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Browse files- .gitattributes +36 -35
- .ragatouille/colbert/indexes/cbseclass10index/0.codes.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/0.metadata.json +6 -0
- .ragatouille/colbert/indexes/cbseclass10index/0.residuals.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/avg_residual.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/buckets.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/centroids.pt +3 -0
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- .ragatouille/colbert/indexes/cbseclass10index/metadata.json +70 -0
- .ragatouille/colbert/indexes/cbseclass10index/pid_docid_map.json +1630 -0
- .ragatouille/colbert/indexes/cbseclass10index/plan.json +64 -0
- README.md +12 -12
- app.py +297 -0
- backend/query_llm.py +175 -0
- backend/semantic_search.py +28 -0
- lancedb/cbse.lance/_latest.manifest +0 -0
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- lancedb/cbse.lance/data/615853d2-bbd1-453c-88c7-8df0edb43e5e.lance +3 -0
- logo.png +0 -0
- requirements.txt +9 -0
- templates/template.j2 +8 -0
- templates/template_html.j2 +103 -0
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"config":{
|
| 3 |
+
"query_token_id":"[unused0]",
|
| 4 |
+
"doc_token_id":"[unused1]",
|
| 5 |
+
"query_token":"[Q]",
|
| 6 |
+
"doc_token":"[D]",
|
| 7 |
+
"ncells":null,
|
| 8 |
+
"centroid_score_threshold":null,
|
| 9 |
+
"ndocs":null,
|
| 10 |
+
"load_index_with_mmap":false,
|
| 11 |
+
"index_path":null,
|
| 12 |
+
"index_bsize":32,
|
| 13 |
+
"nbits":8,
|
| 14 |
+
"kmeans_niters":20,
|
| 15 |
+
"resume":false,
|
| 16 |
+
"similarity":"cosine",
|
| 17 |
+
"bsize":64,
|
| 18 |
+
"accumsteps":1,
|
| 19 |
+
"lr":0.00001,
|
| 20 |
+
"maxsteps":400000,
|
| 21 |
+
"save_every":null,
|
| 22 |
+
"warmup":20000,
|
| 23 |
+
"warmup_bert":null,
|
| 24 |
+
"relu":false,
|
| 25 |
+
"nway":64,
|
| 26 |
+
"use_ib_negatives":true,
|
| 27 |
+
"reranker":false,
|
| 28 |
+
"distillation_alpha":1.0,
|
| 29 |
+
"ignore_scores":false,
|
| 30 |
+
"model_name":null,
|
| 31 |
+
"query_maxlen":32,
|
| 32 |
+
"attend_to_mask_tokens":false,
|
| 33 |
+
"interaction":"colbert",
|
| 34 |
+
"dim":128,
|
| 35 |
+
"doc_maxlen":256,
|
| 36 |
+
"mask_punctuation":true,
|
| 37 |
+
"checkpoint":"colbert-ir/colbertv2.0",
|
| 38 |
+
"triples":"/future/u/okhattab/root/unit/experiments/2021.10/downstream.distillation.round2.2_score/round2.nway6.cosine.ib/examples.64.json",
|
| 39 |
+
"collection":[
|
| 40 |
+
"list with 1628 elements starting with...",
|
| 41 |
+
[
|
| 42 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n3.1 In terms of Regulation 4 of the Project Import Regulations, 1986 (PIR), the basic requirement for \navailing the benefit of assessment under Tariff Heading No. 98.01 is that the importer should have \nentered into one or more contracts with the su ppliers of the goods for setting up a project. Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs.",
|
| 43 |
+
"Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs. \n3.2 Regulation 5 provides in the manner of registering contracts, which is as follows: \n(i) Before any order is made by the proper officer of Customs permitting the clearance of the \ngoods for home consumption; \n(ii) In the case of goods cleared for hom e consumption without payment of duty subject to re -\nexport in respect of fairs, exhibitions, demonstrations, seminars, congresses and \nconferences, duly sponsored or approved by the Government of India or Trade fair Authority \nof India, as the case may be, b efore the date of payment of duty. \n3.3 To expedite registration, the importers are advised to submit the following documents along with \nthe application for registration: \n(i) Original deed of contract together with true copy thereof.",
|
| 44 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n(ii) Industrial License and letter of intent, SSI Certificate granted by the appropriate authority \nwith a copy thereof. \n \n(iii) Original Import licence, if any, with a list of items showing the dimensions, specifications, \nquantity, quality, value of each item duly attested by the Licensing Authority and a copy \nthereof."
