LangChain Integration

Use AI Foundation Services with LangChain for RAG, chains, and agent workflows. Since AIFS is OpenAI-compatible, you can use the langchain-openai package directly.

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Setup

pip install langchain langchain-openai

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Initialize LLM

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    openai_api_key=os.getenv("OPENAI_API_KEY"),
    openai_api_base=os.getenv("OPENAI_BASE_URL"),
    model_name="Llama-3.3-70B-Instruct",
    streaming=True,
)

# Test
for chunk in llm.stream("Write me a short poem about cloud computing."):
    if chunk.content:
        print(chunk.content, end="", flush=True)

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Initialize Embeddings

from typing import List
from langchain_openai import OpenAIEmbeddings
import os

class AIFSEmbeddings(OpenAIEmbeddings):
    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        embeddings = self.client.create(input=texts, model=self.model)
        return [embed.embedding for embed in embeddings.data]

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

    def embed_query(self, text: str) -> List[float]:
        return self.embed_documents([text])[0]

    async def aembed_query(self, text: str) -> List[float]:
        embed = await self.aembed_documents([text])
        return embed[0]

embed_model = AIFSEmbeddings(
    openai_api_key=os.getenv("OPENAI_API_KEY"),
    openai_api_base=os.getenv("OPENAI_BASE_URL"),
    model="text-embedding-bge-m3",
)

# Test
embeddings = embed_model.embed_documents(["Hello!", "World!"])
print(f"Dimensions: {len(embeddings[0])}")

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Next Steps

• Embeddings Guide — Learn more about embedding models
• LlamaIndex Integration — Alternative RAG framework