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| 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) | |