Netmind integration

import getpass
import os

if not os.getenv("NETMIND_API_KEY"):
    os.environ["NETMIND_API_KEY"] = getpass.getpass("Enter your Netmind API key: ")

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

pip install -qU langchain-netmind

from langchain_netmind import NetmindEmbeddings

embeddings = NetmindEmbeddings(
    model="nvidia/NV-Embed-v2",
)

# 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'

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

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

[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146
[0.022523142397403717, -0.002223758026957512, -0.008578270673751831, -0.006029821466654539, 0.008752