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Upload config.py
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config.py
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"""
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Configuration file for the Law RAG Chatbot application
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"""
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import os
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from typing import Optional
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from pathlib import Path
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from dotenv import load_dotenv
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load_dotenv()
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# Load environment variables from .env file if it exists
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def load_dotenv():
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"""Load environment variables from .env file"""
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env_file = Path('.env')
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if env_file.exists():
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with open(env_file, 'r') as f:
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for line in f:
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line = line.strip()
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if line and not line.startswith('#') and '=' in line:
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key, value = line.split('=', 1)
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os.environ[key] = value
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# Load .env file
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load_dotenv()
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# Hugging Face Configuration
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HF_TOKEN = os.getenv('HF_TOKEN')
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HF_DATASET_NAME = "ymoslem/Law-StackExchange"
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# Groq Configuration
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GROQ_API_KEY = os.getenv('GROQ_API_KEY')
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GROQ_MODEL = "llama3-8b-8192" # or "mixtral-8x7b-32768"
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# Embedding Configuration
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EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
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EMBEDDING_DIMENSION = 384
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# ChromaDB Configuration
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CHROMA_PERSIST_DIR = "./chroma_db"
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CHROMA_COLLECTION_NAME = "law_stackexchange"
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# FastAPI Configuration
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API_TITLE = "Law RAG Chatbot API"
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API_VERSION = "1.0.0"
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API_DESCRIPTION = "RAG-based legal assistance chatbot using Law-StackExchange data"
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HOST = "0.0.0.0"
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PORT = 8000
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# RAG Configuration
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CHUNK_SIZE = 1000
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CHUNK_OVERLAP = 200
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TOP_K_RETRIEVAL = 8 # Increased from 5
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MAX_TOKENS = 4096
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TEMPERATURE = 0.1
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DEFAULT_CONTEXT_LENGTH = 5 # New default context length
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# Token Management Configuration
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MAX_CONTEXT_TOKENS = 4000 # Maximum tokens for context (reserve space for prompt)
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MAX_PROMPT_TOKENS = 6000 # Maximum total prompt tokens (Groq limit)
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MAX_SOURCES = 5 # Maximum number of sources to include
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MAX_SEARCH_VARIATIONS = 2 # Maximum search variations to try
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MAX_LEGAL_CONCEPTS = 2 # Maximum legal concepts to extract
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# Dataset Configuration
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DATASET_SPLIT = "train"
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CACHE_DIR = ".cache"
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# Error Messages
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ERROR_MESSAGES = {
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"no_hf_token": "Hugging Face token not found. Set HF_TOKEN environment variable.",
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"no_groq_key": "Groq API key not found. Set GROQ_API_KEY environment variable.",
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"auth_failed": "Authentication failed: {}",
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"dataset_load_failed": "Failed to load dataset: {}",
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"embedding_failed": "Failed to create embeddings: {}",
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"vector_db_failed": "Failed to setup vector database: {}",
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"llm_failed": "Failed to initialize LLM: {}"
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}
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# API Response Models
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class ChatRequest:
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question: str
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context_length: Optional[int] = 3
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class ChatResponse:
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answer: str
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sources: list
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confidence: float
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processing_time: float
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