autollm下載 - autollm源代碼下載

autollm

其他源碼

v0.1.10

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問題|功能請求

？為什麼要自動？

簡化。統一。放大。

特徵	Autollm	Langchain	Llamaindex	Litellm
100+ LLM	✅	✅	✅	✅
統一API	✅			✅
20+矢量數據庫	✅	✅	✅
成本計算（100+ LLM）	✅			✅
1線RAG LLM引擎	✅
1線FastApi	✅

？安裝

在Python中輕鬆安裝Autollm軟件包> = 3.8環境。

pip install autollm

對於內置數據讀取器（GitHub，PDF，Docx，Ipynb，Epub，Mbox，網站..），請安裝：

pip install autollm[readers]

Quickstart

教程

視頻教程：
- 在幾秒鐘內與API一起製作抹布！！！
- Autollm在幾秒鐘內自動創建LLM應用程序
- Autollm：在幾秒鐘內創建基於抹布的LLM Web應用程序！
- Autollm：基於抹布的LLM應用程序和API幾秒鐘
- Autollm：解鎖100多種語言模型的功率！分步教程
博客文章：
- 自動介紹
COLAB筆記本：
- QuickStart：

在幾秒鐘內創建查詢引擎

 >> > from autollm import AutoQueryEngine , read_files_as_documents

>> > documents = read_files_as_documents ( input_dir = "path/to/documents" )
>> > query_engine = AutoQueryEngine . from_defaults ( documents )

>> > response = query_engine . query (
...     "Why did SafeVideo AI develop this project?"
... )

>> > response . response
"Because they wanted to deploy rag based llm apis in no time!"

高級用法

 >> > from autollm import AutoQueryEngine

>> > query_engine = AutoQueryEngine . from_defaults (
...     documents = documents ,
...     llm_model = "gpt-3.5-turbo" ,
...     llm_max_tokens = "256" ,
...     llm_temperature = "0.1" ,
...     system_prompt = '...' ,
...     query_wrapper_prompt = '...' ,
...     enable_cost_calculator = True ,
...     embed_model = "huggingface/BAAI/bge-large-zh" ,
...     chunk_size = 512 ,
...     chunk_overlap = 64 ,
...     context_window = 4096 ,
...     similarity_top_k = 3 ,
...     response_mode = "compact" ,
...     structured_answer_filtering = False ,
...     vector_store_type = "LanceDBVectorStore" ,
...     lancedb_uri = "./lancedb" ,
...     lancedb_table_name = "vectors" ,
...     exist_ok = True ,
...     overwrite_existing = False ,
... )

>> > response = query_engine . query ( "Who is SafeVideo AI?" )

>> > print ( response . response )
"A startup that provides self hosted AI API's for companies!"

將其轉換為1行的FastApi應用

 >> > import uvicorn

>> > from autollm import AutoFastAPI

>> > app = AutoFastAPI . from_query_engine ( query_engine )

>> > uvicorn . run ( app , host = "0.0.0.0" , port = 8000 )
INFO :    Started server process [ 12345 ]
INFO :    Waiting for application startup .
INFO :    Application startup complete .
INFO :    Uvicorn running on http : // http : // 0.0 . 0.0 : 8000 /

高級用法

 >> > from autollm import AutoFastAPI

>> > app = AutoFastAPI . from_query_engine (
...      query_engine ,
...      api_title = '...' ,
...      api_description = '...' ,
...      api_version = '...' ,
...      api_term_of_service = '...' ,
    )

>> > uvicorn . run ( app , host = "0.0.0.0" , port = 8000 )
INFO :    Started server process [ 12345 ]
INFO :    Waiting for application startup .
INFO :    Application startup complete .
INFO :    Uvicorn running on http : // http : // 0.0 . 0.0 : 8000 /

？特徵

支持100+ LLM

 >> > from autollm import AutoQueryEngine

>> > os . environ [ "HUGGINGFACE_API_KEY" ] = "huggingface_api_key"

>> > llm_model = "huggingface/WizardLM/WizardCoder-Python-34B-V1.0"
>> > llm_api_base = "https://my-endpoint.huggingface.cloud"

