Getting started with LangChain and Weights & Biases



Table of Content

What is LangChain?

Access to Structured Data: Language models can generate responses informed by real-time or specific data.
Chaining Processes: LangChain supports multi-step workflows, combining outputs from various components like data retrieval, reasoning, or algorithms.

What makes LangChain unique?

Real-Time Data Integration: Models interact with structured data, delivering contextually relevant outputs.
Process Chaining: Create workflows that integrate multiple steps for sophisticated AI solutions.
Customizable Templates: Pre-built templates make it easier to adapt workflows for specific needs.

LangChain components

LangChain libraries

Data-Driven Responses: For applications that require language models to generate responses based on up-to-date or specific data, such as generating a weather report based on real-time weather data.
Complex Query Processing: In scenarios where a query requires multiple computational steps, such as retrieving information, processing it, and then generating a language-based output.

LangChain templates

Retrieval-Augmented Generation Chatbot: Create a chatbot tailored to your data using OpenAI and PineconeVectorStore.
Data Extraction with OpenAI Functions: Extract structured data from unstructured sources with OpenAI's function-calling capabilities.
Local Retrieval-Augmented Generation: Develop a data-specific chatbot relying solely on local tools such as Ollama, GPT4all, and Chroma.
OpenAI Functions Agent: Design a chatbot capable of performing actions using OpenAI's function calling and Tavily.
XML Agent: Build an action-oriented chatbot leveraging Anthropic and You.com.

Standardized Reporting: In generating business reports, research summaries, or news articles where a consistent format is desired.
Educational Content Creation: For creating educational materials like quizzes, explanations, or essay questions in a structured manner.

LangServe

Web Applications: For developers looking to incorporate language model functionalities into web applications without embedding the entire model.
Microservices Architecture: In a microservices architecture, where different services need to interact with a language model independently.

LangSmith

Domain-Specific Applications: In sectors like legal, medical, or technical fields where the language model needs to understand and generate domain-specific content.
Customized Customer Service: For tailoring chatbots and virtual assistants to reflect a company's brand voice or to handle specific types of customer queries.

Overview of Weights & Biases

Experiment Tracking: Monitor configurations and metrics.
Visualization: Gain insights into performance and optimization.
Debugging: Easily identify bottlenecks or inefficiencies in workflows.

How does LangChain work?

Storing the data into vector indexes



Vectorization: Convert text into vector representations using embeddings.
Indexing: Use databases like FAISS or Pinecone for efficient similarity searches.

Data retrieval



Query Vectorization: Transform queries into vectors.
Performing the Search: Retrieve the most relevant data from the index.

Chaining



Integration: Combine retrieved data with the language model to generate responses.

Getting started with LangChain and Weights & Biases

Step 1: Installing the necessary libraries

!pip install langchain wandb
!pip install openai
!pip install pinecone-client
!pip install langchain-openai
!pip install tiktoken
!pip install faiss-gpu
!pip install --upgrade openai

Step 2: Importing the necessary libraries

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.chains import create_retrieval_chain
from langchain.prompts import ChatPromptTemplate
from langchain.schema import Document
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
import os

Step 3: Turn on Weights & Biases' logging for LangChain

os.environ["LANGCHAIN_WANDB_TRACING"] = "true"

# optionally set your wandb settings or configs
os.environ["WANDB_PROJECT"] = "langchain-tracing"

Step 4: Set our OpenAI key and initialize a new OpenAI model

# Set OpenAI API key
os.environ['OPENAI_API_KEY'] = "insert your OpenAI API key here"

# Initialize the language model
llm = ChatOpenAI()

Step 5: Loading our LangChain documentation data

loader = PyPDFLoader("/kaggle/input/langchaindocumentationpdf/langChainDocumentation.pdf")
pages = loader.load_and_split()

Step 6: Create a FAISS index from the document

embeddings = OpenAIEmbeddings()
faiss_index = FAISS.from_documents(pages, embeddings)

Step 7: Create a query for the document

# Create a query for the document
query = "What is LangChain?"
docs = faiss_index.similarity_search(query, k=1)

# Access the text of the top result
retrieved_doc = docs[0] if docs else None

Step 8: Set up the prompt template

# Set up the enhanced prompt template
prompt_template = ChatPromptTemplate.from_template(
    "Based on the following information:\n\n{context}\n\n"
    "Can you answer this question: {input}?"
)

# Format the prompt with the actual context and the query
if retrieved_doc:
    formatted_prompt = prompt_template.format(context=retrieved_doc.page_content, input=query)

    # Invoke the language model with the formatted prompt
    response = llm.invoke(formatted_prompt)
    print("Response:\n", response)
else:
    print("No relevant document found.")

Output if the Documentation File Is Empty

content='As per the provided information, there is no mention of "LangChain." It is possible that "LangChain" may refer to something unrelated to the Sunflower Galaxy or the information provided. Could you please provide more context or clarify your question?'

Output if the Documentation File Contains the Actual Data

content='LangChain is a framework that facilitates the development of language model-powered applications. It enables applications to be context-aware and capable of reasoning based on the provided context. LangChain consists of several components, including Python and JavaScript libraries (LangChain Libraries) that provide interfaces and integrations for various components, pre-built chains and agents, as well as LangChain Templates that offer easily deployable reference architectures for different tasks. Additionally, LangServe allows deploying LangChain chains as a REST API, while LangSmith serves as a developer platform for debugging, testing, evaluating, and monitoring chains built on any Language Model (LLM) framework, seamlessly integrating with LangChain. Overall, LangChain simplifies the entire application lifecycle by providing tools for development, productionizing, and deployment.'

Using Weights & Biases to trace LangChain’s activity



Practical tips for beginners to LangChain and Weights & Biases

LangChain

Experiment with different vector storage solutions (e.g., Pinecone, Chroma) to optimize performance.
Start small with basic workflows before scaling to complex chains.

Weights & Biases

Use custom metrics to evaluate language models more effectively.
Track changes over time with detailed logs and visualizations.

Conclusion

Reference