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Showing content from https://github.com/googleapis/langchain-google-memorystore-redis-python/ below:

GitHub - googleapis/langchain-google-memorystore-redis-python

• Client Library Documentation
• Product Documentation

In order to use this library, you first need to go through the following steps:

1. Select or create a Cloud Platform project.
2. Enable billing for your project.
3. Enable the Google Memorystore for Redis API.
4. Setup Authentication.

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Python >= 3.8

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install langchain-google-memorystore-redis

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install langchain-google-memorystore-redis

Use a vector store to store embedded data and perform vector search.

from langchain_google_memorystore_redis import RedisVectorStore

redis_client = redis.from_url("redis://127.0.0.1:6379")

embeddings_service = VertexAIEmbeddings(model_name="textembedding-gecko@003")
vectorstore = RedisVectorStore(
    client=redis_client,
    index_name="my_vector_index",
    embeddings=embeddings_service
)

You can also use a clustered client:

from langchain_google_memorystore_redis import RedisVectorStore

redis_client = redis.cluster.RedisCluster.from_url("redis://127.0.0.1:6379")

embeddings_service = VertexAIEmbeddings(model_name="textembedding-gecko@003")
vectorstore = RedisVectorStore(
    client=redis_client,
    index_name="my_vector_index",
    embeddings=embeddings_service
)

See the full Vector Store tutorial.

Use a document loader to load data as LangChain Documents.

from langchain_google_memorystore_redis import MemorystoreDocumentLoader


loader = MemorystoreDocumentLoader(
    client=redis_client,
    key_prefix="docs:",
    content_fields=set(["page_content"]),
)
docs = loader.lazy_load()

See the full Document Loader tutorial.

Use ChatMessageHistory to store messages and provide conversation history to LLMs.

from langchain_google_memorystore_redis import MemorystoreChatMessageHistory


history = MemorystoreChatMessageHistory(
    client=redis_client,
    session_id="my-session_id"
)

See the full Chat Message History tutorial.

Contributions to this library are always welcome and highly encouraged.

See CONTRIBUTING for more information how to get started.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See Code of Conduct for more information.

Apache 2.0 - See LICENSE for more information.

This is not an officially supported Google product.

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