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update readme example (#140)

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Jerry Liu 2023-03-26 09:34:13 -07:00 committed by GitHub
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@ -30,31 +30,22 @@ Note: Make sure you change the description of the `Tool` to match your use-case.
```python ```python
from llama_index import GPTSimpleVectorIndex, download_loader from llama_index import GPTSimpleVectorIndex, download_loader
from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI from langchain.llms import OpenAI
from langchain.chains.conversation.memory import ConversationBufferMemory from langchain.chains.question_answering import load_qa_chain
# load documents
GoogleDocsReader = download_loader('GoogleDocsReader') GoogleDocsReader = download_loader('GoogleDocsReader')
gdoc_ids = ['1wf-y2pd9C878Oh-FmLH7Q_BQkljdm6TQal-c1pUfrec'] gdoc_ids = ['1wf-y2pd9C878Oh-FmLH7Q_BQkljdm6TQal-c1pUfrec']
loader = GoogleDocsReader() loader = GoogleDocsReader()
documents = loader.load_data(document_ids=gdoc_ids) documents = loader.load_data(document_ids=gdoc_ids)
index = GPTSimpleVectorIndex(documents) langchain_documents = [d.to_langchain_format() for d in documents]
tools = [ # initialize sample QA chain
Tool(
name="Google Doc Index",
func=lambda q: index.query(q),
description=f"Useful when you want answer questions about the Google Documents.",
),
]
llm = OpenAI(temperature=0) llm = OpenAI(temperature=0)
memory = ConversationBufferMemory(memory_key="chat_history") qa_chain = load_qa_chain(llm)
agent_chain = initialize_agent( question="<query here>"
tools, llm, agent="zero-shot-react-description", memory=memory answer = chain.run(input_documents=langchain_documents, question=question)
)
output = agent_chain.run(input="Where did the author go to school?")
``` ```
## How to add a loader ## How to add a loader