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docs: update prompt_templates.mdx (#32405)

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```messages_to_pass = [
    HumanMessage(content="What's the capital of France?"),
    AIMessage(content="The capital of France is Paris."),
    HumanMessage(content="And what about Germany?")
]
formatted_prompt = prompt_template.invoke({"msgs": messages_to_pass})
print(formatted_prompt)```

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
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16
docs/docs/concepts/prompt_templates.mdx
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@ -53,17 +53,29 @@ This is how you use MessagesPlaceholder.
```python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage
from langchain_core.messages import HumanMessage, AIMessage
prompt_template = ChatPromptTemplate([
("system", "You are a helpful assistant"),
MessagesPlaceholder("msgs")
])
# Simple example with one message
prompt_template.invoke({"msgs": [HumanMessage(content="hi!")]})
# More complex example with conversation history
messages_to_pass = [
HumanMessage(content="What's the capital of France?"),
AIMessage(content="The capital of France is Paris."),
HumanMessage(content="And what about Germany?")
]
formatted_prompt = prompt_template.invoke({"msgs": messages_to_pass})
print(formatted_prompt)
```
This will produce a list of two messages, the first one being a system message, and the second one being the HumanMessage we passed in.
This will produce a list of four messages total: the system message plus the three messages we passed in (two HumanMessages and one AIMessage).
If we had passed in 5 messages, then it would have produced 6 messages in total (the system message plus the 5 passed in).
This is useful for letting a list of messages be slotted into a particular spot.