mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
121 lines
3.5 KiB
Plaintext
121 lines
3.5 KiB
Plaintext
### Setup
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To start we'll need to install the OpenAI Python package:
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```bash
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pip install openai
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```
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Accessing the API requires an API key, which you can get by creating an account and heading [here](https://platform.openai.com/account/api-keys). Once we have a key we'll want to set it as an environment variable by running:
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```bash
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export OPENAI_API_KEY="..."
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```
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If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:
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```python
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from langchain.chat_models import ChatOpenAI
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chat = ChatOpenAI(openai_api_key="...")
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```
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otherwise you can initialize without any params:
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```python
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from langchain.chat_models import ChatOpenAI
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chat = ChatOpenAI()
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```
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### Messages
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The chat model interface is based around messages rather than raw text.
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The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`
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### `__call__`
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#### Messages in -> message out
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You can get chat completions by passing one or more messages to the chat model. The response will be a message.
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```python
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from langchain.schema import (
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AIMessage,
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HumanMessage,
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SystemMessage
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)
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chat([HumanMessage(content="Translate this sentence from English to French: I love programming.")])
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```
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<CodeOutputBlock lang="python">
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```
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AIMessage(content="J'aime programmer.", additional_kwargs={})
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```
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</CodeOutputBlock>
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OpenAI's chat model supports multiple messages as input. See [here](https://platform.openai.com/docs/guides/chat/chat-vs-completions) for more information. Here is an example of sending a system and user message to the chat model:
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```python
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messages = [
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SystemMessage(content="You are a helpful assistant that translates English to French."),
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HumanMessage(content="I love programming.")
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]
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chat(messages)
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```
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<CodeOutputBlock lang="python">
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```
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AIMessage(content="J'aime programmer.", additional_kwargs={})
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```
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</CodeOutputBlock>
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### `generate`
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#### Batch calls, richer outputs
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You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter.
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```python
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batch_messages = [
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[
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SystemMessage(content="You are a helpful assistant that translates English to French."),
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HumanMessage(content="I love programming.")
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],
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[
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SystemMessage(content="You are a helpful assistant that translates English to French."),
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HumanMessage(content="I love artificial intelligence.")
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],
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]
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result = chat.generate(batch_messages)
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result
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```
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<CodeOutputBlock lang="python">
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```
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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}})
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```
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</CodeOutputBlock>
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You can recover things like token usage from this LLMResult
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```python
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result.llm_output
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```
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<CodeOutputBlock lang="python">
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```
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{'token_usage': {'prompt_tokens': 57,
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'completion_tokens': 20,
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'total_tokens': 77}}
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```
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</CodeOutputBlock>
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