Merge branch 'master' of github.com:Mintplex-Labs/anything-llm

.vscode/settings.json

@@ -1,5 +1,6 @@
 {
   "cSpell.words": [
-    "openai"
+    "openai",
+    "Weaviate"
   ]
 }

docker/.env.example

@@ -32,6 +32,11 @@ PINECONE_INDEX=
 # Enable all below if you are using vector database: LanceDB.
 # VECTOR_DB="lancedb"
 
+# Enable all below if you are using vector database: Weaviate.
+# VECTOR_DB="weaviate"
+# WEAVIATE_ENDPOINT="http://localhost:8080"
+# WEAVIATE_API_KEY=
+
 # CLOUD DEPLOYMENT VARIRABLES ONLY
 # AUTH_TOKEN="hunter2" # This is the password to your application if remote hosting.
 # NO_DEBUG="true"

docker/Dockerfile

@@ -63,13 +63,13 @@ RUN cd ./frontend/ && yarn build && yarn cache clean
 
 # Setup the server
 FROM server-deps as production-stage
-COPY ./server/ ./server/
+COPY --chown=anythingllm:anythingllm ./server/ ./server/
 
 # Copy built static frontend files to the server public directory
 COPY --from=build-stage /app/frontend/dist ./server/public
 
 # Copy the collector
-COPY ./collector/ ./collector/
+COPY --chown=anythingllm:anythingllm ./collector/ ./collector/
 
 # Install collector dependencies
 RUN cd /app/collector && \

frontend/src/components/Modals/Settings/ExportImport/index.jsx

@@ -7,7 +7,7 @@ import paths from "../../../../utils/paths";
 const noop = () => false;
 export default function ExportOrImportData({ hideModal = noop }) {
   return (
-    <div className="relative w-full max-w-2xl max-h-full">
+    <div className="relative w-full w-full max-h-full">
       <div className="relative bg-white rounded-lg shadow dark:bg-stone-700">
         <div className="flex flex-col items-start justify-between px-6 py-4">
           <p className="text-gray-800 dark:text-stone-200 text-base ">

frontend/src/components/Modals/Settings/LLMSelection/index.jsx

@@ -37,7 +37,7 @@ export default function LLMSelection({
     setHasChanges(!!error ? true : false);
   };
   return (
-    <div className="relative w-full max-w-2xl max-h-full">
+    <div className="relative w-full w-full max-h-full">
       <div className="relative bg-white rounded-lg shadow dark:bg-stone-700">
         <div className="flex items-start justify-between px-6 py-4">
           <p className="text-gray-800 dark:text-stone-200 text-base ">
@@ -59,7 +59,7 @@ export default function LLMSelection({
               <p className="block text-sm font-medium text-gray-800 dark:text-slate-200">
                 LLM providers
               </p>
-              <div className="w-full flex overflow-x-scroll gap-x-4 no-scroll">
+              <div className="w-full flex overflow-x-scroll gap-x-4">
                 <input hidden={true} name="LLMProvider" value={llmChoice} />
                 <LLMProviderOption
                   name="OpenAI"

frontend/src/components/Modals/Settings/MultiUserMode/index.jsx

@@ -39,7 +39,7 @@ export default function MultiUserMode({ hideModal = noop }) {
   };
 
   return (
-    <div className="relative w-full max-w-2xl max-h-full">
+    <div className="relative w-full w-full max-h-full">
       <div className="relative bg-white rounded-lg shadow dark:bg-stone-700">
         <div className="flex items-start justify-between px-6 py-4">
           <p className="text-gray-800 dark:text-stone-200 text-base ">

frontend/src/components/Modals/Settings/PasswordProtection/index.jsx

@@ -41,7 +41,7 @@ export default function PasswordProtection({
   };
 
   return (
-    <div className="relative w-full max-w-2xl max-h-full">
+    <div className="relative w-full w-full max-h-full">
       <div className="relative bg-white rounded-lg shadow dark:bg-stone-700">
         <div className="flex items-start justify-between px-6 py-4">
           <p className="text-gray-800 dark:text-stone-200 text-base ">

frontend/src/components/Modals/Settings/VectorDbs/index.jsx

@@ -3,6 +3,7 @@ import System from "../../../../models/system";
 import ChromaLogo from "../../../../media/vectordbs/chroma.png";
 import PineconeLogo from "../../../../media/vectordbs/pinecone.png";
 import LanceDbLogo from "../../../../media/vectordbs/lancedb.png";
+import WeaviateLogo from "../../../../media/vectordbs/weaviate.png";
 
