Eurelis-Langchain-SolR-VectorStore

This library allows to use a Solr based vector store with the python version of LangChain

Usage

This library assume you already have a running Solr instance with a configured dense vector field

<fieldType name="knn_vector" class="solr.DenseVectorField" vectorDimension="768" similarityFunction="euclidean"/>
<field name="vector" type="knn_vector" indexed="true" stored="true"/>

Be sure to set a vectorDimension value corresponding to what yor embeddings model provide.

from langchain.embeddings import HuggingFaceEmbeddings
from eurelis_langchain_solr_vectorstore import Solr

embeddings = HuggingFaceEmbeddings()  # you are free to use any embeddings method allowed by langchain

vector_store = Solr(embeddings)  # with default core configuration

You can also specify data about the solr instance and core to use:

vector_store = Solr(embeddings, core_kwargs={
    'page_content_field': 'text_t',  # field containing the text content
    'vector_field': 'vector',        # field containing the embeddings of the text content
    'core_name': 'langchain',        # core name
    'url_base': 'http://localhost:8983/solr', # base url to access solr
    'query_handler': 'select', # handler to use to query solr
    'update_handler': 'update', # update handler for solr
    'metadata_fields': [] # additional solr fields to consider as metadata, ie ['id']
})  # with custom default core configuration

In the code above you have both the allowed core arguments and the default value.

Metadata

The Solr based vector store also supports storing and filtering on metadata.

Metadata are mapped into Solr using the following convention: metadata_{key}_{type} with key being the original metadata key, and type is automatically inferred as:

• i for integer fields
• d for float fields
• s for string fields
• b for boolean fields

The vector_search method take an optional where param expecting a dict:

• dict item key: base name of a metadata field
• dict item value: value expected in the metadata field

Example using the vector store as a retriever:

retriever = vector_store.as_retriever()

Example adding filter instructions to the retriever

retriever = vector_store.as_retriever(search_kwargs={'filter': {'language': 'en', 'year': 2000}})

Docker

A docker compose file is present in the etc/docker folder, use it with

docker compose up -d

To launch a solr instance with a core named langchain and a 'vector' field with 768 dimensions.