Quick Start

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

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

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

Lyzr + Azure Cognitive Search

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

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

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
}

Pre-built Agents

​Getting Started with Lyzr Chat Agent SDK

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

​Chat with PDF

​Integrations

​Vector Store Integrations

​Lyzr + Weaviate

​Lyzr + Supabase Pgvector

​Lyzr + Qdrant Vector Store

​Lyzr + LanceDB Vector Store

​Lyzr + Azure Cognitive Search

​LLM Integration

Getting Started with Lyzr Chat Agent SDK

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

Chat with PDF

Integrations

Vector Store Integrations

Lyzr + Weaviate

Lyzr + Supabase Pgvector

Lyzr + Qdrant Vector Store

Lyzr + LanceDB Vector Store

Lyzr + Azure Cognitive Search

LLM Integration