LangChain Memory Store
Dhiraj Patra
Cloud-Native Architect | AI, ML, GenAI Innovator & Mentor | Quantitative Financial Analyst
To add bigger memory space with LangChain, you can leverage the various memory modules that LangChain provides. Here's a brief guide on how to do it:
1. Use a Larger Memory Backend
LangChain allows you to use different types of memory backends. For larger memory capacity, you can use backends like databases or cloud storage. For instance, using a vector database like Pinecone or FAISS can help manage larger context effectively.
2. Implement a Custom Memory Class
You can implement your own memory class to handle larger context. Here’s an example of how to create a custom memory class:
```python
from langchain.memory import BaseMemory
class CustomMemory(BaseMemory):
? ? def init(self):
? ? ? ? self.memory = []
? ? def add_to_memory(self, message):
? ? ? ? self.memory.append(message)
? ??
? ? def get_memory(self):
? ? ? ? return self.memory
? ? def clear_memory(self):
? ? ? ? self.memory = []
```
3. Configure Memory in LangChain
When setting up the chain, you can specify the memory class you want to use:
```python
from langchain import LLMChain
from langchain.llms import OpenAI
# Create an instance of your custom memory class
custom_memory = CustomMemory()
# Initialize the language model
llm = OpenAI(api_key='your_openai_api_key')
# Create the chain with the custom memory
chain = LLMChain(llm=llm, memory=custom_memory)
# Add messages to memory
chain.memory.add_to_memory("Previous context or message")
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# Retrieve memory
context = chain.memory.get_memory()
```
4. Use External Storage
For even larger memory, consider using external storage solutions like a database (e.g., PostgreSQL, MongoDB) or cloud storage (e.g., AWS S3, Google Cloud Storage). You can extend the memory class to interact with these external storage systems.
Example with SQLite:
```python
import sqlite3
from langchain.memory import BaseMemory
class SQLiteMemory(BaseMemory):
? ? def init(self, db_path):
? ? ? ? self.conn = sqlite3.connect(db_path)
? ? ? ? self.cursor = self.conn.cursor()
? ? ? ? self.cursor.execute('''CREATE TABLE IF NOT EXISTS memory (message TEXT)''')
? ? def add_to_memory(self, message):
? ? ? ? self.cursor.execute("INSERT INTO memory (message) VALUES (?)", (message,))
? ? ? ? self.conn.commit()
? ??
? ? def get_memory(self):
? ? ? ? self.cursor.execute("SELECT message FROM memory")
? ? ? ? return [row[0] for row in self.cursor.fetchall()]
? ? def clear_memory(self):
? ? ? ? self.cursor.execute("DELETE FROM memory")
? ? ? ? self.conn.commit()
? ? ? ? self.conn.close()
# Initialize SQLite memory
sqlite_memory = SQLiteMemory('memory.db')
# Create the chain with SQLite memory
chain = LLMChain(llm=llm, memory=sqlite_memory)
```
By using these methods, you can effectively increase the memory capacity for your LangChain application, ensuring it can handle and recall larger contexts across interactions.