Per-User Retrieval
When building a retrieval app, you often have to build it with multiple users in mind. This means that you may be storing data not just for one user, but for many different users, and they should not be able to see eachother’s data. This means that you need to be able to configure your retrieval chain to only retrieve certain information. This generally involves two steps.
Step 1: Make sure the retriever you are using supports multiple users
At the moment, there is no unified flag or filter for this in LangChain.
Rather, each vectorstore and retriever may have their own, and may be
called different things (namespaces, multi-tenancy, etc). For
vectorstores, this is generally exposed as a keyword argument that is
passed in during similaritySearch
. By reading the documentation or
source code, figure out whether the retriever you are using supports
multiple users, and, if so, how to use it.
Note: adding documentation and/or support for multiple users for retrievers that do not support it (or document it) is a GREAT way to contribute to LangChain
Step 2: Add that parameter as a configurable field for the chain
The LangChain config
object is passed through to every Runnable. Here
you can add any fields you’d like to the configurable
object. Later,
inside the chain we can extract these fields.
Step 3: Call the chain with that configurable field
Now, at runtime you can call this chain with configurable field.
Code Example
Let’s see a concrete example of what this looks like in code. We will use Pinecone for this example.
Setup
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/pinecone @langchain/openai @pinecone-database/pinecone @langchain/core
yarn add @langchain/pinecone @langchain/openai @pinecone-database/pinecone @langchain/core
pnpm add @langchain/pinecone @langchain/openai @pinecone-database/pinecone @langchain/core
Set environment variables
We’ll use OpenAI and Pinecone in this example:
OPENAI_API_KEY=your-api-key
PINECONE_API_KEY=your-api-key
PINECONE_INDEX=your-index-name
# Optional, use LangSmith for best-in-class observability
LANGSMITH_API_KEY=your-api-key
LANGCHAIN_TRACING_V2=true
import { OpenAIEmbeddings } from "@langchain/openai";
import { PineconeStore } from "@langchain/pinecone";
import { Pinecone } from "@pinecone-database/pinecone";
import { Document } from "@langchain/core/documents";
const embeddings = new OpenAIEmbeddings();
const pinecone = new Pinecone();
const pineconeIndex = pinecone.Index(Deno.env.get("PINECONE_INDEX"));
const vectorStore = await PineconeStore.fromExistingIndex(
new OpenAIEmbeddings(),
{ pineconeIndex }
);
await vectorStore.addDocuments(
[new Document({ pageContent: "i worked at kensho" })],
{ namespace: "harrison" }
);
[ "39d90a6d-7e97-45cc-a9dc-ebefa47220fc" ]
await vectorStore.addDocuments(
[new Document({ pageContent: "i worked at facebook" })],
{ namespace: "ankush" }
);
[ "75f94962-9135-4385-b71c-36d8345e02aa" ]
The pinecone kwarg for namespace
can be used to separate documents
// This will only get documents for Ankush
await vectorStore
.asRetriever({
filter: {
namespace: "ankush",
},
})
.getRelevantDocuments("where did i work?");
[ Document { pageContent: "i worked at facebook", metadata: {} } ]
// This will only get documents for Harrison
await vectorStore
.asRetriever({
filter: {
namespace: "harrison",
},
})
.getRelevantDocuments("where did i work?");
[ Document { pageContent: "i worked at kensho", metadata: {} } ]
We can now create the chain that we will use to do question-answering over
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import {
RunnableBinding,
RunnableLambda,
RunnablePassthrough,
} from "@langchain/core/runnables";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
This is basic question-answering chain set up.
const template = `Answer the question based only on the following context:
{context}
Question: {question}`;
const prompt = ChatPromptTemplate.fromTemplate(template);
const model = new ChatOpenAI({
model: "gpt-3.5-turbo-0125",
temperature: 0,
});
const retriever = vectorStore.asRetriever();
We can now create the chain using our configurable retriever. It is configurable because we can define any object which will be passed to the chain. From there, we extract the configurable object and pass it to the vectorstore.
import { RunnableSequence } from "@langchain/core/runnables";
const chain = RunnableSequence.from([
{
context: async (input, config) => {
if (!config || !("configurable" in config)) {
throw new Error("No config");
}
const { configurable } = config;
return JSON.stringify(
await vectorStore.asRetriever(configurable).getRelevantDocuments(input)
);
},
question: new RunnablePassthrough(),
},
prompt,
model,
new StringOutputParser(),
]);
We can now invoke the chain with configurable options. search_kwargs
is the id of the configurable field. The value is the search kwargs to
use for Pinecone
await chain.invoke("where did the user work?", {
configurable: { filter: { namespace: "harrison" } },
});
"The user worked at Kensho."
await chain.invoke("where did the user work?", {
configurable: { filter: { namespace: "ankush" } },
});
"The user worked at Facebook."
For more vectorstore implementations for multi-user, please refer to specific pages, such as Milvus.