关于人工智能:手把手教你用-Milvus-和-Towhee-搭建一个-AI-聊天机器人

作为向量数据库的佼佼者，Milvus 实用于各种须要借助高效和可扩大向量搜寻性能的 AI 利用。

举个例子，如果想要搭建一个负责聊天机器人数据管理流程，Milvus 必然是首选向量数据库。那么如何让这个利用程序开发变得易于治理及更好了解，那就须要借助 Towhee（https://towhee.io/）了。Towhee 是一个新兴的机器学习（ML）框架，能够简化了实现和编排简单 ML 模型的过程。

接下来我将介绍如何通过 Python 应用 Milvus + Towhee 搭建一个根底的 AI 聊天机器人。本文会重点解说如何解决、剖析非结构化数据及存储和查问向量数据。

首先，创立一个 Python 虚拟环境来运行聊天机器人。

以下是 Linux shell session（会话）。借助 Shell session 创立并激活环境，将 pip 降级到最新版本。

[egoebelbecker@ares milvus_chatbot]$ python -m venv ./chatbot_venv
[egoebelbecker@ares milvus_chatbot]$ source chatbot_venv/bin/activate
(chatbot_venv) [egoebelbecker@ares milvus_chatbot]$ pip install --upgrade pip
Requirement already satisfied: pip in ./chatbot_venv/lib64/python3.11/site-packages (22.2.2)
Collecting pip
  Using cached pip-23.1.2-py3-none-any.whl (2.1 MB)
Installing collected packages: pip
  Attempting uninstall: pip
    Found existing installation: pip 22.2.2
    Uninstalling pip-22.2.2:
      Successfully uninstalled pip-22.2.2
Successfully installed pip-23.1.2

接下来，装置运行代码所需的软件包：Pandas、Jupyter、Langchain、Towhee、Unstructured、Milvus、PymMilvus、sentence_transformers 和 Gradio。

