To pass this lesson, use the getHistory()
and saveHistory()
methods to save and retrieve the conversation history from the database.
-
Add a config object to the
chain.invoke()
call, passing{ configurable: { sessionId } }
-
Within
RunnablePassthrough.assign()
, use thesessionId
fromconfig.configurable?
to retrieve the message history via thegetHistory()
function. Assign this to thehistory
key -
Add a
MessagesPlaceholder
forhistory
to theChatMessagePromptTemplate
-
Save the input and response to the database using the
saveHistory()
function
Solution
typescript
import {
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder
} from "@langchain/core/prompts";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { RunnableSequence, RunnablePassthrough, RunnablePick } from "@langchain/core/runnables";
import { Neo4jVectorStore } from "@langchain/community/vectorstores/neo4j_vector";
import { getHistory, saveHistory } from "./history";
export async function call(
message: string,
sessionId: string
): Promise<string> {
const embeddings = new OpenAIEmbeddings({
openAIApiKey: process.env.OPEN_AI_API_KEY,
});
const store = await Neo4jVectorStore.fromExistingGraph(embeddings, {
url: process.env.NEO4J_URI,
username: process.env.NEO4J_USERNAME,
password: process.env.NEO4J_PASSWORD,
nodeLabel: "Talk",
textNodeProperties: ["title", "description"],
indexName: "talk_embeddings_openai",
embeddingNodeProperty: "embedding",
retrievalQuery: `
RETURN node.description AS text, score,
node {
.time, .title,
url: 'https://athens.cityjsconf.org/'+ node.url,
speaker: [
(node)-[:GIVEN_BY]->(s) |
s { .name, .company, .x_handle, .bio }
][0],
room: [ (node)-[:IN_ROOM]->(r) | r.name ][0],
tags: [ (node)-[:HAS_TAG]->(t) | t.name ]
} AS metadata
`,
});
const retriever = store.asRetriever();
// 1. create a prompt template
const prompt = ChatPromptTemplate.fromMessages([
SystemMessagePromptTemplate.fromTemplate(
`You are a helpful assistant helping users with queries
about the CityJS Athens conference.
Answer the user's question to the best of your ability.
If you do not know the answer, just say you don't know.
`
),
SystemMessagePromptTemplate.fromTemplate(
`
Here are some talks to help you answer the question.
Don't use your pre-trained knowledge to answer the question.
Always include a full link to the meetup.
If the answer isn't included in the documents, say you don't know.
Documents:
{documents}
`
),
new MessagesPlaceholder("history"),
HumanMessagePromptTemplate.fromTemplate(`Question: {message}`),
]);
// 2. choose an LLM
const llm = new ChatOpenAI({
openAIApiKey: process.env.OPENAI_API_KEY,
temperature: 0.9,
});
// 3. parse the response
const parser = new StringOutputParser();
// 4. runnable sequence (LCEL)
const chain = RunnableSequence.from<RunInput, string>([
RunnablePassthrough.assign({
history: (input, config) => getHistory(config?.configurable.sessionId, 3),
documents: new RunnablePick("input").pipe(
retriever.pipe((docs) => JSON.stringify(docs))
),
}),
prompt,
llm,
new StringOutputParser(),
]);
// 5. invoke the chain
const output = await chain.invoke(
{ message },
{
configurable: {
sessionId,
},
}
);
// 6. Save history
await saveHistory(sessionId, message, output);
return output;
}
Summary
Loving your work!