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| import os | |
| from langchain.document_loaders import TextLoader, DirectoryLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from sentence_transformers import SentenceTransformer | |
| import faiss | |
| import torch | |
| import numpy as np | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig | |
| from datetime import datetime | |
| import json | |
| import gradio as gr | |
| class DocumentRetrievalAndGeneration: | |
| def __init__(self, embedding_model_name, lm_model_id, data_folder, faiss_index_path): | |
| self.all_splits = self.load_documents(data_folder) | |
| self.embeddings = SentenceTransformer(embedding_model_name) | |
| self.gpu_index = self.load_faiss_index(faiss_index_path) | |
| self.llm = self.initialize_llm(lm_model_id) | |
| # self.all_splits = self.split_documents() | |
| def load_documents(self, folder_path): | |
| loader = DirectoryLoader(folder_path, loader_cls=TextLoader) | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=250) | |
| documents = loader.load() | |
| all_splits = text_splitter.split_documents(documents) | |
| print('Length of documents:', len(documents)) | |
| print("LEN of all_splits", len(all_splits)) | |
| return all_splits | |
| def load_faiss_index(self, faiss_index_path): | |
| cpu_index = faiss.read_index(faiss_index_path) | |
| gpu_resource = faiss.StandardGpuResources() | |
| gpu_index = faiss.index_cpu_to_gpu(gpu_resource, 0, cpu_index) | |
| return gpu_index | |
| def initialize_llm(self, model_id): | |
| bnb_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.bfloat16 | |
| ) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| generate_text = pipeline( | |
| model=model, | |
| tokenizer=tokenizer, | |
| return_full_text=True, | |
| task='text-generation', | |
| temperature=0.6, | |
| max_new_tokens=2048, | |
| ) | |
| return generate_text | |
| def query_and_generate_response(self, query): | |
| query_embedding = self.embeddings.encode(query, convert_to_tensor=True).cpu().numpy() | |
| distances, indices = self.gpu_index.search(np.array([query_embedding]), k=5) | |
| content = "" | |
| for idx in indices[0]: | |
| content += "-" * 50 + "\n" | |
| content += self.all_splits[idx].page_content + "\n" | |
| print("CHUNK",idx) | |
| print(self.all_splits[idx].page_content) | |
| print("############################") | |
| prompt=f""" | |
| You are a knowledgeable assistant with access to a comprehensive database. | |
| I need you to answer my question and provide related information in a specific format. | |
| I have provided five relatable json files {content}, choose the most suitable chunks for answering the query | |
| Here's what I need: | |
| Include a final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point. | |
| content | |
| Here's my question: | |
| Query:{query} | |
| Solution==> | |
| Example1 | |
| Query: "How to use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM", | |
| Solution: "To use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM, you need to modify the configuration file of the NDK application. Specifically, change the processor reference from 'A15_0' to 'IPU1_0'.", | |
| Example2 | |
| Query: "Can BQ25896 support I2C interface?", | |
| Solution: "Yes, the BQ25896 charger supports the I2C interface for communication.", | |
| """ | |
| # prompt = f"Query: {query}\nSolution: {content}\n" | |
| # Encode and prepare inputs | |
| messages = [{"role": "user", "content": prompt}] | |
| encodeds = self.llm.tokenizer.apply_chat_template(messages, return_tensors="pt") | |
| model_inputs = encodeds.to(self.llm.device) | |
| # Perform inference and measure time | |
| start_time = datetime.now() | |
| generated_ids = self.llm.model.generate(model_inputs, max_new_tokens=1000, do_sample=True) | |
| elapsed_time = datetime.now() - start_time | |
| # Decode and return output | |
| decoded = self.llm.tokenizer.batch_decode(generated_ids) | |
| generated_response = decoded[0] | |
| print("Generated response:", generated_response) | |
| print("Time elapsed:", elapsed_time) | |
| print("Device in use:", self.llm.device) | |
| return generated_response, content | |
| def qa_infer_gradio(self, query): | |
| response = self.query_and_generate_response(query) | |
| return response | |
| if __name__ == "__main__": | |
| # Example usage | |
| embedding_model_name = 'flax-sentence-embeddings/all_datasets_v3_MiniLM-L12' | |
| lm_model_id = "mistralai/Mistral-7B-Instruct-v0.2" | |