(chatbot_venv) [egoebelbecker@ares milvus_chatbot]$ pip install pandas jupyter langchain towhee unstructured milvus pymilvus sentence_transformers gradio
Collecting pandas
  Obtaining dependency information for pandas from https://files.pythonhosted.org/packages/d0/28/88b81881c056376254618fad622a5e94b5126db8c61157ea1910cd1c040a/pandas-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata
  Using cached pandas-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)
Collecting jupyter
  Using cached jupyter-1.0.0-py2.py3-none-any.whl (2.7 kB)
(snip)
Installing collected packages: webencodings, wcwidth, pytz, pure-eval, ptyprocess, pickleshare, json5, ipython-genutils, filetype, fastjsonschema, executing, backcall, zipp, XlsxWriter, xlrd, widgetsnbextension, websocket-client, webcolors, urllib3, uri-template, tzdata, typing-extensions, traitlets, tqdm, tornado, tinycss2, tenacity, tabulate, soupsieve, sniffio, six, send2trash, rpds-py, rfc3986-validator, rfc3986, regex, pyzmq, PyYAML, python-magic, python-json-logger, pypandoc, pygments, pycparser, psutil, prompt-toolkit, prometheus-client, platformdirs, pkginfo, pillow, pexpect, parso, pandocfilters, packaging, overrides, olefile, numpy, nest-asyncio, mypy-extensions, multidict, more-itertools, mistune, mdurl, markupsafe, markdown, lxml, jupyterlab-widgets, jupyterlab-pygments, jsonpointer, joblib, jeepney, idna, greenlet, frozenlist, fqdn, et-xmlfile, docutils, defusedxml, decorator, debugpy, click, charset-normalizer, chardet, certifi, babel, attrs, async-timeout, async-lru, yarl, typing-inspect, terminado, SQLAlchemy, rfc3339-validator, requests, referencing, qtpy, python-pptx, python-docx, python-dateutil, pydantic, pdf2image, openpyxl, numexpr, nltk, msg-parser, matplotlib-inline, marshmallow, markdown-it-py, jupyter-core, jinja2, jedi, jaraco.classes, importlib-metadata, comm, cffi, bleach, beautifulsoup4, asttokens, anyio, aiosignal, stack-data, rich, requests-toolbelt, readme-renderer, pandas, openapi-schema-pydantic, langsmith, jupyter-server-terminals, jupyter-client, jsonschema-specifications, dataclasses-json, cryptography, arrow, argon2-cffi-bindings, aiohttp, SecretStorage, pdfminer.six, langchain, jsonschema, isoduration, ipython, argon2-cffi, unstructured, nbformat, keyring, ipykernel, twine, qtconsole, nbclient, jupyter-events, jupyter-console, ipywidgets, towhee, nbconvert, jupyter-server, notebook-shim, jupyterlab-server, jupyter-lsp, jupyterlab, notebook, jupyter
Successfully installed PyYAML-6.0.1 SQLAlchemy-2.0.19 SecretStorage-3.3.3 XlsxWriter-3.1.2 aiohttp-3.8.5 aiosignal-1.3.1 anyio-3.7.1 argon2-cffi-21.3.0 argon2-cffi-bindings-21.2.0 arrow-1.2.3 asttokens-2.2.1 async-lru-2.0.4 async-timeout-4.0.2 attrs-23.1.0 babel-2.12.1 backcall-0.2.0 beautifulsoup4-4.12.2 bleach-6.0.0 certifi-2023.7.22 cffi-1.15.1 chardet-5.1.0 charset-normalizer-3.2.0 click-8.1.6 comm-0.1.3 cryptography-41.0.2 dataclasses-json-0.5.14 debugpy-1.6.7 decorator-5.1.1 defusedxml-0.7.1 docutils-0.20.1 et-xmlfile-1.1.0 executing-1.2.0 fastjsonschema-2.18.0 filetype-1.2.0 fqdn-1.5.1 frozenlist-1.4.0 greenlet-2.0.2 idna-3.4 importlib-metadata-6.8.0 ipykernel-6.25.0 ipython-8.14.0 ipython-genutils-0.2.0 ipywidgets-8.0.7 isoduration-20.11.0 jaraco.classes-3.3.0 jedi-0.19.0 jeepney-0.8.0 jinja2-3.1.2 joblib-1.3.1 json5-0.9.14 jsonpointer-2.4 jsonschema-4.18.4 jsonschema-specifications-2023.7.1 jupyter-1.0.0 jupyter-client-8.3.0 jupyter-console-6.6.3 jupyter-core-5.3.1 jupyter-events-0.7.0 jupyter-lsp-2.2.0 jupyter-server-2.7.0 jupyter-server-terminals-0.4.4 jupyterlab-4.0.3 jupyterlab-pygments-0.2.2 jupyterlab-server-2.24.0 jupyterlab-widgets-3.0.8 keyring-24.2.0 langchain-0.0.248 langsmith-0.0.15 lxml-4.9.3 markdown-3.4.4 markdown-it-py-3.0.0 markupsafe-2.1.3 marshmallow-3.20.1 matplotlib-inline-0.1.6 mdurl-0.1.2 mistune-3.0.1 more-itertools-10.0.0 msg-parser-1.2.0 multidict-6.0.4 mypy-extensions-1.0.0 nbclient-0.8.0 nbconvert-7.7.3 nbformat-5.9.2 nest-asyncio-1.5.7 nltk-3.8.1 notebook-7.0.1 notebook-shim-0.2.3 numexpr-2.8.4 numpy-1.25.2 olefile-0.46 openapi-schema-pydantic-1.2.4 openpyxl-3.1.2 overrides-7.3.1 packaging-23.1 pandas-2.0.3 pandocfilters-1.5.0 parso-0.8.3 pdf2image-1.16.3 pdfminer.six-20221105 pexpect-4.8.0 pickleshare-0.7.5 pillow-10.0.0 pkginfo-1.9.6 platformdirs-3.10.0 prometheus-client-0.17.1 prompt-toolkit-3.0.39 psutil-5.9.5 ptyprocess-0.7.0 pure-eval-0.2.2 pycparser-2.21 pydantic-1.10.12 pygments-2.15.1 pypandoc-1.11 python-dateutil-2.8.2 python-docx-0.8.11 python-json-logger-2.0.7 python-magic-0.4.27 python-pptx-0.6.21 pytz-2023.3 pyzmq-25.1.0 qtconsole-5.4.3 qtpy-2.3.1 readme-renderer-40.0 referencing-0.30.0 regex-2023.6.3 requests-2.31.0 requests-toolbelt-1.0.0 rfc3339-validator-0.1.4 rfc3986-2.0.0 rfc3986-validator-0.1.1 rich-13.5.1 rpds-py-0.9.2 send2trash-1.8.2 six-1.16.0 sniffio-1.3.0 soupsieve-2.4.1 stack-data-0.6.2 tabulate-0.9.0 tenacity-8.2.2 terminado-0.17.1 tinycss2-1.2.1 tornado-6.3.2 towhee-1.1.1 tqdm-4.65.0 traitlets-5.9.0 twine-4.0.2 typing-extensions-4.7.1 typing-inspect-0.9.0 tzdata-2023.3 unstructured-0.8.7 uri-template-1.3.0 urllib3-2.0.4 wcwidth-0.2.6 webcolors-1.13 webencodings-0.5.1 websocket-client-1.6.1 widgetsnbextension-4.0.8 xlrd-2.0.1 yarl-1.9.2 zipp-3.16.2
(chatbot_venv) [egoebelbecker@ares milvus_chatbot]$

