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Using DynamoDB with LangChain - Amazon DynamoDB
Chat message historyVector store backed by vector indexesAdditional resources

Using DynamoDB with LangChain

LangChain is a framework for building applications with large language models (LLMs). Two LangChain integrations use DynamoDB: the DynamoDBChatMessageHistory class stores conversation history so a model can converse back and forth with a user across sessions, and the DynamoDBVectorStore class implements the LangChain vector store interface on DynamoDB vector indexes for similarity search.

Chat message history

The DynamoDBChatMessageHistory class, in the langchain-community package for Python and the @langchain/community package for JavaScript, persists chat messages in a DynamoDB table. The class expects an existing table whose partition key is a string attribute named SessionId (configurable through primary_key_name). Create the table with the AWS CLI:

aws dynamodb create-table \
  --table-name SessionTable \
  --attribute-definitions AttributeName=SessionId,AttributeType=S \
  --key-schema AttributeName=SessionId,KeyType=HASH \
  --billing-mode PAY_PER_REQUEST

Install the packages, then read and write history keyed by a session ID:

pip install langchain-community boto3
from langchain_community.chat_message_histories import DynamoDBChatMessageHistory

history = DynamoDBChatMessageHistory(
    table_name="SessionTable",
    session_id="user-42",
)

history.add_user_message("Hello!")
history.add_ai_message("How can I help you today?")

print(history.messages)

Each chat session's messages are stored under its session_id, so a returning user picks up the conversation where they left off. The constructor also supports composite keys for isolating history by application details such as a user ID (key), Time to Live-based expiry of old sessions (ttl), and a cap on stored messages (history_size). For the full API, see the Python reference and the JavaScript reference.

Vector store backed by vector indexes

The DynamoDBVectorStore class, in the langchain-aws package, implements the LangChain vector store interface using DynamoDB vector indexes (see Using vector indexes in DynamoDB). Documents are stored as regular DynamoDB items and searched through the table's vector index with the SearchVectors API, so LangChain retrievers and RAG chains run similarity searches directly against the table that holds your data.

Install the package:

pip install langchain-aws boto3

Provide a table name and an embedding function. The table and vector index are created on first write if they don't exist:

from langchain_aws.embeddings import BedrockEmbeddings
from langchain_aws.vectorstores.dynamodb import DynamoDBVectorStore

vector_store = DynamoDBVectorStore.from_texts(
    ["hello", "developer", "wife"],
    embedding=BedrockEmbeddings(model_id="amazon.titan-embed-text-v2:0"),
    table_name="my-documents",
)

docs = vector_store.similarity_search("greeting", k=2)

To scope searches, pass partition_attribute when you construct the store. The vector index is then created with a search schema partition key on that document metadata field, and each search examines only one value of it, such as a collection, category, or tenant, rather than the whole corpus. Every search then supplies the value through filter={partition_attribute: value} or a store-level default_partition_value. The partition key is part of the index schema and cannot be changed after the index is created.

Keep the following in mind:

  • Like a global secondary index, the vector index is eventually consistent: a search issued immediately after add_texts may not include the just-written documents until the index catches up.

  • SearchVectors returns at most the top 100 matches per query, so the store's k parameter is capped at 100.

  • The index's dimensions, distance function, and search schema are fixed at creation. The store validates them against an existing index rather than proceeding with a mismatch.

For the full API, see DynamoDBVectorStore in the langchain-aws repository.

Additional resources