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-rw-r--r--README.md16
-rw-r--r--g4f/models.py6
-rw-r--r--interference/app.py70
3 files changed, 81 insertions, 11 deletions
diff --git a/README.md b/README.md
index dc148e7d..ed8dc577 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ pip install -U g4f
#### Prerequisites:
-1. [Download and install Python](https://www.python.org/downloads/) (Version 3.x is recommended).
+1. [Download and install Python](https://www.python.org/downloads/) (Version 3.10 is recommended).
#### Setting up the project:
@@ -278,6 +278,9 @@ asyncio.run(run_all())
### interference openai-proxy api (use with openai python package)
+If you want to use the embedding function, you need to get a huggingface token. You can get one at https://huggingface.co/settings/tokens make sure your role is set to write. If you have your token, just use it instead of the OpenAI api-key.
+
+
get requirements:
```sh
@@ -293,7 +296,7 @@ python3 -m interference.app
```py
import openai
-openai.api_key = ""
+openai.api_key = "Empty if you don't use embeddings, otherwise your hugginface token"
openai.api_base = "http://localhost:1337"
@@ -518,12 +521,9 @@ for message in response:
print(message, flush=True, end='')
```
-## ChatGPT clone
-
-> We are currently implementing new features and trying to scale it, but please be patient as it may be unstable.
-> https://chat.g4f.ai/chat
-> This site was developed by me and includes **gpt-4/3.5**, **internet access** and **gpt-jailbreak's** like DAN
-> Run locally here: https://github.com/xtekky/chatgpt-clone.
+## Contributors
+A list of the contributors is available [here](https://github.com/xtekky/gpt4free/graphs/contributors)
+The [`Vercel.py`](https://github.com/xtekky/gpt4free/blob/main/g4f/Provider/Vercel.py) file contains code from [vercel-llm-api](https://github.com/ading2210/vercel-llm-api) by [@ading2210](https://github.com/ading2210), which is licenced under the [GNU GPL v3](https://www.gnu.org/licenses/gpl-3.0.txt)
## Copyright:
diff --git a/g4f/models.py b/g4f/models.py
index f1b0ec31..9889f0d5 100644
--- a/g4f/models.py
+++ b/g4f/models.py
@@ -146,6 +146,12 @@ gpt_35_turbo_16k_0613 = Model(
name = 'openai:gpt-3.5-turbo-16k-0613',
base_provider = 'openai')
+gpt_35_turbo_0613 = Model(
+ name = 'openai:gpt-3.5-turbo-0613',
+ base_provider = 'openai',
+ best_provider = [
+ Aivvm, ChatgptLogin])
+
gpt_4_0613 = Model(
name = 'openai:gpt-4-0613',
base_provider = 'openai',
diff --git a/interference/app.py b/interference/app.py
index 1b1af22f..15e5bc80 100644
--- a/interference/app.py
+++ b/interference/app.py
@@ -3,10 +3,10 @@ import random
import string
import time
from typing import Any
-
+import requests
from flask import Flask, request
from flask_cors import CORS
-
+from transformers import AutoTokenizer
from g4f import ChatCompletion
app = Flask(__name__)
@@ -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()