使用 Chainlit, Langchain 及 Elasticsearch 轻松实现对 PDF 文件的查询

在我之前的文章 "Elasticsearch:与多个 PDF 聊天 | LangChain Python 应用教程(免费 LLMs 和嵌入)" 里,我详述如何使用 Streamlit,Langchain, Elasticsearch 及 OpenAI 来针对 PDF 进行聊天。在今天的文章中,我将使用 Chainlit 来展示如使用 Langchain 及 Elasticsearch 针对 PDF 文件进行查询。

为方便大家学习,我的代码在地址 GitHub - liu-xiao-guo/langchain-openai-chainlit: Chat with your documents (pdf, csv, text) using Openai model, LangChain and Chainlit 进行下载。

安装

安装 Elasticsearch 及 Kibana

如果你还没有安装好自己的 Elasticsearch 及 Kibana,那么请参考一下的文章来进行安装:

在安装的时候,请选择 Elastic Stack 8.x 进行安装。在安装的时候,我们可以看到如下的安装信息:

拷贝 Elasticsearch 证书

我们把 Elasticsearch 的证书拷贝到当前的目录下:

复制代码
$ pwd
/Users/liuxg/python/elser
$ cp ~/elastic/elasticsearch-8.12.0/config/certs/http_ca.crt .
$ ls http_ca.crt 
http_ca.crt

安装 Python 依赖包

我们在当前的目录下打入如下的命令:

复制代码
python3 -m venv .venv
source .venv/bin/activate

然后,我们再打入如下的命令:

复制代码
$ pwd
/Users/liuxg/python/langchain-openai-chainlit
$ source .venv/bin/activate
(.venv) $ pip3 install -r requirements.txt

运行应用

有关 Chainlit 的更多知识请参考 Overview - Chainlit。这里就不再赘述。有关 pdf_qa.py 的代码如下:

pdf_qa.py

复制代码
# Import necessary modules and define env variables

# from langchain.embeddings.openai import OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_openai import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
import os
import io
import chainlit as cl
import PyPDF2
from io import BytesIO

from pprint import pprint
import inspect
# from langchain.vectorstores import ElasticsearchStore
from langchain_community.vectorstores import ElasticsearchStore
from elasticsearch import Elasticsearch

from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

OPENAI_API_KEY= os.getenv("OPENAI_API_KEY")
ES_USER = os.getenv("ES_USER")
ES_PASSWORD = os.getenv("ES_PASSWORD")
elastic_index_name='pdf_docs'

# text_splitter and system template

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)

system_template = """Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
The "SOURCES" part should be a reference to the source of the document from which you got your answer.

Example of your response should be:

```
The answer is foo
SOURCES: xyz
```

Begin!
----------------
{summaries}"""


messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}


@cl.on_chat_start
async def on_chat_start():

    # Sending an image with the local file path
    elements = [
    cl.Image(name="image1", display="inline", path="./robot.jpeg")
    ]
    await cl.Message(content="Hello there, Welcome to AskAnyQuery related to Data!", elements=elements).send()
    files = None

    # Wait for the user to upload a PDF file
    while files is None:
        files = await cl.AskFileMessage(
            content="Please upload a PDF file to begin!",
            accept=["application/pdf"],
            max_size_mb=20,
            timeout=180,
        ).send()

    file = files[0]

    # print("type: ", type(file))
    # print("file: ", file)
    # pprint(vars(file))
    # print(file.content)
 
    msg = cl.Message(content=f"Processing `{file.name}`...")
    await msg.send()

    # Read the PDF file
    # pdf_stream = BytesIO(file.content)
    with open(file.path, 'rb') as f:
        pdf_content = f.read()
    pdf_stream = BytesIO(pdf_content)
    pdf = PyPDF2.PdfReader(pdf_stream)
    pdf_text = ""
    for page in pdf.pages:
        pdf_text += page.extract_text()

    # Split the text into chunks
    texts = text_splitter.split_text(pdf_text)

    # Create metadata for each chunk
    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]

    # Create a Chroma vector store
    embeddings = OpenAIEmbeddings()
  
    url = f"https://{ES_USER}:{ES_PASSWORD}@localhost:9200"
 
    connection = Elasticsearch(
        hosts=[url], 
        ca_certs = "./http_ca.crt", 
        verify_certs = True
    )

    docsearch = None
    
    if not connection.indices.exists(index=elastic_index_name):
        print("The index does not exist, going to generate embeddings")   
        docsearch = await cl.make_async(ElasticsearchStore.from_texts)( 
                texts,
                embedding = embeddings, 
                es_url = url, 
                es_connection = connection,
                index_name = elastic_index_name, 
                es_user = ES_USER,
                es_password = ES_PASSWORD,
                metadatas=metadatas
        )
    else: 
        print("The index already existed")
        
        docsearch = ElasticsearchStore(
            es_connection=connection,
            embedding=embeddings,
            es_url = url, 
            index_name = elastic_index_name, 
            es_user = ES_USER,
            es_password = ES_PASSWORD    
        )

