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AimlapiEmbeddings

This will help 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​

ProviderPackage
AI/ML APIlangchain-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 https://aimlapi.com/app/ 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_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation​

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

%pip install -qU langchain-aimlapi
Note: you may need to restart the kernel to use updated packages.

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
API Reference:InMemoryVectorStore
'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])
[-0.0011368310078978539, 0.00714730704203248, -0.014703838154673576, -0.034064359962940216, 0.011239

Embed multiple texts:​

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])
[-0.0011398226488381624, 0.007080476265400648, -0.014682820066809654, -0.03407655283808708, 0.011276
[-0.005510928109288216, 0.016650190576910973, -0.011078780516982079, -0.03116573952138424, -0.003735

API Reference​

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