 const noop = () => false;
 export default function VectorDBSelection({
@@ -37,7 +38,7 @@ export default function VectorDBSelection({
     setHasChanges(!!error ? true : false);
   };
   return (
-    <div className="relative w-full max-w-2xl max-h-full">
+    <div className="relative w-full w-full max-h-full">
       <div className="relative bg-white rounded-lg shadow dark:bg-stone-700">
         <div className="flex items-start justify-between px-6 py-4">
           <p className="text-gray-800 dark:text-stone-200 text-base ">
@@ -59,7 +60,7 @@ export default function VectorDBSelection({
               <p className="block text-sm font-medium text-gray-800 dark:text-slate-200">
                 Vector database providers
               </p>
-              <div className="w-full flex overflow-x-scroll gap-x-4 no-scroll">
+              <div className="w-full flex overflow-x-scroll gap-x-4">
                 <input hidden={true} name="VectorDB" value={vectorDB} />
                 <VectorDBOption
                   name="Chroma"
@@ -79,6 +80,15 @@ export default function VectorDBSelection({
                   image={PineconeLogo}
                   onClick={updateVectorChoice}
                 />
+                <VectorDBOption
+                  name="Weaviate"
+                  value="weaviate"
+                  link="weaviate.io"
+                  description="Open source local and cloud hosted multi-modal vector database."
+                  checked={vectorDB === "weaviate"}
+                  image={WeaviateLogo}
+                  onClick={updateVectorChoice}
+                />
                 <VectorDBOption
                   name="LanceDB"
                   value="lancedb"
@@ -171,6 +181,41 @@ export default function VectorDBSelection({
                   </p>
                 </div>
               )}
+              {vectorDB === "weaviate" && (
+                <>
+                  <div>
+                    <label className="block mb-2 text-sm font-medium text-gray-800 dark:text-slate-200">
+                      Weaviate Endpoint
+                    </label>
+                    <input
+                      type="url"
+                      name="WeaviateEndpoint"
+                      disabled={!canDebug}
+                      className="bg-gray-50 border border-gray-500 text-gray-900 placeholder-gray-500 text-sm rounded-lg dark:bg-stone-700 focus:border-stone-500 block w-full p-2.5 dark:text-slate-200 dark:placeholder-stone-500 dark:border-slate-200"
+                      placeholder="http://localhost:8080"
+                      defaultValue={settings?.WeaviateEndpoint}
+                      required={true}
+                      autoComplete="off"
+                      spellCheck={false}
+                    />
+                  </div>
+                  <div>
+                    <label className="block mb-2 text-sm font-medium text-gray-800 dark:text-slate-200">
+                      Api Key
+                    </label>
+                    <input
+                      type="password"
+                      name="WeaviateApiKey"
+                      disabled={!canDebug}
+                      className="bg-gray-50 border border-gray-500 text-gray-900 placeholder-gray-500 text-sm rounded-lg dark:bg-stone-700 focus:border-stone-500 block w-full p-2.5 dark:text-slate-200 dark:placeholder-stone-500 dark:border-slate-200"
+                      placeholder="sk-123Abcweaviate"
+                      defaultValue={settings?.WeaviateApiKey}
+                      autoComplete="off"
+                      spellCheck={false}
+                    />
+                  </div>
+                </>
+              )}
             </div>
           </div>
           <div className="w-full p-4">

frontend/src/components/Modals/Settings/index.jsx

@@ -46,7 +46,7 @@ export default function SystemSettingsModal({ hideModal = noop }) {
         className="flex fixed top-0 left-0 right-0 w-full h-full"
         onClick={hideModal}
       />
-      <div className="relative w-full max-w-2xl max-h-full">
+      <div className="relative w-full w-full md:w-1/2 max-h-full">
         <div className="relative bg-white rounded-lg shadow dark:bg-stone-700">
           <div className="flex flex-col gap-y-1 border-b dark:border-gray-600 px-4 pt-4 ">
             <div className="flex items-start justify-between rounded-t ">

frontend/src/components/WorkspaceChat/ChatContainer/ChatHistory/Citation/index.jsx

@@ -22,7 +22,7 @@ export default function Citations({ sources = [] }) {
 
   return (
     <div className="flex flex-col mt-4 justify-left">
-      <div className="no-scroll flex flex-col justify-left overflow-x-scroll ">
+      <div className="flex flex-col justify-left overflow-x-scroll ">
         <div className="w-full flex overflow-x-scroll items-center gap-4 mt-1 doc__source">
           {combineLikeSources(sources).map((source) => (
             <Citation id={source?.id || v4()} source={source} />

server/.env.example

@@ -31,6 +31,12 @@ PINECONE_INDEX=
 # Enable all below if you are using vector database: LanceDB.
 # VECTOR_DB="lancedb"
 