拜访链接 https://gist.github.com/egoebelbecker/07059b88a1c4daa96ec07937f8ca77b3 获取涵盖本教程所有代码的 Jupyter Notebook。下载 Notebook，启动 Jupyter 并加载 Notebook。

chatbot_venv) [egoebelbecker@ares milvus_chatbot]$ jupyter notebook milvus_chatbot.ipynb 
[I 2023-07-31 11:29:01.748 ServerApp] Package notebook took 0.0000s to import
[I 2023-07-31 11:29:01.759 ServerApp] Package jupyter_lsp took 0.0108s to import
[W 2023-07-31 11:29:01.759 ServerApp] A `_jupyter_server_extension_points` function was not found in jupyter_lsp. Instead, a `_jupyter_server_extension_paths` function was found and will be used for now. This function name will be deprecated in future releases of Jupyter Server.
[I 2023-07-31 11:29:01.764 ServerApp] Package jupyter_server_terminals took 0.0045s to import
[I 2023-07-31 11:29:01.765 ServerApp] Package jupyterlab took 0.0000s to import
[I 2023-07-31 11:29:02.124 ServerApp] Package notebook_shim took 0.0000s to import

所有准备就绪后，就能够搭建聊天机器人了。

机器人须要存储文档块以及应用 Towhee 提取出的文档块向量。在这个步骤中，咱们须要用到 Milvus。

装置轻量版 Milvus Lite，应用以下命令运行 Milvus 服务器：

(chatbot_venv) [egoebelbecker@ares milvus_chatbot]$ milvus-server

    __  _________ _   ____  ______
   /  |/  /  _/ /| | / / / / / __/
  / /|_/ // // /_| |/ / /_/ /\ \
 /_/  /_/___/____/___/\____/___/ {Lite}

 Welcome to use Milvus!

 Version:   v2.2.12-lite
 Process:   139309
 Started:   2023-07-31 12:43:43
 Config:    /home/egoebelbecker/.milvus.io/milvus-server/2.2.12/configs/milvus.yaml
 Logs:      /home/egoebelbecker/.milvus.io/milvus-server/2.2.12/logs

