Load Quip documents into LlamaIndex for RAG pipelines
LlamaIndex reader that loads content from Quip documents by thread ID, enabling retrieval of collaborative document data for indexing and search workflows.
Why it matters
Extract and load content from Quip collaborative documents into LlamaIndex data structures, enabling users to build retrieval-augmented generation (RAG) applications and AI agents that can query and reason over their Quip documentation.
Outcomes
What it gets done
Authenticate with Quip API using access tokens
Retrieve thread content from specified Quip thread IDs
Transform Quip documents into LlamaIndex document format
Enable downstream use as a LangChain agent tool
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/li-reader-readers-quip | bash Overview
LlamaIndex Readers Integration: Quip
The Quip Reader is a LlamaIndex integration that loads content from Quip documents by constructing queries based on thread IDs. It retrieves thread content and formats it as documents for use in LlamaIndex indexing and search pipelines, and can also function as a Tool in LangChain Agents. Use this reader when you need to incorporate Quip document content into LlamaIndex-powered search or retrieval systems. It requires a Quip access token and thread IDs for operation.
What it does
The Quip Reader integration for LlamaIndex enables loading data from Quip documents into your LlamaIndex workflows. It constructs queries to retrieve thread content based on thread IDs.
When to use - and when NOT to
Use this reader when you need to incorporate Quip document content into LlamaIndex-powered search, retrieval, or question-answering systems. Use it when you have specific thread IDs you want to retrieve and process.
This reader requires a Quip access token for authentication and thread IDs for content retrieval.
Inputs and outputs
You provide a Quip access token for authentication and a list of thread IDs corresponding to the documents you want to load. The reader returns documents containing the thread content, formatted for use within LlamaIndex pipelines.
from llama_index.readers.quip import QuipReader
# Initialize QuipReader
reader = QuipReader(access_token="<Access Token>")
# Load data from Quip
documents = reader.load_data(thread_ids=["<Thread ID 1>", "<Thread ID 2>"])
Integrations
This reader is designed to be used as a way to load data into LlamaIndex. It can also be used as a Tool in LangChain Agents.
Who it's for
This integration is designed for developers and data engineers building search, retrieval-augmented generation (RAG), or question-answering systems that need to incorporate Quip documents.
Installation is straightforward via pip:
pip install llama-index-readers-quip
Source README
LlamaIndex Readers Integration: Quip
Overview
The Quip Reader enables loading data from Quip documents. It constructs queries to retrieve thread content based on thread IDs.
Installation
You can install the Quip Reader via pip:
pip install llama-index-readers-quip
Usage
from llama_index.readers.quip import QuipReader
### Initialize QuipReader
reader = QuipReader(access_token="<Access Token>")
### Load data from Quip
documents = reader.load_data(thread_ids=["<Thread ID 1>", "<Thread ID 2>"])
This loader is designed to be used as a way to load data into
LlamaIndex and/or subsequently
used as a Tool in a LangChain Agent.
FAQ
Common questions
Discussion
Questions & comments · 0
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