langchain/libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb

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__ModuleName__Embeddings

• TODO: Make sure API reference link is correct

This will help you get started with ModuleName embedding models using LangChain. For detailed documentation on __ModuleName__Embeddings features and configuration options, please refer to the API reference.

Overview

Integration details

Provider	Package
ModuleName	package_name

Setup

• TODO: Update with relevant info.

To access ModuleName embedding models you'll need to create a/an ModuleName account, get an API key, and install the __package_name__ integration package.

Credentials

• TODO: Update with relevant info.

Head to (TODO: link) to sign up to ModuleName and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:

In [ ]:

import getpass
import os

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

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

In [ ]:

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

Installation

The LangChain ModuleName integration lives in the __package_name__ package:

In [ ]:

%pip install -qU __package_name__

Instantiation

Now we can instantiate our model object and generate chat completions:

• TODO: Update model instantiation with relevant params.

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from __module_name__ import __ModuleName__Embeddings

embeddings = __ModuleName__Embeddings(
    model="model-name",
)

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 our RAG tutorials.

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.

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# 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

Direct Usage

Under the hood, the vectorstore and retriever implementations are calling embeddings.embed_documents(...) and embeddings.embed_query(...) to create embeddings for the text(s) used in from_texts and retrieval invoke 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 with embed_query:

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single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100]) # Show the first 100 characters of the vector

Embed multiple texts

You can embed multiple texts with embed_documents:

In [ ]:

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

API Reference

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