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60 lines
2.1 KiB
Markdown
60 lines
2.1 KiB
Markdown
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# Google Doc Loader
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This loader takes in IDs of Google Docs and parses their text into `Document`s. To use this loader, you will need to authenticate with a Google Account the first time you run it locally. You can extract a Google Doc's ID directly from its URL. For example, the ID of `https://docs.google.com/document/d/1wf-y2pd9C878Oh-FmLH7Q_BQkljdm6TQal-c1pUfrec/edit` is `1wf-y2pd9C878Oh-FmLH7Q_BQkljdm6TQal-c1pUfrec`.
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## Usage
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To use this loader, you simply need to pass in an array of Google Doc IDs.
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```python
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from loader_hub import GoogleDocsReader
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gdoc_ids = ['1wf-y2pd9C878Oh-FmLH7Q_BQkljdm6TQal-c1pUfrec']
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documents = GoogleDocsReader.load_data(document_ids=gdoc_ids)
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```
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## Examples
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This loader is designed to be used as a way to load data into [GPT Index](https://github.com/jerryjliu/gpt_index/tree/main/gpt_index) and/or subsequently used as a Tool in a [LangChain](https://github.com/hwchase17/langchain) Agent.
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### GPT Index
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```python
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from loader_hub import GoogleDocsReader
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from gpt_index import GPTSimpleVectorIndex
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gdoc_ids = ['1wf-y2pd9C878Oh-FmLH7Q_BQkljdm6TQal-c1pUfrec']
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documents = GoogleDocsReader.load_data(document_ids=gdoc_ids)
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index = GPTSimpleVectorIndex(documents)
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index.query('Where did the author go to school?')
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```
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### LangChain
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Note: Make sure you change the description of the `Tool` to match your use-case.
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```python
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from loader_hub import GoogleDocsReader
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from gpt_index import GPTSimpleVectorIndex
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from langchain.agents import initialize_agent, Tool
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from langchain.llms import OpenAI
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from langchain.chains.conversation.memory import ConversationBufferMemory
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gdoc_ids = ['1wf-y2pd9C878Oh-FmLH7Q_BQkljdm6TQal-c1pUfrec']
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documents = GoogleDocsReader.load_data(document_ids=gdoc_ids)
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index = GPTSimpleVectorIndex(documents)
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tools = [
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Tool(
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name="Google Doc Index",
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func=lambda q: index.query(q),
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description=f"Useful when you want answer questions about the Google Documents.",
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),
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]
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llm = OpenAI(temperature=0)
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memory = ConversationBufferMemory(memory_key="chat_history")
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agent_chain = initialize_agent(
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tools, llm, agent="zero-shot-react-description", memory=memory
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)
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```
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