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LangChain for GenAI & NLP

Welcome to my project! This repository showcases my journey and learnings as I explore LangChain, Generative AI, and Natural Language Processing (NLP). Through this repository, I will share my work on building powerful chains, prompt templates, embeddings, memory management, multi-agent systems, LangGraph, and more, as I continue to delve into the world of modern AI. Follow me as I continue to explore new tools, algorithms, and techniques!

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🚀 Starting My Chain Here

I’m excited to start creating a variety of chains using LangChain, an open-source framework designed to make it easier to work with large language models (LLMs) and connect them with external data sources. Here's what I've learned so far:

Key Topics I've Explored:

• Prompt Templates: Using LangChain’s ChatPromptTemplate and other prompt tools to create dynamic, reusable templates for interacting with LLMs.
• Output Parsers: Parsing outputs from language models to make them more structured and useful for downstream tasks.
• Document Loaders: Efficiently loading and handling external documents for processing with LLMs.
• Text Splitters: Breaking text into smaller, manageable chunks using CharacterTextSplitter and RecursiveTextSplitter to improve processing speed and accuracy.
• Embeddings: Integrating embeddings from HuggingFace, Google Generative GenAI, and other sources for powerful document retrieval and search capabilities.
• Vector Stores: Storing and retrieving vector embeddings efficiently using tools like FAISS, Chroma, Pinecone, and others for high-performance document retrieval and similarity search.
• Contextual Compression: Leveraging contextual compression techniques to optimize input and output data for LLMs, ensuring more efficient processing and delivering state-of-the-art results in natural language understanding and generation tasks.
• LLMChain: Using LangChain’s LLMChain to build powerful, modular chains that connect multiple LLM calls, transforming workflows into multi-step processes.
• Memory: Implementing Memory features like WindowBufferMemory to manage long-term interactions, store information, and maintain context across sessions.
• VectorStoreRetriever: Using LangChain’s VectorStoreRetriever to perform efficient document retrieval using vector embeddings from a vector store.
• Agents & Multi-Agents: Building intelligent agents that can autonomously make decisions, reason about tasks, and even use external tools to complete complex workflows.
• LangGraph: Creating and visualizing AI workflows with LangChain’s LangGraph, which helps structure multi-step processes and visualize the relationships between tasks.

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🔧 Technologies & Tools Used:

• LangChain: A framework for developing applications powered by LLMs.
• HuggingFace Embeddings: Pre-trained models for converting text into embeddings for document retrieval.
• Google Generative GenAI: Leveraging Google's advanced generative models for high-quality text generation and understanding.
• Text Splitters: Using advanced text splitting techniques to break down long documents into more digestible chunks.
• Prompt Templates: Dynamically creating prompts to interact with LLMs in specific ways.
• Vector Stores: Using FAISS, Chroma, Pinecone, and other vector stores to manage and search through high-dimensional embeddings efficiently.
• Contextual Compression: Techniques to compress large inputs while retaining context, optimizing performance and achieving state-of-the-art results in natural language generation tasks.
• LLMChain: Creating multi-step workflows and integrating LLMs to build chains that combine multiple tasks or LLM calls.
• Memory: Managing session-based memory using tools like WindowBufferMemory to track interactions over time.
• VectorStoreRetriever: Efficiently retrieving documents or information using vector embeddings stored in a vector store.
• Agents: Building intelligent agents that can reason about tasks and autonomously interact with external APIs and systems.
• Multi-Agents: Managing multiple agents working together to complete complex workflows or tasks in parallel.
• LangGraph: Visualizing and managing complex AI workflows and task dependencies using the LangGraph module.

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📝 Key Features & Learnings

1. Prompt Templates

• Templates define how the input data is structured and guide the language model's behavior.
• Example: Using a chat-based prompt to build a dynamic conversation flow.

2. Output Parsers

• After generating a response, it's important to parse and structure the output.
• Example: Parsing text for specific pieces of information such as names, dates, or key concepts.

3. Document Loaders

• Load documents from various sources (e.g., PDFs, CSVs) and make them ready for processing by the model.
• Example: Using document loaders to bring in knowledge from multiple sources for retrieval and generation.

4. Text Splitters

• CharacterTextSplitter: Splits large documents into smaller text blocks by a specific number of characters.
• RecursiveTextSplitter: Splits documents in a more recursive manner based on semantic boundaries like paragraphs and headings.

5. Embeddings

• HuggingFace Embeddings: Transform text data into vector embeddings for search and retrieval tasks.
• Google Generative GenAI Embeddings: Integrating Google's GenAI embeddings to enhance text generation and retrieval capabilities.

