Update app.py

app.py

@@ -1,64 +1,147 @@
+import os
+import chromadb
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings, HuggingFacePipeline
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import create_retrieval_chain, LLMChain
+from langchain.prompts import PromptTemplate
+from collections import OrderedDict
+
+# Load embeddings model
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Load Chroma database (Avoid reprocessing documents)
+CHROMA_PATH = "./chroma_db"
+if not os.path.exists(CHROMA_PATH):
+    raise FileNotFoundError("ChromaDB folder not found. Make sure it's uploaded to the repo.")
+
+chroma_client = chromadb.PersistentClient(path=CHROMA_PATH)
+db = Chroma(embedding_function=embeddings, client=chroma_client)
+
+# Load the model
+model_name = "google/flan-t5-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Create pipeline
+qa_pipeline = pipeline(
+    "text2text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=0,
+    max_length=512,
+    min_length=50,
+    do_sample=False,
+    repetition_penalty=1.2
 )
 
+# Wrap pipeline in LangChain
+llm = HuggingFacePipeline(pipeline=qa_pipeline)
+retriever = db.as_retriever(search_kwargs={"k": 3})
+
+
+def clean_context(context_list, max_tokens=350, min_length=50):
+    """
+    Cleans retrieved document context:
+    - Removes duplicates while preserving order
+    - Limits total token count
+    - Ensures useful, non-repetitive context
+    """
+
+    # Preserve order while removing duplicates
+    unique_texts = list(OrderedDict.fromkeys([doc.page_content.strip() for doc in context_list]))
+
+    # Remove very short texts (e.g., headers)
+    filtered_texts = [text for text in unique_texts if len(text.split()) > min_length]
+
+    # Avoid near-duplicate entries
+    deduplicated_texts = []
+    seen_texts = set()
+    for text in filtered_texts:
+        if not any(text in s for s in seen_texts):  # Avoid near-duplicates
+            deduplicated_texts.append(text)
+            seen_texts.add(text)
+
+    # Limit context based on token count
+    trimmed_context = []
+    total_tokens = 0
+    for text in deduplicated_texts:
+        tokenized_text = tokenizer.encode(text, add_special_tokens=False)
+        token_count = len(tokenized_text)
+
+        if total_tokens + token_count > max_tokens:
+            remaining_tokens = max_tokens - total_tokens
+            if remaining_tokens > 20:
+                trimmed_context.append(tokenizer.decode(tokenized_text[:remaining_tokens]))
+            break
+
+        trimmed_context.append(text)
+        total_tokens += token_count
+
+    return "\n\n".join(trimmed_context) if trimmed_context else "No relevant context found."
+
+# Define prompt
+prompt_template = PromptTemplate(
+    template="""
+    You are a Kubernetes instructor. Answer the question based on the provided context.
+    If the context does not provide an answer, say "I don't have enough information."
+
+    Context:
+    {context}
+
+    Question:
+    {input}
+
+    Answer:
+    """,
+    input_variables=["context", "input"]
+)
+
+llm_chain = LLMChain(llm=llm, prompt=prompt_template)
+qa_chain = create_retrieval_chain(retriever, llm_chain)
+
+# Query function
+def get_k8s_answer(query):
+    retrieved_context = retriever.get_relevant_documents(query)
+    cleaned_context = clean_context(retrieved_context, max_tokens=350)  # Ensure context size is within limits
+
+    # Ensure total input tokens < 512 before passing to model
+    input_text = f"Context:\n{cleaned_context}\n\nQuestion: {query}\nAnswer:"
+    total_tokens = len(tokenizer.encode(input_text, add_special_tokens=True))
+
+    if total_tokens > 512:
+      # Trim context further to fit within the limit
+      allowed_tokens = 512 - len(tokenizer.encode(query, add_special_tokens=True)) - 50  # 50 tokens for the model's response
+      cleaned_context = clean_context(retrieved_context, max_tokens=allowed_tokens)
+
+      # Recalculate total tokens
+      input_text = f"Context:\n{cleaned_context}\n\nQuestion: {query}\nAnswer:"
+      total_tokens = len(tokenizer.encode(input_text, add_special_tokens=True))
+
+      if total_tokens > 512:
+          return "Error: Even after trimming, input is too large."
+
+    response = qa_chain.invoke({"input": query, "context": cleaned_context})
+    return response
+
+def get_k8s_answer_text(query):
+  model_full_answer = get_k8s_answer(query)
+  if 'answer' in model_full_answer.keys():
+    if 'text' in model_full_answer['answer'].keys():
+      return model_full_answer['answer']['text']
+  return "Error"
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=get_k8s_answer_text,
+    inputs=gr.Textbox(label="Ask a Kubernetes Question"),
+    outputs=gr.Textbox(label="Answer"),
+    title="Kubernetes RAG Assistant",
+    description="Ask any Kubernetes-related question and get a step-by-step answer based on documentation."
+)
 
 if __name__ == "__main__":
     demo.launch()