| data_folder = 'sample_embedding_folder' | |
| faiss_index_path = 'faiss_index_new_model3.index' | |
| doc_retrieval_gen = DocumentRetrievalAndGeneration(embedding_model_name, lm_model_id, data_folder, faiss_index_path) | |
| # Define Gradio interface function | |
| def launch_interface(): | |
| css_code = """ | |
| .gradio-container { | |
| background-color: #daccdb; | |
| } | |
| /* Button styling for all buttons */ | |
| button { | |
| background-color: #927fc7; /* Default color for all other buttons */ | |
| color: black; | |
| border: 1px solid black; | |
| padding: 10px; | |
| margin-right: 10px; | |
| font-size: 16px; /* Increase font size */ | |
| font-weight: bold; /* Make text bold */ | |
| } | |
| """ | |
| EXAMPLES = ["Can the VIP and CSI2 modules operate simultaneously? ", | |
| "I'm using Code Composer Studio 5.4.0.00091 and enabled FPv4SPD16 floating point support for CortexM4 in TDA2. However, after building the project, the .asm file shows --float_support=vfplib instead of FPv4SPD16. Why is this happening?", | |
| "Could you clarify the maximum number of cameras that can be connected simultaneously to the video input ports on the TDA2x SoC, considering it supports up to 10 multiplexed input ports and includes 3 dedicated video input modules?"] | |
| file_path = "ticketNames.txt" | |
| # Read the file content | |
| with open(file_path, "r") as file: | |
| content = file.read() | |
| ticket_names = json.loads(content) | |
| dropdown = gr.Dropdown(label="Sample queries", choices=ticket_names) | |
| # Define Gradio interface | |
| interface = gr.Interface( | |
| fn=doc_retrieval_gen.qa_infer_gradio, | |
| inputs=[gr.Textbox(label="QUERY", placeholder="Enter your query here")], | |
| allow_flagging='never', | |
| examples=EXAMPLES, | |
| cache_examples=False, | |
| outputs=[gr.Textbox(label="SOLUTION"), gr.Textbox(label="RELATED QUERIES")], | |
| css=css_code | |
| ) | |
| # Launch Gradio interface | |
| interface.launch(debug=True) | |
| # Launch the interface | |
| launch_interface() | |
| # import os | |
| # import json | |
| # from langchain.document_loaders import TextLoader, DirectoryLoader | |
| # from langchain.vectorstores import FAISS | |
| # from sentence_transformers import SentenceTransformer | |
| # import faiss | |
| # import torch | |
| # import numpy as np | |
| # from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig | |
| # from datetime import datetime | |
| # import gradio as gr | |
| # class DocumentRetrievalAndGeneration: | |
| # def __init__(self, embedding_model_name, lm_model_id, data_folder, faiss_index_path): | |
| # self.documents = self.load_documents(data_folder) | |
| # self.embeddings = SentenceTransformer(embedding_model_name) | |
| # self.gpu_index = self.load_faiss_index(faiss_index_path) | |
| # self.llm = self.initialize_llm(lm_model_id) | |
| # def load_documents(self, folder_path): | |
| # loader = DirectoryLoader(folder_path, loader_cls=TextLoader) | |
| # documents = loader.load() | |
| # print('Length of documents:', len(documents)) | |
| # return documents | |
| # def load_faiss_index(self, faiss_index_path): | |
| # cpu_index = faiss.read_index(faiss_index_path) | |
| # gpu_resource = faiss.StandardGpuResources() | |
| # gpu_index = faiss.index_cpu_to_gpu(gpu_resource, 0, cpu_index) | |
| # return gpu_index | |
| # def initialize_llm(self, model_id): | |
| # bnb_config = BitsAndBytesConfig( | |
| # load_in_4bit=True, | |
| # bnb_4bit_use_double_quant=True, | |
| # bnb_4bit_quant_type="nf4", | |
| # bnb_4bit_compute_dtype=torch.bfloat16 | |
| # ) | |
| # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config) | |
| # tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| # generate_text = pipeline( | |
| # model=model, | |
| # tokenizer=tokenizer, | |
| # return_full_text=True, | |
| # task='text-generation', | |
| # temperature=0.6, | |
| # max_new_tokens=2048, | |
| # ) | |
| # return generate_text | |
| # def query_and_generate_response(self, query): | |
| # query_embedding = self.embeddings.encode(query, convert_to_tensor=True).cpu().numpy() | |
| # distances, indices = self.gpu_index.search(np.array([query_embedding]), k=5) | |
| # # content = "" | |
| # # for idx in indices[0]: | |
| # # content += "-" * 50 + "\n" | |
| # # content += self.documents[idx].page_content + "\n" | |
| # # print(self.documents[idx].page_content) | |
| # # print("############################") | |
| # content = "" | |
| # all_splits=build_faiss_index.all_splits | |
| # for idx in indices[0]: | |
