refactor global vars

.gitignore

@@ -0,0 +1 @@
+.env

app.py

@@ -13,93 +13,9 @@ import os
 import json
 import numpy as np
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-
-## Global Variables
-
-title = """
-# 👋🏻Welcome to 🙋🏻‍♂️Tonic's 📽️Nvidia 🛌🏻Embed V-1 !"""
-
-description = """
-You can use this Space to test out the current model [nvidia/NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1). 🐣a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark (MTEB benchmark)(as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks.
-You can also use 📽️Nvidia 🛌🏻Embed V-1 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/NV-Embed?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
-Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻  [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to 🌟 [MultiTonic](https://github.com/MultiTonic) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
-"""
-
-tasks = {
-        'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim',
-        'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia',
-        'FEVER': 'Given a claim, retrieve documents that support or refute the claim',
-        'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question',
-        'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question',
-        'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query',
-        'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question',
-        'NQ': 'Given a question, retrieve Wikipedia passages that answer the question',
-        'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question',
-        'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper',
-}
-
-intention_prompt= """
-  "type": "object",
-  "properties": {
-    "ClimateFEVER": {
-      "type": "boolean",
-      "description" : "select this for climate science related text"
-    },
-    "DBPedia": {
-      "type": "boolean",
-      "description" : "select this for encyclopedic related knowledge"
-    },
-    "FEVER": {
-      "type": "boolean",
-      "description": "select this to verify a claim or embed a claim"
-    },
-    "FiQA2018": {
-      "type": "boolean",
-      "description" : "select this for financial questions or topics"
-    },
-    "HotpotQA": {
-      "type": "boolean",
-      "description" : "select this for a multi-hop question or for texts that provide multihop claims"
-    },
-    "MSMARCO": {
-      "type": "boolean",
-      "description": "Given a web search query, retrieve relevant passages that answer the query"
-    },
-    "NFCorpus": {
-      "type": "boolean",
-      "description" : "Given a question, retrieve relevant documents that best answer the question"
-    },
-    "NQ": {
-      "type": "boolean",
-      "description" : "Given a question, retrieve Wikipedia passages that answer the question"
-    },
-    "QuoraRetrieval": {
-      "type": "boolean",
-      "description": "Given a question, retrieve questions that are semantically equivalent to the given question"
-    },
-    "SCIDOCS": {
-      "type": "boolean",
-      "description": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper"
-    }
-  },
-  "required": [
-    "ClimateFEVER",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-  ]
-produce a complete json schema."
-
-you will recieve a text , classify the text according to the schema above. ONLY PROVIDE THE FINAL JSON , DO NOT PRODUCE ANY ADDITION INSTRUCTION :"""
+import openai
+from openai import OpenAI
+from globalvars import API_BASE, API_KEY, intention_prompt
 
 ## add chroma vector store
 
@@ -112,10 +28,15 @@ model = AutoModel.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True).
 
 ## Make intention Mapper 
 
-
-
-## Change to Yi API Client
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+intention_client = OpenAI(
+    api_key=API_KEY,
+    base_url=API_BASE
+)
+intention_completion = intention_client.chat.completions.create(
+    model="yi-large",
+    messages=[{"role": "system", "content": intention_prompt},{"role": "user", "content": inputext}]
+)
+# print(completion)
 
 def respond(
     message,

globalvars.py

@@ -0,0 +1,86 @@
+## Global Variables
+
+API_BASE = "https://api.01.ai/v1"
+API_KEY = "your key"
+
+title = """
+# 👋🏻Welcome to 🙋🏻‍♂️Tonic's 📽️Nvidia 🛌🏻Embed V-1 !"""
+
+description = """
+You can use this Space to test out the current model [nvidia/NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1). 🐣a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark (MTEB benchmark)(as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks.
+You can also use 📽️Nvidia 🛌🏻Embed V-1 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/NV-Embed?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
+Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻  [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to 🌟 [MultiTonic](https://github.com/MultiTonic) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
+"""
+
+tasks = {
+        'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim',
+        'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia',
+        'FEVER': 'Given a claim, retrieve documents that support or refute the claim',
+        'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question',
+        'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question',
+        'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query',
+        'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question',
+        'NQ': 'Given a question, retrieve Wikipedia passages that answer the question',
+        'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question',
+        'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper',
+}
+
+intention_prompt= """
+  "type": "object",
+  "properties": {
+    "ClimateFEVER": {
+      "type": "boolean",
+      "description" : "select this for climate science related text"
+    },
+    "DBPedia": {
+      "type": "boolean",
+      "description" : "select this for encyclopedic related knowledge"
+    },
+    "FEVER": {
+      "type": "boolean",
+      "description": "select this to verify a claim or embed a claim"
+    },
+    "FiQA2018": {
+      "type": "boolean",
+      "description" : "select this for financial questions or topics"
+    },
+    "HotpotQA": {
+      "type": "boolean",
+      "description" : "select this for a multi-hop question or for texts that provide multihop claims"
+    },
+    "MSMARCO": {
+      "type": "boolean",
+      "description": "Given a web search query, retrieve relevant passages that answer the query"
+    },
+    "NFCorpus": {
+      "type": "boolean",
+      "description" : "Given a question, retrieve relevant documents that best answer the question"
+    },
+    "NQ": {
+      "type": "boolean",
+      "description" : "Given a question, retrieve Wikipedia passages that answer the question"
+    },
+    "QuoraRetrieval": {
+      "type": "boolean",
+      "description": "Given a question, retrieve questions that are semantically equivalent to the given question"
+    },
+    "SCIDOCS": {
+      "type": "boolean",
+      "description": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper"
+    }
+  },
+  "required": [
+    "ClimateFEVER",
+    "DBPedia",
+    "FEVER",
+    "FiQA2018",
+    "HotpotQA",
+    "MSMARCO",
+    "NFCorpus",
+    "NQ",
+    "QuoraRetrieval",
+    "SCIDOCS",
+  ]
+produce a complete json schema."
+
+you will recieve a text , classify the text according to the schema above. ONLY PROVIDE THE FINAL JSON , DO NOT PRODUCE ANY ADDITION INSTRUCTION :"""