LangChain Integration

Use LongParser as a document loader in any LangChain RAG pipeline.

Install

pip install "longparser[langchain]"

DocumentLoader

from longparser.integrations.langchain import LongParserLoader

loader = LongParserLoader(
    file_path="report.pdf",
    chunk_max_tokens=512,
)

documents = loader.load()
# Returns List[langchain_core.documents.Document]

Each document has: - page_content — chunk text - metadata.chunk_id — unique chunk ID - metadata.page_numbers — source page numbers - metadata.chunk_type — section | table | equation | etc. - metadata.section_path — hierarchy path

With FAISS

from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from longparser.integrations.langchain import LongParserLoader

docs = LongParserLoader("report.pdf").load()
vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())

results = vectorstore.similarity_search("key findings", k=5)

With Chroma

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

docs = LongParserLoader("report.pdf").load()
vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())

Streaming QA Chain

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-5.3"),
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
)

answer = qa.invoke("What are the revenue figures?")
print(answer["result"])