>> > AutoQueryEngine . from_defaults (
...     documents = '...' ,
...     llm_model = llm_model ,
...     llm_api_base = llm_api_base ,
... )

更多LLM：

擁抱面 - ollama示例：

 >> > from autollm import AutoQueryEngine

>> > llm_model = "ollama/llama2"
>> > llm_api_base = "http://localhost:11434"

>> > AutoQueryEngine . from_defaults (
...     documents = '...' ,
...     llm_model = llm_model ,
...     llm_api_base = llm_api_base ,
... )

Microsoft Azure- OpenAI示例：

 >> > from autollm import AutoQueryEngine

>> > os . environ [ "AZURE_API_KEY" ] = ""
>> > os . environ [ "AZURE_API_BASE" ] = ""
>> > os . environ [ "AZURE_API_VERSION" ] = ""

>> > llm_model = "azure/<your_deployment_name>" )

>> > AutoQueryEngine . from_defaults (
...     documents = '...' ,
...     llm_model = llm_model
... )

Google -Vertexai示例：

 >> > from autollm import AutoQueryEngine

>> > os . environ [ "VERTEXAI_PROJECT" ] = "hardy-device-38811"  # Your Project ID`
>> > os . environ [ "VERTEXAI_LOCATION" ] = "us-central1"  # Your Location

>> > llm_model = "text-bison@001"

>> > AutoQueryEngine . from_defaults (
...     documents = '...' ,
...     llm_model = llm_model
... )

AWS BEDROCK -CLAUDE V2示例：

 >> > from autollm import AutoQueryEngine

>> > os . environ [ "AWS_ACCESS_KEY_ID" ] = ""
>> > os . environ [ "AWS_SECRET_ACCESS_KEY" ] = ""
>> > os . environ [ "AWS_REGION_NAME" ] = ""

>> > llm_model = "anthropic.claude-v2"

>> > AutoQueryEngine . from_defaults (
...     documents = '...' ,
...     llm_model = llm_model
... )

支持20多個載體

？專家提示： autollm默認為lancedb作為矢量存儲：它不用設置，無服務器和100倍更具成本效益！

更多矢量：

QDRANTVECTORSTORE示例：

 >> > from autollm import AutoQueryEngine
>> > import qdrant_client

>> > vector_store_type = "QdrantVectorStore"
>> > client = qdrant_client . QdrantClient (
...     url = "http://<host>:<port>" ,
...     api_key = "<qdrant-api-key>"
... )
>> > collection_name = "quickstart"

>> > AutoQueryEngine . from_defaults (
...     documents = '...' ,
...     vector_store_type = vector_store_type ,
...     client = client ,
...     collection_name = collection_name ,
... )

100+ LLM的自動化成本計算

 >> > from autollm import AutoServiceContext

>> > service_context = AutoServiceContext ( enable_cost_calculation = True )

# Example verbose output after query
Embedding Token Usage : 7
LLM Prompt Token Usage : 1482
LLM Completion Token Usage : 47
LLM Total Token Cost : $ 0.002317

用1線創建FastApi應用程序

例子

 >> > from autollm import AutoFastAPI

>> > app = AutoFastAPI . from_config ( config_path , env_path )

在這裡， config和env應由您的配置和環境文件路徑代替。

創建FastApi應用程序後，在終端中運行以下命令以使其啟動並運行：

uvicorn main:app

從駱駝索引遷移

從駱駝索引切換？我們已經覆蓋了你。

容易遷移

 >> > from llama_index import StorageContext , ServiceContext , VectorStoreIndex
>> > from llama_index . vectorstores import LanceDBVectorStore

>> > from autollm import AutoQueryEngine

>> > vector_store = LanceDBVectorStore ( uri = "./.lancedb" )
>> > storage_context = StorageContext . from_defaults ( vector_store = vector_store )
>> > service_context = ServiceContext . from_defaults ()
>> > index = VectorStoreIndex . from_documents (
        documents = documents ,
        storage_context = storage_contex ,
        service_context = service_context ,
    )

>> > query_engine = AutoQueryEngine . from_instances ( index )