 'stick', model=model).data[0].embedding\n",
     "\n",
     "# comparing cosine similarity, automobiles vs automobiles should be 1.0, i.e exactly the same, while automobiles vs dinosaurs should be between 0 and 1, i.e. not the same\n",
     "print(cosine_similarity(automobile_embedding, automobile_embedding))\n",
@@ -100,7 +96,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.13"
   },
   "orig_nbformat": 4
  },