This package contains the ObjectBox integrations for LangChain. This page will show you how to use the ObjectBox vector store in your LangChain projects.
Install the langchain-objectbox package from PyPI via pip.
pip install langchain-objectbox
In Python import the ObjectBox vector store which is available under fully qualified class
path langchain_objectbox.vectorstores.ObjectBox, e.g.:
from langchain_objectbox.vectorstores import ObjectBox
Create an ObjectBox VectorStore using e.g. one of the from_ class methods e.g. from_texts class method.
NOTE: Ensure to set argument embedding_dimensions along with the dimensions used in your embeddings model.
obx_vectorstore = ObjectBox.from_texts(texts, embeddings, embedding_dimensions=768)
Note: we have also fully runnable examples in the examples directory.
This small example demonstrates how to create an ObjectBox vector store with a list of texts and then perform a
similarity search.
Note that the DeterministicFakeEmbedding is a not a real embedding model and thus does not match semantically.
from langchain_core.embeddings.fake import DeterministicFakeEmbedding
from langchain_objectbox.vectorstores import ObjectBox
texts = ["foo", "bar", "baz"]
obx_vectorstore = ObjectBox.from_texts(
texts,
DeterministicFakeEmbedding(size=10),
embedding_dimensions=10,
)
result = obx_vectorstore.similarity_search("foo", k=1)
print(result)Prerequisites:
- Ollama as local LLM: See installation notes on https://python.langchain.com/docs/get_started/quickstart
pip install langchain bs4
from langchain_objectbox.vectorstores import ObjectBox
from langchain_community.llms import Ollama
llm = Ollama(model="llama2")
from langchain_community.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://docs.smith.langchain.com/user_guide")
docs = loader.load()
from langchain_community.embeddings import OllamaEmbeddings
embeddings = OllamaEmbeddings()
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts.chat import ChatPromptTemplate
prompt = ChatPromptTemplate.from_template("""Answer the following question based only on the provided context:
<context>
{context}
</context>
Question: {input}""")
from langchain_text_splitters import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter()
documents = text_splitter.split_documents(docs)
vector = ObjectBox.from_documents(documents, embeddings, embedding_dimensions=768)
document_chain = create_stuff_documents_chain(llm, prompt)
from langchain_core.documents import Document
from langchain.chains import create_retrieval_chain
retriever = vector.as_retriever()
retrieval_chain = create_retrieval_chain(retriever, document_chain)
response = retrieval_chain.invoke({"input": "how can langsmith help with testing?"})
print(response["answer"])If you are interested in developing the langchain-objectbox package itself (just "just" using it),
you can check the "developer-notes.md" page.
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