Building an AI Memory System with LangChain and Pinecone from Scratch

Building an AI Memory System with LangChain and Pinecone from Scratch

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Building an AI Memory System with LangChain and Pinecone from Scratch

Recently, have you been overwhelmed by various AI applications? With the emergence of ChatGPT, Wenxin Yiyan, and more, it can be dazzling. However, did you know that these AI applications all share a common point – they utilize a magical framework called LangChain. Today, we will discuss how to use LangChain and Pinecone to build a simple AI memory system by ourselves.

What is LangChain?

In simple terms, LangChain is a toolbox that helps you connect large language models (like GPT) with external data. It allows your AI not only to talk but also to look up information, perform calculations, and even execute certain actions.

Explaining it with code might be more intuitive:

from langchain import OpenAI, LLMChain
from langchain.prompts import PromptTemplate

# Initialize OpenAI model
llm = OpenAI(temperature=0.9)

# Define a prompt template
prompt = PromptTemplate(
    input_variables=["topic"],
    template="Tell me a joke about {topic}"
)

# Create an LLMChain
chain = LLMChain(llm=llm, prompt=prompt)

# Use the chain to generate content
print(chain.run("Python"))

See, with just a few lines of code, you can make the AI tell you a joke related to Python.

Building an AI Memory System with LangChain and Pinecone from ScratchIsn’t that cool?

Pinecone: The Memory Database for AI

Pinecone is a vector database.

Building an AI Memory System with LangChain and Pinecone from ScratchFor example, it’s like a notebook for AI, capable of storing a large amount of information and quickly retrieving it when needed.

Here’s how to use it:

import pinecone
from langchain.embeddings import OpenAIEmbeddings

# Initialize Pinecone
pinecone.init(api_key="your API key", environment="your environment")

# Create an index
index = pinecone.Index("my-first-index")

# Create embeddings
embeddings = OpenAIEmbeddings()
text = "Python is the best programming language"
vect = embeddings.embed_query(text)

# Store the vector
index.upsert([("id1", vect, {"text": text})])

This code stores the sentence “Python is the best programming language” into Pinecone. When AI needs it later, it can quickly find this information.

Friendly reminder: Don’t forget to replace “your API key” and “your environment”, otherwise the code won’t run.

Combining Use: Building an AI Memory System

Now that we know how to use these two tools, let’s get down to building an AI memory system.

First, we need a function to store information:

def store_info(text):
    vector = embeddings.embed_query(text)
    index.upsert([(str(uuid.uuid4()), vector, {"text": text})])

# Example usage
store_info("LangChain is a powerful AI development framework")

Next, we need a function to retrieve information:

def retrieve_info(query):
    query_vector = embeddings.embed_query(query)
    results = index.query(query_vector, top_k=1, include_metadata=True)
    return results['matches'][0]['metadata']['text']

# Example usage
related_info = retrieve_info("What is LangChain")
print(related_info)

Finally, we combine these to create an AI that can remember conversation content:

def chat_with_memory(user_input):
    # Retrieve relevant information
    context = retrieve_info(user_input)
    
    # Create a memory-enabled prompt
    prompt = f"Based on the following information: {context}\nAnswer the question: {user_input}"
    
    # Use LLM to generate a response
    response = chain.run(prompt)
    
    # Store conversation content
    store_info(f"Q: {user_input}\nA: {response}")
    
    return response

# Example usage
print(chat_with_memory("What are the features of LangChain?"))

See, this is a simple AI memory system.

Building an AI Memory System with LangChain and Pinecone from ScratchIt can remember your conversations and use previous dialogue content to answer new questions.

Building such a system isn’t too difficult, right? The key is to understand what each part does and then combine them like building blocks. If you find anything confusing, just try a few more times; practice makes perfect.

Python and AI may seem complex, but if you take it slow, it’s not that hard. The important thing is not to be afraid of making mistakes and to get hands-on; you’ll find that you can create some really cool things. Good luck, future AI master!

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