Updated version

(Clone) ES 1a: Configure External models for Automation.py

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+# Databricks notebook source
+# MAGIC %md
+# MAGIC # External Models for Email Response Automation
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Install MLflow with external models support
+
+# COMMAND ----------
+
+# MAGIC %pip install mlflow[genai]>=2.9.0
+# MAGIC %pip install --upgrade mlflow
+# MAGIC %pip install --upgrade langchain
+# MAGIC dbutils.library.restartPython()
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC In this notebook, we will use External Models for automation of the email response. External models are third-party models hosted outside of Databricks. Supported by Model Serving, external models allow you to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. For this specific problem, we have picked OpenAI.
+# MAGIC
+# MAGIC https://docs.databricks.com/en/generative-ai/external-models/index.html
+
+# COMMAND ----------
+
+# MAGIC %run "./ES 0a: Intro & Config"
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ### Read the emails from the bronze layer
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC Lets read the raw emails persisted in the bronze layer in a Dataframe.
+# MAGIC
+
+# COMMAND ----------
+
+emails_silver=spark \
+                .read \
+                .table(config['table_emails_bronze'])
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Create OpenAI Completion endpoint for our solution
+
+# COMMAND ----------
+
+import mlflow.deployments
+
+client = mlflow.deployments.get_deploy_client("databricks")
+client.create_endpoint(
+    name="Email-OpenAI-Completion-Endpoint",
+    config={
+        "served_entities": [{
+            "external_model": {
+                "name": "gpt-3.5-turbo-instruct",
+                "provider": "openai",
+                "task": "llm/v1/completions",
+                "openai_config": {
+                    "openai_api_key": "{{secrets/email_openai_secret_scope/openai_api_key}}",
+                }
+            }
+        }]
+    }
+)
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Setup Langchain for the external model
+# MAGIC
+# MAGIC We will setup Langchain to define the prompt template that will retrieve email Catagory, Sentiment, Synopsis and possible reply.
+# MAGIC
+# MAGIC The possible reply can be based on the templated and embedding can be used for it. However in this solution, we can not defined it.
+
+# COMMAND ----------
+
+import mlflow
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms import Databricks
+
+gateway = Databricks(
+    host="https://" + spark.conf.get("spark.databricks.workspaceUrl"), 
+    endpoint_name="Email-OpenAI-Completion-Endpoint",
+    temperature=0.1,
+)
+
+# Build Prompt Template
+template = """
+Given the following email text, categorise whether the email is a job request, customer query or generic email where no action required. It should capture sentiment of the email as positive, negative or neutral. Also it should create a short summary of the email. In addition, it should draft possible reply to email.
+
+The output should be structured as a JSON dictionary of dictionaries. First attribute name is "Category" which categorises of the email as three possible values - Job, Query or No Action. Second json attribute name is Sentiment with possible values - positive, negative or neutral. Third json attribute name is "Synopsis" which should capture short email summary. Forth JSON attribute name "Reply" should be possibly email reply to the original email.
+
+Email summary begin here: {email_body}"""
+
+prompt = PromptTemplate(template=template, input_variables=["email_body"])
+
+# Build LLM Chain
+llm_chain = LLMChain(prompt=prompt, llm=gateway)
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Test one of the emails with the OpenAI API
+
+# COMMAND ----------
+
+# Retrieve a single review for testing
+test_single_review = emails_silver.limit(1).select("email_body_clean").collect()[0][0]
+
+# print(test_single_review)
+# Predict on the review
+response_string = llm_chain.invoke(test_single_review)
+
+# Print string
+print(response_string)

