This commit is contained in:
abc 2023-10-12 14:35:18 +01:00
parent 86248b44bc
commit dc502a22de

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@ -3,10 +3,10 @@ import random
import string
import time
import requests
# import requests
from flask import Flask, request
from flask_cors import CORS
from transformers import AutoTokenizer
# from transformers import AutoTokenizer
from g4f import ChatCompletion
@ -95,67 +95,67 @@ def chat_completions():
# Get the embedding from huggingface
def get_embedding(input_text, token):
huggingface_token = token
embedding_model = "sentence-transformers/all-mpnet-base-v2"
max_token_length = 500
# def get_embedding(input_text, token):
# huggingface_token = token
# embedding_model = "sentence-transformers/all-mpnet-base-v2"
# max_token_length = 500
# Load the tokenizer for the 'all-mpnet-base-v2' model
tokenizer = AutoTokenizer.from_pretrained(embedding_model)
# Tokenize the text and split the tokens into chunks of 500 tokens each
tokens = tokenizer.tokenize(input_text)
token_chunks = [
tokens[i : i + max_token_length]
for i in range(0, len(tokens), max_token_length)
]
# # Load the tokenizer for the 'all-mpnet-base-v2' model
# tokenizer = AutoTokenizer.from_pretrained(embedding_model)
# # Tokenize the text and split the tokens into chunks of 500 tokens each
# tokens = tokenizer.tokenize(input_text)
# token_chunks = [
# tokens[i : i + max_token_length]
# for i in range(0, len(tokens), max_token_length)
# ]
# Initialize an empty list
embeddings = []
# # Initialize an empty list
# embeddings = []
# Create embeddings for each chunk
for chunk in token_chunks:
# Convert the chunk tokens back to text
chunk_text = tokenizer.convert_tokens_to_string(chunk)
# # Create embeddings for each chunk
# for chunk in token_chunks:
# # Convert the chunk tokens back to text
# chunk_text = tokenizer.convert_tokens_to_string(chunk)
# Use the Hugging Face API to get embeddings for the chunk
api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{embedding_model}"
headers = {"Authorization": f"Bearer {huggingface_token}"}
chunk_text = chunk_text.replace("\n", " ")
# # Use the Hugging Face API to get embeddings for the chunk
# api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{embedding_model}"
# headers = {"Authorization": f"Bearer {huggingface_token}"}
# chunk_text = chunk_text.replace("\n", " ")
# Make a POST request to get the chunk's embedding
response = requests.post(
api_url,
headers=headers,
json={"inputs": chunk_text, "options": {"wait_for_model": True}},
)
# # Make a POST request to get the chunk's embedding
# response = requests.post(
# api_url,
# headers=headers,
# json={"inputs": chunk_text, "options": {"wait_for_model": True}},
# )
# Parse the response and extract the embedding
chunk_embedding = response.json()
# Append the embedding to the list
embeddings.append(chunk_embedding)
# # Parse the response and extract the embedding
# chunk_embedding = response.json()
# # Append the embedding to the list
# embeddings.append(chunk_embedding)
# averaging all the embeddings
# this isn't very effective
# someone a better idea?
num_embeddings = len(embeddings)
average_embedding = [sum(x) / num_embeddings for x in zip(*embeddings)]
embedding = average_embedding
return embedding
# # averaging all the embeddings
# # this isn't very effective
# # someone a better idea?
# num_embeddings = len(embeddings)
# average_embedding = [sum(x) / num_embeddings for x in zip(*embeddings)]
# embedding = average_embedding
# return embedding
@app.route("/embeddings", methods=["POST"])
def embeddings():
input_text_list = request.get_json().get("input")
input_text = " ".join(map(str, input_text_list))
token = request.headers.get("Authorization").replace("Bearer ", "")
embedding = get_embedding(input_text, token)
# @app.route("/embeddings", methods=["POST"])
# def embeddings():
# input_text_list = request.get_json().get("input")
# input_text = " ".join(map(str, input_text_list))
# token = request.headers.get("Authorization").replace("Bearer ", "")
# embedding = get_embedding(input_text, token)
return {
"data": [{"embedding": embedding, "index": 0, "object": "embedding"}],
"model": "text-embedding-ada-002",
"object": "list",
"usage": {"prompt_tokens": None, "total_tokens": None},
}
# return {
# "data": [{"embedding": embedding, "index": 0, "object": "embedding"}],
# "model": "text-embedding-ada-002",
# "object": "list",
# "usage": {"prompt_tokens": None, "total_tokens": None},
# }
def run_api():