add flex flow with LangChain sample

apps/11_promptflow/README.md

@@ -36,8 +36,42 @@ $ pip install -r requirements.txt
 [Prompt flow > Quick start](https://microsoft.github.io/promptflow/how-to-guides/quick-start.html) provides a quick start guide to Prompt flow.
 Some of the examples are extracted from [github.com/microsoft/promptflow/examples](https://github.com/microsoft/promptflow/tree/main/examples) to guide you through the basic usage of Prompt flow.
 
+**Set up connection**
+
+```shell
+$ cd apps/11_promptflow
+
+# List connections
+$ pf connection list
+
+# Set parameters
+$ AZURE_OPENAI_KEY=<your_api_key>
+$ AZURE_OPENAI_ENDPOINT=<your_api_endpoint>
+$ CONNECTION_NAME=open_ai_connection
+
+# Delete connection (if needed)
+$ pf connection delete \
+    --name $CONNECTION_NAME
+
+# Create connection
+$ pf connection create \
+    --file connection_azure_openai.yaml \
+    --set api_key=$AZURE_OPENAI_KEY \
+    --set api_base=$AZURE_OPENAI_ENDPOINT \
+    --name $CONNECTION_NAME
+
+# Show connection
+$ pf connection show \
+    --name $CONNECTION_NAME
+```
+
 ### [chat_minimal](https://github.com/microsoft/promptflow/tree/main/examples/flex-flows/chat-minimal)
 
+A chat flow defined using function with minimal code. It demonstrates the minimal code to have a chat flow.
+
+Tracing feature is available in Prompt flow, which allows you to trace the flow of the conversation. You can see its implementation in this example.
+Details are available in [Tracing](https://microsoft.github.io/promptflow/how-to-guides/tracing/index.html)
+
 **Run as normal Python script**
 
 ```shell
@@ -67,6 +101,8 @@ $ pf run create \
     --stream
 ```
 
+`--column-mapping` is used to map the data in the JSONL file to the flow. For more details, refer to [Use column mapping](https://microsoft.github.io/promptflow/how-to-guides/run-and-evaluate-a-flow/use-column-mapping.html).
+
 ### playground_chat
 
 ```shell
@@ -79,30 +115,6 @@ $ pf flow init \
 
 $ cd playground_chat
 
-# Set parameters
-$ CONNECTION_NAME=open_ai_connection
-$ AZURE_OPENAI_KEY=<your_api_key>
-$ AZURE_OPENAI_ENDPOINT=<your_api_endpoint>
-
-# List connections
-$ pf connection list
-
-
-# Delete connection (if needed)
-$ pf connection delete \
-    --name $CONNECTION_NAME
-
-# Create connection
-$ pf connection create \
-    --file azure_openai.yaml \
-    --set api_key=$AZURE_OPENAI_KEY \
-    --set api_base=$AZURE_OPENAI_ENDPOINT \
-    --name $CONNECTION_NAME
-
-# Show connection
-$ pf connection show \
-    --name $CONNECTION_NAME
-
 # Interact with chat flow
 $ pf flow test \
     --flow . \
@@ -230,14 +242,38 @@ $ pf run create \
 $ pf run show-details --name $RUN_NAME
 ```
 
+[Tutorial: How prompt flow helps on quality improvement](https://github.com/microsoft/promptflow/blob/main/examples/tutorials/flow-fine-tuning-evaluation/promptflow-quality-improvement.md) provides a detailed guide on how to use Prompt flow to improve the quality of your LLM applications.
+
 ### [eval-chat-math](https://github.com/microsoft/promptflow/tree/main/examples/flows/evaluation/eval-chat-math)
 
 This example shows how to evaluate the answer of math questions, which can compare the output results with the standard answers numerically.
 Details are available in the [eval-chat-math/README.md](./eval-chat-math/README.md).
 To understand how to operate the flow in VS Code, you can refer to the [Build your high quality LLM apps with Prompt flow](https://www.youtube.com/watch?v=gcIe6nk2gA4).
 This video shows how to evaluate the answer of math questions and guide you to tune the prompts using variants.
 
-<!-- TODO: rag, tracing, deployments -->
+### flex_flow_langchain
+
+To guide you through working with LangChain, we provide an example flex flow that
+
+```shell
+$ cd apps/11_promptflow/flex_flow_langchain
+$ pf flow test \
+    --flow main:LangChainRunner \
+    --inputs question="What's 2+2?" \
+    --init custom_connection=open_ai_connection
+
+$ RUN_NAME=flex_flow_langchain-$(date +%s)
+$ pf run create \
+    --name $RUN_NAME \
+    --flow . \
+    --data ./data.jsonl \
+    --column-mapping question='${data.question}' \
+    --stream
+
+$ pf run show-details --name $RUN_NAME
+```
+
+<!-- TODO: rag, deployments -->
 
 ## References
 

apps/11_promptflow/flex_flow_langchain/data.jsonl

@@ -0,0 +1,2 @@
+{"question": "What's 4+4?"}
+{"question": "What's 4x4?"}

apps/11_promptflow/flex_flow_langchain/flow.flex.yaml

@@ -0,0 +1,8 @@
+$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
+entry: main:LangChainRunner
+sample:
+  inputs:
+    input: What's 2+2?
+    prediction: What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.
+  init:
+    custom_connection: open_ai_connection

apps/11_promptflow/flex_flow_langchain/main.py

@@ -0,0 +1,52 @@
+from dataclasses import dataclass
+
+from langchain_openai import AzureChatOpenAI
+from promptflow.client import PFClient
+from promptflow.connections import CustomConnection
+from promptflow.tracing import trace
+
+
+@dataclass
+class Result:
+    answer: str
+
+
+class LangChainRunner:
+    def __init__(self, custom_connection: CustomConnection):
+        # https://python.langchain.com/v0.2/docs/integrations/chat/azure_chat_openai/
+        self.llm = AzureChatOpenAI(
+            temperature=0,
+            api_key=custom_connection.secrets["api_key"],
+            api_version=custom_connection.configs["api_version"],
+            azure_endpoint=custom_connection.configs["api_base"],
+            model="gpt-4o",
+        )
+
+    @trace
+    def __call__(
+        self,
+        question: str,
+    ) -> Result:
+        response = self.llm.invoke(
+            [
+                (
+                    "system",
+                    "You are asking me to do some math, I can help with that.",
+                ),
+                ("human", question),
+            ],
+        )
+        return Result(answer=response.content)
+
+
+if __name__ == "__main__":
+    from promptflow.tracing import start_trace
+
+    start_trace()
+    pf = PFClient()
+    connection = pf.connections.get(name="open_ai_connection")
+    runner = LangChainRunner(custom_connection=connection)
+    result = runner(
+        question="What's 2+2?",
+    )
+    print(result)