 Ctrl+C to exit …

或者，运行 Notebook 中的代码：

from milvus import default_server

# 启动 Milvus 服务
default_server.start()

# 进行 Milvus 服务
default_server.stop()

设置利用变量并获取 OpenAI API 密钥

接下来，设置变量并清理旧的 SQLite 文件，咱们将用 SQLite 存储聊天历史记录。

MILVUS_URI – Milvus 服务器连贯信息，解析为主机和端口。
MILVUS_HOST – Milvus 运行的主机。
MILVUS_PORT – 服务器监听的端口。
DROP_EXIST – 在启动时删除现有的 Milvus 汇合。
EMBED_MODEL – 用于生成 embedding 向量的 sentence_transformers 模型
COLLECTION_NAME – 用于存储向量数据的 Milvus collection 名称
DIM – 模型生成的文本向量维度
OPENAI_API_KEY – 大语言模型（LLM）API 的密钥

import getpass
import os

MILVUS_URI = 'http://localhost:19530'
[MILVUS_HOST, MILVUS_PORT] = MILVUS_URI.split('://')[1].split(':')
DROP_EXIST = True
EMBED_MODEL = 'all-mpnet-base-v2'
COLLECTION_NAME = 'chatbot_demo'
DIM = 768

OPENAI_API_KEY = getpass.getpass('Enter your OpenAI API key:')

if os.path.exists('./sqlite.db'):
    os.remove('./sqlite.db')

运行上述代码定义变量并输出 OpenAI API 密钥。

接下来，须要下载数据并存储在 Milvus 中。不过在此之前，先学习一下如何应用 pipeline 解决非结构化数据。

我会用 Towhee 官网主页作为文档起源的示例来进行演示，大家也能够尝试其余不同文档网站，理解 pipeline 如何解决不同的数据集。

以下代码应用 Towhee pipeline：

input – 创立新 pipeline，传入源数据。
map – 应用 ops.text_loader() 解析 URL 并将其映射为 ‘doc’。
flat_map – 应用 ops.text_splitter() 将文档拆分成多个片段，以便后续存储。
output – 抉择数据输入，准备就绪能够应用。

将此 pipeline 传入 DataCollection 察看其工作原理。

from towhee import pipe, ops, DataCollectionpipe_load = (  

from towhee import pipe, ops, DataCollection

pipe_load = (pipe.input('source')
        .map('source', 'doc', ops.text_loader())
        .flat_map('doc', 'doc_chunks', ops.text_splitter(chunk_size=300))
        .output('source', 'doc_chunks')
)
DataCollection(pipe_load('https://towhee.io')).show()

以下为输入：

接着，参考以下示例 embedding pipeline 将这些文档块转化为向量。pipeline 通过 map() 在每个文档块上运行 ops.sentence_embedding.sbert()。在示例中，咱们传入了 1 个文本块。

pipe_embed = (pipe.input('doc_chunk')
        .map('doc_chunk', 'vec', ops.sentence_embedding.sbert(model_name=EMBED_MODEL))
        .map('vec', 'vec', ops.np_normalize())
        .output('doc_chunk', 'vec')
)

text = '''SOTA Models

We provide 700+ pre-trained embedding models spanning 5 fields (CV, NLP, Multimodal, Audio, Medical), 15 tasks, and 140+ model architectures.
These include BERT, CLIP, ViT, SwinTransformer, data2vec, etc.
'''

DataCollection(pipe_embed(text)).show()

运行此代码查看这个 pipeline 如何将单个文档片段转换成向量。

创立 1 个 Collection 来存储数据。

以下代码中，咱们应用 MILVUS_HOST 和 MILVUS_PORT 连贯至 Milvus，删除所有现有 Collection，并定义了 create_collection() 函数以创立 1 个全新的 Collection。

新 Collection 的 Schema 如下所示：

id – 标识符，数据类型为整数。
embedding – 向量，数据类型为浮点向量。
text – 向量对应的文档块文本，数据类型为字符串。

from pymilvus import (
    connections, utility, Collection,
    CollectionSchema, FieldSchema, DataType
)


def create_collection(collection_name):
    connections.connect(host=MILVUS_HOST, port=MILVUS_PORT)
    
    has_collection = utility.has_collection(collection_name)
    
    if has_collection:
        collection = Collection(collection_name)
        if DROP_EXIST:
            collection.drop()
        else:
            return collection

