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8516a03a02
This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
import logging
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from typing import List, Optional
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import requests
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from langchain_core.embeddings import Embeddings
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from pydantic import BaseModel
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logger = logging.getLogger(__name__)
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class LlamafileEmbeddings(BaseModel, Embeddings):
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"""Llamafile lets you distribute and run large language models with a
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single file.
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To get started, see: https://github.com/Mozilla-Ocho/llamafile
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To use this class, you will need to first:
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1. Download a llamafile.
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2. Make the downloaded file executable: `chmod +x path/to/model.llamafile`
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3. Start the llamafile in server mode with embeddings enabled:
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`./path/to/model.llamafile --server --nobrowser --embedding`
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Example:
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.. code-block:: python
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from langchain_community.embeddings import LlamafileEmbeddings
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embedder = LlamafileEmbeddings()
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doc_embeddings = embedder.embed_documents(
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[
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"Alpha is the first letter of the Greek alphabet",
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"Beta is the second letter of the Greek alphabet",
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]
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)
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query_embedding = embedder.embed_query(
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"What is the second letter of the Greek alphabet"
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)
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"""
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base_url: str = "http://localhost:8080"
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"""Base url where the llamafile server is listening."""
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request_timeout: Optional[int] = None
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"""Timeout for server requests"""
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def _embed(self, text: str) -> List[float]:
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try:
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response = requests.post(
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url=f"{self.base_url}/embedding",
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headers={
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"Content-Type": "application/json",
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},
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json={
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"content": text,
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},
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timeout=self.request_timeout,
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)
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except requests.exceptions.ConnectionError:
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raise requests.exceptions.ConnectionError(
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f"Could not connect to Llamafile server. Please make sure "
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f"that a server is running at {self.base_url}."
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)
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# Raise exception if we got a bad (non-200) response status code
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response.raise_for_status()
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contents = response.json()
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if "embedding" not in contents:
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raise KeyError(
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"Unexpected output from /embedding endpoint, output dict "
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"missing 'embedding' key."
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)
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embedding = contents["embedding"]
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# Sanity check the embedding vector:
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# Prior to llamafile v0.6.2, if the server was not started with the
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# `--embedding` option, the embedding endpoint would always return a
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# 0-vector. See issue:
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# https://github.com/Mozilla-Ocho/llamafile/issues/243
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# So here we raise an exception if the vector sums to exactly 0.
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if sum(embedding) == 0.0:
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raise ValueError(
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"Embedding sums to 0, did you start the llamafile server with "
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"the `--embedding` option enabled?"
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)
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return embedding
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed documents using a llamafile server running at `self.base_url`.
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llamafile server should be started in a separate process before invoking
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this method.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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doc_embeddings = []
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for text in texts:
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doc_embeddings.append(self._embed(text))
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return doc_embeddings
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using a llamafile server running at `self.base_url`.
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llamafile server should be started in a separate process before invoking
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this method.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return self._embed(text)
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