Configuration

from langchat import LangChatConfig

config = LangChatConfig.from_env()  # Load from environment variables

config = LangChatConfig(
    # Required: List of OpenAI API keys
    openai_api_keys=["sk-...", "sk-..."],  # Multiple keys for rotation
    
    # Optional: OpenAI model settings
    openai_model="gpt-4o-mini",  # Default: "gpt-4o-mini"
    openai_temperature=1.0,  # Default: 1.0 (0.0-2.0)
    openai_embedding_model="text-embedding-3-large",  # Default: "text-embedding-3-large"
    
    # Optional: Retry configuration
    max_llm_retries=2  # Default: 2 (retries per API key)
)

config = LangChatConfig(
    # Required: Pinecone API key
    pinecone_api_key="pcsk-...",
    
    # Required: Pinecone index name (must be pre-created)
    pinecone_index_name="your-index-name"
)

config = LangChatConfig(
    # Required: Supabase project URL
    supabase_url="https://xxxxx.supabase.co",
    
    # Required: Supabase API key (anon key)
    supabase_key="eyJhbGc..."
)

config = LangChatConfig(
    # Number of documents to retrieve
    retrieval_k=5,  # Default: 5
    
    # Top N results after reranking
    reranker_top_n=3,  # Default: 3
    
    # Reranker model
    reranker_model="ms-marco-MiniLM-L-12-v2",  # Default: "ms-marco-MiniLM-L-12-v2"
    
    # Reranker cache directory
    reranker_cache_dir="rerank_models"  # Default: "rerank_models"
)

config = LangChatConfig(
    # Maximum chat history messages to keep in memory
    max_chat_history=20,  # Default: 20
    
    # Conversation buffer window size
    memory_window=20  # Default: 20
)

config = LangChatConfig(
    # Port for API server
    server_port=8000  # Default: 8000
)

config = LangChatConfig(
    # Timezone for date/time formatting
    timezone="Asia/Dhaka"  # Default: "Asia/Dhaka"
)

config = LangChatConfig(
    # Custom system prompt template
    # Use single braces {context}, {chat_history}, {question} for variables
    # LangChain's PromptTemplate handles substitution automatically
    system_prompt_template="""You are a helpful assistant.
    
    Use the following context to answer questions:
    {context}
    
    Chat history: {chat_history}
    
    Question: {question}
    Answer:""",
    
    # Custom standalone question prompt
    standalone_question_prompt="""Convert this question to a standalone search query.
    
    Chat History: {chat_history}
    Question: {question}
    Standalone query:""",
    
    # Enable verbose chain output for debugging
    # Shows detailed prompt formatting and chain execution in Rich panels
    verbose_chains=False  # Set to True for debugging
)

# OpenAI
export OPENAI_API_KEYS="key1,key2"  # Comma-separated for multiple keys
export OPENAI_API_KEY="key1"  # Alternative: single key
export OPENAI_MODEL="gpt-4o-mini"
export OPENAI_TEMPERATURE="1.0"
export OPENAI_EMBEDDING_MODEL="text-embedding-3-large"

# Pinecone
export PINECONE_API_KEY="pcsk-..."
export PINECONE_INDEX_NAME="your-index-name"

# Supabase
export SUPABASE_URL="https://xxxxx.supabase.co"
export SUPABASE_KEY="eyJhbGc..."

# Vector Search
export RETRIEVAL_K="5"
export RERANKER_TOP_N="3"
export RERANKER_MODEL="ms-marco-MiniLM-L-12-v2"
export RERANKER_CACHE_DIR="rerank_models"

# Session
export MAX_CHAT_HISTORY="20"
export MEMORY_WINDOW="20"

# Server
export SERVER_PORT="8000"
export PORT="8000"  # Alternative

# Timezone
export TIMEZONE="Asia/Dhaka"

# Debugging
export VERBOSE_CHAINS="False"  # Set to "True" to enable verbose chain output

config = LangChatConfig.from_env()

from langchat import LangChatConfig

config = LangChatConfig(
    openai_api_keys=["sk-..."],
    pinecone_api_key="pcsk-...",
    pinecone_index_name="my-index",
    supabase_url="https://xxxxx.supabase.co",
    supabase_key="eyJ..."
)

from langchat import LangChatConfig
import os

config = LangChatConfig(
    # Multiple API keys for rotation
    openai_api_keys=os.getenv("OPENAI_API_KEYS").split(","),
    openai_model="gpt-4o-mini",
    openai_temperature=0.8,
    openai_embedding_model="text-embedding-3-large",
    max_llm_retries=2,
    
    # Pinecone
    pinecone_api_key=os.getenv("PINECONE_API_KEY"),
    pinecone_index_name=os.getenv("PINECONE_INDEX_NAME"),
    
    # Supabase
    supabase_url=os.getenv("SUPABASE_URL"),
    supabase_key=os.getenv("SUPABASE_KEY"),
    
    # Vector search
    retrieval_k=10,
    reranker_top_n=5,
    
    # Session
    max_chat_history=50,
    memory_window=50,
    
    # Server
    server_port=int(os.getenv("PORT", "8000"))
)

config = LangChatConfig(
    # ... other config ...
    
    # Custom prompts for education domain
    # Use single braces for template variables
    system_prompt_template="""You are an expert education consultant.
    
    Help students find the best universities based on their profiles.
    
    Context: {context}
    History: {chat_history}
    Question: {question}
    Answer:""",
    
    standalone_question_prompt="""Convert this education question to a standalone search query.
    
    Chat History: {chat_history}
    Question: {question}
    Standalone query:""",
    
    # Enable verbose output for debugging chains
    verbose_chains=False
)

config = LangChatConfig.from_env()

config = LangChatConfig(
    openai_api_keys=["sk-..."],
    # ... other options ...
)

config = LangChatConfig.from_env()
# Override specific settings
config.openai_model = "gpt-4"
config.server_port = 8080

from langchat import LangChat

try:
    config = LangChatConfig(
        openai_api_keys=[],  # Empty list
        # ... other config ...
    )
    langchat = LangChat(config=config)
except ValueError as e:
    print(f"Configuration error: {e}")

# ❌ Bad
config = LangChatConfig(
    openai_api_keys=["sk-abc123..."],  # Hardcoded
    supabase_key="eyJ..."
)

# ✅ Good
config = LangChatConfig.from_env()  # From environment

config = LangChatConfig(
    openai_api_keys=[
        "sk-key1",
        "sk-key2",
        "sk-key3"
    ],
    max_llm_retries=2  # Total: 6 retries
)

# More results = better accuracy, slower
config.retrieval_k = 10
config.reranker_top_n = 5

# Fewer results = faster, potentially less accurate
config.retrieval_k = 3
config.reranker_top_n = 2

config.system_prompt_template = """Your custom prompt here..."""