    # 创立 collection
    fields = [FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),
        FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, dim=DIM),
        FieldSchema(name='text', dtype=DataType.VARCHAR, max_length=500)
    ]
    schema = CollectionSchema(
        fields=fields,
        description="Towhee demo",
        enable_dynamic_field=True
        )
    collection = Collection(name=collection_name, schema=schema)
    
    index_params = {
        'metric_type': 'IP',
        'index_type': 'IVF_FLAT',
        'params': {'nlist': 1024}
        }
    collection.create_index(
        field_name='embedding', 
        index_params=index_params
    )
    return collection

当初，将文本向量插入 Milvus。

以下代码能够：

创立新 Collection
加载新文档数据
将新文档切块
应用 EMBED_MODEL 为文本快生成向量
将文本块向量和对应文本块数据插入到 Milvus

load_data = (pipe.input('collection_name', 'source')
        .map('collection_name', 'collection', create_collection)
        .map('source', 'doc', ops.text_loader())
        .flat_map('doc', 'doc_chunk', ops.text_splitter(chunk_size=300))
        .map('doc_chunk', 'vec', ops.sentence_embedding.sbert(model_name=EMBED_MODEL))
        .map('vec', 'vec', ops.np_normalize())
        .map(('collection_name', 'vec', 'doc_chunk'), 'mr',
             ops.ann_insert.osschat_milvus(host=MILVUS_HOST, port=MILVUS_PORT))
        .output('mr')
)

通过以下代码，咱们将 Frodo Baggins 的百科页面内容转化为文本快向量并插入到 Milvus 中。

project_name = 'towhee_demo'
data_source = 'https://en.wikipedia.org/wiki/Frodo_Baggins'
mr = load_data(COLLECTION_NAME, data_source)

print('Doc chunks inserted:', len(mr.to_list()))

最终一共插入 408 个本文块向量：

2023-07-31 16:50:53,369 - 139993906521792 - node.py-node:167 - INFO: Begin to run Node-_input2023-07-31 16:50:53,371 - 139993906521792 - node.py-node:167 - INFO: Begin to run Node-create_collection-02023-07-31 16:50:53,373 - 139993881343680 - node.py-node:167 - INFO: Begin to run Node-text-loader-12023-07-31 16:50:53,374 - 139993898129088 - node.py-node:167 - INFO: Begin to run Node-text-splitter-22023-07-31 16:50:53,376 - 139993872950976 - node.py-node:167 - INFO: Begin to run Node-sentence-embedding/sbert-32023-07-31 16:50:53,377 - 139993385268928 - node.py-node:167 - INFO: Begin to run Node-np-normalize-42023-07-31 16:50:53,378 - 139993376876224 - node.py-node:167 - INFO: Begin to run Node-ann-insert/osschat-milvus-52023-07-31 16:50:53,379 - 139993368483520 - node.py-node:167 - INFO: Begin to run Node-_output

(snip)

Categories:
2023-07-31 18:07:53,530 - 140552729257664 - logger.py-logger:14 - DETAIL: Skipping sentence because does not exceed 5 word tokens
Categories
2023-07-31 18:07:53,532 - 140552729257664 - logger.py-logger:14 - DETAIL: Skipping sentence because does not exceed 3 word tokens
Hidden categories
2023-07-31 18:07:53,533 - 140552729257664 - logger.py-logger:14 - DETAIL: Skipping sentence because does not exceed 3 word tokens
Hidden categories
2023-07-31 18:07:53,533 - 140552729257664 - logger.py-logger:14 - DETAIL: Not narrative. Text does not contain a verb:

Hidden categories: 
2023-07-31 18:07:53,534 - 140552729257664 - logger.py-logger:14 - DETAIL: Skipping sentence because does not exceed 5 word tokens
Hidden categories