6. Vector Stores

• FAISS: A popular library for efficient similarity search and clustering of embeddings, widely used for large-scale document retrieval tasks.
• Chroma: An open-source vector database for storing and querying embeddings that integrates with LangChain to facilitate powerful search and retrieval.
• Pinecone: A managed vector database solution that enables high-performance, real-time similarity search and indexing of vector embeddings.
• Weaviate: A vector search engine for machine learning models that also integrates well with LangChain for document retrieval and other use cases.

7. Contextual Compression

• Contextual Compression involves using techniques that compress the input data while retaining the most important contextual information. This method allows large documents or inputs to be handled efficiently by LLMs without losing key information, improving the model’s processing speed and delivering high-quality outputs. By applying contextual compression, I aim to optimize the interaction with language models, achieving state-of-the-art performance for NLP tasks.

8. LLMChain

• The LLMChain module helps build complex workflows by chaining multiple LLM calls together. This allows for modular, reusable components in your NLP pipeline, making it easier to handle tasks like document generation, summarization, and more.

9. Memory (WindowBufferMemory)

• Memory is essential for maintaining context across interactions. I’ve explored using WindowBufferMemory to manage the model’s memory, enabling it to recall past interactions or outputs and provide more context-aware responses over time.

10. VectorStoreRetriever

• VectorStoreRetriever is used to retrieve relevant documents or pieces of information from a vector store based on their semantic similarity to a given query. This tool is essential for building document-based applications where context retrieval is key.

11. Agents & Multi-Agents

• Agents in LangChain allow models to autonomously interact with external tools or APIs to achieve a goal. I’ve experimented with using agents to handle tasks like querying external data or processing information across multiple steps.
• Multi-Agents take this concept further, enabling the coordination of multiple agents working in parallel or sequentially to accomplish more complex goals. This is ideal for scenarios where tasks can be divided and worked on simultaneously, speeding up the process and improving efficiency.

12. LangGraph

• LangGraph is a visualization tool within LangChain that helps design and manage complex workflows. It allows users to graphically represent the steps and dependencies in a chain, providing clarity on how tasks are related. This makes it easier to debug and optimize workflows as they grow in complexity.

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🧑‍💻 Code Snippets & Examples

Example 1: Creating a Prompt Template

from langchain.prompts import ChatPromptTemplate

# Define a simple chat-based prompt template
chat_prompt = ChatPromptTemplate.from_messages([
    ("ai", "You are a helpful assistant. Based on the user's question '{question}', you will provide an answer."),
    ("user", "{question}")
])

formatted_prompt = chat_prompt.format_messages(question="Who is Elon Musk?")

Example 2: Loading Documents and Embeddings

from langchain_huggingface.embeddings import HuggingFaceEmbeddings

def embeddings(data):
    if not isinstance(data, str):
        data = str(data)
    embeddings = HuggingFaceEmbeddings()
    vector = embeddings.embed_query(data)
    return vector

# Example data and embeddings
data = "Information about Elon Musk"
result_vectors = embeddings(data)

Example 3: Using a Text Splitter

from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter

# Split a long document into smaller chunks
text = "This is a very long document that needs to be split into smaller chunks."

splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=20)
chunks = splitter.split_text(text)

print(chunks)

Example 4: Using a Vector Store (FAISS) for Document Retrieval

import faiss
import numpy as np

# Create random embeddings for 100 documents
embeddings = np.random.random((100, 128)).astype('float32')

# Create a FAISS index and add the embeddings
index = faiss.IndexFlatL2(128)  # Use L2 distance for search
index.add(embeddings)

# Now, query with a new embedding (for example, from a user's question)
query = np.random.random((1, 128)).astype('float32')
distances, indices = index.search(query, k=5)

print("Closest matches:", indices)
print("Distances:", distances)

Example 5: Using an Agent to Query External Tools

from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType

tools = [
    Tool(
        name="calculator",
        func=my_calculation_function,  # Define this function to use the tool
        description="Performs mathematical operations"
    )
]

agent = initialize_agent(tools, AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
response = agent.run("What is 3 + 5?")
print(response)

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📈 What’s Coming Next

This is just the beginning of my journey with LangChain, and there's much more to explore! Some exciting topics I plan to cover next include:

• Advanced document retrieval techniques
• Building multi-step chains for more complex workflows
• Integrating with external APIs for real-time data access
• Fine-tuning language models on custom datasets
• Exploring multi-agent coordination in real-world scenarios
• Designing complex workflows with LangGraph

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🌟 Follow Me on GitHub!

If you find this project interesting and want to stay updated with my progress, feel free to follow me on GitHub! I’ll be pushing more code, tutorials, and ideas as I continue exploring LangChain and its potential.

👉 Follow me on GitHub

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🤝 Contribute

If you have any suggestions, ideas, or improvements, feel free to open an issue or create a pull request. Let’s learn together!

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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