AimlapiEmbeddings

This guide helps you get started with AI/ML API embedding models using LangChain. For detailed documentation on AimlapiEmbeddings features and configuration options, please refer to the API reference.

Overview

Integration details

Class	Package	Local	JS support	Downloads	Version
AimlapiEmbeddings	langchain-aimlapi	❌	❌

Setup

To access AI/ML API embedding models you’ll need to create an account, get an API key, and install the langchain-aimlapi integration package.

Credentials

Head to aimlapi.com to sign up and generate an API key. Once you’ve done this set the AIMLAPI_API_KEY environment variable:

import getpass
import os

if not os.getenv("AIMLAPI_API_KEY"):
    os.environ["AIMLAPI_API_KEY"] = getpass.getpass("Enter your AI/ML API key: ")

To enable automated tracing of your model calls, set your LangSmith API key:

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

Installation

The LangChain AI/ML API integration lives in the langchain-aimlapi package:

pip install -qU langchain-aimlapi

Instantiation

Now we can instantiate our embeddings model and perform embedding operations:

from langchain_aimlapi import AimlapiEmbeddings

embeddings = AimlapiEmbeddings(
    model="text-embedding-ada-002",
)

Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows. Below is how to index and retrieve data using the embeddings object we initialized above with InMemoryVectorStore.

from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

retriever = vectorstore.as_retriever()

retrieved_documents = retriever.invoke("What is LangChain?")
retrieved_documents[0].page_content

'LangChain is the framework for building context-aware reasoning applications'

Direct usage

You can directly call embed_query and embed_documents for custom embedding scenarios.

Embed single text

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])

Embed multiple texts

text2 = "LangGraph is a library for building stateful, multi-actor applications with LLMs"

vectors = embeddings.embed_documents([text, text2])
for vector in vectors:
    print(str(vector)[:100])

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API reference

For detailed documentation on AimlapiEmbeddings features and configuration options, please refer to the API reference.

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