Doc chunks inserted: 408

Milvus 中曾经存储了文本块向量，当初能够进行向量查问了。

以下函数创立了 1 个查问 pipeline。留神，这是本教程中最为要害的一个步骤！

ops.ann_search.osschat_milvus(host=MILVUS_HOST, port=MILVUS_PORT, 
                              **{'metric_type': 'IP', 'limit': 3, 'output_fields': ['text']}))

OSSChat_milvus（https://towhee.io/ann-search/osschat-milvus）查问 Milvus 向量数据库中与查问文本相匹配的文档片段。

以下为整个查问 pipeline 代码：

pipe_search = (pipe.input('collection_name', 'query')
        .map('query', 'query_vec', ops.sentence_embedding.sbert(model_name=EMBED_MODEL))
        .map('query_vec', 'query_vec', ops.np_normalize())
        .map(('collection_name', 'query_vec'), 'search_res',
             ops.ann_search.osschat_milvus(host=MILVUS_HOST, port=MILVUS_PORT,
                                           **{'metric_type': 'IP', 'limit': 3, 'output_fields': ['text']}))
        .flat_map('search_res', ('id', 'score', 'text'), lambda x: (x[0], x[1], x[2]))
        .output('query', 'text', 'score')
)

当初，能够尝试查问以下问题：

query = 'Who is Frodo Baggins?'
DataCollection(pipe_search(project_name, query)).show()

不难发现，咱们应用的模型返还了 3 个相匹配的后果（注：后面 ann_search.osschat_milvus 中指定了 limit=3）：

接着，须要在聊天机器人中退出 LLM。这样，用户就能够和聊天机器人发展对话了。本示例中，咱们将应用 OpenAI ChatGPT 背地的模型服务：GPT-3.5。

为了使 LLM 答复更精确，咱们须要存储用户和机器人的聊天记录，并在查问时调用这些记录，能够用 SQLite 实现聊天记录的治理。

以下函数用于调取聊天记录：

<section id="nice" data-tool="mdnice 编辑器" data-website="https://www.mdnice.com" style="font-size: 16px; padding: 0 10px; line-height: 1.6; word-spacing: 0px; letter-spacing: 0px; word-break: break-word; word-wrap: break-word; text-align: left; color: #3E3E3E; font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light,'PingFang SC', Cambria, Cochin, Georgia, Times,'Times New Roman', serif;"><pre class="custom" data-tool="mdnice 编辑器" style="margin-top: 10px; margin-bottom: 10px; border-radius: 5px; box-shadow: rgba(0, 0, 0, 0.55) 0px 2px 10px; text-align: left;"><span style="display: block; background: url(https://files.mdnice.com/user/3441/876cad08-0422-409d-bb5a-08afec5da8ee.svg); height: 30px; width: 100%; background-size: 40px; background-repeat: no-repeat; background-color: #282c34; margin-bottom: -7px; border-radius: 5px; background-position: 10px 10px;"></span><code class="hljs" style="overflow-x: auto; padding: 16px; color: #abb2bf; display: -webkit-box; font-family: Operator Mono, Consolas, Monaco, Menlo, monospace; font-size: 12px; -webkit-overflow-scrolling: touch; padding-top: 15px; background: #282c34; border-radius: 5px;">query = 'Who is Frodo Baggins?'
DataCollection(pipe_search(project_name, query)).show()
</code></pre>
</section>

以下函数用户存储聊天记录：

pipe_add_history = (pipe.input('collection_name', 'session', 'question', 'answer')
        .map(('collection_name', 'session', 'question', 'answer'), 'history', ops.chat_message_histories.sql(method='add'))
        .output('history')
)

