In order to use this library, you first need to go through the following steps:
- Select or create a Cloud Platform project.
- Enable billing for your project.
- Enable the Google Cloud Spanner API.
- 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 >= 3.9
pip install virtualenv virtualenv <your-env> source <your-env>/bin/activate <your-env>/bin/pip install langchain-google-spanner
pip install virtualenv virtualenv <your-env> <your-env>\Scripts\activate <your-env>\Scripts\pip.exe install langchain-google-spanner
Use a vector store to store embedded data and perform vector search.
from langchain_google_sapnner import SpannerVectorstore from langchain.embeddings import VertexAIEmbeddings embeddings_service = VertexAIEmbeddings(model_name="textembedding-gecko@003") vectorstore = SpannerVectorStore( instance_id="my-instance", database_id="my-database", table_name="my-table", embeddings=embedding_service )
See the full Vector Store tutorial.
Use a document loader to load data as LangChain Documents.
from langchain_google_spanner import SpannerLoader loader = SpannerLoader( instance_id="my-instance", database_id="my-database", query="SELECT * from my_table_name" ) docs = loader.lazy_load()
See the full Document Loader tutorial.
Use ChatMessageHistory to store messages and provide conversation
history to LLMs.
from langchain_google_spanner import SpannerChatMessageHistory history = SpannerChatMessageHistory( instance_id="my-instance", database_id="my-database", table_name="my_table_name", session_id="my-session_id" )
See the full Chat Message History tutorial.
Use SpannerGraphStore to store nodes and edges extracted from documents.
from langchain_google_spanner import SpannerGraphStore graph = SpannerGraphStore( instance_id="my-instance", database_id="my-database", graph_name="my_graph", )
See the full Spanner Graph Store tutorial.
Use SpannerGraphQAChain for question answering over a graph stored in Spanner Graph.
from langchain_google_spanner import SpannerGraphStore, SpannerGraphQAChain from langchain_google_vertexai import ChatVertexAI graph = SpannerGraphStore( instance_id="my-instance", database_id="my-database", graph_name="my_graph", ) llm = ChatVertexAI() chain = SpannerGraphQAChain.from_llm( llm, graph=graph, allow_dangerous_requests=True ) chain.invoke("query=Where does Sarah's sibling live?")
See the full Spanner Graph QA Chain 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.