add system prompt for intention mapper

app.py

@@ -1,11 +1,121 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+import spaces
+import torch
+import torch.nn.functional as F
+from torch.nn import DataParallel
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+import threading
+import queue
+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
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+## 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 :"""
+
+## add chroma vector store
+
+
+## use instruct embeddings
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True)
+model = AutoModel.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True).to(device)
+
+
+## Make intention Mapper 
+
+
+
+## Change to Yi API Client
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 def respond(
     message,