Merge pull request #43 from getzep/v2-integration

feat: Add support for v2 api

.eslintrc.json

@@ -42,10 +42,12 @@
       "max-classes-per-file": 0,
       "multiline-ternary": 0,
       "no-unused-vars": "off",
+      "no-underscore-dangle": "off",
       "no-shadow": 0,
       "@typescript-eslint/no-unused-vars": "error",
       "no-useless-constructor": "off",
       "@typescript-eslint/no-useless-constructor": "error",
+      "import/prefer-default-export": "off",
       "import/extensions": [
          "error",
          "ignorePackages",
@@ -61,7 +63,7 @@
          {
             "code": 100,
             "tabWidth": 3,
-            "comments": 100,
+            "comments": 150,
             "ignoreComments": false,
             "ignoreTrailingComments": true,
             "ignoreUrls": true,

.github/workflows/doc-build.yml

@@ -21,16 +21,16 @@ jobs:
         uses: actions/setup-node@v3
         with:
           node-version: 20
-          cache: 'npm'
+          cache: 'yarn'
 
       - name: Install Dependencies
-        run: npm ci
+        run: yarn install --frozen-lockfile
 
       - name: Build Project
-        run: npm run build
+        run: yarn build
 
       - name: Install TypeDoc
-        run: npm install typedoc typedoc-plugin-markdown
+        run: yarn add typedoc typedoc-plugin-markdown
 
       - name: Generate Documentation
         run: npx typedoc --out docs --excludePrivate --excludeInternal --excludeExternals src/

.github/workflows/node-build-test.yml

@@ -20,7 +20,7 @@ jobs:
       uses: actions/setup-node@v3
       with:
         node-version: ${{ matrix.node-version }}
-        cache: 'npm'
-    - run: npm ci
-    - run: npm run build --if-present
-    - run: npm test
+        cache: 'yarn'
+    - run: yarn install --frozen-lockfile
+    - run: yarn build
+    - run: yarn test

.github/workflows/npm-publish.yml

@@ -17,14 +17,14 @@ jobs:
         node-version: '18.x'
         registry-url: 'https://registry.npmjs.org'
     - name: Install dependencies
-      run: npm ci
-    - name: Publish package release
-      if: "!github.event.release.prerelease"
-      run: npm publish
-      env:
-        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
+      run: yarn install --frozen-lockfile
+#    - name: Publish package release
+#      if: "!github.event.release.prerelease"
+#      run: yarn publish
+#      env:
+#        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
     - name: Publish package pre-release
       if: "github.event.release.prerelease"
-      run: npm publish --tag next
+      run: yarn publish --tag next
       env:
         NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}