+# Enable all below if you are using vector database: Weaviate.
+# VECTOR_DB="weaviate"
+# WEAVIATE_ENDPOINT="http://localhost:8080"
+# WEAVIATE_API_KEY=
+
+
 # CLOUD DEPLOYMENT VARIRABLES ONLY
 # AUTH_TOKEN="hunter2" # This is the password to your application if remote hosting.
 # STORAGE_DIR= # absolute filesystem path with no trailing slash

server/endpoints/system.js

@@ -60,6 +60,12 @@ function systemEndpoints(app) {
               ChromaEndpoint: process.env.CHROMA_ENDPOINT,
             }
           : {}),
+        ...(vectorDB === "weaviate"
+          ? {
+              WeaviateEndpoint: process.env.WEAVIATE_ENDPOINT,
+              WeaviateApiKey: process.env.WEAVIATE_API_KEY,
+            }
+          : {}),
         LLMProvider: llmProvider,
         ...(llmProvider === "openai"
           ? {

server/package.json

@@ -27,6 +27,7 @@
     "dotenv": "^16.0.3",
     "express": "^4.18.2",
     "extract-zip": "^2.0.1",
+    "graphql": "^16.7.1",
     "jsonwebtoken": "^8.5.1",
     "langchain": "^0.0.90",
     "moment": "^2.29.4",
@@ -39,7 +40,8 @@
     "sqlite3": "^5.1.6",
     "uuid": "^9.0.0",
     "uuid-apikey": "^1.5.3",
-    "vectordb": "0.1.12"
+    "vectordb": "0.1.12",
+    "weaviate-ts-client": "^1.4.0"
   },
   "devDependencies": {
     "nodemon": "^2.0.22",

server/utils/helpers/camelcase.js

@@ -0,0 +1,143 @@
+const UPPERCASE = /[\p{Lu}]/u;
+const LOWERCASE = /[\p{Ll}]/u;
+const LEADING_CAPITAL = /^[\p{Lu}](?![\p{Lu}])/gu;
+const IDENTIFIER = /([\p{Alpha}\p{N}_]|$)/u;
+const SEPARATORS = /[_.\- ]+/;
+
+const LEADING_SEPARATORS = new RegExp("^" + SEPARATORS.source);
+const SEPARATORS_AND_IDENTIFIER = new RegExp(
+  SEPARATORS.source + IDENTIFIER.source,
+  "gu"
+);
+const NUMBERS_AND_IDENTIFIER = new RegExp("\\d+" + IDENTIFIER.source, "gu");
+
+const preserveCamelCase = (
+  string,
+  toLowerCase,
+  toUpperCase,
+  preserveConsecutiveUppercase
+) => {
+  let isLastCharLower = false;
+  let isLastCharUpper = false;
+  let isLastLastCharUpper = false;
+  let isLastLastCharPreserved = false;
+
+  for (let index = 0; index < string.length; index++) {
+    const character = string[index];
+    isLastLastCharPreserved = index > 2 ? string[index - 3] === "-" : true;
+
+    if (isLastCharLower && UPPERCASE.test(character)) {
+      string = string.slice(0, index) + "-" + string.slice(index);
+      isLastCharLower = false;
+      isLastLastCharUpper = isLastCharUpper;
+      isLastCharUpper = true;
+      index++;
+    } else if (
+      isLastCharUpper &&
+      isLastLastCharUpper &&
+      LOWERCASE.test(character) &&
+      (!isLastLastCharPreserved || preserveConsecutiveUppercase)
+    ) {
+      string = string.slice(0, index - 1) + "-" + string.slice(index - 1);
+      isLastLastCharUpper = isLastCharUpper;
+      isLastCharUpper = false;
+      isLastCharLower = true;
+    } else {
+      isLastCharLower =
+        toLowerCase(character) === character &&
+        toUpperCase(character) !== character;
+      isLastLastCharUpper = isLastCharUpper;
+      isLastCharUpper =
+        toUpperCase(character) === character &&
+        toLowerCase(character) !== character;
+    }
+  }
+
+  return string;
+};
+
+const preserveConsecutiveUppercase = (input, toLowerCase) => {
+  LEADING_CAPITAL.lastIndex = 0;
+
+  return input.replace(LEADING_CAPITAL, (m1) => toLowerCase(m1));
+};
+
+const postProcess = (input, toUpperCase) => {
+  SEPARATORS_AND_IDENTIFIER.lastIndex = 0;
+  NUMBERS_AND_IDENTIFIER.lastIndex = 0;
+
+  return input
+    .replace(SEPARATORS_AND_IDENTIFIER, (_, identifier) =>
+      toUpperCase(identifier)
+    )
+    .replace(NUMBERS_AND_IDENTIFIER, (m) => toUpperCase(m));
+};
+
+function camelCase(input, options) {
+  if (!(typeof input === "string" || Array.isArray(input))) {
+    throw new TypeError("Expected the input to be `string | string[]`");
+  }
+
+  options = {
+    pascalCase: true,
+    preserveConsecutiveUppercase: false,
+    ...options,
+  };
+
+  if (Array.isArray(input)) {
+    input = input
+      .map((x) => x.trim())
+      .filter((x) => x.length)
+      .join("-");
+  } else {
+    input = input.trim();
+  }
+
+  if (input.length === 0) {
+    return "";
+  }
+
+  const toLowerCase =
+    options.locale === false
+      ? (string) => string.toLowerCase()
+      : (string) => string.toLocaleLowerCase(options.locale);
+
+  const toUpperCase =
+    options.locale === false
+      ? (string) => string.toUpperCase()
+      : (string) => string.toLocaleUpperCase(options.locale);
+
+  if (input.length === 1) {
+    if (SEPARATORS.test(input)) {
+      return "";
+    }
+
+    return options.pascalCase ? toUpperCase(input) : toLowerCase(input);
+  }
+
+  const hasUpperCase = input !== toLowerCase(input);
+
+  if (hasUpperCase) {
+    input = preserveCamelCase(
+      input,
+      toLowerCase,
+      toUpperCase,
+      options.preserveConsecutiveUppercase
+    );
+  }
+
+  input = input.replace(LEADING_SEPARATORS, "");
+  input = options.preserveConsecutiveUppercase
+    ? preserveConsecutiveUppercase(input, toLowerCase)
+    : toLowerCase(input);
+
+  if (options.pascalCase) {
+    input = toUpperCase(input.charAt(0)) + input.slice(1);
+  }
+
+  return postProcess(input, toUpperCase);
+}
+
+module.exports = {
+  camelCase,
+};

