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https://github.com/wassname/stampy-chat.git
synced 2026-09-11 12:50:34 +08:00
Bug fixes success
This commit is contained in:
henri123lemoine
+142
-160
@@ -1,12 +1,137 @@
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# ---------------------------------- web code ----------------------------------
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# -------------------------------- non-web-code --------------------------------
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import time
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import os
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import json
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import numpy as np
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from typing import List
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import openai
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from openai.error import RateLimitError
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try:
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import config
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openai.api_key = config.OPENAI_API_KEY
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except ImportError:
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openai.api_key = os.environ.get('OPENAI_API_KEY')
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# OpenAI models
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EMBEDDING_MODEL = "text-embedding-ada-002"
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COMPLETIONS_MODEL = "gpt-3.5-turbo"
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# OpenAI parameters
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LEN_EMBEDDINGS = 1536
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MAX__TOKEN_LEN_PROMPT = 4095 # This may be 8191, unsure.
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# Paths
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import pathlib
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project_path = pathlib.Path(__file__).parent
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PATH_TO_DATASET_JSON = project_path / "data" / "dataset.json" # Path to the saved dataset (.json) file, containing the dataset class object.
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class Dataset:
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def __init__(self, path_to_dataset: str = PATH_TO_DATASET_JSON):
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self.path_to_dataset = path_to_dataset # .json
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self.load_dataset()
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def load_dataset(self): # Load the dataset from the saved .json file
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with open(self.path_to_dataset, 'rb') as f:
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dataset_dict = json.load(f)
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self.metadata = dataset_dict['metadata']
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self.embedding_strings = dataset_dict['embedding_strings']
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self.embeddings_metadata_index = dataset_dict['embeddings_metadata_index']
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self.articles_count = dataset_dict['articles_count']
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self.total_articles_count = dataset_dict['total_articles_count']
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self.total_char_count = dataset_dict['total_char_count']
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self.total_word_count = dataset_dict['total_word_count']
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self.total_sentence_count = dataset_dict['total_sentence_count']
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self.total_block_count = dataset_dict['total_block_count']
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self.sources_so_far = dataset_dict['sources_so_far']
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self.info_types = dataset_dict['info_types']
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self.embeddings = np.array(dataset_dict['embeddings'])
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class Block:
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def __init__(self, title: str, author: str, date: str, url: str, tags: str, text: str):
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self.title = title
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self.author = author
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self.date = date
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self.url = url
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self.tags = tags
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self.text = text
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def get_embedding(text: str) -> np.ndarray:
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"""Get the embedding for a given text. The function will retry with exponential backoff if the API rate limit is reached, up to 4 times.
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Args:
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text (str): The text to get the embedding for.
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Returns:
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np.ndarray: The embedding for the given text.
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"""
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max_retries = 4
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max_wait_time = 10
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for attempt in range(max_retries):
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try:
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result = openai.Embedding.create(
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model=EMBEDDING_MODEL,
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input=text
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)
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return result["data"][0]["embedding"]
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except RateLimitError as e:
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if attempt + 1 == max_retries:
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raise e
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wait_time = min(max_wait_time, (2 ** attempt)) # Exponential backoff
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time.sleep(wait_time)
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def get_top_k_blocks(user_query: str, k: int = 10, HyDE: bool = False) -> List[Block]:
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"""Get the top k blocks that are most semantically similar to the query, using the provided dataset.
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Args:
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query (str): The query to be searched for.
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k (int, optional): The number of blocks to return.
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HyDE (bool, optional): Whether to use HyDE or not. Defaults to False.
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Returns:
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List[Block]: A list of the top k blocks that are most semantically similar to the query.
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"""
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# Get the dataset (in data/dataset.json)
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metadataset = Dataset()
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# Get the embedding for the query.
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query_embedding = get_embedding(user_query)
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# If HyDE is enabled, produce a no-context ChatCompletion to the query.