(Clone) ES 1b: Configure Foundation models for Automation.py

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+# Databricks notebook source
+# MAGIC %md
+# MAGIC # Foundation Models for Email Response Automation
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC Databricks Model Serving supports any Foundation Model, be it a fully custom model, a Databricks-managed model, or a third-party Foundation Model. This flexibility allows you to choose the right model for the right job, keeping you ahead of future advances in the range of available models. 
+# MAGIC
+# MAGIC You can get started with Foundation Model APIs on a pay-per-token basis, which significantly reduces operational costs. Alternatively, for workloads requiring fine-tuned models or performance guarantees, you can switch to Provisioned Throughput.
+# MAGIC
+# MAGIC Please refer Foundation Model API documentation for more details:
+# MAGIC https://docs.databricks.com/en/machine-learning/foundation-models/index.html?_gl=1*licvcu*_gcl_au*ODA1MDc0NDEzLjE3MDMzMjEzNDk
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Install the SDK on a Databricks Notebook
+
+# COMMAND ----------
+
+# MAGIC %pip install mlflow==2.9.0 langchain==0.0.344 databricks-sdk==0.12.0 
+
+# COMMAND ----------
+
+!pip install databricks-genai-inference
+
+dbutils.library.restartPython()
+
+# COMMAND ----------
+
+# MAGIC %run "./ES 0a: Intro & Config"
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ### Read the emails from the bronze layer
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC Lets read the raw emails persisted in the bronze layer in a Dataframe.
+# MAGIC
+
+# COMMAND ----------
+
+emails_silver=spark \
+                .read \
+                .table(config['table_emails_bronze'])
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Create Mistral-8X7b-instruct Completion endpoint for our solution
+# MAGIC
+# MAGIC We analysed multiple foundation models and Mistral is providing better results for the email automation in our case. However any foundation model can be used based on your preference and uniqueness of your data
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Setup Langchain for the Foundation model
+# MAGIC
+# MAGIC We will setup Langchain to define the prompt template that will retrieve email Catagory, Sentiment, Synopsis and possible reply.
+# MAGIC
+# MAGIC The possible reply can be based on the templated and embedding can be used for it. However in this solution, we can not defined it.
+
+# COMMAND ----------
+
+import mlflow
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms import Databricks
+from langchain.chat_models import ChatDatabricks
+from langchain.schema.output_parser import StrOutputParser
+
+prompt = PromptTemplate(
+  input_variables = ["question"],
+  template = "You are an assistant. Give a short answer to this question: {question}"
+)
+chat_model = ChatDatabricks(endpoint="databricks-mixtral-8x7b-instruct", max_tokens = 500)
+
+# Build Prompt Template
+template = """
+<s>[INST] <<SYS>>
+Given the following email text, categorise whether the email is a job request, customer query or generic email where no action required. It should capture sentiment of the email as positive, negative or neutral. Also it should create a short summary of the email. In addition, it should draft possible reply to email. the output of the questions should only be a JSON dictionary of dictionaries
+
+The output should be structured as a JSON dictionary of dictionaries. First attribute name is "Category" which categorises of the email as three possible values - Job, Query or No Action. Second json attribute name is Sentiment with possible values - positive, negative or neutral. Third json attribute name is "Synopsis" which should capture short email summary in 2-3 lines. Forth JSON attribute name "Reply" should be possibly email reply to the original email.
+<</SYS>>
+Email summary begin here DO NOT give answer except a JSON and No other text : {email_body}  [/INST] """
+
+prompt = PromptTemplate(template=template, input_variables=["email_body"])
+
+# Build LLM Chain
+llm_chain = LLMChain(prompt=prompt, llm=chat_model)
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Test one of the emails with the Mistral model API
+
+# COMMAND ----------
+
+# Retrieve a single review for testing
+test_single_review = emails_silver.limit(2).select("email_body_clean").collect()[0][0]
+
+# print(test_single_review)
+# Predict on the review
+response = llm_chain.run(test_single_review)
+
+print(f"response.text:{response}")