server/utils/helpers/index.js

@@ -10,6 +10,9 @@ function getVectorDbClass() {
     case "lancedb":
       const { LanceDb } = require("../vectorDbProviders/lance");
       return LanceDb;
+    case "weaviate":
+      const { Weaviate } = require("../vectorDbProviders/weaviate");
+      return Weaviate;
     default:
       throw new Error("ENV: No VECTOR_DB value found in environment!");
   }

server/utils/helpers/updateENV.js

@@ -39,6 +39,15 @@ const KEY_MAPPING = {
     envKey: "CHROMA_ENDPOINT",
     checks: [isValidURL, validChromaURL],
   },
+  WeaviateEndpoint: {
+    envKey: "WEAVIATE_ENDPOINT",
+    checks: [isValidURL],
+  },
+  WeaviateApiKey: {
+    envKey: "WEAVIATE_API_KEY",
+    checks: [],
+  },
+
   PineConeEnvironment: {
     envKey: "PINECONE_ENVIRONMENT",
     checks: [],
@@ -103,7 +112,7 @@ function validOpenAIModel(input = "") {
 }
 
 function supportedVectorDB(input = "") {
-  const supported = ["chroma", "pinecone", "lancedb"];
+  const supported = ["chroma", "pinecone", "lancedb", "weaviate"];
   return supported.includes(input)
     ? null
     : `Invalid VectorDB type. Must be one of ${supported.join(", ")}.`;

server/utils/vectorDbProviders/weaviate/WEAVIATE_SETUP.md

@@ -0,0 +1,17 @@
+# How to setup a local (or cloud) Weaviate Vector Database
+
+[Get a Weaviate Cloud instance](https://weaviate.io/developers/weaviate/quickstart#create-an-instance).
+[Set up Weaviate locally on Docker](https://weaviate.io/developers/weaviate/installation/docker-compose).
+
+Fill out the variables in the "Vector Database" tab of settings. Select Weaviate as your provider and fill out the appropriate fields
+with the information from either of the above steps.
+
+### How to get started _Development mode only_
+
+After setting up either the Weaviate cloud or local dockerized instance you just need to set these variable in `.env.development` or defined them at runtime via the UI.
+
+```
+VECTOR_DB="weaviate"
+WEAVIATE_ENDPOINT='http://localhost:8080'
+WEAVIATE_API_KEY= # Optional
+```