搭建一个 Pipeline 将查问传递至 LLM 中。

这个 LLM 查问 Pipeline 能够：

依据用户查问问题搜寻 Milvus 向量数据库
调取并存储以后聊天记录
将用户查问问题、Milvus 搜寻后果、聊天记录三者一并传入 ChatGPT
记录本轮问题和答案
返回最终答复

chat = (pipe.input('collection_name', 'query', 'session')
        .map('query', 'query_vec', ops.sentence_embedding.sbert(model_name=EMBED_MODEL))
        .map('query_vec', 'query_vec', ops.np_normalize())
        .map(('collection_name', 'query_vec'), 'search_res',
            ops.ann_search.osschat_milvus(host=MILVUS_HOST,
                                        port=MILVUS_PORT,
                                        **{'metric_type': 'IP', 'limit': 3, 'output_fields': ['text']}))
        .map('search_res', 'knowledge', lambda y: [x[2] for x in y])
        .map(('collection_name', 'session'), 'history', ops.chat_message_histories.sql(method='get'))
        .map(('query', 'knowledge', 'history'), 'messages', ops.prompt.question_answer())
        .map('messages', 'answer', ops.LLM.OpenAI(api_key=OPENAI_API_KEY,
                                                model_name='gpt-3.5-turbo',
                                                temperature=0.8))
        .map(('collection_name', 'session', 'query', 'answer'), 'new_history', ops.chat_message_histories.sql(method='add'))
        .output('query', 'history', 'answer',)
)

在连贯至图形用户界面（GUI）前，咱们须要先测试以下这个 Pipeline。

new_query = 'Where did Frodo take the ring?'
DataCollection(chat(COLLECTION_NAME, new_query, session_id)).show()

以下为输入和后果：

祝贺你！这个 Pipeline 搭建胜利了！接下来能够搭建 Gradio 界面吧！

首先，须要一些函数通过 UUID 来创立 session ID，承受并响应界面上的用户查问。

import uuidimport io

def create_session_id():
    uid = str(uuid.uuid4())
    suid = ''.join(uid.split('-'))
    return 'sess_' + suid


def respond(session, query):
    res = chat(COLLECTION_NAME, query, session).get_dict()
    answer = res['answer']
    response = res['history']
    response.append((query, answer))
    return response

接着，Gradio 界面通过这些函数搭建聊天机器人。Blocks API 用于搭建聊天机器人界面。发送信息（Send Message）按钮通过响应函数将申请发送至 ChatGPT。

import gradio as gr

with gr.Blocks() as demo:
    session_id = gr.State(create_session_id)

    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown('''## Chat''')
            conversation = gr.Chatbot(label='conversation').style(height=300)
            question = gr.Textbox(label='question', value=None)
    
            send_btn = gr.Button('Send Message')
            send_btn.click(
                fn=respond,
                inputs=[
                    session_id,
                    question
                ],
                outputs=conversation,
            )

demo.launch(server_name='127.0.0.1', server_port=8902)

界面如下所示：

至此，一个联合向量检索和 LLM 生成的智能聊天机器人就搭建实现啦！

回顾一下，咱们首先创立了 Towhee pipeline 来解决非结构化数据，并将其转化为向量并存储在 Milvus 向量数据库中。而后，搭建了一个查问 Pipeline，在聊天机器人中接入 LLM。最终，一个根底的聊天机器人界面便搭建实现。

简言之，Milvus 高度可扩大，提供高效的向量相似性搜寻性能，可能帮忙开发者轻松搭建聊天机器人、举荐零碎、图片或文本辨认等 ML 和 AI 利用。期待大家用 Milvus 搭建更出更棒的利用！

本文作者

Eric Goebelbecker 现居纽约，有着 25 年的金融市场从业教训。他负责为金融资讯替换（FIX）协定网络和市场数据分析系统搭建基础设施。Eric 热衷于摸索各种晋升团队工作效率的工具和软件。

如果在应用 Milvus 或 Zilliz 产品有任何问题，可增加小助手微信“zilliz-tech”退出交换群。
欢送关注微信公众号“Zilliz”，理解最新资讯。

本文由 mdnice 多平台公布

关于人工智能:手把手教你用-Milvus-和-Towhee-搭建一个-AI-聊天机器人

01. 设置环境

02. 搭建聊天机器人

文档存储

示例流水线（pipeline）

示例 Embedding pipeline

设置 Milvus

插入 Pipeline

03. 检索知识库

04. 退出大语言模型（LLM）

聊天记录

LLM 查问 Pipeline

Gradio 界面

05. 总结