Framework Adapters¶

snowloader’s core loaders produce framework-agnostic SnowDocument objects. The adapter layer converts these into the document types that LangChain and LlamaIndex expect, with zero business logic - all the real work happens in the core loaders.

LangChain¶

The LangChain adapters implement langchain_core.document_loaders.BaseLoader, so they work with any LangChain vector store, retriever, or chain.

pip install snowloader[langchain]

Available adapters:

ServiceNowIncidentLoader
ServiceNowKBLoader
ServiceNowCMDBLoader
ServiceNowChangeLoader
ServiceNowProblemLoader
ServiceNowCatalogLoader

Usage:

from snowloader import SnowConnection
from snowloader.adapters.langchain import ServiceNowIncidentLoader

conn = SnowConnection(instance_url="...", username="...", password="...")

loader = ServiceNowIncidentLoader(connection=conn, query="active=true")

# load() returns list[langchain_core.documents.Document]
docs = loader.load()

# lazy_load() yields one Document at a time
for doc in loader.lazy_load():
    print(doc.page_content[:100])

# Delta sync
from datetime import datetime, timezone
updated = loader.load_since(datetime(2024, 1, 1, tzinfo=timezone.utc))

RAG pipeline example:

from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings

docs = ServiceNowIncidentLoader(connection=conn).load()
vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

All loader parameters (query, fields, include_journals, ci_class, include_relationships, etc.) are passed through to the underlying core loader via **kwargs.

LlamaIndex¶

The LlamaIndex adapters implement llama_index.core.readers.base.BaseReader, so they work with any LlamaIndex index.

pip install snowloader[llamaindex]

Available readers:

ServiceNowIncidentReader
ServiceNowKBReader
ServiceNowCMDBReader
ServiceNowChangeReader
ServiceNowProblemReader
ServiceNowCatalogReader

Usage:

from snowloader import SnowConnection
from snowloader.adapters.llamaindex import ServiceNowIncidentReader

conn = SnowConnection(instance_url="...", username="...", password="...")

reader = ServiceNowIncidentReader(connection=conn, query="active=true")

# load_data() returns list[llama_index.core.schema.Document]
docs = reader.load_data()

# Delta sync
from datetime import datetime, timezone
updated = reader.load_data_since(datetime(2024, 1, 1, tzinfo=timezone.utc))

Metadata exclusion:

By default, sys_id is excluded from LLM metadata (it is still available in the metadata dict, but marked so embedding models and LLMs skip it). You can customize this:

reader = ServiceNowIncidentReader(
    connection=conn,
    excluded_llm_metadata_keys=["sys_id", "sys_created_on", "sys_updated_on"],
)

RAG pipeline example:

from llama_index.core import VectorStoreIndex

docs = ServiceNowIncidentReader(connection=conn).load_data()
index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query("What incidents are related to email?")