ChatPipeshift
This will help you get started with Pipeshiftchat models. For detailed documentation of all ChatPipeshift features and configurations head to theAPI reference.
Overview
Integration details
| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |
|---|---|---|---|---|---|---|
| ChatPipeshift | langchain-pipeshift | ❌ | - | ❌ |
Model features
| Tool calling | Structured output | JSON mode | Image input | Audio input | Video input | Token-level streaming | Native async | Token usage | Logprobs |
|---|---|---|---|---|---|---|---|---|---|
| ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | - |
Setup
To access Pipeshift models you'll need to create an account on Pipeshift, get an API key, and install thelangchain-pipeshift integration package.
Credentials
Head toPipeshift to sign up to Pipeshift and generate an API key. Once you've done this set the PIPESHIFT_API_KEY environment variable:
import getpass
import os
ifnot os.getenv("PIPESHIFT_API_KEY"):
os.environ["PIPESHIFT_API_KEY"]= getpass.getpass("Enter your Pipeshift API key: ")
If you want to get automated tracing of your model calls you can also set yourLangSmith API key by uncommenting below:
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
Installation
The LangChain Pipeshift integration lives in thelangchain-pipeshift package:
%pip install-qU langchain-pipeshift
Note: you may need to restart the kernel to use updated packages.
Instantiation
Now we can instantiate our model object and generate chat completions:
from langchain_pipeshiftimport ChatPipeshift
llm= ChatPipeshift(
model="meta-llama/Meta-Llama-3.1-8B-Instruct",
temperature=0,
max_tokens=512,
# other params...
)
Invocation
messages=[
(
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
),
("human","I love programming."),
]
ai_msg= llm.invoke(messages)
ai_msg
AIMessage(content='Here is the translation:\n\nJe suis amoureux du programme. \n\nHowever, a more common translation would be:\n\nJ\'aime programmer.\n\nNote that "Je suis amoureux" typically implies romantic love, whereas "J\'aime" is a more casual way to express affection or enjoyment for an activity, in this case, programming.', additional_kwargs={}, response_metadata={}, id='run-5cad8e5c-d089-44a8-8dcd-22736cde7d7b-0')
print(ai_msg.content)
Here is the translation:
Je suis amoureux du programme.
However, a more common translation would be:
J'aime programmer.
Note that "Je suis amoureux" typically implies romantic love, whereas "J'aime" is a more casual way to express affection or enjoyment for an activity, in this case, programming.
Chaining
We canchain our model with a prompt template like so:
from langchain_core.promptsimport ChatPromptTemplate
prompt= ChatPromptTemplate(
[
(
"system",
"You are a helpful assistant that translates {input_language} to {output_language}.",
),
("human","{input}"),
]
)
chain= prompt| llm
chain.invoke(
{
"input_language":"English",
"output_language":"German",
"input":"I love programming.",
}
)
AIMessage(content="Das ist schön! Du liebst Programmieren! (That's great! You love programming!)\n\nWould you like to know the German translation of a specific programming-related term or phrase, or would you like me to help you with something else?", additional_kwargs={}, response_metadata={}, id='run-8a4b7d56-23d9-43a7-8fb2-e05f556d94bd-0')
API reference
For detailed documentation of all ChatPipeshift features and configurations head to the API reference:https://dashboard.pipeshift.com/docs
Related
- Chat modelconceptual guide
- Chat modelhow-to guides