|
| 45 |
+
]
|
| 46 |
+
],
|
| 47 |
+
"queries":"/future/u/okhattab/data/MSMARCO/queries.train.tsv",
|
| 48 |
+
"index_name":"cbseclass10index",
|
| 49 |
+
"overwrite":false,
|
| 50 |
+
"root":".ragatouille/",
|
| 51 |
+
"experiment":"colbert",
|
| 52 |
+
"index_root":null,
|
| 53 |
+
"name":"2024-08/15/14.23.27",
|
| 54 |
+
"rank":0,
|
| 55 |
+
"nranks":1,
|
| 56 |
+
"amp":true,
|
| 57 |
+
"gpus":1,
|
| 58 |
+
"avoid_fork_if_possible":false
|
| 59 |
+
},
|
| 60 |
+
"num_chunks":1,
|
| 61 |
+
"num_partitions":4096,
|
| 62 |
+
"num_embeddings":202637,
|
| 63 |
+
"avg_doclen":124.4699017199,
|
| 64 |
+
"RAGatouille":{
|
| 65 |
+
"index_config":{
|
| 66 |
+
"index_type":"PLAID",
|
| 67 |
+
"index_name":"cbseclass10index"
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
}
|
.ragatouille/colbert/indexes/cbseclass10index/pid_docid_map.json
ADDED
|
@@ -0,0 +1,1630 @@
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|
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|
| 1618 |
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| 1619 |
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| 1620 |
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|
| 1621 |
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| 1622 |
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| 1623 |
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|
| 1625 |
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|
| 1626 |
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|
| 1627 |
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|
| 1628 |
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|
| 1629 |
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"1627":"17dc8801-a1dc-486f-8ebf-814bc37467fb"
|
| 1630 |
+
}
|
.ragatouille/colbert/indexes/cbseclass10index/plan.json
ADDED
|
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"config": {
|
| 3 |
+
"query_token_id": "[unused0]",
|
| 4 |
+
"doc_token_id": "[unused1]",
|
| 5 |
+
"query_token": "[Q]",
|
| 6 |
+
"doc_token": "[D]",
|
| 7 |
+
"ncells": null,
|
| 8 |
+
"centroid_score_threshold": null,
|
| 9 |
+
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|
| 10 |
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|
| 11 |
+
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|
| 12 |
+
"index_bsize": 32,
|
| 13 |
+
"nbits": 8,
|
| 14 |
+
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|
| 15 |
+
"resume": false,
|
| 16 |
+
"similarity": "cosine",
|
| 17 |
+
"bsize": 64,
|
| 18 |
+
"accumsteps": 1,
|
| 19 |
+
"lr": 1e-5,
|
| 20 |
+
"maxsteps": 400000,
|
| 21 |
+
"save_every": null,
|
| 22 |
+
"warmup": 20000,
|
| 23 |
+
"warmup_bert": null,
|
| 24 |
+
"relu": false,
|
| 25 |
+
"nway": 64,
|
| 26 |
+
"use_ib_negatives": true,
|
| 27 |
+
"reranker": false,
|
| 28 |
+
"distillation_alpha": 1.0,
|
| 29 |
+
"ignore_scores": false,
|
| 30 |
+
"model_name": null,
|
| 31 |
+
"query_maxlen": 32,
|
| 32 |
+
"attend_to_mask_tokens": false,
|
| 33 |
+
"interaction": "colbert",
|
| 34 |
+
"dim": 128,
|
| 35 |
+
"doc_maxlen": 256,
|
| 36 |
+
"mask_punctuation": true,
|
| 37 |
+
"checkpoint": "colbert-ir\/colbertv2.0",
|
| 38 |
+
"triples": "\/future\/u\/okhattab\/root\/unit\/experiments\/2021.10\/downstream.distillation.round2.2_score\/round2.nway6.cosine.ib\/examples.64.json",
|
| 39 |
+
"collection": [
|
| 40 |
+
"list with 1628 elements starting with...",
|
| 41 |
+
[
|
| 42 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n3.1 In terms of Regulation 4 of the Project Import Regulations, 1986 (PIR), the basic requirement for \navailing the benefit of assessment under Tariff Heading No. 98.01 is that the importer should have \nentered into one or more contracts with the su ppliers of the goods for setting up a project. Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs.",
|
| 43 |
+
"Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs. \n3.2 Regulation 5 provides in the manner of registering contracts, which is as follows: \n(i) Before any order is made by the proper officer of Customs permitting the clearance of the \ngoods for home consumption; \n(ii) In the case of goods cleared for hom e consumption without payment of duty subject to re -\nexport in respect of fairs, exhibitions, demonstrations, seminars, congresses and \nconferences, duly sponsored or approved by the Government of India or Trade fair Authority \nof India, as the case may be, b efore the date of payment of duty. \n3.3 To expedite registration, the importers are advised to submit the following documents along with \nthe application for registration: \n(i) Original deed of contract together with true copy thereof.",
|
| 44 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n(ii) Industrial License and letter of intent, SSI Certificate granted by the appropriate authority \nwith a copy thereof. \n \n(iii) Original Import licence, if any, with a list of items showing the dimensions, specifications, \nquantity, quality, value of each item duly attested by the Licensing Authority and a copy \nthereof."
|
| 45 |
+
]
|
| 46 |
+
],
|
| 47 |
+
"queries": "\/future\/u\/okhattab\/data\/MSMARCO\/queries.train.tsv",
|
| 48 |
+
"index_name": "cbseclass10index",
|
| 49 |
+
"overwrite": false,
|
| 50 |
+
"root": ".ragatouille\/",
|
| 51 |
+
"experiment": "colbert",
|
| 52 |
+
"index_root": null,
|
| 53 |
+
"name": "2024-08\/15\/14.23.27",
|
| 54 |
+
"rank": 0,
|
| 55 |
+
"nranks": 1,
|
| 56 |
+
"amp": true,
|
| 57 |
+
"gpus": 1,
|
| 58 |
+
"avoid_fork_if_possible": false
|
| 59 |
+
},
|
| 60 |
+
"num_chunks": 1,
|
| 61 |
+
"num_partitions": 4096,
|
| 62 |
+
"num_embeddings_est": 202637.00051879883,
|
| 63 |
+
"avg_doclen_est": 124.46990203857422
|
| 64 |
+
}
|
README.md
CHANGED
|
@@ -1,12 +1,12 @@
|
|
| 1 |
-
---
|
| 2 |
-
title:
|
| 3 |
-
emoji:
|
| 4 |
-
colorFrom:
|
| 5 |
-
colorTo:
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 4.
