NetmindEmbeddings
This will help you get started with Netmind embedding models using LangChain. For detailed documentation onNetmindEmbeddings features and configuration options, please refer to theAPI reference.
Overview
Integration details
| Provider | Package |
|---|---|
| Netmind | langchain-netmind |
Setup
To access Netmind embedding models you'll need to create a/an Netmind account, get an API key, and install thelangchain-netmind integration package.
Credentials
Head tohttps://www.netmind.ai/ to sign up to Netmind and generate an API key. Once you've done this set the NETMIND_API_KEY environment variable:
import getpass
import os
ifnot os.getenv("NETMIND_API_KEY"):
os.environ["NETMIND_API_KEY"]= getpass.getpass("Enter your Netmind API key: ")
If you want to get automated tracing of your model calls you can also set yourLangSmith API key by uncommenting below:
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
Installation
The LangChain Netmind integration lives in thelangchain-netmind package:
%pip install-qU langchain-netmind
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m24.0[0m[39;49m -> [0m[32;49m25.0.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
Note: you may need to restart the kernel to use updated packages.
Instantiation
Now we can instantiate our model object:
from langchain_netmindimport NetmindEmbeddings
embeddings= NetmindEmbeddings(
model="nvidia/NV-Embed-v2",
)
Indexing and Retrieval
Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see ourRAG tutorials.
Below, see how to index and retrieve data using theembeddings object we initialized above. In this example, we will index and retrieve a sample document in theInMemoryVectorStore.
# Create a vector store with a sample text
from langchain_core.vectorstoresimport 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'
Direct Usage
Under the hood, the vectorstore and retriever implementations are callingembeddings.embed_documents(...) andembeddings.embed_query(...) to create embeddings for the text(s) used infrom_texts and retrievalinvoke operations, respectively.
You can directly call these methods to get embeddings for your own use cases.
Embed single texts
You can embed single texts or documents withembed_query:
single_vector= embeddings.embed_query(text)
print(str(single_vector)[:100])# Show the first 100 characters of the vector
[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146
Embed multiple texts
You can embed multiple texts withembed_documents:
text2=(
"LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors= embeddings.embed_documents([text, text2])
for vectorin two_vectors:
print(str(vector)[:100])# Show the first 100 characters of the vector
[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146
[0.022523142397403717, -0.002223758026957512, -0.008578270673751831, -0.006029821466654539, 0.008752
API Reference
For detailed documentation onNetmindEmbeddings features and configuration options, please refer to the:
Related
- Embedding modelconceptual guide
- Embedding modelhow-to guides