LangChain Integration

Introduction

For RAG use case, it is very important to leverage the state-of-the-art framework to perform search, filter context and refinement of user query. This section show a quick tutorial how you can use our services API with LangChain to demonstrate the in-context learning capability of LLM.

Quickstart

Step 0: Install prerequiste package

pip install langchain
pip install langchain-openai

Step 1: Provide Base URL and API key to check available model name

Python

import os
import openai
from openai import OpenAI

print("OpenAI version: ",openai.__version__)
client = OpenAI(
    api_key=os.getenv('API_KEY'),
    base_url=os.getenv('API_BASE'),
)

print("==========Available models==========")
models = client.models.list()
for model in models.data:
  print(model.id)

info

Above code will show all the LLM and Embedding models that available dedicated for your key.

Example output:

==========Available models==========
Llama-3-70B-Instruct
Mixtral-8x7B-Instruct-v0.1
CodeLlama-2
jina-embeddings-v2-base-de
text-embedding-bge-m3
gpt-35-turbo
text-embedding-ada-002
gpt-4-32k-canada
gpt-4-32k-france
mistral-large-32k-france
gpt-4-turbo-128k-france

Step 2: Initialize LLM with OpenAILike class.

Python

from langchain_openai import ChatOpenAI
import os

model = "Llama-3-70B-Instruct"

llm = ChatOpenAI(openai_api_key=os.getenv('API_KEY'), 
              openai_api_base=os.getenv('API_BASE'),
              model_name="Mixtral-8x7B-Instruct-v0.1",
             streaming=True)

# Test run
for chunk in llm.stream("Write me a song about sparkling water."):
  if chunk.content:
    print(chunk.content,end="", flush=True)

Example output:

(Verse 1)
In the heat of the day, when the sun is high,
I reach for something to quench my thirst, not just any old drink will do.
I want something crisp, something clean, something that sparkles and shines,
...

Step 3: Choose an embedding models.

Python

from typing import List
from langchain_openai import OpenAIEmbeddings
import os

class OTC_Embeddings(OpenAIEmbeddings):
    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace transformer model.

        Args:
            texts: The list of texts to embed.

        Returns:
            List of embeddings, one for each text.
        """
        

        #texts = list(map(lambda x: x.replace("\n", " "), texts))

        embeddings = self.client.create(
            input=texts, model=self.model
        )
        embed_list = [embed.embedding for embed in embeddings.data]
        return embed_list 

    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
        embeddings = await self.async_client.create(
            input=texts, model=self.model
        )
        embed_list = [embed.embedding for embed in embeddings.data]
        return embed_list 


    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]

    async def aembed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        embed =  await self.aembed_documents([text])
        return embed[0]


# Test run
embed_model = OTC_Embeddings(openai_api_key=os.getenv('API_KEY'), 
                            openai_api_base=os.getenv('API_BASE'),
                            model="text-embedding-bge-m3")
embeddings = embeddings_model.embed_documents(
    [
      "Hi there!",
      "Oh, hello!",
      "What's your name?",
      "My friends call me World",
      "Hello World!"
    ]
)
print(len(embeddings), len(embeddings[0]))

Example output:

(5, 1024)

Introduction​

Quickstart​

Introduction

Quickstart