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
| 1 |
+
---
|
| 2 |
+
title: Adwitiya Chat & Quizbot
|
| 3 |
+
emoji: 📚
|
| 4 |
+
colorFrom: red
|
| 5 |
+
colorTo: indigo
|
| 6 |
+
sdk: gradio
|
| 7 |
+
sdk_version: 4.36.0
|
| 8 |
+
app_file: app.py
|
| 9 |
+
pinned: false
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
app.py
ADDED
|
@@ -0,0 +1,297 @@
|
|
|
|
|
|
|
|
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|
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|
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|
| 1 |
+
import requests
|
| 2 |
+
import gradio as gr
|
| 3 |
+
from ragatouille import RAGPretrainedModel
|
| 4 |
+
import logging
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
from time import perf_counter
|
| 7 |
+
from sentence_transformers import CrossEncoder
|
| 8 |
+
from huggingface_hub import InferenceClient
|
| 9 |
+
from jinja2 import Environment, FileSystemLoader
|
| 10 |
+
import numpy as np
|
| 11 |
+
from os import getenv
|
| 12 |
+
from backend.query_llm import generate_hf, generate_qwen
|
| 13 |
+
from backend.semantic_search import table, retriever
|
| 14 |
+
from huggingface_hub import InferenceClient
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
# Bhashini API translation function
|
| 18 |
+
api_key = getenv('API_KEY')
|
| 19 |
+
user_id = getenv('USER_ID')
|
| 20 |
+
|
| 21 |
+
def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") -> dict:
|
| 22 |
+
"""Translates text from source language to target language using the Bhashini API."""
|
| 23 |
+
|
| 24 |
+
if not text.strip():
|
| 25 |
+
print('Input text is empty. Please provide valid text for translation.')
|
| 26 |
+
return {"status_code": 400, "message": "Input text is empty", "translated_content": None, "speech_content": None}
|
| 27 |
+
else:
|
| 28 |
+
print('Input text - ',text)
|
| 29 |
+
print(f'Starting translation process from {from_code} to {to_code}...')
|
| 30 |
+
print(f'Starting translation process from {from_code} to {to_code}...')
|
| 31 |
+
gr.Warning(f'Translating to {to_code}...')
|
| 32 |
+
|
| 33 |
+
url = 'https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/getModelsPipeline'
|
| 34 |
+
headers = {
|
| 35 |
+
"Content-Type": "application/json",
|
| 36 |
+
"userID": user_id,
|
| 37 |
+
"ulcaApiKey": api_key
|
| 38 |
+
}
|
| 39 |
+
payload = {
|
| 40 |
+
"pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}}}],
|
| 41 |
+
"pipelineRequestConfig": {"pipelineId": "64392f96daac500b55c543cd"}
|
| 42 |
+
}
|
| 43 |
+
|
| 44 |
+
print('Sending initial request to get the pipeline...')
|
| 45 |
+
response = requests.post(url, json=payload, headers=headers)
|
| 46 |
+
|
| 47 |
+
if response.status_code != 200:
|
| 48 |
+
print(f'Error in initial request: {response.status_code}')
|
| 49 |
+
return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": None}
|
| 50 |
+
|
| 51 |
+
print('Initial request successful, processing response...')
|
| 52 |
+
response_data = response.json()
|
| 53 |
+
service_id = response_data["pipelineResponseConfig"][0]["config"][0]["serviceId"]
|
| 54 |
+
callback_url = response_data["pipelineInferenceAPIEndPoint"]["callbackUrl"]
|
| 55 |
+
|
| 56 |
+
print(f'Service ID: {service_id}, Callback URL: {callback_url}')
|
| 57 |
+
|
| 58 |
+
headers2 = {
|
| 59 |
+
"Content-Type": "application/json",
|
| 60 |
+
response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["name"]: response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["value"]
|
| 61 |
+
}
|
| 62 |
+
compute_payload = {
|
| 63 |
+
"pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}, "serviceId": service_id}}],
|
| 64 |
+
"inputData": {"input": [{"source": text}], "audio": [{"audioContent": None}]}
|
| 65 |
+
}
|
| 66 |
+
|
| 67 |
+
print(f'Sending translation request with text: "{text}"')
|
| 68 |
+
compute_response = requests.post(callback_url, json=compute_payload, headers=headers2)
|
| 69 |
+
|
| 70 |
+
if compute_response.status_code != 200:
|
| 71 |
+
print(f'Error in translation request: {compute_response.status_code}')
|
| 72 |
+
return {"status_code": compute_response.status_code, "message": "Error in translation", "translated_content": None}
|
| 73 |
+
|
| 74 |
+
print('Translation request successful, processing translation...')
