Quick Start

Pre-built chat agent by Lyzr

Getting Started with Lyzr Chat Agent SDK

Lyzr’s Chat Agent is powered by a state-of-the-art chatbot architecture that super abstracts all the complexity of building an advanced LLM-powered chatbot. This enables developers to focus more on data quality, prompt quality, and the application use case instead of spending countless hours stitching together various building blocks and indexes to build the backend RAG pipeline.

Lyzr’s Chat Agent integrates all the building blocks of a chatbot

What are the various methods and arguments that you could pass to Lyzr’s ChatBot class?

Methods

Method	What it does?
pdf_chat	chat with PDF documents
website_chat	automatically scrape the website content and chat with website data
docx_chat	chat with Microsoft Word documents
txt_chat	chat with flat files
youtube_chat	chat with youtube content (must have transcriptions)
webpage_chat	automatically scrape the webpage content and chat with webpage data

Chat with PDF

Sample Code 👇

import os
from lyzr import ChatBot

# Set your OpenAI API key
os.environ['OPENAI_API_KEY'] = 'sk-'

# Initialize the PDF Chatbot with the path to the PDF file
chatbot = ChatBot.pdf_chat(
    input_files=["PATH/TO/YOUR/PDF/FILE"],
)

# Ask a question related to the PDF content
response = chatbot.chat("Your question here")

# Print the chatbot's response
print(response.response)

# Access source nodes for additional information
for n, source in enumerate(response.source_nodes):
    print(f"Source {n+1}")
    print(source.text)

Types of Arguments

pdf_chat(
        input_dir: Optional[str] = None,
        input_files: Optional[List] = None,
        exclude_hidden: bool = True,
        filename_as_id: bool = True,
        recursive: bool = True,
        required_exts: Optional[List[str]] = None,
        system_prompt: str = None,
        query_wrapper_prompt: str = None,
        embed_model: Union[str, EmbedType] = "default",
        llm_params: dict = None,
        vector_store_params: dict = None,
        service_context_params: dict = None,
        chat_engine_params: dict = None,
        retriever_params: dict = None,
    ):

input_dir · string

Use input_dir to parse all the .pdf files from a directory.

input_files · list

Pass a list of .pdf file paths.

exclude_hidden · boolean

Set to true to ignore hidden files when using input_dir.

filename_as_id · boolean

Set to true to consider the filename as the id for indexing the parsed data.

recursive · boolean

Set to true to parse files from all subdirectories.

system_prompt · string

System-wide prompt to be prepended to all input prompts, used to guide system “decision making”.

query_wrapper_prompt · string

A specific wrapper instruction for passed-in input queries.

embed_model · string

The default embed model is OpenAI text-embedding-ada-002. Default fallback model is bge from Hugging Face.

llm_params · object

Default language model is OpenAI gpt-4-0125-preview. Default temperature is 0.

vector_store_params · object

The default vector store is Embedded Weaviate DB.

service_context_params · object

Default chunk_size is 1024 tokens. Default overlap is 20 tokens.

chat_engine_params · object

Default is none.

retriever_params · object

Default is none.

Integrations

Vector Store Integrations

Lyzr + Weaviate

Local Embedded

vector_store_params = {
    "vector_store_type": "WeaviateVectorStore",
    "index_name": "IndexName" # first letter should be capital
}

Cloud / Self Hosted

vector_store_params = {
    "vector_store_type": "WeaviateVectorStore",
    "url": "https://sample..weaviate.network",
    "api_key": "DB_API_KEY",
    "index_name": "IndexName" # first letter should be capital
}

Lyzr + Supabase Pgvector

Install vecs and supabase

pip install vecs supabase

vector_store_params = {
    "vector_store_type": "SupabaseVectorStore",
    "postgres_connection_string": "postgresql://<user>:<password>@<host>:<port>/<db_name>",
    "collection_name":"base_demo",
  }

Lyzr + Qdrant Vector Store

!pip install -U qdrant_client

Local Embedded

client = qdrant_client.QdrantClient(
        location=":memory:" 
)
vector_store_params = {
    "vector_store_type": "QdrantVectorStore",
    "client": client,
    "collection_name":"base_demo",
}

Cloud / Self Hosted

client = qdrant_client.QdrantClient( 
    uri="http://<host>:<port>"
    api_key="<qdrant-api-key>",
)
vector_store_params = {
    "vector_store_type": "QdrantVectorStore",
    "client": client,
    "collection_name":"base_demo",
}

Lyzr + LanceDB Vector Store

!pip install -U lancedb

Local Embedded

vector_store_params = {
    "vector_store_type": "LanceDBVectorStore",
    "uri": "/tmp/lancedb"),
    "table_name":"base_demo",
}

Lyzr + Azure Cognitive Search

pip install azure-search-documents==11.4.0  azure-identity

Cloud / Self Hosted

from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents import SearchClient
from azure.core.credentials import AzureKeyCredential

search_service_name = getpass.getpass("Azure Cognitive Search Service Name")

key = getpass.getpass("Azure Cognitive Search Key")

cognitive_search_credential = AzureKeyCredential(key)

service_endpoint = f"https://{search_service_name}.search.windows.net"

index_name = "quickstart"

index_client = SearchIndexClient(
    endpoint=service_endpoint,
    credential=cognitive_search_credential,
)

vector_store_params = {
    "vector_store_type": "CognitiveSearchVectorStore",
    "search_or_index_client": index_client),
    "index_name":index_name,
}

LLM Integration

Lyzr + OpenAI

llm_params = {
    "model":"gpt-4-0125-preview",
    "api_key":"sk-"
    "temperature":0,
    "top_p":0,
}

Lyzr + Azure OpenAI

llm_params = {
    "model": "azure/gpt-35-turbo-16k",
    "api_base": "https://<deployment_namer>.azure.com/",
    "api_version": "2024-02-15-preview",
    "api_key": "<YOUR-API-KEY>",
    "max_retries": 1
}

Assistant

Responses are generated using AI and may contain mistakes.