1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
import { getModelBuilder } from "https://esm.town/v/webup/getModelBuilder";
export async function getMemoryBuilder(spec: {
type: "buffer" | "summary" | "vector";
provider?: "openai";
} = { type: "buffer" }, options = {}) {
const { cond, matches } = await import("npm:lodash-es");
const setup = cond([
[
matches({ type: "buffer" }),
async () => {
const { BufferMemory } = await import("npm:langchain/memory");
return new BufferMemory();
},
],
[
matches({ type: "summary", provider: "openai" }),
async () => {
const { ConversationSummaryMemory } = await import(
"npm:langchain/memory"
);
const builder = await getModelBuilder();
const llm = await builder();
return new ConversationSummaryMemory({ llm, ...options });
},
],
[
matches({ type: "vector", provider: "openai" }),
async () => {
const { VectorStoreRetrieverMemory } = await import(
"npm:langchain/memory"
);
const { MemoryVectorStore } = await import(
"npm:langchain/vectorstores/memory"
);
const builder = await getModelBuilder({
type: "embedding",
provider: "openai",
});
const model = await builder();
const vectorStore = new MemoryVectorStore(model);
return new VectorStoreRetrieverMemory({
// 1 is how many documents to return, you might want to return more, eg. 4
vectorStoreRetriever: vectorStore.asRetriever(1),
...options,
});
},
],
]);
return () => setup(spec);
}