|
| 75 |
+
compute_response_data = compute_response.json()
|
| 76 |
+
translated_content = compute_response_data["pipelineResponse"][0]["output"][0]["target"]
|
| 77 |
+
|
| 78 |
+
print(f'Translation successful. Translated content: "{translated_content}"')
|
| 79 |
+
return {"status_code": 200, "message": "Translation successful", "translated_content": translated_content}
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
# Existing chatbot functions
|
| 83 |
+
VECTOR_COLUMN_NAME = "vector"
|
| 84 |
+
TEXT_COLUMN_NAME = "text"
|
| 85 |
+
HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
|
| 86 |
+
proj_dir = Path(__file__).parent
|
| 87 |
+
|
| 88 |
+
logging.basicConfig(level=logging.INFO)
|
| 89 |
+
logger = logging.getLogger(__name__)
|
| 90 |
+
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=HF_TOKEN)
|
| 91 |
+
env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
|
| 92 |
+
|
| 93 |
+
template = env.get_template('template.j2')
|
| 94 |
+
template_html = env.get_template('template_html.j2')
|
| 95 |
+
|
| 96 |
+
# def add_text(history, text):
|
| 97 |
+
# history = [] if history is None else history
|
| 98 |
+
# history = history + [(text, None)]
|
| 99 |
+
# return history, gr.Textbox(value="", interactive=False)
|
| 100 |
+
|
| 101 |
+
def bot(history, cross_encoder):
|
| 102 |
+
|
| 103 |
+
top_rerank = 25
|
| 104 |
+
top_k_rank = 20
|
| 105 |
+
query = history[-1][0] if history else ''
|
| 106 |
+
print('\nQuery: ',query )
|
| 107 |
+
print('\nHistory:',history)
|
| 108 |
+
if not query:
|
| 109 |
+
gr.Warning("Please submit a non-empty string as a prompt")
|
| 110 |
+
raise ValueError("Empty string was submitted")
|
| 111 |
+
|
| 112 |
+
logger.warning('Retrieving documents...')
|
| 113 |
+
|
| 114 |
+
if cross_encoder == '(HIGH ACCURATE) ColBERT':
|
| 115 |
+
gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
|
| 116 |
+
RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
|
| 117 |
+
RAG_db = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
|
| 118 |
+
documents_full = RAG_db.search(query, k=top_k_rank)
|
| 119 |
+
|
| 120 |
+
documents = [item['content'] for item in documents_full]
|
| 121 |
+
prompt = template.render(documents=documents, query=query)
|
| 122 |
+
prompt_html = template_html.render(documents=documents, query=query)
|
| 123 |
+
|
| 124 |
+
generate_fn = generate_hf
|
| 125 |
+
|
| 126 |
+
history[-1][1] = ""
|
| 127 |
+
for character in generate_fn(prompt, history[:-1]):
|
| 128 |
+
history[-1][1] = character
|
| 129 |
+
yield history, prompt_html
|
| 130 |
+
else:
|
| 131 |
+
document_start = perf_counter()
|
| 132 |
+
|
| 133 |
+
query_vec = retriever.encode(query)
|
| 134 |
+
doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
|
| 135 |
+
|
| 136 |
+
documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
|
| 137 |
+
documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
|
| 138 |
+
|
| 139 |
+
query_doc_pair = [[query, doc] for doc in documents]
|
| 140 |
+
if cross_encoder == '(FAST) MiniLM-L6v2':
|
| 141 |
+
cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
|
| 142 |
+
elif cross_encoder == '(ACCURATE) BGE reranker':
|
| 143 |
+
cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
|
| 144 |
+
|
| 145 |
+
cross_scores = cross_encoder1.predict(query_doc_pair)
|
| 146 |
+
sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
|
| 147 |
+
|
| 148 |
+
documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
|
| 149 |
+
|
| 150 |
+
document_time = perf_counter() - document_start
|
| 151 |
+
|
| 152 |
+
prompt = template.render(documents=documents, query=query)
|
| 153 |
+
prompt_html = template_html.render(documents=documents, query=query)
|
| 154 |
+
|
| 155 |
+
#generate_fn = generate_hf
|
| 156 |
+
generate_fn=generate_qwen
|
| 157 |
+
# Create a new history entry instead of modifying the tuple directly
|
| 158 |
+
new_history = history[:-1] + [ (prompt, "") ] # query replaced prompt
|
| 159 |
+
output=''
|
| 160 |
+
# for character in generate_fn(prompt, history[:-1]):
|
| 161 |
+
# #new_history[-1] = (query, character)
|
| 162 |
+
# output+=character
|
| 163 |
+
output=generate_fn(prompt, history[:-1])
|
| 164 |
+
|
| 165 |
+
print('Output:',output)
|
| 166 |
+
new_history[-1] = (prompt, output) #query replaced with prompt
|
| 167 |
+
print('New History',new_history)
|
| 168 |
+
#print('prompt html',prompt_html)# Update the last tuple with new text
|
| 169 |
+
|
| 170 |
+
history_list = list(history[-1])
|
| 171 |
+
history_list[1] = output # Assuming `character` is what you want to assign
|
| 172 |
+
# Update the history with the modified list converted back to a tuple
|
| 173 |
+
history[-1] = tuple(history_list)
|
| 174 |
+
|
| 175 |
+
#history[-1][1] = character
|
| 176 |
+
# yield new_history, prompt_html
|
| 177 |
+
