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Showing content from https://python.langchain.com/docs/integrations/text_embedding/predictionguard below:

PredictionGuardEmbeddings | 🦜️🔗 LangChain

Prediction Guard is a secure, scalable GenAI platform that safeguards sensitive data, prevents common AI malfunctions, and runs on affordable hardware.

This integration shows how to use the Prediction Guard embeddings integration with Langchain. This integration supports text and images, separately or together in matched pairs.

To access Prediction Guard models, contact us here to get a Prediction Guard API key and get started.

Once you have a key, you can set it with

import os

os.environ["PREDICTIONGUARD_API_KEY"] = "<Prediction Guard API Key"

%pip install --upgrade --quiet langchain-predictionguard

First, install the Prediction Guard and LangChain packages. Then, set the required env vars and set up package imports.

from langchain_predictionguard import PredictionGuardEmbeddings

embeddings = PredictionGuardEmbeddings(model="bridgetower-large-itm-mlm-itc")

Prediction Guard embeddings generation supports both text and images. This integration includes that support spread across various functions.

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

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

The vectorstore and retriever implementations are calling embeddings.embed_documents(...) and embeddings.embed_query(...) to create embeddings from the texts used in the from_texts and retrieval invoke operations.

These methods can be directly called with the following commands.

text = "This is an embedding example."
single_vector = embeddings.embed_query(text)

single_vector[:5]

[0.01456777285784483,
 -0.08131945133209229,
 -0.013045587576925755,
 -0.09488929063081741,
 -0.003087474964559078]

docs = [
    "This is an embedding example.",
    "This is another embedding example.",
]

two_vectors = embeddings.embed_documents(docs)

for vector in two_vectors:
    print(vector[:5])

[0.01456777285784483, -0.08131945133209229, -0.013045587576925755, -0.09488929063081741, -0.003087474964559078]
[-0.0015021917643025517, -0.08883760124444962, -0.0025286630261689425, -0.1052245944738388, 0.014225339516997337]

image = [
    "https://farm4.staticflickr.com/3300/3497460990_11dfb95dd1_z.jpg",
]
single_vector = embeddings.embed_images(image)

print(single_vector[0][:5])

[0.0911610797047615, -0.034427884966135025, 0.007927080616354942, -0.03500846028327942, 0.022317267954349518]

images = [
    "https://fastly.picsum.photos/id/866/200/300.jpg?hmac=rcadCENKh4rD6MAp6V_ma-AyWv641M4iiOpe1RyFHeI",
    "https://farm4.staticflickr.com/3300/3497460990_11dfb95dd1_z.jpg",
]

two_vectors = embeddings.embed_images(images)

for vector in two_vectors:
    print(vector[:5])

[0.1593627631664276, -0.03636132553219795, -0.013229663483798504, -0.08789524435997009, 0.062290553003549576]
[0.0911610797047615, -0.034427884966135025, 0.007927080616354942, -0.03500846028327942, 0.022317267954349518]

inputs = [
    {
        "text": "This is an embedding example.",
        "image": "https://farm4.staticflickr.com/3300/3497460990_11dfb95dd1_z.jpg",
    },
]
single_vector = embeddings.embed_image_text(inputs)

print(single_vector[0][:5])

[0.0363212488591671, -0.10172265768051147, -0.014760786667466164, -0.046511903405189514, 0.03860781341791153]

inputs = [
    {
        "text": "This is an embedding example.",
        "image": "https://fastly.picsum.photos/id/866/200/300.jpg?hmac=rcadCENKh4rD6MAp6V_ma-AyWv641M4iiOpe1RyFHeI",
    },
    {
        "text": "This is another embedding example.",
        "image": "https://farm4.staticflickr.com/3300/3497460990_11dfb95dd1_z.jpg",
    },
]
two_vectors = embeddings.embed_image_text(inputs)

for vector in two_vectors:
    print(vector[:5])

[0.11867266893386841, -0.05898813530802727, -0.026179173961281776, -0.10747235268354416, 0.07684746384620667]
[0.026654226705431938, -0.10080841928720474, -0.012732953764498234, -0.04365091398358345, 0.036743905395269394]

For detailed documentation of all PredictionGuardEmbeddings features and configurations check out the API reference: https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.predictionguard.PredictionGuardEmbeddings.html

• Embedding model conceptual guide
• Embedding model how-to guides

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