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escape special characters
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app.py
CHANGED
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@@ -1,4 +1,4 @@
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#
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import spaces
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from torch.nn import DataParallel
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from torch import Tensor
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@@ -17,7 +17,7 @@ import gradio as gr
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import torch
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import torch.nn.functional as F
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from dotenv import load_dotenv
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from utils import load_env_variables, parse_and_route
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from globalvars import API_BASE, intention_prompt, tasks, system_message, model_name , metadata_prompt
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@@ -49,12 +49,12 @@ class EmbeddingGenerator:
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@spaces.GPU
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def compute_embeddings(self, input_text: str):
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intention_completion = self.intention_client.chat.completions.create(
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model="yi-large",
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messages=[
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{"role": "system", "content": intention_prompt},
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{"role": "user", "content":
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]
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)
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intention_output = intention_completion.choices[0].message['content']
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@@ -71,14 +71,14 @@ class EmbeddingGenerator:
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return f"Error: Task '{selected_task}' not found. Please select a valid task."
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query_prefix = f"Instruct: {task_description}\nQuery: "
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queries = [
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# Get the metadata
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metadata_completion = self.intention_client.chat.completions.create(
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model="yi-large",
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messages=[
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{"role": "system", "content": metadata_prompt},
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{"role": "user", "content":
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]
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)
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metadata_output = metadata_completion.choices[0].message['content']
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@@ -93,12 +93,9 @@ class EmbeddingGenerator:
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# Normalize embeddings
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query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
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embeddings_list = query_embeddings.detach().cpu().numpy().tolist()
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# Include metadata in the embeddings
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embeddings_with_metadata = [{"embedding": emb, "metadata": metadata} for emb in embeddings_list]
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self.clear_cuda_cache()
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return
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def extract_metadata(self, metadata_output: str):
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# Regex pattern to extract key-value pairs
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@@ -143,8 +140,18 @@ def add_documents_to_chroma(client, collection, documents: list, embedding_funct
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)
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def query_chroma(client, collection_name: str, query_text: str, embedding_function: MyEmbeddingFunction):
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db = Chroma(client=client, collection_name=collection_name, embedding_function=embedding_function)
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return result_docs
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top_p,
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):
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retrieved_text = query_documents(message)
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": f"{retrieved_text}\n\n{message}"})
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response = ""
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for message in intention_client.chat_completion(
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messages,
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# app.py
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import spaces
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from torch.nn import DataParallel
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from torch import Tensor
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import torch
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import torch.nn.functional as F
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from dotenv import load_dotenv
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from utils import load_env_variables, parse_and_route , escape_special_characters
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from globalvars import API_BASE, intention_prompt, tasks, system_message, model_name , metadata_prompt
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@spaces.GPU
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def compute_embeddings(self, input_text: str):
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escaped_input_text = escape_special_characters(input_text)
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intention_completion = self.intention_client.chat.completions.create(
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model="yi-large",
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messages=[
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{"role": "system", "content": escape_special_characters(intention_prompt)},
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{"role": "user", "content": escaped_input_text}
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]
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)
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intention_output = intention_completion.choices[0].message['content']
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return f"Error: Task '{selected_task}' not found. Please select a valid task."
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query_prefix = f"Instruct: {task_description}\nQuery: "
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queries = [escaped_input_text]
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# Get the metadata
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metadata_completion = self.intention_client.chat.completions.create(
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model="yi-large",
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messages=[
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{"role": "system", "content": escape_special_characters(metadata_prompt)},
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{"role": "user", "content": escaped_input_text}
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]
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)
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metadata_output = metadata_completion.choices[0].message['content']
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# Normalize embeddings
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query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
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embeddings_list = query_embeddings.detach().cpu().numpy().tolist()
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self.clear_cuda_cache()
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return embeddings_list, metadata
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def extract_metadata(self, metadata_output: str):
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# Regex pattern to extract key-value pairs
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)
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def query_chroma(client, collection_name: str, query_text: str, embedding_function: MyEmbeddingFunction):
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# Compute query embeddings and metadata
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query_embeddings, query_metadata = embedding_function.embedding_generator.compute_embeddings(query_text)
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# Initialize Chroma with the collection
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db = Chroma(client=client, collection_name=collection_name, embedding_function=embedding_function)
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# Perform similarity search using the query embeddings and metadata
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result_docs = db.similarity_search(
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query_embeddings=query_embeddings,
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query_metadata=query_metadata
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)
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return result_docs
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top_p,
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):
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retrieved_text = query_documents(message)
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messages = [{"role": "system", "content": escape_special_characters(system_message)}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": f"{retrieved_text}\n\n{escape_special_characters(message)}"})
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response = ""
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for message in intention_client.chat_completion(
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messages,
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utils.py
CHANGED
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@@ -30,4 +30,37 @@ def parse_and_route(example_output: str):
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else:
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return {true_task: "Task description not found"}
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else:
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return "No true task found in the example output"
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else:
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return {true_task: "Task description not found"}
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else:
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return "No true task found in the example output"
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import json
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def escape_special_characters(text: str) -> str:
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"""
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Escapes special characters in the given text for JSON and cURL compatibility.
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"""
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escaped_text = json.dumps(text)[1:-1]
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curl_escaped_text = escaped_text.replace(" ", "\\ ")
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curl_escaped_text = curl_escaped_text.replace("&", "\\&")
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curl_escaped_text = curl_escaped_text.replace(";", "\\;")
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curl_escaped_text = curl_escaped_text.replace("(", "\\(")
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curl_escaped_text = curl_escaped_text.replace(")", "\\)")
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curl_escaped_text = curl_escaped_text.replace("$", "\\$")
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curl_escaped_text = curl_escaped_text.replace("`", "\\`")
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curl_escaped_text = curl_escaped_text.replace("|", "\\|")
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curl_escaped_text = curl_escaped_text.replace("*", "\\*")
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curl_escaped_text = curl_escaped_text.replace("?", "\\?")
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curl_escaped_text = curl_escaped_text.replace("<", "\\<")
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curl_escaped_text = curl_escaped_text.replace(">", "\\>")
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curl_escaped_text = curl_escaped_text.replace("!", "\\!")
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curl_escaped_text = curl_escaped_text.replace("{", "\\{")
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curl_escaped_text = curl_escaped_text.replace("}", "\\}")
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curl_escaped_text = curl_escaped_text.replace("[", "\\[")
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curl_escaped_text = curl_escaped_text.replace("]", "\\]")
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curl_escaped_text = curl_escaped_text.replace("#", "\\#")
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curl_escaped_text = curl_escaped_text.replace("%", "\\%")
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curl_escaped_text = curl_escaped_text.replace("^", "\\^")
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curl_escaped_text = curl_escaped_text.replace("=", "\\=")
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curl_escaped_text = curl_escaped_text.replace("~", "\\~")
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curl_escaped_text = curl_escaped_text.replace("'", "\\'")
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return curl_escaped_text
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