mirror of
https://github.com/wassname/stampy-chat.git
synced 2026-09-11 12:50:34 +08:00
chunk unification
This commit is contained in:
Fraser
+9
-49
@@ -17,22 +17,15 @@ class handler(BaseHTTPRequestHandler):
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self.wfile.write(chat(data['query'], data['history']).encode('utf-8'))
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# ------------------------------- chat gpt stuff -------------------------------
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# --------------------------------- chat stuff ---------------------------------
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from api.get_blocks import get_top_k_blocks, Block
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from typing import List, Dict
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import openai
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import os
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import requests
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from typing import List, Dict
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try:
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import tiktoken
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except ImportError as e:
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print(e)
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print("Please install tiktoken with `pip install tiktoken`")
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import openai
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OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
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openai.api_key = OPENAI_API_KEY
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from api.get_blocks import get_top_k_blocks, Block
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import tiktoken
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# OpenAI models
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EMBEDDING_MODEL = "text-embedding-ada-002"
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@@ -44,39 +37,10 @@ LEN_EMBEDDINGS = 1536
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MAX_TOKEN_LEN_PROMPT = 4095 # This may be 8191, unsure.
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TRUNCATE_CONTEXT = 2000
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def moderate_query(query: str) -> List[str]:
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"""This function uses the OpenAI Moderation API to check if a query contains any offensive language.
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# OpenAI API key
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OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
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openai.api_key = OPENAI_API_KEY
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Args:
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query (str): The query to be checked.
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Raises:
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Exception: If the API call fails.
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Returns:
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List[str]: A list of categories that the query was flagged for.
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"""
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headers = {"Content-Type": "application/json","Authorization": f"Bearer {OPENAI_API_KEY}"}
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data = {"input": query}
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response = requests.post(MODERATION_ENDPOINT, headers=headers, data=json.dumps(data))
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flagged_categories = []
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if response.status_code == 200:
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moderation_results = response.json()
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flagged = moderation_results['results'][0]['flagged']
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categories = moderation_results['results'][0]['categories']
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if flagged:
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for category, is_flagged in categories.items():
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if is_flagged:
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flagged_categories.append(category)
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else:
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raise Exception(f"Error: {response.status_code} {response.reason}")
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return flagged_categories
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def limit_tokens(text: str, max_tokens: int, encoding_name: str = "cl100k_base") -> str:
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encoding = tiktoken.get_encoding(encoding_name)
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@@ -197,10 +161,6 @@ def chat(query: str, history: List[Dict[str, str]] = [], k: str = 10, mode: str
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"""
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# 1. Check if the query is offensive
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flagged_categories: List[str] = moderate_query(query)
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if len(flagged_categories) > 0:
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return f"Your query contains offensive language. Please try again."
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# 2. Find the top-k most relevant blocks from the Alignment Research Dataset
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top_k_blocks: List[Block] = get_top_k_blocks(query, k, HyDE)
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+37
-11
@@ -1,8 +1,10 @@
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import time
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import numpy as np
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import json
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from typing import List
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import dataclasses
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import itertools
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import json
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import numpy as np
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import openai
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import time
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EMBEDDING_MODEL = "text-embedding-ada-002"
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COMPLETIONS_MODEL = "gpt-3.5-turbo"
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@@ -32,14 +34,14 @@ class Dataset:
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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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@dataclasses.dataclass
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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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title: str
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author: str
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date: str
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url: str
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tags: str
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text: str
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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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@@ -111,4 +113,28 @@ def get_top_k_blocks(user_query: str, k: int = 10, HyDE: bool = False) -> List[B
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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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return unify(blocks)
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# for all blocks that are "the same" (same title, author, date, url, tags),
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# combine their text with "\n\n...\n\n" in between, returning the list.
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def unify(blocks: List[Block]) -> List[Block]:
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key = lambda block: (block.title, block.author, block.date, block.url, block.tags)
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blocks.sort(key=key)
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unified_blocks: List[Block] = []
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for k, g in itertools.groupby(blocks, key=key):
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text = "\n\n\n[...]\n\n\n".join([block.text for block in g])
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unified_blocks.append(Block(k[0], k[1], k[2], k[3], k[4], text))
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return unified_blocks
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+1
-123
@@ -2,6 +2,7 @@
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import json
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import dataclasses
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from api.get_blocks import get_top_k_blocks
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from http.server import BaseHTTPRequestHandler
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@dataclasses.dataclass
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@@ -30,127 +31,4 @@ class handler(BaseHTTPRequestHandler):
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data = json.loads(post_data)
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self.wfile.write(json.dumps(get_top_k_blocks(data['query']), cls = Encoder).encode('utf-8'))
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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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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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@@ -40,8 +40,13 @@ const ShowSemanticEntry: React.FC<{entry: SemanticEntry}> = ({entry}) => {
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<h3 className="text-xl flex-1">{entry.title}</h3>
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<p className="flex-1 text-right my-0">{entry.author} - {entry.date}</p>
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</div>
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<p className="text-sm">{entry.text}</p>
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{ entry.text.split("\n").map((paragraph, i) => {
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const p = paragraph.trim();
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if (p === "") return <></>;
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if (p === "[...]") return <hr key={i} />;
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return <p className="text-sm" key={i}> {paragraph} </p>
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})
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}
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<a href={entry.url}>Read more</a>
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</div>
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