yield history, prompt_html
|
| 178 |
+
# new_history,prompt_html
|
| 179 |
+
# history[-1][1] = ""
|
| 180 |
+
# for character in generate_fn(prompt, history[:-1]):
|
| 181 |
+
# history[-1][1] = character
|
| 182 |
+
# yield history, prompt_html
|
| 183 |
+
|
| 184 |
+
#def translate_text(response_text, selected_language):
|
| 185 |
+
|
| 186 |
+
def translate_text(selected_language,history):
|
| 187 |
+
|
| 188 |
+
iso_language_codes = {
|
| 189 |
+
"Hindi": "hi",
|
| 190 |
+
"Gom": "gom",
|
| 191 |
+
"Kannada": "kn",
|
| 192 |
+
"Dogri": "doi",
|
| 193 |
+
"Bodo": "brx",
|
| 194 |
+
"Urdu": "ur",
|
| 195 |
+
"Tamil": "ta",
|
| 196 |
+
"Kashmiri": "ks",
|
| 197 |
+
"Assamese": "as",
|
| 198 |
+
"Bengali": "bn",
|
| 199 |
+
"Marathi": "mr",
|
| 200 |
+
"Sindhi": "sd",
|
| 201 |
+
"Maithili": "mai",
|
| 202 |
+
"Punjabi": "pa",
|
| 203 |
+
"Malayalam": "ml",
|
| 204 |
+
"Manipuri": "mni",
|
| 205 |
+
"Telugu": "te",
|
| 206 |
+
"Sanskrit": "sa",
|
| 207 |
+
"Nepali": "ne",
|
| 208 |
+
"Santali": "sat",
|
| 209 |
+
"Gujarati": "gu",
|
| 210 |
+
"Odia": "or"
|
| 211 |
+
}
|
| 212 |
+
|
| 213 |
+
to_code = iso_language_codes[selected_language]
|
| 214 |
+
response_text = history[-1][1] if history else ''
|
| 215 |
+
print('response_text for translation',response_text)
|
| 216 |
+
translation = bhashini_translate(response_text, to_code=to_code)
|
| 217 |
+
return translation['translated_content']
|
| 218 |
+
|
| 219 |
+
|
| 220 |
+
# Gradio interface
|
| 221 |
+
with gr.Blocks(theme='gradio/soft') as CHATBOT:
|
| 222 |
+
history_state = gr.State([])
|
| 223 |
+
with gr.Row():
|
| 224 |
+
with gr.Column(scale=10):
|
| 225 |
+
gr.HTML(value="""<div style="color: #FF4500;"><h1>m-</h1>MITHRA<h1><span style="color: #008000">student Manual Chatbot </span></h1></div>""")
|
| 226 |
+
gr.HTML(value=f"""<p style="font-family: sans-serif; font-size: 16px;">Using GenAI for CBIC Capacity Building - A free chat bot developed by National Customs Targeting Center using Open source LLMs for CBIC Officers</p>""")
|
| 227 |
+
gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;">Developed by NCTC,Mumbai. Suggestions may be sent to <a href="mailto:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</a>.</p>""")
|
| 228 |
+
|
| 229 |
+
with gr.Column(scale=3):
|
| 230 |
+
gr.Image(value='logo.png', height=200, width=200)
|
| 231 |
+
|
| 232 |
+
chatbot = gr.Chatbot(
|
| 233 |
+
[],
|
| 234 |
+
elem_id="chatbot",
|
| 235 |
+
avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
|
| 236 |
+
'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
|
| 237 |
+
bubble_full_width=False,
|
| 238 |
+
show_copy_button=True,
|
| 239 |
+
show_share_button=True,
|
| 240 |
+
)
|
| 241 |
+
|
| 242 |
+
with gr.Row():
|
| 243 |
+
txt = gr.Textbox(
|
| 244 |
+
scale=3,
|
| 245 |
+
show_label=False,
|
| 246 |
+
placeholder="Enter text and press enter",
|
| 247 |
+
container=False,
|
| 248 |
+
)
|
| 249 |
+
txt_btn = gr.Button(value="Submit text", scale=1)
|
| 250 |
+
|
| 251 |
+
cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], value='(ACCURATE) BGE reranker', label="Embeddings", info="Only First query to Colbert may take little time)")
|
| 252 |
+
language_dropdown = gr.Dropdown(
|
| 253 |
+
choices=[
|
| 254 |
+
"Hindi", "Gom", "Kannada", "Dogri", "Bodo", "Urdu", "Tamil", "Kashmiri", "Assamese", "Bengali", "Marathi",
|
| 255 |
+
"Sindhi", "Maithili", "Punjabi", "Malayalam", "Manipuri", "Telugu", "Sanskrit", "Nepali", "Santali",
|
| 256 |
+
"Gujarati", "Odia"
|
| 257 |
+
],
|
| 258 |
+
value="Hindi", # default to Hindi
|
| 259 |
+
label="Select Language for Translation"
|
| 260 |
+
)
|
| 261 |
+
|
| 262 |
+
prompt_html = gr.HTML()
|
| 263 |
+
|
| 264 |
+
translated_textbox = gr.Textbox(label="Translated Response")
|
| 265 |
+
def update_history_and_translate(txt, cross_encoder, history_state, language_dropdown):
|
| 266 |
+
print('History state',history_state)
|
| 267 |
+
history = history_state
|
| 268 |
+
history.append((txt, ""))
|
| 269 |
+
#history_state.value=(history)
|
| 270 |
+
|
| 271 |
+
# Call bot function
|
| 272 |
+
# bot_output = list(bot(history, cross_encoder))
|
| 273 |
+
bot_output = next(bot(history, cross_encoder))
|
| 274 |
+
print('bot_output',bot_output)
|
| 275 |
+
#history, prompt_html = bot_output[-1]
|
| 276 |
+
history, prompt_html = bot_output
|
| 277 |
+
print('History',history)
|
| 278 |
+
# Update the history state
|
| 279 |
+
history_state[:] = history
|
| 280 |
+
|
| 281 |
+
# Translate text
|
| 282 |
+
translated_text = translate_text(language_dropdown, history)
|
| 283 |
+
return history, prompt_html, translated_text
|
| 284 |
+
|
| 285 |
+
txt_msg = txt_btn.click(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox])
|
| 286 |
+
txt_msg = txt.submit(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox])