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if HyDE:
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messages = [
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{"role": "system", "content": "You are a knowledgeable AI Alignment assistant."},
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{"role": "user", "content": f"Do your best to answer the question/instruction, even if you don't know the correct answer or action for sure.\nQ: {user_query}"},
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]
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HyDE_completion = openai.ChatCompletion.create(
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model=COMPLETIONS_MODEL,
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messages=messages
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)["choices"][0]["message"]["content"]
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HyDe_completion_embedding = get_embedding(f"Question: {user_query}\n\nAnswer: {HyDE_completion}")
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similarity_scores = np.dot(metadataset.embeddings, HyDe_completion_embedding)
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else:
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similarity_scores = np.dot(metadataset.embeddings, query_embedding)
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ordered_blocks = np.argsort(similarity_scores)[::-1] # Sort the blocks by similarity score
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top_k_block_indices = ordered_blocks[:k] # Get the top k indices of the blocks
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top_k_metadata_indexes = [metadataset.embeddings_metadata_index[i] for i in top_k_block_indices]
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# Get the top k blocks (title, author, date, url, tags, text)
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top_k_texts = [metadataset.embedding_strings[i] for i in top_k_block_indices] # Get the top k texts
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top_k_metadata = [metadataset.metadata[i] for i in top_k_metadata_indexes] # Get the top k metadata (title, author, date, url, tags)
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# Combine the top k texts and metadata into a list of Block objects
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top_k_metadata_and_text = [list(top_k_metadata[i]) + [top_k_texts[i]] for i in range(k)]
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blocks = [Block(*block) for block in top_k_metadata_and_text]
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return blocks
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# ---------------------------------- web code ----------------------------------
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from http.server import BaseHTTPRequestHandler
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def get_top_k_blocks(user_query: str, k: int = 10, HyDE: bool = False):
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return "Hello World"
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class handler(BaseHTTPRequestHandler):
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@@ -19,162 +144,19 @@ class handler(BaseHTTPRequestHandler):
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data = json.loads(post_data)
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results = {}
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query = data['query']
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if 'k' in data:
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k = data['k']
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else:
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k=10
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if 'HyDE' in data:
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HyDE = data['HyDE']
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else:
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HyDE = False
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for i, block in enumerate(get_top_k_blocks(data['query'])):
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for i, block in enumerate(get_top_k_blocks(query, k=k, HyDE=HyDE)):
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results[i] = json.dumps(block.__dict__)
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self.wfile.write(json.dumps(results).encode('utf-8'))
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# -------------------------------- non-web-code --------------------------------
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# import time
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# import pickle
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# import os
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# import numpy as np
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# import openai
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# from openai.error import RateLimitError
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# from functools import wraps
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# from typing import Callable, List, Type
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# # OpenAI API key
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# try:
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# import config
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# openai.api_key = config.OPENAI_API_KEY
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# except ImportError:
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# openai.api_key = os.environ.get('OPENAI_API_KEY')
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# # OpenAI models
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# EMBEDDING_MODEL = "text-embedding-ada-002"
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# COMPLETIONS_MODEL = "gpt-3.5-turbo"
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# # OpenAI parameters
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# LEN_EMBEDDINGS = 1536
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# MAX_LEN_PROMPT = 4095 # This may be 8191, unsure.
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# # Paths
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# from pathlib import Path
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# project_path = Path(__file__).parent.parent.parent
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# PATH_TO_DATA = project_path / "web" / "api" / "data" / "alignment_texts.jsonl" # Path to the dataset .jsonl file.
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# PATH_TO_EMBEDDINGS = project_path / "web" / "api" / "data" / "embeddings.npy" # Path to the saved embeddings (.npy) file.
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# PATH_TO_DATASET = project_path / "web" / "api" / "data" / "dataset.pkl" # Path to the saved dataset (.pkl) file, containing the dataset class object.
