add files

Dockerfile

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+FROM python:3.10-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git curl && \
+    rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy project files
+COPY . /app
+
+# Create cache directory
+RUN mkdir -p /app/cache && chmod -R 777 /app/cache
+
+# Set environment variables
+ENV TRANSFORMERS_CACHE=/app/cache \
+    HF_HOME=/app/cache \
+    SENTENCE_TRANSFORMERS_HOME=/app/cache \
+    PORT=7860 \
+    PYTHONUNBUFFERED=1
+
+# Install Python dependencies
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt && \
+    pip install gradio
+
+# Expose Gradio port
+EXPOSE 7860
+
+# Run the app
+CMD ["python", "app.py"]

README.md

@@ -1 +1,11 @@
+---
+title: Customer Support RAG
+emoji: 💻
+colorFrom: blue
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.28.0
+app_file: app.py
+pinned: false
+---
 # TroubleGraph

app.py

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+mport gradio as gr
+from gliner import GLiNER
+from langchain_community.vectorstores import Qdrant
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_qdrant import QdrantVectorStore
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import RunnableMap
+from langchain_core.output_parsers import StrOutputParser
+from langchain.memory import ChatMessageHistory
+import re
+from datasets import load_dataset
+from langchain.schema import Document
+import os
+
+# Initialize once
+gliner_model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Qdrant setup
+doc_store = QdrantVectorStore.from_existing_collection(
+    embedding=embedding_model,
+    collection_name="customer_support_docsv1",
+    url=os.getenv("QDRANT_URL"),
+    api_key=os.getenv("QDRANT_API_KEY"),
+)
+
+retriever = doc_store.as_retriever(search_type="similarity", search_kwargs={"k": 1})
+llm = ChatGroq(api_key=os.getenv("GROQ_API_KEY"), model="meta-llama/llama-4-scout-17b-16e-instruct")
+
+chat_prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are an intelligent assistant. Use context and chat history to answer."),
+    MessagesPlaceholder("chat_history"),
+    ("human", "{query}")
+])
+
+rag_chain = RunnableMap({
+    "context": lambda x: retriever.invoke(x["query"]),
+    "query": lambda x: x["query"],
+    "chat_history": lambda x: x["chat_history"]
+}) | chat_prompt | llm | StrOutputParser()
+
+# Shared memory
+memory = ChatMessageHistory()
+
+# Gradio handler
+def chat_fn(message, history_list):
+    # Use LangChain-style history for context
+    response = rag_chain.invoke({
+        "query": message,
+        "chat_history": memory.messages
+    })
+
+    # Append new messages to the LangChain memory
+    memory.add_user_message(message)
+    memory.add_ai_message(response)
+
+    return response
+
+chatbot = gr.ChatInterface(fn=chat_fn, title="🛠️ Customer Support Chatbot")
+chatbot.launch()

requirements.txt

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+gradio
+gliner
+qdrant-client
+sentence-transformers
+datasets
+langchain
+langchain-community
+langchain-qdrant
+langchain-groq