|
| 287 |
+
|
| 288 |
+
examples = ['My transhipment cargo is missing','can u explain and tabulate difference between b 17 bond and a warehousing bond',
|
| 289 |
+
'What are benefits of the AEO Scheme and eligibility criteria?',
|
| 290 |
+
'What are penalties for customs offences? ', 'what are penalties to customs officers misusing their powers under customs act?','What are eligibility criteria for exemption from cost recovery charges','list in detail what is procedure for obtaining new approval for openeing a CFS attached to an ICD']
|
| 291 |
+
|
| 292 |
+
gr.Examples(examples, txt)
|
| 293 |
+
|
| 294 |
+
|
| 295 |
+
# Launch the Gradio application
|
| 296 |
+
CHATBOT.launch(share=True,debug=True)
|
| 297 |
+
|
backend/query_llm.py
ADDED
|
@@ -0,0 +1,175 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
|
| 3 |
+
import openai
|
| 4 |
+
import gradio as gr
|
| 5 |
+
|
| 6 |
+
from os import getenv
|
| 7 |
+
from typing import Any, Dict, Generator, List
|
| 8 |
+
|
| 9 |
+
from huggingface_hub import InferenceClient
|
| 10 |
+
from transformers import AutoTokenizer
|
| 11 |
+
from gradio_client import Client
|
| 12 |
+
#tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
|
| 13 |
+
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
|
| 14 |
+
#tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
|
| 15 |
+
temperature = 0.5
|
| 16 |
+
top_p = 0.7
|
| 17 |
+
repetition_penalty = 1.2
|
| 18 |
+
|
| 19 |
+
OPENAI_KEY = getenv("OPENAI_API_KEY")
|
| 20 |
+
HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
|
| 21 |
+
|
| 22 |
+
# hf_client = InferenceClient(
|
| 23 |
+
# "mistralai/Mistral-7B-Instruct-v0.1",
|
| 24 |
+
# token=HF_TOKEN
|
| 25 |
+
# )
|
| 26 |
+
|
| 27 |
+
client = Client("Qwen/Qwen1.5-110B-Chat-demo")
|
| 28 |
+
hf_client = InferenceClient(
|
| 29 |
+
"mistralai/Mixtral-8x7B-Instruct-v0.1",
|
| 30 |
+
token=HF_TOKEN
|
| 31 |
+
)
|
| 32 |
+
def format_prompt(message: str, api_kind: str):
|
| 33 |
+
"""
|
| 34 |
+
Formats the given message using a chat template.
|
| 35 |
+
|
| 36 |
+
Args:
|
| 37 |
+
message (str): The user message to be formatted.
|
| 38 |
+
|
| 39 |
+
Returns:
|
| 40 |
+
str: Formatted message after applying the chat template.
|
| 41 |
+
"""
|
| 42 |
+
|
| 43 |
+
# Create a list of message dictionaries with role and content
|
| 44 |
+
messages: List[Dict[str, Any]] = [{'role': 'user', 'content': message}]
|
| 45 |
+
|
| 46 |
+
if api_kind == "openai":
|
| 47 |
+
return messages
|
| 48 |
+
elif api_kind == "hf":
|
| 49 |
+
return tokenizer.apply_chat_template(messages, tokenize=False)
|
| 50 |
+
elif api_kind:
|
| 51 |
+
raise ValueError("API is not supported")
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
def generate_hf(prompt: str, history: str, temperature: float = 0.5, max_new_tokens: int = 4000,
|
| 55 |
+
top_p: float = 0.95, repetition_penalty: float = 1.0) -> Generator[str, None, str]:
|
| 56 |
+
"""
|
| 57 |
+
Generate a sequence of tokens based on a given prompt and history using Mistral client.
|
| 58 |
+
|
| 59 |
+
Args:
|
| 60 |
+
prompt (str): The initial prompt for the text generation.
|
| 61 |
+
history (str): Context or history for the text generation.
|
| 62 |
+
temperature (float, optional): The softmax temperature for sampling. Defaults to 0.9.
|
| 63 |
+
max_new_tokens (int, optional): Maximum number of tokens to be generated. Defaults to 256.
|
| 64 |
+
top_p (float, optional): Nucleus sampling probability. Defaults to 0.95.
|
| 65 |
+
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
|
| 66 |
+
|
| 67 |
+
Returns:
|
| 68 |
+
Generator[str, None, str]: A generator yielding chunks of generated text.
|
| 69 |
+
Returns a final string if an error occurs.
|
| 70 |
+
"""
|
| 71 |
+
|
| 72 |
+
temperature = max(float(temperature), 1e-2) # Ensure temperature isn't too low
|
| 73 |
+
top_p = float(top_p)
|
| 74 |
+
|
| 75 |
+
generate_kwargs = {
|
| 76 |
+
'temperature': temperature,
|
| 77 |
+
'max_new_tokens': max_new_tokens,
|
| 78 |
+
'top_p': top_p,
|
| 79 |
+
'repetition_penalty': repetition_penalty,
|
| 80 |
+
'do_sample': True,
|
| 81 |
+
'seed': 42,
|
| 82 |
+
}
|
| 83 |
+
|
| 84 |
+
formatted_prompt = format_prompt(prompt, "hf")
|
| 85 |
+
|
| 86 |
+
try:
|
| 87 |
+
stream = hf_client.text_generation(formatted_prompt, **generate_kwargs,
|
| 88 |
+
stream=True, details=True, return_full_text=False)
|
| 89 |
+
output = ""
|
| 90 |
+
for response in stream:
|
| 91 |
+
output += response.token.text
|
| 92 |
+
yield output
|
| 93 |
+
|
| 94 |
+
except Exception as e:
|
| 95 |
+
if "Too Many Requests" in str(e):
|
| 96 |
+
print("ERROR: Too many requests on Mistral client")
|
| 97 |
+
gr.Warning("Unfortunately Mistral is unable to process")
|
| 98 |
+
return "Unfortunately, I am not able to process your request now."
