Langchain with InterSystems IRIS

Langchain with support for InterSystems IRIS

Install

pip install langchain-iris

Example

import os
from dotenv import load_dotenv
from langchain.docstore.document import Document

from langchain.document_loaders import TextLoader

from langchain.text_splitter import CharacterTextSplitter

from langchain.embeddings.openai import OpenAIEmbeddings

from langchain.embeddings import HuggingFaceEmbeddings

from langchain.embeddings.fastembed import FastEmbedEmbeddings
from langchain_iris import IRISVector
loader = TextLoader("state_of_the_union.txt")

documents = loader.load()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)

docs = text_splitter.split_documents(documents)
CONNECTION_STRING = 'iris://_SYSTEM:SYS@localhost:1972/USER'
load_dotenv(override=True)
embeddings = OpenAIEmbeddings()
COLLECTION_NAME = "state_of_the_union_test"
db = IRISVector.from_documents(

embedding=embeddings,

documents=docs,

collection_name=COLLECTION_NAME,

connection_string=CONNECTION_STRING,

)
query = "What did the president say about Ketanji Brown Jackson"

docs_with_score = db.similarity_search_with_score(query)
for doc, score in docs_with_score:

print("-" * 80)

print("Score: ", score)

print(doc.page_content)

print("-" * 80)