OpenAI Agent with Query Engine Tools¶

Build Query Engine Tools¶

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

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%pip install llama-index-agent-openai

%pip install llama-index-agent-openai

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!pip install llama-index

!pip install llama-index

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from llama_index.core import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    StorageContext,
    load_index_from_storage,
)

from llama_index.core.tools import QueryEngineTool, ToolMetadata

from llama_index.core import ( SimpleDirectoryReader, VectorStoreIndex, StorageContext, load_index_from_storage, ) from llama_index.core.tools import QueryEngineTool, ToolMetadata

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try:
    storage_context = StorageContext.from_defaults(
        persist_dir="./storage/lyft"
    )
    lyft_index = load_index_from_storage(storage_context)

    storage_context = StorageContext.from_defaults(
        persist_dir="./storage/uber"
    )
    uber_index = load_index_from_storage(storage_context)

    index_loaded = True
except:
    index_loaded = False

try: storage_context = StorageContext.from_defaults( persist_dir="./storage/lyft" ) lyft_index = load_index_from_storage(storage_context) storage_context = StorageContext.from_defaults( persist_dir="./storage/uber" ) uber_index = load_index_from_storage(storage_context) index_loaded = True except: index_loaded = False

Download Data

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!mkdir -p 'data/10k/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'

!mkdir -p 'data/10k/' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'

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if not index_loaded:
    # load data
    lyft_docs = SimpleDirectoryReader(
        input_files=["./data/10k/lyft_2021.pdf"]
    ).load_data()
    uber_docs = SimpleDirectoryReader(
        input_files=["./data/10k/uber_2021.pdf"]
    ).load_data()

    # build index
    lyft_index = VectorStoreIndex.from_documents(lyft_docs)
    uber_index = VectorStoreIndex.from_documents(uber_docs)

    # persist index
    lyft_index.storage_context.persist(persist_dir="./storage/lyft")
    uber_index.storage_context.persist(persist_dir="./storage/uber")

if not index_loaded: # load data lyft_docs = SimpleDirectoryReader( input_files=["./data/10k/lyft_2021.pdf"] ).load_data() uber_docs = SimpleDirectoryReader( input_files=["./data/10k/uber_2021.pdf"] ).load_data() # build index lyft_index = VectorStoreIndex.from_documents(lyft_docs) uber_index = VectorStoreIndex.from_documents(uber_docs) # persist index lyft_index.storage_context.persist(persist_dir="./storage/lyft") uber_index.storage_context.persist(persist_dir="./storage/uber")

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lyft_engine = lyft_index.as_query_engine(similarity_top_k=3)
uber_engine = uber_index.as_query_engine(similarity_top_k=3)

lyft_engine = lyft_index.as_query_engine(similarity_top_k=3) uber_engine = uber_index.as_query_engine(similarity_top_k=3)

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query_engine_tools = [
    QueryEngineTool(
        query_engine=lyft_engine,
        metadata=ToolMetadata(
            name="lyft_10k",
            description=(
                "Provides information about Lyft financials for year 2021. "
                "Use a detailed plain text question as input to the tool."
            ),
        ),
    ),
    QueryEngineTool(
        query_engine=uber_engine,
        metadata=ToolMetadata(
            name="uber_10k",
            description=(
                "Provides information about Uber financials for year 2021. "
                "Use a detailed plain text question as input to the tool."
            ),
        ),
    ),
]

query_engine_tools = [ QueryEngineTool( query_engine=lyft_engine, metadata=ToolMetadata( name="lyft_10k", description=( "Provides information about Lyft financials for year 2021. " "Use a detailed plain text question as input to the tool." ), ), ), QueryEngineTool( query_engine=uber_engine, metadata=ToolMetadata( name="uber_10k", description=( "Provides information about Uber financials for year 2021. " "Use a detailed plain text question as input to the tool." ), ), ), ]

Setup OpenAI Agent¶

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from llama_index.agent.openai import OpenAIAgent

from llama_index.agent.openai import OpenAIAgent

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agent = OpenAIAgent.from_tools(query_engine_tools, verbose=True)

agent = OpenAIAgent.from_tools(query_engine_tools, verbose=True)

Let's Try It Out!¶

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agent.chat_repl()

agent.chat_repl()

===== Entering Chat REPL =====
Type "exit" to exit.

=== Calling Function ===
Calling function: lyft_10k with args: {
  "input": "What was Lyft's revenue growth in 2021?"
}
Got output: 
Lyft's revenue growth in 2021 was 36%.
========================
=== Calling Function ===
Calling function: uber_10k with args: {
  "input": "What was Uber's revenue growth in 2021?"
}
Got output: 
Uber's revenue growth in 2021 was 57%.
========================
Assistant: Lyft's revenue growth in 2021 was 36%, while Uber's revenue growth in 2021 was 57%.