server/utils/vectorDbProviders/weaviate/index.js

@@ -0,0 +1,503 @@
+const { default: weaviate } = require("weaviate-ts-client");
+const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
+const { storeVectorResult, cachedVectorInformation } = require("../../files");
+const { v4: uuidv4 } = require("uuid");
+const { toChunks, getLLMProvider } = require("../../helpers");
+const { chatPrompt } = require("../../chats");
+const { camelCase } = require("../../helpers/camelcase");
+
+const Weaviate = {
+  name: "Weaviate",
+  connect: async function () {
+    if (process.env.VECTOR_DB !== "weaviate")
+      throw new Error("Weaviate::Invalid ENV settings");
+
+    const weaviateUrl = new URL(process.env.WEAVIATE_ENDPOINT);
+    const options = {
+      scheme: weaviateUrl.protocol?.replace(":", "") || "http",
+      host: weaviateUrl?.host,
+      ...(process.env?.WEAVIATE_API_KEY?.length > 0
+        ? { apiKey: new weaviate.ApiKey(process.env?.WEAVIATE_API_KEY) }
+        : {}),
+    };
+    const client = weaviate.client(options);
+    const isAlive = await await client.misc.liveChecker().do();
+    if (!isAlive)
+      throw new Error(
+        "Weaviate::Invalid Alive signal received - is the service online?"
+      );
+    return { client };
+  },
+  heartbeat: async function () {
+    await this.connect();
+    return { heartbeat: Number(new Date()) };
+  },
+  totalIndicies: async function () {
+    const { client } = await this.connect();
+    const collectionNames = await this.allNamespaces(client);
+    var totalVectors = 0;
+    for (const name of collectionNames) {
+      totalVectors += await this.namespaceCountWithClient(client, name);
+    }
+    return totalVectors;
+  },
+  namespaceCountWithClient: async function (client, namespace) {
+    try {
+      const response = await client.graphql
+        .aggregate()
+        .withClassName(camelCase(namespace))
+        .withFields("meta { count }")
+        .do();
+      return (
+        response?.data?.Aggregate?.[camelCase(namespace)]?.[0]?.meta?.count || 0
+      );
+    } catch (e) {
+      console.error(`Weaviate:namespaceCountWithClient`, e.message);
+      return 0;
+    }
+  },
+  namespaceCount: async function (namespace = null) {
+    try {
+      const { client } = await this.connect();
+      const response = await client.graphql
+        .aggregate()
+        .withClassName(camelCase(namespace))
+        .withFields("meta { count }")
+        .do();
+
+      return (
+        response?.data?.Aggregate?.[camelCase(namespace)]?.[0]?.meta?.count || 0
+      );
+    } catch (e) {
+      console.error(`Weaviate:namespaceCountWithClient`, e.message);
+      return 0;
+    }
+  },
+  similarityResponse: async function (client, namespace, queryVector) {
+    const result = {
+      contextTexts: [],
+      sourceDocuments: [],
+    };
+
+    const weaviateClass = await this.namespace(client, namespace);
+    const fields = weaviateClass.properties.map((prop) => prop.name).join(" ");
+    const queryResponse = await client.graphql
+      .get()
+      .withClassName(camelCase(namespace))
+      .withFields(`${fields} _additional { id }`)
+      .withNearVector({ vector: queryVector })
+      .withLimit(4)
+      .do();
+
+    const responses = queryResponse?.data?.Get?.[camelCase(namespace)];
+    responses.forEach((response) => {
+      // In Weaviate we have to pluck id from _additional and spread it into the rest
+      // of the properties.
+      const {
+        _additional: { id },
+        ...rest
+      } = response;
+      result.contextTexts.push(rest.text);
+      result.sourceDocuments.push({ ...rest, id });
+    });
+
+    return result;
+  },
+  allNamespaces: async function (client) {
+    try {
+      const { classes = [] } = await client.schema.getter().do();
+      return classes.map((classObj) => classObj.class);
+    } catch (e) {
+      console.error("Weaviate::AllNamespace", e);
+      return [];
+    }
+  },
+  namespace: async function (client, namespace = null) {
+    if (!namespace) throw new Error("No namespace value provided.");
+    if (!(await this.namespaceExists(client, namespace))) return null;
+
+    const weaviateClass = await client.schema
+      .classGetter()
+      .withClassName(camelCase(namespace))
+      .do();
+
+    return {
+      ...weaviateClass,
+      vectorCount: await this.namespaceCount(namespace),
+    };
+  },
+  addVectors: async function (client, vectors = []) {
+    const response = { success: true, errors: new Set([]) };
+    const results = await client.batch
+      .objectsBatcher()
+      .withObjects(...vectors)
+      .do();
+
+    results.forEach((res) => {
+      const { status, errors = [] } = res.result;
+      if (status === "SUCCESS" || errors.length === 0) return;
+      response.success = false;
+      response.errors.add(errors.error?.[0]?.message || null);
+    });
+
+    response.errors = [...response.errors];
+    return response;
+  },
+  hasNamespace: async function (namespace = null) {
+    if (!namespace) return false;
+    const { client } = await this.connect();
+    const weaviateClasses = await this.allNamespaces(client);
+    return weaviateClasses.includes(camelCase(namespace));
+  },
+  namespaceExists: async function (client, namespace = null) {
+    if (!namespace) throw new Error("No namespace value provided.");
+    const weaviateClasses = await this.allNamespaces(client);
+    return weaviateClasses.includes(camelCase(namespace));
+  },
+  deleteVectorsInNamespace: async function (client, namespace = null) {
+    await client.schema.classDeleter().withClassName(camelCase(namespace)).do();
+    return true;
+  },
+  addDocumentToNamespace: async function (
+    namespace,
+    documentData = {},
+    fullFilePath = null
+  ) {
+    const { DocumentVectors } = require("../../../models/vectors");
+    try {
+      const {
+        pageContent,
+        docId,
+        id: _id, // Weaviate will abort if `id` is present in properties
