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fvdb

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FVDB- faiss周围的薄瓷器

fvdb是FAISS矢量数据库周围的简单，最小的包装器。它使用具有归一化向量的L2索引。

它使用faiss-cpu软件包和sentence-transformers进行嵌入。如果您需要GPU版本的faiss（可能不是），则可以手动安装faiss-gpu并在fvdb/db.hy中使用GPUIndexFlatL2而不是IndexFlatL2 。即使使用faiss-cpu您仍然可以使用GPU文本嵌入模型。

如果启用了摘要（不是默认值，请参见下面的配置部分），则将与提取物一起存储提取物的摘要。

它与trag息息。

特征

与分数的相似性搜索
句子转换器嵌入式的选择
结果的有用格式（JSON，列表...）
CLI访问
提取摘要

除纯文本以外的任何输入（Markdown，Asciidoc，RST，源代码等）都超出了范围。您应该在单独的步骤中转换为纯文本中的许多可用软件包（非结构化，trafiltura，docling等）之一。

用法

 import hy # fvdb is written in Hy, but you can use it from python too
from fvdb import faiss , ingest , similar , sources , write

# data ingestion
v = faiss ()
ingest ( v , "doc.md" )
ingest ( v , "docs-dir" )
write ( v , "/tmp/test.fvdb" ) # defaults to $XDG_DATA_HOME/fvdb (~/.local/share/fvdb/ on Linux)

# search
results = similar ( v , "some query text" )
results = marginal ( v , "some query text" ) # not yet implemented

# information, management
sources ( v )
    { ...
      'docs-dir/Once More to the Lake.txt' ,
      'docs-dir/Politics and the English Language.txt' ,
      'docs-dir/Reflections on Gandhi.txt' ,
      'docs-dir/Shooting an elephant.txt' ,
      'docs-dir/The death of the moth.txt' ,
      ... }

info ( v )
    {   'records' : 42 ,
        'embeddings' : 42 ,
        'embedding_dimension' : 1024 ,
        'is_trained' : True ,
        'path' : '/tmp/test-vdb' ,
        'sources' : 24 ,
        'embedding_model' : 'Alibaba-NLP/gte-large-en-v1.5' }

nuke ( v )

这些也可以从命令行获得。

$ # defaults to $XDG_DATA_HOME/fvdb (~/.local/share/fvdb/ on Linux)
# data ingestion (saves on exit)
$ fvdb ingest doc.md
    Adding 2 records

$ fvdb ingest docs-dir
    Adding 42 records

$ # search
$ fvdb similar -j " some query text " > results.json   # --json / -j gives json output

$ fvdb similar -r 2 " George Orwell's formative experience as a policeman in colonial Burma "
    # defaults to tabulated output (not all fields will be shown)
       score  source                             added                               page    length
    --------  ---------------------------------- --------------------------------  ------  --------
    0.579925  docs-dir/A hanging.txt             2024-11-05T11:37:26.232773+00:00       0      2582
    0.526988  docs-dir/Shooting an elephant.txt  2024-11-05T11:37:43.891659+00:00       0      3889

$ fvdb marginal " some query text "                       # not yet implemented

$ # information, management
$ fvdb sources
    ...
    docs-dir/Once More to the Lake.txt
    docs-dir/Politics and the English Language.txt
    docs-dir/Reflections on Gandhi.txt
    docs-dir/Shooting an elephant.txt
    docs-dir/The death of the moth.txt
    ...

$ fvdb info
    -------------------  -----------------------------
    records              44
    embeddings           44
    embedding_dimension  1024
    is_trained           True
    path                 /tmp/test
    sources              24
    embedding_model      Alibaba-NLP/gte-large-en-v1.5
    -------------------  -----------------------------

$ fvdb nuke

配置

寻找$XDG_CONFIG_HOME/fvdb/conf.toml ，否则使用默认值。

您不能在单个FVDB中混合嵌入式型号。

这是一个例子。

 # Sets the default path to something other than $XDG_CONFIG_HOME/fvdb/conf.toml
path = " /tmp/test.fvdb "

# Summaries are useful if you use an embedding model with large maximum sequence length,
# for example, gte-large-en-v1.5 has maximum sequence length of 8192.
summary = true		

# A conservative default model, maximum sequence length of 512,
# so no point using summaries.
embeddings.model = " all-mpnet-base-v2 "

# # Some models need extra options
# embeddings.model = "Alibaba-NLP/gte-large-en-v1.5"
# embeddings.trust_remote_code = true
# # You can put some smaller models on a cpu, but larger models will be slow
# embeddings.device = "cpu"