聊天引擎 - 上下文模式¶

ContextChatEngine是一个简单的聊天模式，建立在对您的数据进行检索的基础上。

对于每次聊天交互：

• 首先使用用户消息从索引中检索文本
• 将检索到的文本设置为系统提示中的上下文
• 返回给用户消息的答复

这种方法简单直接，适用于与知识库直接相关和一般性交互的问题。

如果您在colab上打开这个笔记本，您可能需要安装LlamaIndex 🦙。

In [ ]:

%pip install llama-index-llms-openai

%pip install llama-index-llms-openai

In [ ]:

!pip install llama-index

!pip install llama-index

下载数据¶

In [ ]:

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

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

五行代码皆可开始¶

加载数据并构建索引

In [ ]:

import openai
import os

os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
openai.api_key = os.environ["OPENAI_API_KEY"]

import openai import os os.environ["OPENAI_API_KEY"] = "API_KEY_HERE" openai.api_key = os.environ["OPENAI_API_KEY"]

In [ ]:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data)

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data() index = VectorStoreIndex.from_documents(data)

配置聊天引擎

由于检索到的上下文可能占用大量可用的LLM上下文，让我们确保配置一个较小的聊天历史限制！

In [ ]:

from llama_index.core.memory import ChatMemoryBuffer

memory = ChatMemoryBuffer.from_defaults(token_limit=1500)

chat_engine = index.as_chat_engine(
    chat_mode="context",
    memory=memory,
    system_prompt=(
        "You are a chatbot, able to have normal interactions, as well as talk"
        " about an essay discussing Paul Grahams life."
    ),
)

from llama_index.core.memory import ChatMemoryBuffer memory = ChatMemoryBuffer.from_defaults(token_limit=1500) chat_engine = index.as_chat_engine( chat_mode="context", memory=memory, system_prompt=( "You are a chatbot, able to have normal interactions, as well as talk" " about an essay discussing Paul Grahams life." ), )

与您的数据交谈

In [ ]:

response = chat_engine.chat("Hello!")

response = chat_engine.chat("Hello!")

In [ ]:

print(response)

print(response)

Hello! How can I assist you today?

Sure, I'd be happy to help! What would you like to ask?

In [ ]:

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

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

In [ ]:

print(response)

print(response)

Growing up, Paul Graham had a keen interest in writing and programming. He spent a lot of time writing short stories, although he admits that they weren't particularly good. In terms of programming, he started working with computers in 9th grade when he had access to an IBM 1401 computer at his school. He learned an early version of Fortran and experimented with writing programs on punch cards. However, he found it challenging to figure out what to do with the computer since he didn't have much data to work with. It wasn't until microcomputers became available that he truly delved into programming, starting with a kit-built microcomputer called the Heathkit. Eventually, he convinced his father to buy a TRS-80, which allowed him to write simple games, create a word processor, and explore programming further.

In [ ]:

response = chat_engine.chat("Can you tell me more?")

response = chat_engine.chat("Can you tell me more?")

In [ ]:

print(response)

print(response)

Certainly! As Paul Graham continued to explore programming, he became fascinated with the possibilities it offered. He enjoyed the process of creating something out of nothing and the logical thinking required in programming. During his high school years, he also developed an interest in painting and considered pursuing it as a career.

After high school, Paul Graham attended Cornell University, where he studied philosophy. However, he found himself spending more time programming than studying philosophy. He even started a company called Viaweb with some friends, which aimed to create an online store builder. Viaweb eventually became successful and was acquired by Yahoo in 1998.

After the acquisition, Paul Graham moved to California and became a millionaire. However, he soon realized that he was burnt out from the stress of running Viaweb. He decided to leave Yahoo and pursue his passion for painting. He enrolled in the Accademia di Belle Arti in Florence, Italy, to study painting.

During his time in Florence, Paul Graham immersed himself in the world of art and painting. He experimented with different techniques and styles, particularly focusing on still life paintings. He found joy in closely observing everyday objects and capturing their details on canvas.

After a year in Florence, Paul Graham returned to the United States and worked at a software company called Interleaf. Although he was not particularly enthusiastic about the job, it provided him with a steady income and allowed him to save money to pursue his dream of attending the Rhode Island School of Design (RISD) to further his studies in painting.

Overall, Paul Graham's journey from programming to painting reflects his curiosity and willingness to explore different passions. He has found success in both fields and continues to share his insights and experiences through his writings and lectures.

重置对话状态

In [ ]:

chat_engine.reset()

chat_engine.reset()

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response = chat_engine.chat("Hello! What do you know?")

response = chat_engine.chat("Hello! What do you know?")

In [ ]:

print(response)

print(response)

Hi there! I know a lot about Paul Graham's life. He is an entrepreneur, programmer, and investor who is best known for co-founding the venture capital firm Y Combinator. He is also the author of several essays on technology and startups, including the influential essay "Hackers and Painters". He has had a long and successful career in the tech industry, and his experiences have shaped his views on entrepreneurship and technology.

流式支持¶

In [ ]:

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

llm = OpenAI(model="gpt-3.5-turbo", temperature=0)
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()

index = VectorStoreIndex.from_documents(data)

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.llms.openai import OpenAI llm = OpenAI(model="gpt-3.5-turbo", temperature=0) data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data() index = VectorStoreIndex.from_documents(data)

In [ ]:

chat_engine = index.as_chat_engine(chat_mode="context", llm=llm)

chat_engine = index.as_chat_engine(chat_mode="context", llm=llm)

In [ ]:

response = chat_engine.stream_chat("What did Paul Graham do after YC?")
for token in response.response_gen:
    print(token, end="")

response = chat_engine.stream_chat("What did Paul Graham do after YC?") for token in response.response_gen: print(token, end="")

After stepping down from his role at Y Combinator (YC), Paul Graham focused on pursuing different interests. Initially, he decided to dedicate his time to painting and see how good he could become with focused practice. He spent most of 2014 painting, but eventually ran out of steam and stopped.

Following his break from painting, Graham returned to writing essays and also resumed working on Lisp, a programming language. He delved into the core of Lisp, which involves writing an interpreter in the language itself. Graham continued to write essays and work on Lisp in the years following his departure from YC.