(Clone) ES 2a: Serve External models for Automation.py

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+# Databricks notebook source
+# MAGIC %md
+# MAGIC # External Models for Email Response Automation
+
+# COMMAND ----------
+
+# MAGIC %pip install mlflow==2.9.0 langchain==0.0.344 databricks-sdk==0.12.0
+
+# COMMAND ----------
+
+# MAGIC %pip install mlflow[genai]>=2.9.0
+# MAGIC %pip install --upgrade mlflow
+# MAGIC %pip install --upgrade langchain
+# MAGIC dbutils.library.restartPython()
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC In this notebook, we will use External Models for automation of the email response. External models are third-party models hosted outside of Databricks. Supported by Model Serving, external models allow you to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. For this specific problem, we have picked OpenAI.
+# MAGIC
+# MAGIC https://docs.databricks.com/en/generative-ai/external-models/index.html
+
+# COMMAND ----------
+
+# MAGIC %run "./ES 0a: Intro & Config"
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ### Read the emails from the bronze layer
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC Lets read the raw emails persisted in the bronze layer in a Dataframe.
+# MAGIC
+
+# COMMAND ----------
+
+emails_silver=spark \
+                .read \
+                .table(config['table_emails_bronze'])
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## Setup Langchain for the external model
+# MAGIC
+# MAGIC We will setup Langchain to define the prompt template that will retrieve email Catagory, Sentiment, Synopsis and possible reply.
+# MAGIC
+# MAGIC The possible reply can be based on the templated and embedding can be used for it. However in this solution, we can not defined it.
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ###Create Email Summarisation UDF
+
+# COMMAND ----------
+
+import mlflow
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms import Databricks
+from langchain import OpenAI, PromptTemplate, LLMChain
+from pyspark.sql import functions as SF
+from pyspark.sql.types import StringType
+from tenacity import retry, stop_after_attempt, wait_exponential
+from langchain.chat_models import ChatDatabricks
+from langchain.chat_models import ChatOpenAI
+from langchain.schema.output_parser import StrOutputParser
+from tenacity import retry, stop_after_attempt, wait_exponential
+import os
+
+spark.conf.set("spark.sql.execution.arrow.maxRecordsPerBatch",2)
+spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
+token = dbutils.notebook.entry_point.getDbutils().notebook().getContext().apiToken().get()
+url = spark.conf.get("spark.databricks.workspaceUrl")
+
+
+
+# Create Summary function
+def run_summarisation_pipeline(email_body):
+
+    os.environ['DATABRICKS_TOKEN'] = token
+
+    # Chat GPT LLM Model
+    gateway = Databricks(
+    host="https://" + url, 
+    endpoint_name="Email-OpenAI-Completion-Endpoint"
+    )
+
+    # Build Prompt Template
+    prompt_template_string = """
+    Given the following email text, categorise whether the email is a job request, customer query or generic email where no action required. It should capture sentiment of the email as positive, negative or neutral. Also it should create a short summary of the email. In addition, it should draft possible reply to email.
+
+    The output should be structured as a JSON dictionary of dictionaries. First attribute name is "Category" which categorises of the email as three possible values - Job, Query or No Action. Second json attribute name is Sentiment with possible values - positive, negative or neutral. Third json attribute name is "Synopsis" which should capture short email summary. Forth JSON attribute name "Reply" should be possibly email reply to the original email.
+
+    Email summary begin here: {email_body}"""
+
+    prompt_template = PromptTemplate(template=prompt_template_string, input_variables=["email_body"])
+
+    # Build LLM Chain
+    chain = LLMChain(prompt=prompt_template, llm=gateway)
+
+    @retry(wait=wait_exponential(multiplier=10, min=10, max=1800), stop=stop_after_attempt(7))
+    def _call_with_retry(email_body):
+        return chain.invoke(email_body)
+
+    try:
+        summary_string = _call_with_retry(email_body)
+    except Exception as e:
+        summary_string = f"FAILED: {e.last_attempt.exception()}"
+
+    return summary_string
+
+
+# Create UDF
+summarisation_udf = SF.udf(
+    lambda x: run_summarisation_pipeline(
+        email_body=x
+    ),
+    StringType(),
+)
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ###Process incoming data
+
+# COMMAND ----------
+
+# Repartition for concurrency
+emails_silver = emails_silver.repartition(10)
+
+# Make the requests
+emails_silver_with_summary = (
+    emails_silver
+    .withColumn("summary_string", summarisation_udf(SF.col("email_body_clean")))
+)
+
+
+# Save table
+# (
+#     emails_silver_with_summary
+#     .write
+#     .mode("overwrite")
+#     .option("overwriteSchema", "true")
+#     .saveAsTable(config['table_emails_silver_externalm'])
+# )
+
+# COMMAND ----------
+
+display(emails_silver_with_summary)
+
+# COMMAND ----------
+
+display(emails_silver_with_summary)
+
+# COMMAND ----------
+
+