semantic ai下載 - semantic ai源代碼下載

semantic ai

其他源碼

v0.0.6.1

下載

語義AI徽標

語義ai lib

檢索調查系統（RAG）的開源框架使用語義搜索來檢索預期的結果並藉助LLM（大語言模型）生成人類可讀的對話響應。

語義AI庫文檔在這里文檔

要求

Python 3.10+異步

安裝

 # Using pip
$ python -m pip install semantic-ai

# Manual install
$ python -m pip install .

設置環境變量

在.env文件中設置憑據。僅給出一個連接器，一個索引器和一個LLM模型配置的憑據。其他字段放為空

 # Default
FILE_DOWNLOAD_DIR_PATH= # default directory name 'download_file_dir'
EXTRACTED_DIR_PATH= # default directory name 'extracted_dir'

# Connector (SharePoint, S3, GCP Bucket, GDrive, Confluence etc.,)
CONNECTOR_TYPE= " connector_name " # sharepoint
SHAREPOINT_CLIENT_ID= " client_id "
SHAREPOINT_CLIENT_SECRET= " client_secret "
SHAREPOINT_TENANT_ID= " tenant_id "
SHAREPOINT_HOST_NAME= ' <tenant_name>.sharepoint.com '
SHAREPOINT_SCOPE= ' https://graph.microsoft.com/.default '
SHAREPOINT_SITE_ID= " site_id "
SHAREPOINT_DRIVE_ID= " drive_id "
SHAREPOINT_FOLDER_URL= " folder_url " # /My_folder/child_folder/

# Indexer
INDEXER_TYPE= " <vector_db_name> " # elasticsearch, qdrant, opensearch
ELASTICSEARCH_URL= " <elasticsearch_url> " # give valid url
ELASTICSEARCH_USER= " <elasticsearch_user> " # give valid user
ELASTICSEARCH_PASSWORD= " <elasticsearch_password> " # give valid password
ELASTICSEARCH_INDEX_NAME= " <index_name> "
ELASTICSEARCH_SSL_VERIFY= " <ssl_verify> " # True or False

# Qdrant
QDRANT_URL= " <qdrant_url> "
QDRANT_INDEX_NAME= " <index_name> "
QDRANT_API_KEY= " <apikey> "

# Opensearch
OPENSEARCH_URL= " <opensearch_url> "
OPENSEARCH_USER= " <opensearch_user> "
OPENSEARCH_PASSWORD= " <opensearch_password> "
OPENSEARCH_INDEX_NAME= " <index_name> "

# LLM
LLM_MODEL= " <llm_model> " # llama, openai
LLM_MODEL_NAME_OR_PATH= " " # model name
OPENAI_API_KEY= " <openai_api_key> " # if using openai

# SQL
SQLITE_SQL_PATH= " <database_path> " # sqlit db path

# MYSQL
MYSQL_HOST= " <host_name> " # localhost or Ip Address
MYSQL_USER= " <user_name> "
MYSQL_PASSWORD= " <password> "
MYSQL_DATABASE= " <database_name> "
MYSQL_PORT= " <port> " # default port is 3306

方法1：加載.env文件。 env文件應具有憑據

%load_ext dotenv
%dotenv
%dotenv relative/or/absolute/path/to/.env

(or)

dotenv -f .env run -- python

方法2：

 from semantic_ai . config import Settings
settings = Settings ()

義務

1。導入模塊

 import asyncio
import semantic_ai

2。要從給定源下載文件，請從下載的文件中提取內容，然後在給定向量數據庫中索引提取的數據。

 await semantic_ai . download ()
await semantic_ai . extract ()
await semantic_ai . index ()

下載，提取和索引完成後，我們可以從索引矢量數據庫中生成答案。該代碼下面給出。

3。使用檢索LLM模型從索引向量db產生答案。

 search_obj = await semantic_ai . search ()
query = ""
search = await search_obj . generate ( query )

假設作業運行很長時間，我們可以觀看處理的文件數，文件數量失敗以及存儲在“ extract_dir_path/meta”目錄中的文本文件中的文件名。

例子

連接源並獲取連接對象。我們可以在示例文件夾中看到這一點。示例：SharePoint連接器

 from semantic_ai . connectors import Sharepoint

CLIENT_ID = '<client_id>'  # sharepoint client id
CLIENT_SECRET = '<client_secret>'  # sharepoint client seceret
TENANT_ID = '<tenant_id>'  # sharepoint tenant id
SCOPE = 'https://graph.microsoft.com/.default'  # scope
HOST_NAME = "<tenant_name>.sharepoint.com"  # for example 'contoso.sharepoint.com'

# Sharepoint object creation
connection = Sharepoint (
    client_id = CLIENT_ID ,
    client_secret = CLIENT_SECRET ,
    tenant_id = TENANT_ID ,
    host_name = HOST_NAME ,
    scope = SCOPE
)

結構

1。導入模塊

 import asyncio
import semantic_ai

2。數據庫連接

sqlite：

 from semantic_ai . connectors import Sqlite

file_path = < database_file_path >

sql = Sqlite ( sql_path = file_path )

mysql：

 from semantic_ai . connectors import Mysql

sql = Mysql (
    host = < host_name > ,
    user = < user_name > ,
    password = < password > ,
    database = < database > ,
    port = < port_number >  # 3306 is default port
)

3。使用檢索LLM模型從數據庫中生成答案。

 query = ""
search_obj = await semantic_ai . db_search ( query = query )

在服務器中運行

$ semantic_ai serve -f .env

INFO:     Loading environment from ' .env '
INFO:     Started server process [43973]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

在http://127.0.0.1:8000/semantic-ai上打開瀏覽器