|
| 99 |
+
elif "Authorization header is invalid" in str(e):
|
| 100 |
+
print("Authetification error:", str(e))
|
| 101 |
+
gr.Warning("Authentication error: HF token was either not provided or incorrect")
|
| 102 |
+
return "Authentication error"
|
| 103 |
+
else:
|
| 104 |
+
print("Unhandled Exception:", str(e))
|
| 105 |
+
gr.Warning("Unfortunately Mistral is unable to process")
|
| 106 |
+
return "I do not know what happened, but I couldn't understand you."
|
| 107 |
+
|
| 108 |
+
def generate_qwen(formatted_prompt: str, history: str):
|
| 109 |
+
response = client.predict(
|
| 110 |
+
query=formatted_prompt,
|
| 111 |
+
history=[],
|
| 112 |
+
system='You are wonderful',
|
| 113 |
+
api_name="/model_chat"
|
| 114 |
+
)
|
| 115 |
+
print('Response:',response)
|
| 116 |
+
|
| 117 |
+
#return output
|
| 118 |
+
#return response[1][0][1]
|
| 119 |
+
return response[1][0][1]
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
|
| 123 |
+
def generate_openai(prompt: str, history: str, temperature: float = 0.9, max_new_tokens: int = 256,
|
| 124 |
+
top_p: float = 0.95, repetition_penalty: float = 1.0) -> Generator[str, None, str]:
|
| 125 |
+
"""
|
| 126 |
+
Generate a sequence of tokens based on a given prompt and history using Mistral client.
|
| 127 |
+
|
| 128 |
+
Args:
|
| 129 |
+
prompt (str): The initial prompt for the text generation.
|
| 130 |
+
history (str): Context or history for the text generation.
|
| 131 |
+
temperature (float, optional): The softmax temperature for sampling. Defaults to 0.9.
|
| 132 |
+
max_new_tokens (int, optional): Maximum number of tokens to be generated. Defaults to 256.
|
| 133 |
+
top_p (float, optional): Nucleus sampling probability. Defaults to 0.95.
|
| 134 |
+
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
|
| 135 |
+
|
| 136 |
+
Returns:
|
| 137 |
+
Generator[str, None, str]: A generator yielding chunks of generated text.
|
| 138 |
+
Returns a final string if an error occurs.
|
| 139 |
+
"""
|
| 140 |
+
|
| 141 |
+
temperature = max(float(temperature), 1e-2) # Ensure temperature isn't too low
|
| 142 |
+
top_p = float(top_p)
|
| 143 |
+
|
| 144 |
+
generate_kwargs = {
|
| 145 |
+
'temperature': temperature,
|
| 146 |
+
'max_tokens': max_new_tokens,
|
| 147 |
+
'top_p': top_p,
|
| 148 |
+
'frequency_penalty': max(-2., min(repetition_penalty, 2.)),
|
| 149 |
+
}
|
| 150 |
+
|
| 151 |
+
formatted_prompt = format_prompt(prompt, "openai")
|
| 152 |
+
|
| 153 |
+
try:
|
| 154 |
+
stream = openai.ChatCompletion.create(model="gpt-3.5-turbo-0301",
|
| 155 |
+
messages=formatted_prompt,
|
| 156 |
+
**generate_kwargs,
|
| 157 |
+
stream=True)
|
| 158 |
+
output = ""
|
| 159 |
+
for chunk in stream:
|
| 160 |
+
output += chunk.choices[0].delta.get("content", "")
|
| 161 |
+
yield output
|
| 162 |
+
|
| 163 |
+
except Exception as e:
|
| 164 |
+
if "Too Many Requests" in str(e):
|
| 165 |
+
print("ERROR: Too many requests on OpenAI client")
|
| 166 |
+
gr.Warning("Unfortunately OpenAI is unable to process")
|
| 167 |
+
return "Unfortunately, I am not able to process your request now."
|
| 168 |
+
elif "You didn't provide an API key" in str(e):
|
| 169 |
+
print("Authetification error:", str(e))
|
| 170 |
+
gr.Warning("Authentication error: OpenAI key was either not provided or incorrect")
|
| 171 |
+
return "Authentication error"
|
| 172 |
+
else:
|
| 173 |
+
print("Unhandled Exception:", str(e))
|
| 174 |
+
gr.Warning("Unfortunately OpenAI is unable to process")
|
| 175 |
+
return "I do not know what happened, but I couldn't understand you."
