Chat Engine - OpenAI Agent Mode

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

%pip install llama-index-llms-openai

!pip install llama-index

Download Data

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

--2023-11-20 14:52:58--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.02s   

2023-11-20 14:52:58 (2.86 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

Get started in 5 lines of code

Load data and build index

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI

# Necessary to use the latest OpenAI models that support function calling API
llm = OpenAI(model="gpt-3.5-turbo-0613")
data = SimpleDirectoryReader(input_dir="../data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data)

Configure chat engine

chat_engine = index.as_chat_engine(chat_mode="openai", llm=llm, verbose=True)

Chat with your data

response = chat_engine.chat("Hi")
print(response)

STARTING TURN 1
---------------

Hello! How can I assist you today?

response = chat_engine.chat(
    "Use the tool to answer: Who did Paul Graham hand over YC to?"
)
print(response)

STARTING TURN 1
---------------

=== Calling Function ===
Calling function: query_engine_tool with args: {
  "input": "Who did Paul Graham hand over YC to?"
}
Got output: Paul Graham handed over YC to Sam Altman.
========================

STARTING TURN 2
---------------

Paul Graham handed over Y Combinator (YC) to Sam Altman.

response = chat_engine.stream_chat(
    "Use the tool to answer: Who did Paul Graham hand over YC to?"
)
print(response)

STARTING TURN 1
---------------

=== Calling Function ===
Calling function: query_engine_tool with args: {
  "input": "Who did Paul Graham hand over YC to?"
}
Got output: Paul Graham handed over YC to Sam Altman.
========================

STARTING TURN 2
---------------

Force chat engine to query the index

NOTE: this is a feature unique to the “openai” chat mode (which uses the OpenAIAgent under the hood).

response = chat_engine.chat(
    "What did Paul Graham do growing up?", tool_choice="query_engine_tool"
)

STARTING TURN 1
---------------

=== Calling Function ===
Calling function: query_engine_tool with args: {
  "input": "What did Paul Graham do growing up?"
}
Got output: Growing up, Paul Graham worked on writing and programming. He wrote short stories and also tried his hand at programming on the IBM 1401 computer that his school district had. He later got a microcomputer, a TRS-80, and started programming more extensively, writing simple games and even a word processor.
========================

STARTING TURN 2
---------------

print(response)

Growing up, Paul Graham worked on writing and programming. He wrote short stories and also tried his hand at programming on the IBM 1401 computer that his school district had. He later got a microcomputer, a TRS-80, and started programming more extensively, writing simple games and even a word processor.