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This guide provides a guide to getting started with GreenNodeEmbeddings. It enables you to perform semantic document search using various built-in connectors or your own custom data sources by generating high-quality vector representations of text.

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Overview

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Integration details

ProviderPackage
GreenNodelangchain-greennode

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Setup

To access GreenNode embedding models you’ll need to create a GreenNode account, get an API key, and install the langchain-greennode integration package.

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Credentials

GreenNode requires an API key for authentication, which can be provided either as the api_key parameter during initialization or set as the environment variable GREENNODE_API_KEY. You can obtain an API key by registering for an account on GreenNode Serverless AI. 
import getpass
import os

if not os.getenv("GREENNODE_API_KEY"):
    os.environ["GREENNODE_API_KEY"] = getpass.getpass("Enter your GreenNode API key: ")
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

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Installation

The LangChain GreenNode integration lives in the langchain-greennode package: 
pip install -qU langchain-greennode

Note: you may need to restart the kernel to use updated packages.

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Instantiation

The GreenNodeEmbeddings class can be instantiated with optional parameters for the API key and model name: 
from langchain_greennode import GreenNodeEmbeddings

# Initialize the embeddings model
embeddings = GreenNodeEmbeddings(
    # api_key="YOUR_API_KEY",  # You can pass the API key directly
    model="BAAI/bge-m3"  # The default embedding model
)

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Indexing and Retrieval

Embedding models play a key role in retrieval-augmented generation (RAG) workflows by enabling both the indexing of content and its efficient retrieval. Below, see how to index and retrieve data using the embeddings object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore. 
# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

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

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

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

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

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Direct Usage

The GreenNodeEmbeddings class can be used independently to generate text embeddings without the need for a vector store. This is useful for tasks such as similarity scoring, clustering, or custom processing pipelines.

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Embed single texts

You can embed single texts or documents with embed_query: 
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector

[-0.01104736328125, -0.0281982421875, 0.0035858154296875, -0.0311279296875, -0.0106201171875, -0.039

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Embed multiple texts

You can embed multiple texts with embed_documents: 
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])  # Show the first 100 characters of the vector

[-0.01104736328125, -0.0281982421875, 0.0035858154296875, -0.0311279296875, -0.0106201171875, -0.039
[-0.07177734375, -0.00017452239990234375, -0.002044677734375, -0.0299072265625, -0.0184326171875, -0

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Async Support

GreenNodeEmbeddings supports async operations: 
import asyncio


async def generate_embeddings_async():
    # Embed a single query
    query_result = await embeddings.aembed_query("What is the capital of France?")
    print(f"Async query embedding dimension: {len(query_result)}")

    # Embed multiple documents
    docs = [
        "Paris is the capital of France",
        "Berlin is the capital of Germany",
        "Rome is the capital of Italy",
    ]
    docs_result = await embeddings.aembed_documents(docs)
    print(f"Async document embeddings count: {len(docs_result)}")


await generate_embeddings_async()

Async query embedding dimension: 1024
Async document embeddings count: 3

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Document Similarity Example

import numpy as np
from scipy.spatial.distance import cosine

# Create some documents
documents = [
    "Machine learning algorithms build mathematical models based on sample data",
    "Deep learning uses neural networks with many layers",
    "Climate change is a major global environmental challenge",
    "Neural networks are inspired by the human brain's structure",
]

# Embed the documents
embeddings_list = embeddings.embed_documents(documents)


# Function to calculate similarity
def calculate_similarity(embedding1, embedding2):
    return 1 - cosine(embedding1, embedding2)


# Print similarity matrix
print("Document Similarity Matrix:")
for i, emb_i in enumerate(embeddings_list):
    similarities = []
    for j, emb_j in enumerate(embeddings_list):
        similarity = calculate_similarity(emb_i, emb_j)
        similarities.append(f"{similarity:.4f}")
    print(f"Document {i + 1}: {similarities}")

Document Similarity Matrix:
Document 1: ['1.0000', '0.6005', '0.3542', '0.5788']
Document 2: ['0.6005', '1.0000', '0.4154', '0.6170']
Document 3: ['0.3542', '0.4154', '1.0000', '0.3528']
Document 4: ['0.5788', '0.6170', '0.3528', '1.0000']

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API reference

For more details about the GreenNode Serverless AI API, visit the GreenNode Serverless AI Documentation.
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