PremAI

PremAI is an all-in-one platform that simplifies the creation of robust, production-ready applications powered by Generative AI. By streamlining the development process, PremAI allows you to concentrate on enhancing user experience and driving overall growth for your application. You can quickly start using our platform here.

ChatPremAI

This example goes over how to use LangChain to interact with different chat models with ChatPremAI

Installation and setup

We start by installing langchain and premai-sdk. You can type the following command to install:

pip install premai langchain

Before proceeding further, please make sure that you have made an account on PremAI and already created a project. If not, please refer to the quick start guide to get started with the PremAI platform. Create your first project and grab your API key.

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_community.chat_models import ChatPremAI

API Reference:HumanMessage | SystemMessage | ChatPremAI

Setup PremAI client in LangChain

Once we imported our required modules, let's setup our client. For now let's assume that our project_id is 8. But make sure you use your project-id, otherwise it will throw error.

To use langchain with prem, you do not need to pass any model name or set any parameters with our chat-client. By default it will use the model name and parameters used in the LaunchPad.

Note: If you change the model or any other parameters like temperature or max_tokens while setting the client, it will override existing default configurations, that was used in LaunchPad.

import os
import getpass

if "PREMAI_API_KEY" not in os.environ:
    os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")

chat = ChatPremAI(project_id=8)

Chat Completions

ChatPremAI supports two methods: invoke (which is the same as generate) and stream.

The first one will give us a static result. Whereas the second one will stream tokens one by one. Here's how you can generate chat-like completions.

human_message = HumanMessage(content="Who are you?")

chat.invoke([human_message])

You can provide system prompt here like this:

system_message = SystemMessage(content="You are a friendly assistant.")
human_message = HumanMessage(content="Who are you?")

chat.invoke([system_message, human_message])

You can also change generation parameters while calling the model. Here's how you can do that:

chat.invoke(
    [system_message, human_message],
    temperature = 0.7, max_tokens = 20, top_p = 0.95
)

If you are going to place system prompt here, then it will override your system prompt that was fixed while deploying the application from the platform.

Please note that the current version of ChatPremAI does not support parameters: n and stop.

Streaming

In this section, let's see how we can stream tokens using langchain and PremAI. Here's how you do it.

import sys

for chunk in chat.stream("hello how are you"):
    sys.stdout.write(chunk.content)
    sys.stdout.flush()

Similar to above, if you want to override the system-prompt and the generation parameters, you need to add the following:

import sys

for chunk in chat.stream(
    "hello how are you",
    system_prompt = "You are an helpful assistant", temperature = 0.7, max_tokens = 20
):
    sys.stdout.write(chunk.content)
    sys.stdout.flush()

This will stream tokens one after the other.

PremEmbeddings

In this section we are going to dicuss how we can get access to different embedding model using PremEmbeddings with LangChain. Lets start by importing our modules and setting our API Key.

import os
import getpass
from langchain_community.embeddings import PremEmbeddings


if os.environ.get("PREMAI_API_KEY") is None:
    os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")

We support lots of state of the art embedding models. You can view our list of supported LLMs and embedding models here. For now let's go for text-embedding-3-large model for this example. .

model = "text-embedding-3-large"
embedder = PremEmbeddings(project_id=8, model=model)

query = "Hello, this is a test query"
query_result = embedder.embed_query(query)

# Let's print the first five elements of the query embedding vector

print(query_result[:5])

Setting `model_name` argument in mandatory for PremAIEmbeddings unlike chat.

Finally, let's embed some sample document

documents = [
    "This is document1",
    "This is document2",
    "This is document3"
]

doc_result = embedder.embed_documents(documents)

# Similar to the previous result, let's print the first five element
# of the first document vector

print(doc_result[0][:5])

print(f"Dimension of embeddings: {len(query_result)}")

Dimension of embeddings: 3072

doc_result[:5]

Result:

[-0.02129288576543331, 0.0008162345038726926, -0.004556538071483374, 0.02918623760342598, -0.02547479420900345]