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
%pip install llama-index
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import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
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from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-4o-mini")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-4o-mini")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
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from llama_index.core import StorageContext, load_index_from_storage
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
from llama_index.core import StorageContext, load_index_from_storage
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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from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
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")
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
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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from llama_index.core.tools import QueryEngineTool
query_engine_tools = [
QueryEngineTool.from_defaults(
query_engine=lyft_engine,
name="lyft_10k",
description=(
"Provides information about Lyft financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
QueryEngineTool.from_defaults(
query_engine=uber_engine,
name="uber_10k",
description=(
"Provides information about Uber financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
]
from llama_index.core.tools import QueryEngineTool
query_engine_tools = [
QueryEngineTool.from_defaults(
query_engine=lyft_engine,
name="lyft_10k",
description=(
"Provides information about Lyft financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
QueryEngineTool.from_defaults(
query_engine=uber_engine,
name="uber_10k",
description=(
"Provides information about Uber financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
]
Setup Agent¶
For LLMs like OpenAI that have a function calling API, we should use the FunctionAgent
.
For other LLMs, we can use the ReActAgent
.
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from llama_index.core.agent.workflow import FunctionAgent, ReActAgent
from llama_index.core.workflow import Context
agent = FunctionAgent(tools=query_engine_tools, llm=OpenAI(model="gpt-4o"))
# context to hold the session/state
ctx = Context(agent)
from llama_index.core.agent.workflow import FunctionAgent, ReActAgent
from llama_index.core.workflow import Context
agent = FunctionAgent(tools=query_engine_tools, llm=OpenAI(model="gpt-4o"))
# context to hold the session/state
ctx = Context(agent)
Let's Try It Out!¶
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from llama_index.core.agent.workflow import ToolCallResult, AgentStream
handler = agent.run("What's the revenue for Lyft in 2021 vs Uber?", ctx=ctx)
async for ev in handler.stream_events():
if isinstance(ev, ToolCallResult):
print(
f"Call {ev.tool_name} with args {ev.tool_kwargs}\nReturned: {ev.tool_output}"
)
elif isinstance(ev, AgentStream):
print(ev.delta, end="", flush=True)
response = await handler
from llama_index.core.agent.workflow import ToolCallResult, AgentStream
handler = agent.run("What's the revenue for Lyft in 2021 vs Uber?", ctx=ctx)
async for ev in handler.stream_events():
if isinstance(ev, ToolCallResult):
print(
f"Call {ev.tool_name} with args {ev.tool_kwargs}\nReturned: {ev.tool_output}"
)
elif isinstance(ev, AgentStream):
print(ev.delta, end="", flush=True)
response = await handler
Call lyft_10k with args {'input': "What was Lyft's revenue for the year 2021?"} Returned: Lyft's revenue for the year 2021 was $3,208,323,000. Call uber_10k with args {'input': "What was Uber's revenue for the year 2021?"} Returned: Uber's revenue for the year 2021 was $17.455 billion. In 2021, Lyft's revenue was approximately $3.21 billion, while Uber's revenue was significantly higher at $17.455 billion.