Memory-augmented retrieval in LangChain - Step-by-Step Execution

Concept Flow - Memory-augmented retrieval

User Query Input

↓

Check Memory for Context

↓

Retrieve Relevant Memory Entries

↓

Combine Query + Memory Context

↓

Send to Retriever/LLM

↓

Get Response

↓

Update Memory with New Info

↓

Output Answer

The system takes a user query, checks stored memory for relevant context, combines them, sends to the retriever or language model, then updates memory with new information before outputting the answer.

Execution Sample

LangChain

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain

# Assume llm and retriever are defined (e.g., llm = ChatOpenAI(), retriever = vectorstore.as_retriever())
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = ConversationalRetrievalChain.from_llm(llm, retriever, memory=memory)
response = chain.invoke({"question": "What is AI?"})["answer"]

This code creates a memory buffer and a conversational retrieval chain that uses this memory to answer the question 'What is AI?'.

Execution Table

Step	Action	Memory State Before	Memory Retrieved	Combined Query	Response Generated	Memory State After
1	User inputs query 'What is AI?'	Empty	None	What is AI?	N/A	Empty
2	Retrieve relevant memory entries	Empty	None	What is AI?	N/A	Empty
3	Send combined query to retriever/LLM	Empty	None	What is AI?	AI is Artificial Intelligence...	Empty
4	Update memory with new info	Empty	None	What is AI?	AI is Artificial Intelligence...	Memory updated with Q&A
5	Output answer	Memory updated with Q&A	N/A	What is AI?	AI is Artificial Intelligence...	Memory updated with Q&A

💡 Process ends after outputting the answer and updating memory with the new conversation.

Variable Tracker

Variable	Start	After Step 1	After Step 4	Final
memory	Empty	Empty	Contains Q: 'What is AI?' and A: 'AI is Artificial Intelligence...'	Contains Q: 'What is AI?' and A: 'AI is Artificial Intelligence...'
query	N/A	'What is AI?'	'What is AI?'	'What is AI?'
response	N/A	N/A	'AI is Artificial Intelligence...'	'AI is Artificial Intelligence...'

Key Moments - 3 Insights

Why does the system check memory before sending the query to the retriever?

Checking memory first helps add context from past conversations, making the response more relevant. See execution_table rows 2 and 3 where memory retrieval happens before generating the response.

What happens if memory is empty at the start?

If memory is empty, the system sends the query alone to the retriever or LLM, as shown in execution_table row 3, and then updates memory after getting the response (row 4).

How does memory get updated after the response?

After generating the response, the system stores the question and answer pair in memory for future context, as shown in execution_table row 4 and variable_tracker for 'memory'.

Visual Quiz - 3 Questions

Look at the execution table, what is the memory state before the first retrieval?

A Contains previous Q&A

B Empty

C Contains only the query

D Contains the response

💡 Hint

Check execution_table row 2 under 'Memory State Before'

At which step does the system update the memory with new information?

A Step 4

B Step 3

C Step 2

D Step 5

💡 Hint

Look at execution_table row 4 under 'Action' and 'Memory State After'

If the memory already contained relevant context, how would the combined query change?

A It would be only the new query

B It would be empty

C It would include the query plus memory context

D It would be only the memory context

💡 Hint

Refer to concept_flow where query and memory context combine before sending to retriever

Concept Snapshot

Memory-augmented retrieval:
- Takes user query
- Retrieves relevant past memory
- Combines query + memory context
- Sends combined input to retriever or LLM
- Gets response
- Updates memory with new Q&A
- Outputs answer
This improves responses by using conversation history.

Full Transcript

Memory-augmented retrieval means the system remembers past conversations or information. When a user asks a question, the system first looks into its memory to find related context. It then combines this context with the new question and sends it to a retriever or language model to get a better answer. After getting the answer, it updates the memory with the new question and answer pair. This way, the system learns and improves over time by using past information to help answer new questions.