ChatContextual
这将帮助您开始使用Contextual AI的Grounded语言模型聊天模型。
要了解更多关于上下文人工智能的信息，请访问我们的文档。
此集成需要 contextual-client Python SDK。了解更多相关信息，请点击这里。
概览
此集成调用了Contextual AI的Grounded语言模型。
集成详情
| 类 | 包 | 本地 | 可序列化的 | JS 支持 | 软件包下载 | 最新包裹 |
|---|---|---|---|---|---|---|
| ChatContextual | langchain-contextual | ❌ | beta | ❌ |  |  |
模型特性
| 工具调用 | 结构化输出 | JSON模式 | 图像输入 | 音频输入 | 视频输入 | 令牌级流式传输 | 原生异步 | 令牌使用量 | 对数概率 |
|---|---|---|---|---|---|---|---|---|---|
| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
设置
要访问上下文模型，您需要创建一个Contextual AI帐户，获取API密钥，并安装langchain-contextual集成包。
凭据
前往 app.contextual.ai 注册 Contextual 并生成 API 密钥。完成后，请设置 CONTEXTUAL_AI_API_KEY 环境变量：
import getpass
import os
if not os.getenv("CONTEXTUAL_AI_API_KEY"):
os.environ["CONTEXTUAL_AI_API_KEY"] = getpass.getpass(
"Enter your Contextual API key: "
)
如果你想自动跟踪模型调用，也可以通过取消注释下面的内容来设置你的 LangSmith API 密钥：
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
安装
LangChain 上下文集成位于 langchain-contextual 包中：
%pip install -qU langchain-contextual
实例化
现在我们可以实例化我们的模型对象并生成聊天补全。
聊天客户端可以通过以下附加设置进行实例化：
| 参数 | 类型 | 描述 | 默认 |
|---|---|---|---|
| temperature | Optional[float] | The sampling temperature, which affects the randomness in the response. Note that higher temperature values can reduce groundedness. | 0 |
| top_p | Optional[float] | A parameter for nucleus sampling, an alternative to temperature which also affects the randomness of the response. Note that higher top_p values can reduce groundedness. | 0.9 |
| max_new_tokens | Optional[int] | The maximum number of tokens that the model can generate in the response. Minimum is 1 and maximum is 2048. | 1024 |
from langchain_contextual import ChatContextual
llm = ChatContextual(
model="v1", # defaults to `v1`
api_key="",
temperature=0, # defaults to 0
top_p=0.9, # defaults to 0.9
max_new_tokens=1024, # defaults to 1024
)
调用
上下文基础语言模型在调用ChatContextual.invoke方法时接受额外的kwargs。
这些额外的输入是：
| 参数 | 类型 | 描述 |
|---|---|---|
| knowledge | list[str] | Required: A list of strings of knowledge sources the grounded language model can use when generating a response. |
| system_prompt | Optional[str] | Optional: Instructions the model should follow when generating responses. Note that we do not guarantee that the model follows these instructions exactly. |
| avoid_commentary | Optional[bool] | Optional (Defaults to False): Flag to indicate whether the model should avoid providing additional commentary in responses. Commentary is conversational in nature and does not contain verifiable claims; therefore, commentary is not strictly grounded in available context. However, commentary may provide useful context which improves the helpfulness of responses. |
# include a system prompt (optional)
system_prompt = "You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability."
# provide your own knowledge from your knowledge-base here in an array of string
knowledge = [
"There are 2 types of dogs in the world: good dogs and best dogs.",
"There are 2 types of cats in the world: good cats and best cats.",
]
# create your message
messages = [
("human", "What type of cats are there in the world and what are the types?"),
]
# invoke the GLM by providing the knowledge strings, optional system prompt
# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument
ai_msg = llm.invoke(
messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True
)
print(ai_msg.content)
链式调用
我们可以将上下文模型与输出解析器链接起来。
from langchain_core.output_parsers import StrOutputParser
chain = llm | StrOutputParser
chain.invoke(
messages, knowledge=knowledge, systemp_prompt=system_prompt, avoid_commentary=True
)
API 参考：StrOutputParser
API 参考
有关ChatContextual所有功能和配置的详细文档，请访问Github页面： https://github.com/ContextualAI//langchain-contextual