From 5746c813d04c065f4fb17551bc03983ace2e1e14 Mon Sep 17 00:00:00 2001 From: henri123lemoine Date: Sat, 25 Mar 2023 00:09:40 -0400 Subject: [PATCH] bigger test dataset.py --- web/api/embeddings.py | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/web/api/embeddings.py b/web/api/embeddings.py index bc567bf..6bdfcc2 100644 --- a/web/api/embeddings.py +++ b/web/api/embeddings.py @@ -36,14 +36,13 @@ from typing import List, Tuple import os -import numpy as np # TODO: Add to requirements.txt -from tenacity import ( # TODO: Add to requirements.txt +import numpy as np +from tenacity import ( retry, stop_after_attempt, wait_random_exponential, ) -import openai # TODO: Add to requirements.txt - +import openai os.environ.get('OPENAI_API_KEY') openai.api_key = os.environ.get('OPENAI_API_KEY') @@ -61,12 +60,14 @@ PATH_TO_DATA = project_path / "src" / "data" / "alignment_texts.jsonl" # Path to PATH_TO_EMBEDDINGS = project_path / "src" / "data" / "embeddings.npy" # Path to the saved embeddings (.npy) file. # BAD PATH_TO_DATASET = project_path / "src" / "data" / "dataset.pkl" # Path to the saved dataset (.pkl) file, containing the dataset class object. # BAD + @retry(wait=wait_random_exponential(min=1, max=10), stop=stop_after_attempt(4)) def get_embedding(text: str) -> np.ndarray: result = openai.Embedding.create(model=EMBEDDING_MODEL, input=text) return result["data"][0]["embedding"] -def get_top_k_blocks(user_query: str, k: int, HyDE: bool = False) -> List[str]: + +def get_top_k_blocks(user_query: str, k: int, HyDE: bool = False) -> List[Link]: """Get the top k blocks that are most semantically similar to the query, using the provided dataset. Args: @@ -79,7 +80,7 @@ def get_top_k_blocks(user_query: str, k: int, HyDE: bool = False) -> List[str]: """ # Get the dataset with open(PATH_TO_DATASET, "rb") as f: - dataset = pickle.load(f) + metadataset = pickle.load(f) # Get the embedding for the query. query_embedding = get_embedding(user_query) @@ -98,30 +99,29 @@ def get_top_k_blocks(user_query: str, k: int, HyDE: bool = False) -> List[str]: )["choices"][0]["text"] HyDe_completion_embedding = get_embedding(f"Question: {user_query}\n\nAnswer: {HyDE_completion}") - similarity_scores = np.dot(dataset.metadataset.embeddings, HyDe_completion_embedding) + similarity_scores = np.dot(metadataset.embeddings, HyDe_completion_embedding) else: - similarity_scores = np.dot(dataset.metadataset.embeddings, query_embedding) + similarity_scores = np.dot(metadataset.embeddings, query_embedding) ordered_blocks = np.argsort(similarity_scores)[::-1] # Sort the blocks by similarity score top_k_indices = ordered_blocks[:k] # Get the top k indices - top_k = [dataset.metadataset.embedding_strings[i] for i in top_k_indices] # Get the top k strings + + # Get associated strings + top_k_strings = [metadataset.embedding_strings[i] for i in top_k_indices] # Get the top k strings # Get associated links + top_k_links = [metadataset.metadata[metadataset.embeddings_metadata_index[i]][3] for i in top_k_indices] # Get the top k sources - return top_k + links = [] + for string, link in zip(top_k_strings, top_k_links): + links.append(Link(link, string)) + + return links def embeddings(query): # write a function here that takes a query, returns a bunch of semantically similar links - - return [ \ - Link('https://www.lesswrong.com/posts/FinfRNLMfbq5ESxB9/microsoft-research-paper-claims-sparks-of-artificial', \ - 'Microsoft Research Paper Claims Sparks of Artificial Intelligence'), \ - Link('https://www.lesswrong.com/posts/XhfBRM7oRcpNZwjm8/abstracts-should-be-either-actually-short-tm-or-broken-into', \ - 'Abstracts should be either actually short™ or broken into'), \ - Link('https://www.lesswrong.com/posts/ohXcBjGvazPAxq2ex/continue-working-on-hard-alignment-don-t-give-up', \ - 'Continue working on hard alignment, don\'t give up'), \ - Link('https://www.lesswrong.com/posts/' + query + '/this-is-a-test', \ - 'This is a test of ' + query), \ - ] + link_list = get_top_k_blocks(query, 4) + + return link_list