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* Embeddings plugin hook + OpenAI implementation * llm.get_embedding_model(name) function * llm embed command, for returning embeddings or saving them to SQLite * Tests using an EmbedDemo embedding model * llm embed-models list and emeb-models default commands * llm embed-db path and llm embed-db collections commands
21 lines
804 B
Markdown
21 lines
804 B
Markdown
(embeddings-binary)=
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# Binary embedding formats
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The default output format of the `llm embed` command is a JSON array of floating point numbers.
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LLM stores embeddings in a more space-efficient format: little-endian binary sequences of 32-bit floating point numbers, each represented using 4 bytes.
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The following Python functions can be used to convert between the two formats:
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```python
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import struct
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def encode(values):
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return struct.pack("<" + "f" * len(values), *values)
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def decode(binary):
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return struct.unpack("<" + "f" * (len(binary) // 4), binary)
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```
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When using `llm embed` directly, the default output format is JSON.
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Use `--format blob` for the binary output, `--format hex` for that binary output as hexadecimal and `--format base64` for that binary output encoded using base64.
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