README.md

@@ -36,3 +36,31 @@ This is the TypeScript / JavaScript client package for the Zep service. For more
 npm install @getzep/zep-js
 ```
 
+## Zep cloud Installation
+In order to install zep js sdk with zep-cloud support, you will need to install
+a pre-release version tagged with `@next`.
+
+```bash
+npm install @getzep/zep-js@next
+```
+
+You will also need to provide a Zep Project API key to your zep client for cloud support.
+You can find out about zep projects in our [cloud docs](https://help.getzep.com/projects.html)
+
+### Using langchain zep classes with `zep-js@next`:
+In the pre-release version `zep-js` sdk comes with `ZepChatMessageHistory` and `ZepVectorStore`
+classes that are compatible with [`Langchain's JS expression language`](https://js.langchain.com/docs/expression_language/)
+
+In order to use these classes in your application, you need to make sure that you have 
+`@langchain/core` package installed:
+
+```bash
+npm install @langchain/core@^0.1.23
+```
+
+You can import these classes in the following way:
+
+```typescript
+import { ZepChatMessageHistory, ZepVectorStore } from "@getzep/zep-js/langchain"
+```
+

__tests__/setup/setupJest.js

@@ -1,2 +1,2 @@
-//setupJest.js or similar file
-require('jest-fetch-mock').enableMocks()
+// setupJest.js or similar file
+require("jest-fetch-mock").enableMocks();

examples/documents/index.ts

@@ -21,13 +21,13 @@ import { Document, IDocument, ISearchQuery, ZepClient } from "../../src";
  */
 async function checkEmbeddingStatus(
    client: ZepClient,
-   collectionName: string
+   collectionName: string,
 ): Promise<void> {
    let c = await client.document.getCollection(collectionName);
 
    while (c.status !== "ready") {
       console.log(
-         `Embedding status: ${c.document_embedded_count}/${c.document_count} documents embedded`
+         `Embedding status: ${c.document_embedded_count}/${c.document_count} documents embedded`,
       );
       // Wait for 1 second
       await new Promise((resolve) => setTimeout(resolve, 1000));
@@ -47,7 +47,7 @@ function readChunkFromFile(file: string, chunkSize: number): string[] {
    const text = fs.readFileSync(file, "utf8");
    const chunks = naiveSplitText(text, chunkSize);
    console.log(
-      `Splitting text into ${chunks.length} chunks of max size ${chunkSize} characters.`
+      `Splitting text into ${chunks.length} chunks of max size ${chunkSize} characters.`,
    );
    return chunks;
 }
@@ -61,7 +61,7 @@ function printResults(results: IDocument[]): void {
       console.log(
          `${result.content} - ${JSON.stringify(result.metadata)} -> ${
             result.score
-         }\n`
+         }\n`,
       );
    }
 }
@@ -117,20 +117,20 @@ function naiveSplitText(text: string, maxChunkSize: number): string[] {
  * This demonstrates how to use the ZepClient to interact with a Zep API.
  */
 async function main() {
+   const projectApiKey = process.env.ZEP_API_KEY;
+   const projectApiUrl = process.env.ZEP_API_URL;
+
    const file = "babbages_calculating_engine.txt";
-   const zepApiUrl = "http://localhost:8000";
    const maxChunkSize = 500;
    const collectionName = `babbage${faker.string.alphanumeric({ length: 8 })}`;
 
    console.log(`Creating collection ${collectionName}`);
 
-   const client = await ZepClient.init(zepApiUrl);
+   const client = await ZepClient.init(projectApiKey, projectApiUrl);
    const collection = await client.document.addCollection({
       name: collectionName,
-      embeddingDimensions: 1536, // this must match the embedding dimensions of your embedding model
       description: "Babbage's Calculating Engine", // optional
       metadata: { qux: faker.string.sample() }, // optional
-      isAutoEmbedded: true, // optional (default: true) - whether Zep should  automatically embed documents
    });
 
    console.log(`Created collection ${collectionName}`);
@@ -143,17 +143,17 @@ async function main() {
             content: chunk,
             document_id: faker.system.fileName(), // optional document ID used in your system
             metadata: { foo: faker.string.sample(), bar: "qux" }, // optional metadata
-         })
+         }),
    );
 
    console.log(
-      `Adding ${documents.length} documents to collection ${collectionName}`
+      `Adding ${documents.length} documents to collection ${collectionName}`,
    );
 
    const uuids = await collection.addDocuments(documents);
 
    console.log(
-      `Added ${uuids.length} documents to collection ${collectionName}`
+      `Added ${uuids.length} documents to collection ${collectionName}`,
    );
 
    await checkEmbeddingStatus(client, collectionName);
@@ -164,10 +164,10 @@ async function main() {
       {
          text: query,
       },
-      3
+      3,
    );
    console.log(
-      `Found ${searchResults.length} documents matching query '${query}'`
+      `Found ${searchResults.length} documents matching query '${query}'`,
    );
    printResults(searchResults);
 
@@ -180,14 +180,14 @@ async function main() {
 
    const mmrSearchResults = await collection.search(mmrSearchQuery, 3);
    console.log(
-      `Found ${mmrSearchResults.length} documents matching MMR query '${query}'`
+      `Found ${mmrSearchResults.length} documents matching MMR query '${query}'`,
    );
    printResults(mmrSearchResults);
 
    // Search by embedding
    const interestingDocument = searchResults[0];
    console.log(
-      `Searching for documents similar to:\n${interestingDocument.content}\n`
+      `Searching for documents similar to:\n${interestingDocument.content}\n`,
    );
    if (!interestingDocument.embedding) {
       throw new Error("No embedding found for document");
@@ -197,10 +197,10 @@ async function main() {
       {
          embedding: vectorToSearch,
       },
-      3
+      3,
    );
    console.log(
-      `Found ${embeddingSearchResults.length} documents matching embedding`
+      `Found ${embeddingSearchResults.length} documents matching embedding`,
    );
    printResults(embeddingSearchResults);
 
@@ -214,10 +214,10 @@ async function main() {
          text: query,
          metadata: metadataQuery,
       },
-      3
+      3,
    );
    console.log(
-      `Found ${newSearchResults.length} documents matching query '${query}' ${metadataQuery}`
+      `Found ${newSearchResults.length} documents matching query '${query}' ${metadataQuery}`,
    );
    printResults(newSearchResults);
 
@@ -228,7 +228,7 @@ async function main() {
             where: { jsonpath: '$[*] ? (@.this_key_does == "not exist")' },
          },
       },
-      3
+      3,
    );
    console.log("Returned array length:", embeddingSearchResults.length);
 
@@ -239,7 +239,7 @@ async function main() {
    }
    console.log(`Deleting document ${documentToDelete}`);
    await collection.deleteDocument(documentToDelete);
-
+   console.log(`Deleted document ${documentToDelete}`);
    // Get a list of documents in the collection by UUID
    const docsToGet = uuids.slice(48, 50);
    console.log(`Getting documents: ${docsToGet}`);

examples/langchain/message_history_example.ts

@@ -0,0 +1,59 @@
+import { ZepClient } from "../../src";
+import { ZepChatMessageHistory } from "../../src/langchain";
+import { ChatOpenAI } from "@langchain/openai";
+import {
+   ChatPromptTemplate,
+   MessagesPlaceholder,
+} from "@langchain/core/prompts";
+import { RunnableWithMessageHistory } from "@langchain/core/runnables";
+import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
+async function main() {
+   const zepClient = await ZepClient.init(
+      process.env.ZEP_API_KEY,
+      process.env.ZEP_API_URL,
+   );
+
+   const prompt = ChatPromptTemplate.fromMessages([
+      ["system", "Answer the user's question below. Be polite and helpful:"],
+      new MessagesPlaceholder("history"),
+      ["human", "{question}"],
+   ]);
+
+   const chain = prompt
+      .pipe(
+         new ChatOpenAI({
+            temperature: 0.8,
+            modelName: "gpt-3.5-turbo-1106",
+         }),
+      )
+      .withConfig({
+         callbacks: [new ConsoleCallbackHandler()],
+      });
+
+   const chainWithHistory = new RunnableWithMessageHistory({
+      runnable: chain,
+      getMessageHistory: (sessionId) =>
+         new ZepChatMessageHistory({
+            client: zepClient,
+            sessionId: sessionId,
+            memoryType: "perpetual",
+         }),
+      inputMessagesKey: "question",
+      historyMessagesKey: "history",
+   });
+
+   const result = await chainWithHistory.invoke(
+      {
+         question: "What did we talk about earlier?",
+      },
+      {
+         configurable: {
+            sessionId: "06d114e2-7c6e-4268-91b9-63a4b9645bbf",
+         },
+      },
+   );
+
+   console.log("result", result);
+}
+
+main();