Create app.py

app.py

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+import os
+import torch
+from torch import cuda, bfloat16
+from transformers import AutoTokenizer, pipeline, BitsAndBytesConfig
+from langchain.llms import HuggingFacePipeline
+from langchain.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+import gradio as gr
+from langchain.embeddings import HuggingFaceEmbeddings
+from transformers import InferenceClient
+
+# Load the Hugging Face token from environment
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+# Load the Mistral model and tokenizer
+model_id = 'mistralai/Mistral-7B-Instruct-v0.3'
+client = InferenceClient(model_id)
+
+# Define stopping criteria
+class StopOnTokens:
+    def __call__(self, input_ids, scores, **kwargs):
+        for stop_ids in stop_token_ids:
+            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
+                return True
+        return False
+
+# Define stopping criteria list
+stop_list = ['\nHuman:', '\n```\n']
+stop_token_ids = [client.tokenizer(x)['input_ids'] for x in stop_list]
+stop_token_ids = [torch.LongTensor(x).to(cuda.current_device() if cuda.is_available() else 'cpu') for x in stop_token_ids]
+
+# Create text generation pipeline
+def generate(prompt, history, system_prompt=None, temperature=0.2, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0):
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+
+    formatted_prompt = format_prompt(prompt, history, system_prompt)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+llm = HuggingFacePipeline(pipeline=generate)
+
+# Load the stored FAISS index
+try:
+    vectorstore = FAISS.load_local('faiss_index', HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cuda"}))
+    print("Loaded embedding successfully")
+except ImportError as e:
+    print("FAISS could not be imported. Make sure FAISS is installed correctly.")
+    raise e
+
+# Set up the Conversational Retrieval Chain
+chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
+
+chat_history = []
+
+def format_prompt(query):
+    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.
+    Here's what I need:
+    1. A brief, general response to my question based on related answers retrieved.
+    2. A JSON-formatted output containing:
+       - "question": The original question.
+       - "answer": The detailed answer.
+       - "related_questions": A list of related questions and their answers, each as a dictionary with the keys:
+         - "question": The related question.
+         - "answer": The related answer.
+    Here's my question:
+    {query}
+    Include a brief final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point.
+    """
+    return prompt
+
+def qa_infer(query):
+    formatted_prompt = format_prompt(query)
+    result = chain({"question": formatted_prompt, "chat_history": chat_history})
+    for doc in result['source_documents']:
+        print("-"*50)
+        print("Retrieved Document:", doc.page_content)
+    print("#"*100)
+    print(result['answer'])
+    return result['answer']
+
+EXAMPLES = ["How to use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM", 
+            "Can BQ25896 support I2C interface?", 
+            "Does TDA2 vout support bt656 8-bit mode?"]
+
+demo = gr.Interface(fn=qa_infer, inputs="text", allow_flagging='never', examples=EXAMPLES, cache_examples=False, outputs="text")
+demo.launch()