+        ...metadata
+      } = documentData;
+      if (!pageContent || pageContent.length == 0) return false;
+
+      console.log("Adding new vectorized document into namespace", namespace);
+      const cacheResult = await cachedVectorInformation(fullFilePath);
+      if (cacheResult.exists) {
+        const { client } = await this.connect();
+        const weaviateClassExits = await this.hasNamespace(namespace);
+        if (!weaviateClassExits) {
+          await client.schema
+            .classCreator()
+            .withClass({
+              class: camelCase(namespace),
+              description: `Class created by AnythingLLM named ${camelCase(
+                namespace
+              )}`,
+              vectorizer: "none",
+            })
+            .do();
+        }
+
+        const { chunks } = cacheResult;
+        const documentVectors = [];
+        const vectors = [];
+
+        for (const chunk of chunks) {
+          // Before sending to Weaviate and saving the records to our db
+          // we need to assign the id of each chunk that is stored in the cached file.
+          chunk.forEach((chunk) => {
+            const id = uuidv4();
+            const flattenedMetadata = this.flattenObjectForWeaviate(
+              chunk.properties
+            );
+            documentVectors.push({ docId, vectorId: id });
+            const vectorRecord = {
+              id,
+              class: camelCase(namespace),
+              vector: chunk.vector || chunk.values || [],
+              properties: { ...flattenedMetadata },
+            };
+            vectors.push(vectorRecord);
+          });
+
+          const { success: additionResult, errors = [] } =
+            await this.addVectors(client, vectors);
+          if (!additionResult) {
+            console.error("Weaviate::addVectors failed to insert", errors);
+            throw new Error("Error embedding into Weaviate");
+          }
+        }
+
+        await DocumentVectors.bulkInsert(documentVectors);
+        return true;
+      }
+
+      // If we are here then we are going to embed and store a novel document.
+      // We have to do this manually as opposed to using LangChains `Chroma.fromDocuments`
+      // because we then cannot atomically control our namespace to granularly find/remove documents
+      // from vectordb.
+      const textSplitter = new RecursiveCharacterTextSplitter({
+        chunkSize: 1000,
+        chunkOverlap: 20,
+      });
+      const textChunks = await textSplitter.splitText(pageContent);
+
+      console.log("Chunks created from document:", textChunks.length);
+      const LLMConnector = getLLMProvider();
+      const documentVectors = [];
+      const vectors = [];
+      const vectorValues = await LLMConnector.embedChunks(textChunks);
+      const submission = {
+        ids: [],
+        vectors: [],
+        properties: [],
+      };
+
+      if (!!vectorValues && vectorValues.length > 0) {
+        for (const [i, vector] of vectorValues.entries()) {
+          const flattenedMetadata = this.flattenObjectForWeaviate(metadata);
+          const vectorRecord = {
+            class: camelCase(namespace),
+            id: uuidv4(),
+            vector: vector,
+            // [DO NOT REMOVE]
+            // LangChain will be unable to find your text if you embed manually and dont include the `text` key.
+            // https://github.com/hwchase17/langchainjs/blob/5485c4af50c063e257ad54f4393fa79e0aff6462/langchain/src/vectorstores/weaviate.ts#L133
+            properties: { ...flattenedMetadata, text: textChunks[i] },
+          };
+
+          submission.ids.push(vectorRecord.id);
+          submission.vectors.push(vectorRecord.values);
+          submission.properties.push(metadata);
+
+          vectors.push(vectorRecord);
+          documentVectors.push({ docId, vectorId: vectorRecord.id });
+        }
+      } else {
+        console.error(
+          "Could not use OpenAI to embed document chunks! This document will not be recorded."
+        );
+      }
+
+      const { client } = await this.connect();
+      const weaviateClassExits = await this.hasNamespace(namespace);
+      if (!weaviateClassExits) {
+        await client.schema
+          .classCreator()
+          .withClass({
+            class: camelCase(namespace),
+            description: `Class created by AnythingLLM named ${camelCase(
+              namespace
+            )}`,
+            vectorizer: "none",
+          })
+          .do();
+      }
+
+      if (vectors.length > 0) {
+        const chunks = [];
+        for (const chunk of toChunks(vectors, 500)) chunks.push(chunk);
+
+        console.log("Inserting vectorized chunks into Weaviate collection.");
+        const { success: additionResult, errors = [] } = await this.addVectors(
+          client,
+          vectors
+        );
+        if (!additionResult) {
+          console.error("Weaviate::addVectors failed to insert", errors);
+          throw new Error("Error embedding into Weaviate");
+        }
+        await storeVectorResult(chunks, fullFilePath);
+      }
+
+      await DocumentVectors.bulkInsert(documentVectors);
+      return true;
+    } catch (e) {
+      console.error(e);
+      console.error("addDocumentToNamespace", e.message);
+      return false;
+    }
+  },
+  deleteDocumentFromNamespace: async function (namespace, docId) {
+    const { DocumentVectors } = require("../../../models/vectors");
+    const { client } = await this.connect();
+    if (!(await this.namespaceExists(client, namespace))) return;
+
+    const knownDocuments = await DocumentVectors.where(`docId = '${docId}'`);
+    if (knownDocuments.length === 0) return;
+
+    for (const doc of knownDocuments) {
+      await client.data
+        .deleter()
+        .withClassName(camelCase(namespace))
+        .withId(doc.vectorId)
+        .do();
+    }
+
+    const indexes = knownDocuments.map((doc) => doc.id);
