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Simplify HumanMessages in the quick start guide (#4026)

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In the section `Get Message Completions from a Chat Model` of the quick
start guide, the HumanMessage doesn't need to include `Translate this
sentence from English to French.` when there is a system message.

Simplify HumanMessages in these examples can further demonstrate the
power of LLM.
This commit is contained in:
Gengliang Wang
2023-05-03 22:45:03 -07:00
committed by GitHub
parent 087a4bd2b8
commit 8af25867cb
2 changed files with 12 additions and 12 deletions
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10
docs/getting_started/getting_started.md
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@@ -316,7 +316,7 @@ You can also pass in multiple messages for OpenAI's gpt-3.5-turbo and gpt-4 mode
```python
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
HumanMessage(content="I love programming.")
]
chat(messages)
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
@@ -327,22 +327,22 @@ You can go one step further and generate completions for multiple sets of messag
batch_messages = [
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
HumanMessage(content="I love programming.")
],
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love artificial intelligence.")
HumanMessage(content="I love artificial intelligence.")
],
]
result = chat.generate(batch_messages)
result
# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}})
# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})
```
You can recover things like token usage from this LLMResult:
```
result.llm_output['token_usage']
# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
# -> {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}
```