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authorchatgpt-tricks <143020293+chatgpt-tricks@users.noreply.github.com>2023-09-19 19:43:04 +0200
committerGitHub <noreply@github.com>2023-09-19 19:43:04 +0200
commit596e1d899fd09d0f903d26b055b9e5c50e76ca49 (patch)
treee322427dc3f809a55c00b966447cf4db90848bc9 /interference
parent~ | Merge pull request #917 from Jasonnor/patch-1 (diff)
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Diffstat (limited to '')
-rw-r--r--interference/app.py68
1 files changed, 66 insertions, 2 deletions
diff --git a/interference/app.py b/interference/app.py
index 1b1af22f..c3848e41 100644
--- a/interference/app.py
+++ b/interference/app.py
@@ -3,7 +3,7 @@ import random
import string
import time
from typing import Any
-
+import requests
from flask import Flask, request
from flask_cors import CORS
@@ -88,9 +88,73 @@ def chat_completions():
return app.response_class(streaming(), mimetype="text/event-stream")
+#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
+
+ # 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 = []
+
+ # 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", " ")
+
+ # 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)
+
+ #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)
+ return {
+ "data": [
+ {
+ "embedding": embedding,
+ "index": 0,
+ "object": "embedding"
+ }
+ ],
+ "model": "text-embedding-ada-002",
+ "object": "list",
+ "usage": {
+ "prompt_tokens": None,
+ "total_tokens": None
+ }
+ }
+
def main():
app.run(host="0.0.0.0", port=1337, debug=True)
if __name__ == "__main__":
- main() \ No newline at end of file
+ main()