+    await DocumentVectors.deleteIds(indexes);
+    return true;
+  },
+  query: async function (reqBody = {}) {
+    const { namespace = null, input, workspace = {} } = reqBody;
+    if (!namespace || !input) throw new Error("Invalid request body");
+
+    const { client } = await this.connect();
+    if (!(await this.namespaceExists(client, namespace))) {
+      return {
+        response: null,
+        sources: [],
+        message: "Invalid query - no documents found for workspace!",
+      };
+    }
+
+    const LLMConnector = getLLMProvider();
+    const queryVector = await LLMConnector.embedTextInput(input);
+    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+      client,
+      namespace,
+      queryVector
+    );
+
+    const prompt = {
+      role: "system",
+      content: `${chatPrompt(workspace)}
+    Context:
+    ${contextTexts
+          .map((text, i) => {
+            return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
+          })
+          .join("")}`,
+    };
+    const memory = [prompt, { role: "user", content: input }];
+    const responseText = await LLMConnector.getChatCompletion(memory, {
+      temperature: workspace?.openAiTemp ?? 0.7,
+    });
+
+    return {
+      response: responseText,
+      sources: this.curateSources(sourceDocuments),
+      message: false,
+    };
+  },
+  // This implementation of chat uses the chat history and modifies the system prompt at execution
+  // this is improved over the regular langchain implementation so that chats do not directly modify embeddings
+  // because then multi-user support will have all conversations mutating the base vector collection to which then
+  // the only solution is replicating entire vector databases per user - which will very quickly consume space on VectorDbs
+  chat: async function (reqBody = {}) {
+    const {
+      namespace = null,
+      input,
+      workspace = {},
+      chatHistory = [],
+    } = reqBody;
+    if (!namespace || !input) throw new Error("Invalid request body");
+
+    const { client } = await this.connect();
+    if (!(await this.namespaceExists(client, namespace))) {
+      return {
+        response: null,
+        sources: [],
+        message: "Invalid query - no documents found for workspace!",
+      };
+    }
+
+    const LLMConnector = getLLMProvider();
+    const queryVector = await LLMConnector.embedTextInput(input);
+    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+      client,
+      namespace,
+      queryVector
+    );
+    const prompt = {
+      role: "system",
+      content: `${chatPrompt(workspace)}
+    Context:
+    ${contextTexts
+          .map((text, i) => {
+            return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
+          })
+          .join("")}`,
+    };
+    const memory = [prompt, ...chatHistory, { role: "user", content: input }];
+    const responseText = await LLMConnector.getChatCompletion(memory, {
+      temperature: workspace?.openAiTemp ?? 0.7,
+    });
+
+    return {
+      response: responseText,
+      sources: this.curateSources(sourceDocuments),
+      message: false,
+    };
+  },
+  "namespace-stats": async function (reqBody = {}) {
+    const { namespace = null } = reqBody;
+    if (!namespace) throw new Error("namespace required");
+    const { client } = await this.connect();
+    const stats = await this.namespace(client, namespace);
+    return stats
+      ? stats
+      : { message: "No stats were able to be fetched from DB for namespace" };
+  },
+  "delete-namespace": async function (reqBody = {}) {
+    const { namespace = null } = reqBody;
+    const { client } = await this.connect();
+    const details = await this.namespace(client, namespace);
+    await this.deleteVectorsInNamespace(client, namespace);
+    return {
+      message: `Namespace ${camelCase(namespace)} was deleted along with ${details?.vectorCount
+        } vectors.`,
+    };
+  },
+  reset: async function () {
+    const { client } = await this.connect();
+    const weaviateClasses = await this.allNamespaces(client);
+    for (const weaviateClass of weaviateClasses) {
+      await client.schema.classDeleter().withClassName(weaviateClass).do();
+    }
+    return { reset: true };
+  },
+  curateSources: function (sources = []) {
+    const documents = [];
+    for (const source of sources) {
+      if (Object.keys(source).length > 0) {
+        documents.push(source);
+      }
+    }
+
+    return documents;
+  },
+  flattenObjectForWeaviate: function (obj = {}) {
+    // Note this function is not generic, it is designed specifically for Weaviate
+    // https://weaviate.io/developers/weaviate/config-refs/datatypes#introduction
+    // Credit to LangchainJS
+    // https://github.com/hwchase17/langchainjs/blob/5485c4af50c063e257ad54f4393fa79e0aff6462/langchain/src/vectorstores/weaviate.ts#L11C1-L50C3
+    const flattenedObject = {};
+
+    for (const key in obj) {
+      if (!Object.hasOwn(obj, key)) {
+        continue;
+      }
+      const value = obj[key];
+      if (typeof obj[key] === "object" && !Array.isArray(value)) {
+        const recursiveResult = this.flattenObjectForWeaviate(value);
+
+        for (const deepKey in recursiveResult) {
+          if (Object.hasOwn(obj, key)) {
+            flattenedObject[`${key}_${deepKey}`] = recursiveResult[deepKey];
+          }
+        }
+      } else if (Array.isArray(value)) {
+        if (
+          value.length > 0 &&
+          typeof value[0] !== "object" &&
+          // eslint-disable-next-line @typescript-eslint/no-explicit-any
+          value.every((el) => typeof el === typeof value[0])
+        ) {
+          // Weaviate only supports arrays of primitive types,
+          // where all elements are of the same type
+          flattenedObject[key] = value;
+        }
+      } else {
+        flattenedObject[key] = value;
+      }
+    }
+
+    return flattenedObject;
+  },
+};
+
+module.exports.Weaviate = Weaviate;

