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author | Gustaf Rydholm <gustaf.rydholm@gmail.com> | 2024-04-05 00:42:02 +0200 |
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committer | Gustaf Rydholm <gustaf.rydholm@gmail.com> | 2024-04-05 00:42:02 +0200 |
commit | 1cf0a401054c3e3ebde60bfd73ad15e39bc531e6 (patch) | |
tree | 633314ef342c35213cfa01607dd3c98e77b7cdd2 /rag/llms/encoder.py | |
parent | 633f180eb0ccdc4772d5d705873cef1e33507976 (diff) |
Rename llm
Diffstat (limited to 'rag/llms/encoder.py')
-rw-r--r-- | rag/llms/encoder.py | 24 |
1 files changed, 0 insertions, 24 deletions
diff --git a/rag/llms/encoder.py b/rag/llms/encoder.py deleted file mode 100644 index 758b523..0000000 --- a/rag/llms/encoder.py +++ /dev/null @@ -1,24 +0,0 @@ -from typing import List -import ollama -import numpy as np - -# FIXME: .env -ENCODER_MODEL = "mxbai-embed-large" - - -class Encoder: - def __init__(self) -> None: - self.query_prompt = "Represent this sentence for searching relevant passages: " - - def __encode(self, prompt: str) -> np.ndarray: - x = ollama.embeddings(model=ENCODER_MODEL, prompt=prompt) - x = np.array([x["embedding"]]).astype("float32") - return x - - def encode(self, doc: List[str]) -> List[np.ndarray]: - return [self.__encode(chunk) for chunk in doc] - - def query(self, query: str) -> np.ndarray: - query = self.query_prompt + query - return self.__encode(query) - |