|
backend/semantic_search.py
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
import logging
|
| 3 |
+
import lancedb
|
| 4 |
+
import os
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
from sentence_transformers import SentenceTransformer
|
| 7 |
+
#from FlagEmbedding import LLMEmbedder, FlagReranker # Al document present here https://github.com/FlagOpen/FlagEmbedding/tree/master
|
| 8 |
+
#EMB_MODEL_NAME = "thenlper/gte-base"
|
| 9 |
+
EMB_MODEL_NAME = 'BAAI/llm-embedder'
|
| 10 |
+
task = "qa" # Encode for a specific task (qa, icl, chat, lrlm, tool, convsearch)
|
| 11 |
+
#EMB_MODEL_NAME = LLMEmbedder('BAAI/llm-embedder', use_fp16=False) # Load model (automatically use GPUs)
|
| 12 |
+
|
| 13 |
+
#reranker_model = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) # use_fp16 speeds up computation with a slight performance degradation
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
#EMB_MODEL_NAME = "thenlper/gte-base"
|
| 17 |
+
#DB_TABLE_NAME = "Huggingface_docs"
|
| 18 |
+
DB_TABLE_NAME = "cbse"
|
| 19 |
+
# Setting up the logging
|
| 20 |
+
logging.basicConfig(level=logging.INFO)
|
| 21 |
+
logger = logging.getLogger(__name__)
|
| 22 |
+
retriever = SentenceTransformer(EMB_MODEL_NAME)
|
| 23 |
+
|
| 24 |
+
# db
|
| 25 |
+
db_uri = os.path.join(Path(__file__).parents[1], "lancedb")
|
| 26 |
+
print(f'DB URL is {db_uri}')
|
| 27 |
+
db = lancedb.connect(db_uri)
|
| 28 |
+
table = db.open_table(DB_TABLE_NAME)
|
lancedb/cbse.lance/_latest.manifest
ADDED
|
Binary file (236 Bytes). View file
|
|
|
lancedb/cbse.lance/_transactions/0-bf8e63bb-3e01-448f-95c6-f20996e7415f.txn
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
$bf8e63bb-3e01-448f-95c6-f20996e7415f�U text ���������*string084vector ���������*fixed_size_list:float:76808
|
lancedb/cbse.lance/_transactions/1-76260358-b577-4610-83d2-64fb3c14ebc6.txn
ADDED
|
Binary file (98 Bytes). View file
|
|
|
lancedb/cbse.lance/_versions/1.manifest
ADDED
|
Binary file (181 Bytes). View file
|
|
|
lancedb/cbse.lance/_versions/2.manifest
ADDED
|
Binary file (236 Bytes). View file
|
|
|
lancedb/cbse.lance/data/615853d2-bbd1-453c-88c7-8df0edb43e5e.lance
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1611699c7fdfa82b6674466912c66b6b5dbf1f21fba597cf5fc01381aacb828b
|
| 3 |
+
size 16009653
|
logo.png
ADDED
|
requirements.txt
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
sentence-transformers==2.6.1
|
| 3 |
+
firebase_admin
|
| 4 |
+
huggingface-hub==0.19.3
|
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spaces
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ragatouille
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+
transformers
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+
lancedb
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+
gradio==4.10.0
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templates/template.j2
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Instructions: You are assistant to Customs officers .You have to answer their questions and tasks asked by them.Use the following information in the Context section to answer the Query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
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Context:
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{% for doc in documents %}
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---
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{{ doc }}
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{% endfor %}
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---
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Query: {{ query }}
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templates/template_html.j2
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Information Page</title>
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<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap">
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<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&display=swap">
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<style>
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* {
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font-family: "Source Sans Pro";
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}
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.instructions > * {
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color: #111 !important;
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}
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details.doc-box * {
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color: #111 !important;
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}
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.dark {
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background: #111;
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color: white;
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}
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.doc-box {
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padding: 10px;
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margin-top: 10px;
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background-color: #baecc2;
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border-radius: 6px;
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color: #111 !important;
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max-width: 700px;
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box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
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}
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.doc-full {
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margin: 10px 14px;
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line-height: 1.6rem;
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}
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.instructions {
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color: #111 !important;
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background: #b7bdfd;
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display: block;
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border-radius: 6px;
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padding: 6px 10px;
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line-height: 1.6rem;
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max-width: 700px;
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box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
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}
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| 53 |
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.query {
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| 54 |
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color: #111 !important;
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background: #ffbcbc;
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display: block;
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| 57 |
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border-radius: 6px;
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padding: 6px 10px;
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| 59 |
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line-height: 1.6rem;
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max-width: 700px;
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box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
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}
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</style>
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</head>
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<body>
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| 66 |
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<div class="prose svelte-1ybaih5" id="component-6">
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| 67 |
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<h2>Prompt</h2>
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| 68 |
+
Below is the prompt that is given to the model. <hr>
|
| 69 |
+
<h2>Instructions</h2>
|
| 70 |
+
<span class="instructions">You are assistant to Customs officers .You have to answer their questions and tasks asked by them.Use the following information in the Context section to answer the Query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
| 71 |
+
<span style="font-weight: bold;">don't try to make up an answer.</span></span><br>
|
| 72 |
+
<h2>Context</h2>
|
| 73 |
+
{% for doc in documents %}
|
| 74 |
+
<details class="doc-box">
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| 75 |
+
<summary>
|
| 76 |
+
<b>Doc {{ loop.index }}:</b> <span class="doc-short">{{ doc[:100] }}...</span>
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| 77 |
+
</summary>
|
| 78 |
+
<div class="doc-full">{{ doc }}</div>
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| 79 |
+
</details>
|
| 80 |
+
{% endfor %}
|
| 81 |
+
|
| 82 |
+
<h2>Query</h2>
|
| 83 |
+
<span class="query">{{ query }}</span>
|
| 84 |
+
</div>
|
| 85 |
+
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| 86 |
+
<script>
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| 87 |
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document.addEventListener("DOMContentLoaded", function() {
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| 88 |
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const detailsElements = document.querySelectorAll('.doc-box');
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| 89 |
+
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| 90 |
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detailsElements.forEach(detail => {
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| 91 |
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detail.addEventListener('toggle', function() {
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| 92 |
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const docShort = this.querySelector('.doc-short');
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| 93 |
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if (this.open) {
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| 94 |
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docShort.style.display = 'none';
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| 95 |
+
} else {
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| 96 |
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docShort.style.display = 'inline';
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| 97 |
+
}
|
| 98 |
+
});
|
| 99 |
+
});
|
| 100 |
+
});
|
| 101 |
+
</script>
|
| 102 |
+
</body>
|
| 103 |
+
</html>
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