mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
b7c71e2e07
* **Description:** adds `LlamafileEmbeddings` class implementation for generating embeddings using [llamafile](https://github.com/Mozilla-Ocho/llamafile)-based models. Includes related unit tests and notebook showing example usage. * **Issue:** N/A * **Dependencies:** N/A
120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
import logging
|
|
from typing import List, Optional
|
|
|
|
import requests
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class LlamafileEmbeddings(BaseModel, Embeddings):
|
|
"""Llamafile lets you distribute and run large language models with a
|
|
single file.
|
|
|
|
To get started, see: https://github.com/Mozilla-Ocho/llamafile
|
|
|
|
To use this class, you will need to first:
|
|
|
|
1. Download a llamafile.
|
|
2. Make the downloaded file executable: `chmod +x path/to/model.llamafile`
|
|
3. Start the llamafile in server mode with embeddings enabled:
|
|
|
|
`./path/to/model.llamafile --server --nobrowser --embedding`
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.embeddings import LlamafileEmbeddings
|
|
embedder = LlamafileEmbeddings()
|
|
doc_embeddings = embedder.embed_documents(
|
|
[
|
|
"Alpha is the first letter of the Greek alphabet",
|
|
"Beta is the second letter of the Greek alphabet",
|
|
]
|
|
)
|
|
query_embedding = embedder.embed_query(
|
|
"What is the second letter of the Greek alphabet"
|
|
)
|
|
|
|
"""
|
|
|
|
base_url: str = "http://localhost:8080"
|
|
"""Base url where the llamafile server is listening."""
|
|
|
|
request_timeout: Optional[int] = None
|
|
"""Timeout for server requests"""
|
|
|
|
def _embed(self, text: str) -> List[float]:
|
|
try:
|
|
response = requests.post(
|
|
url=f"{self.base_url}/embedding",
|
|
headers={
|
|
"Content-Type": "application/json",
|
|
},
|
|
json={
|
|
"content": text,
|
|
},
|
|
timeout=self.request_timeout,
|
|
)
|
|
except requests.exceptions.ConnectionError:
|
|
raise requests.exceptions.ConnectionError(
|
|
f"Could not connect to Llamafile server. Please make sure "
|
|
f"that a server is running at {self.base_url}."
|
|
)
|
|
|
|
# Raise exception if we got a bad (non-200) response status code
|
|
response.raise_for_status()
|
|
|
|
contents = response.json()
|
|
if "embedding" not in contents:
|
|
raise KeyError(
|
|
"Unexpected output from /embedding endpoint, output dict "
|
|
"missing 'embedding' key."
|
|
)
|
|
|
|
embedding = contents["embedding"]
|
|
|
|
# Sanity check the embedding vector:
|
|
# Prior to llamafile v0.6.2, if the server was not started with the
|
|
# `--embedding` option, the embedding endpoint would always return a
|
|
# 0-vector. See issue:
|
|
# https://github.com/Mozilla-Ocho/llamafile/issues/243
|
|
# So here we raise an exception if the vector sums to exactly 0.
|
|
if sum(embedding) == 0.0:
|
|
raise ValueError(
|
|
"Embedding sums to 0, did you start the llamafile server with "
|
|
"the `--embedding` option enabled?"
|
|
)
|
|
|
|
return embedding
|
|
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
|
"""Embed documents using a llamafile server running at `self.base_url`.
|
|
llamafile server should be started in a separate process before invoking
|
|
this method.
|
|
|
|
Args:
|
|
texts: The list of texts to embed.
|
|
|
|
Returns:
|
|
List of embeddings, one for each text.
|
|
"""
|
|
doc_embeddings = []
|
|
for text in texts:
|
|
doc_embeddings.append(self._embed(text))
|
|
return doc_embeddings
|
|
|
|
def embed_query(self, text: str) -> List[float]:
|
|
"""Embed a query using a llamafile server running at `self.base_url`.
|
|
llamafile server should be started in a separate process before invoking
|
|
this method.
|
|
|
|
Args:
|
|
text: The text to embed.
|
|
|
|
Returns:
|
|
Embeddings for the text.
|
|
"""
|
|
return self._embed(text)
|