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# class Dataset:
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# pass
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# class Block:
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# def __init__(self, title: str, author: str, date: str, url: str, tags: str, text: str):
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# self.title = title
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# self.author = author
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# self.date = date
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# self.url = url
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# self.tags = tags
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# self.text = text
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# def retry_on_exception_types(exception_types: List[Type[Exception]], stop_after_attempt: int, max_wait_time: int) -> Callable:
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# def decorator(func: Callable) -> Callable:
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# @wraps(func)
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# def wrapper(*args, **kwargs):
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# attempts = 0
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# while attempts < stop_after_attempt:
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# try:
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# return func(*args, **kwargs)
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# except tuple(exception_types) as e:
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# if attempts + 1 == stop_after_attempt:
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# raise e
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# wait_time = min(max_wait_time, (2 ** attempts)) # Exponential backoff
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# time.sleep(wait_time)
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# attempts += 1
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# return wrapper
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# return decorator
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# @retry_on_exception_types(exception_types=[RateLimitError], stop_after_attempt=4, max_wait_time=10)
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# def get_embedding(text: str) -> np.ndarray:
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# """Get the embedding for a given text. The wrapper function will retry with exponential backoffthe request if the API rate limit is reached, up to 4 times.
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# Args:
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# text (str): The text to get the embedding for.
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# Returns:
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# np.ndarray: The embedding for the given text.
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# """
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# result = openai.Embedding.create(
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# model=EMBEDDING_MODEL,
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# input=text
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# )
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# return result["data"][0]["embedding"]
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# def get_top_k_blocks(user_query: str, k: int = 10, HyDE: bool = False) -> List[Block]:
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# """Get the top k blocks that are most semantically similar to the query, using the provided dataset.
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# Args:
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# query (str): The query to be searched for.
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# k (int, optional): The number of blocks to return.
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# HyDE (bool, optional): Whether to use HyDE or not. Defaults to False.
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# Returns:
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# List[Block]: A list of the top k blocks that are most semantically similar to the query.
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# """
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# # Get the dataset
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# with open(PATH_TO_DATASET, "rb") as f:
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# metadataset = pickle.load(f)
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# # Get the embedding for the query.
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# query_embedding = get_embedding(user_query)
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# # If HyDE is enabled, produce a no-context ChatCompletion to the query.
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# if HyDE:
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# messages = [
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# {"role": "system", "content": "You are a knowledgeable AI Alignment assistant."},
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# {"role": "user", "content": f"Do your best to answer the question/instruction, even if you don't know the correct answer or action for sure.\nQ: {user_query}"},
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# ]
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# HyDE_completion = openai.ChatCompletion.create(
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# model=COMPLETIONS_MODEL,
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# messages=messages
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# )["choices"][0]["message"]["content"]
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# HyDe_completion_embedding = get_embedding(f"Question: {user_query}\n\nAnswer: {HyDE_completion}")
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# similarity_scores = np.dot(metadataset.embeddings, HyDe_completion_embedding)
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# else:
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# similarity_scores = np.dot(metadataset.embeddings, query_embedding)
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# ordered_blocks = np.argsort(similarity_scores)[::-1] # Sort the blocks by similarity score
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# top_k_text_indices = ordered_blocks[:k] # Get the top k indices of the blocks
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# top_k_metadata_indexes = [metadataset.embeddings_metadata_index[i] for i in top_k_text_indices]
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# # Get the top k blocks (title, author, date, url, tags, text)
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# top_k_texts = [metadataset.embedding_strings[i] for i in top_k_text_indices] # Get the top k texts
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# top_k_metadata = [metadataset.metadata[i] for i in top_k_metadata_indexes] # Get the top k metadata (title, author, date, url, tags)
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# print(f"Top {k} blocks for query: '{user_query}'")
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# print("=========================================")
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# print(f"Top_{k}_metadata: {top_k_metadata}")
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# # Combine the top k texts and metadata into a list of Block objects
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# top_k_metadata_and_text = [list(top_k_metadata[i]) + [top_k_texts[i]] for i in range(k)]
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# top_k_blocks = [Block(*block) for block in top_k_metadata_and_text]
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# return top_k_blocks
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# if __name__ == "__main__":
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# # Test the embeddings function
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# query = "What is the best way to learn about AI alignment?"
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# k = 8
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# HyDE = True
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# blocks = get_top_k_blocks(query, k, HyDE)
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# for block in blocks:
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# print(f"Title: {block.title}")
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# print(f"Author: {block.author}")
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# print(f"Date: {block.date}")
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# print(f"URL: {block.url}")
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# print(f"Tags: {block.tags}")
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# print(f"Text: {block.text}")
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# print()
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# print()
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