# Langchain with InterSystems IRIS
[Langchain](https://github.com/langchain-ai/langchain) with support for InterSystems IRIS
## Install
```shell
pip install langchain-iris
```
## Example
```python
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)
```
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"description": "# Langchain with InterSystems IRIS\n\n[Langchain](https://github.com/langchain-ai/langchain) with support for InterSystems IRIS\n\n\n## Install\n\n```shell\npip install langchain-iris\n```\n\n## Example\n\n```python\nimport os\nfrom dotenv import load_dotenv\n\nfrom langchain.docstore.document import Document\nfrom langchain.document_loaders import TextLoader\nfrom langchain.text_splitter import CharacterTextSplitter\nfrom langchain.embeddings.openai import OpenAIEmbeddings\nfrom langchain.embeddings import HuggingFaceEmbeddings\nfrom langchain.embeddings.fastembed import FastEmbedEmbeddings\n\nfrom langchain_iris import IRISVector\n\nloader = TextLoader(\"state_of_the_union.txt\")\ndocuments = loader.load()\ntext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\ndocs = text_splitter.split_documents(documents)\n\nCONNECTION_STRING = 'iris://_SYSTEM:SYS@localhost:1972/USER'\n\nload_dotenv(override=True)\n\nembeddings = OpenAIEmbeddings()\n\nCOLLECTION_NAME = \"state_of_the_union_test\"\n\ndb = IRISVector.from_documents(\n embedding=embeddings,\n documents=docs,\n collection_name=COLLECTION_NAME,\n connection_string=CONNECTION_STRING,\n)\n\nquery = \"What did the president say about Ketanji Brown Jackson\"\ndocs_with_score = db.similarity_search_with_score(query)\n\nfor doc, score in docs_with_score:\n print(\"-\" * 80)\n print(\"Score: \", score)\n print(doc.page_content)\n print(\"-\" * 80)\n```\n",
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