| # content += "-" * 50 + "\n" | |
| # content+=all_splits[idx].page_content | |
| # print(all_splits[idx].page_content) | |
| # print("############################") | |
| # prompt=f""" | |
| # You are a knowledgeable assistant with access to a comprehensive database. | |
| # I need you to answer my question and provide related information in a specific format. | |
| # I have provided five relatable json files {content}, choose the most suitable chunks for answering the query | |
| # Here's what I need: | |
| # Include a final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point. | |
| # content | |
| # Here's my question: | |
| # Query:{query} | |
| # Solution==> | |
| # Example1 | |
| # Query: "How to use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM", | |
| # Solution: "To use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM, you need to modify the configuration file of the NDK application. Specifically, change the processor reference from 'A15_0' to 'IPU1_0'.", | |
| # Example2 | |
| # Query: "Can BQ25896 support I2C interface?", | |
| # Solution: "Yes, the BQ25896 charger supports the I2C interface for communication.", | |
| # """ | |
| # # prompt = f"Query: {query}\nSolution: {content}\n" | |
| # # Encode and prepare inputs | |
| # messages = [{"role": "user", "content": prompt}] | |
| # encodeds = self.llm.tokenizer.apply_chat_template(messages, return_tensors="pt") | |
| # model_inputs = encodeds.to(self.llm.device) | |
| # # Perform inference and measure time | |
| # start_time = datetime.now() | |
| # generated_ids = self.llm.model.generate(model_inputs, max_new_tokens=1000, do_sample=True) | |
| # elapsed_time = datetime.now() - start_time | |
| # # Decode and return output | |
| # decoded = self.llm.tokenizer.batch_decode(generated_ids) | |
| # generated_response = decoded[0] | |
| # print("Generated response:", generated_response) | |
| # print("Time elapsed:", elapsed_time) | |
| # print("Device in use:", self.llm.device) | |
| # return generated_response,content | |
| # def qa_infer_gradio(self, query): | |
| # response = self.query_and_generate_response(query) | |
| # return response | |
| # if __name__ == "__main__": | |
| # # Example usage | |
| # embedding_model_name = 'flax-sentence-embeddings/all_datasets_v3_MiniLM-L12' | |
| # lm_model_id = "mistralai/Mistral-7B-Instruct-v0.2" | |
| # data_folder = 'sample_embedding_folder' | |
| # faiss_index_path = 'faiss_index_new_model3.index' | |
| # doc_retrieval_gen = DocumentRetrievalAndGeneration(embedding_model_name, lm_model_id, data_folder, faiss_index_path) | |
| # # Define Gradio interface function | |
| # def launch_interface(): | |
| # css_code = """ | |
| # .gradio-container { | |
| # background-color: #daccdb; | |
| # } | |
| # /* Button styling for all buttons */ | |
| # button { | |
| # background-color: #927fc7; /* Default color for all other buttons */ | |
| # color: black; | |
| # border: 1px solid black; | |
| # padding: 10px; | |
| # margin-right: 10px; | |
| # font-size: 16px; /* Increase font size */ | |
| # font-weight: bold; /* Make text bold */ | |
| # } | |
| # """ | |
| # EXAMPLES = ["Does the VIP modules & CSI2 module could work simultaneously? ", | |
| # "I'm using Code Composer Studio 5.4.0.00091 and enabled FPv4SPD16 floating point support for CortexM4 in TDA2. However, after building the project, the .asm file shows --float_support=vfplib instead of FPv4SPD16. Why is this happening?", | |
| # "Could you clarify the maximum number of cameras that can be connected simultaneously to the video input ports on the TDA2x SoC, considering it supports up to 10 multiplexed input ports and includes 3 dedicated video input modules?"] | |
| # file_path = "ticketNames.txt" | |
| # # Read the file content | |
| # with open(file_path, "r") as file: | |
| # content = file.read() | |
| # ticket_names = json.loads(content) | |
| # dropdown = gr.Dropdown(label="Sample queries", choices=ticket_names) | |
| # # Define Gradio interface | |
| # interface = gr.Interface( | |
| # fn=doc_retrieval_gen.qa_infer_gradio, | |
| # inputs=[gr.Textbox(label="QUERY", placeholder="Enter your query here")], | |
| # allow_flagging='never', | |
| # examples=EXAMPLES, | |
| # cache_examples=False, | |
| # outputs=[gr.Textbox(label="SOLUTION"), gr.Textbox(label="RELATED QUERIES")], | |
| # css=css_code | |
| # ) | |
| # # Launch Gradio interface | |
| # interface.launch(debug=True) | |
| # # Launch the interface | |
| # launch_interface() | |