github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-06-28 17:38:36 +00:00
Code Issues Packages Projects Releases Wiki Activity

Simplify HumanMessages in the quick start guide (#4026)

Browse Source 
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
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 12 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
docs/getting_started/getting_started.md
View File

@ -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}
```

14
docs/modules/models/chat/getting_started.ipynb
View File

@ -107,7 +107,7 @@
"source": [
"messages = [\n",
" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
" HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
" HumanMessage(content=\"I love programming.\")\n",
"]\n",
"chat(messages)"
]
@ -131,7 +131,7 @@
{
"data": {
"text/plain": [
"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}})"
]
},
"execution_count": 5,
@ -143,11 +143,11 @@
"batch_messages = [\n",
" [\n",
" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
" HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
" HumanMessage(content=\"I love programming.\")\n",
" ],\n",
" [\n",
" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
" HumanMessage(content=\"Translate this sentence from English to French. I love artificial intelligence.\")\n",
" HumanMessage(content=\"I love artificial intelligence.\")\n",
" ],\n",
"]\n",
"result = chat.generate(batch_messages)\n",
@ -171,9 +171,9 @@
{
"data": {
"text/plain": [
"{'token_usage': {'prompt_tokens': 71,\n",
" 'completion_tokens': 18,\n",
" 'total_tokens': 89}}"
"{'token_usage': {'prompt_tokens': 57,\n",
" 'completion_tokens': 20,\n",
" 'total_tokens': 77}}"
]
},
"execution_count": 6,