    # Create a chain that uses the Chroma vector store
    chain = RetrievalQAWithSourcesChain.from_chain_type(
        ChatOpenAI(temperature=0),
        chain_type="stuff",
        retriever=docsearch.as_retriever(search_kwargs={"k": 4}),
    )

    # Save the metadata and texts in the user session
    cl.user_session.set("metadatas", metadatas)
    cl.user_session.set("texts", texts)

    # Let the user know that the system is ready
    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
    await msg.update()

    cl.user_session.set("chain", chain)


@cl.on_message
async def main(message:str):

    chain = cl.user_session.get("chain")  # type: RetrievalQAWithSourcesChain
    print("chain type: ", type(chain))
    cb = cl.AsyncLangchainCallbackHandler(
        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
    )
    cb.answer_reached = True
    
    print("message: ", message)
    pprint(vars(message))
    print(message.content)
    res = await chain.acall(message.content, callbacks=[cb])

    answer = res["answer"]
    sources = res["sources"].strip()
    source_elements = []
    
    # Get the metadata and texts from the user session
    metadatas = cl.user_session.get("metadatas")
    all_sources = [m["source"] for m in metadatas]
    texts = cl.user_session.get("texts")
    
    print("texts: ", texts)

    if sources:
        found_sources = []

        # Add the sources to the message
        for source in sources.split(","):
            source_name = source.strip().replace(".", "")
            # Get the index of the source
            try:
                index = all_sources.index(source_name)
            except ValueError:
                continue
            text = texts[index]
            found_sources.append(source_name)
            # Create the text element referenced in the message
            source_elements.append(cl.Text(content=text, name=source_name))

        if found_sources:
            answer += f"\nSources: {', '.join(found_sources)}"
        else:
            answer += "\nNo sources found"

    if cb.has_streamed_final_answer:
        cb.final_stream.elements = source_elements
        await cb.final_stream.update()
    else:
        await cl.Message(content=answer, elements=source_elements).send()

我们可以使用如下的命令来运行:

复制代码
export ES_USER="elastic"
export ES_PASSWORD="xnLj56lTrH98Lf_6n76y"
export OPENAI_API_KEY="YourOpenAiKey"

chainlit run pdf_qa.py -w

(.venv) $ chainlit run pdf_qa.py -w
2024-02-14 10:58:30 - Loaded .env file
2024-02-14 10:58:33 - Your app is available at http://localhost:8000
2024-02-14 10:58:34 - Translation file for en not found. Using default translation en-US.
2024-02-14 10:58:35 - 2 changes detected

我们先选择项目自带的 pdf 文件:

复制代码
Is sample PDF download critical to an organization?
复制代码
Does comprehensive PDF testing have various advantages?
相关推荐
叶凡要飞14 分钟前
RTX5060Ti安装双系统ubuntu22.04各种踩坑点(黑屏,引导区修复、装驱动、server版本安装)
人工智能·python·yolo·ubuntu·机器学习·操作系统
叶庭云18 分钟前
一文掌握 CodeX CLI 安装以及使用!
人工智能·openai·安装·使用教程·codex cli·编码智能体·vibe coding 终端
Apache Flink22 分钟前
Flink Agents 0.1.0 发布公告
大数据·flink
yuluo_YX22 分钟前
VSR 项目解析
人工智能·python
cdming1 小时前
微软Win11双AI功能来袭:“AI管家”+聊天机器人重构桌面交互体验
人工智能·microsoft·机器人
计算衎1 小时前
python通过win32com库调用UDE工具来做开发调试实现自动化源码,以及UDE的知识点介绍
python·c/c++·pywin32·ude·com api
Full Stack Developme1 小时前
java.nio 包详解
java·python·nio
罗西的思考1 小时前
[Agent] ACE(Agentic Context Engineering)和Dynamic Cheatsheet学习笔记
人工智能·机器学习
fantasy_arch1 小时前
transformer-注意力评分函数
人工智能·深度学习·transformer
逐云者1231 小时前
自动驾驶强化学习的价值对齐:奖励函数设计的艺术与科学
人工智能·机器学习·自动驾驶·自动驾驶奖励函数·奖励函数黑客防范·智能驾驶价值对齐