server/yarn.lock

@@ -130,6 +130,11 @@
   dependencies:
     googleapis-common "^6.0.3"
 
+"@graphql-typed-document-node/core@^3.1.1":
+  version "3.2.0"
+  resolved "https://registry.yarnpkg.com/@graphql-typed-document-node/core/-/core-3.2.0.tgz#5f3d96ec6b2354ad6d8a28bf216a1d97b5426861"
+  integrity sha512-mB9oAsNCm9aM3/SOv4YtBMqZbYj10R7dkq8byBqxGY/ncFwhf2oQzMV+LCRlWoDSEBJ3COiR1yeDvMtsoOsuFQ==
+
 "@mapbox/node-pre-gyp@^1.0.0", "@mapbox/node-pre-gyp@^1.0.10":
   version "1.0.11"
   resolved "https://registry.yarnpkg.com/@mapbox/node-pre-gyp/-/node-pre-gyp-1.0.11.tgz#417db42b7f5323d79e93b34a6d7a2a12c0df43fa"
@@ -921,6 +926,11 @@ extend@^3.0.2:
   resolved "https://registry.yarnpkg.com/extend/-/extend-3.0.2.tgz#f8b1136b4071fbd8eb140aff858b1019ec2915fa"
   integrity sha512-fjquC59cD7CyW6urNXK0FBufkZcoiGG80wTuPujX590cB5Ttln20E2UB4S/WARVqhXffZl2LNgS+gQdPIIim/g==
 
+extract-files@^9.0.0:
+  version "9.0.0"
+  resolved "https://registry.yarnpkg.com/extract-files/-/extract-files-9.0.0.tgz#8a7744f2437f81f5ed3250ed9f1550de902fe54a"
+  integrity sha512-CvdFfHkC95B4bBBk36hcEmvdR2awOdhhVUYH6S/zrVj3477zven/fJMYg7121h4T1xHZC+tetUpubpAhxwI7hQ==
+
 extract-zip@^2.0.1:
   version "2.0.1"
   resolved "https://registry.yarnpkg.com/extract-zip/-/extract-zip-2.0.1.tgz#663dca56fe46df890d5f131ef4a06d22bb8ba13a"
@@ -986,6 +996,15 @@ follow-redirects@^1.14.8:
   resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.15.2.tgz#b460864144ba63f2681096f274c4e57026da2c13"
   integrity sha512-VQLG33o04KaQ8uYi2tVNbdrWp1QWxNNea+nmIB4EVM28v0hmP17z7aG1+wAkNzVq4KeXTq3221ye5qTJP91JwA==
 
+form-data@^3.0.0:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/form-data/-/form-data-3.0.1.tgz#ebd53791b78356a99af9a300d4282c4d5eb9755f"
+  integrity sha512-RHkBKtLWUVwd7SqRIvCZMEvAMoGUp0XU+seQiZejj0COz3RI3hWP4sCv3gZWWLjJTd7rGwcsF5eKZGii0r/hbg==
+  dependencies:
+    asynckit "^0.4.0"
+    combined-stream "^1.0.8"
+    mime-types "^2.1.12"
+
 form-data@^4.0.0:
   version "4.0.0"
   resolved "https://registry.yarnpkg.com/form-data/-/form-data-4.0.0.tgz#93919daeaf361ee529584b9b31664dc12c9fa452"
@@ -1154,6 +1173,21 @@ graceful-fs@^4.2.0, graceful-fs@^4.2.6:
   resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.11.tgz#4183e4e8bf08bb6e05bbb2f7d2e0c8f712ca40e3"
   integrity sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==
 
+graphql-request@^5.1.0:
+  version "5.2.0"
+  resolved "https://registry.yarnpkg.com/graphql-request/-/graphql-request-5.2.0.tgz#a05fb54a517d91bb2d7aefa17ade4523dc5ebdca"
+  integrity sha512-pLhKIvnMyBERL0dtFI3medKqWOz/RhHdcgbZ+hMMIb32mEPa5MJSzS4AuXxfI4sRAu6JVVk5tvXuGfCWl9JYWQ==
+  dependencies:
+    "@graphql-typed-document-node/core" "^3.1.1"
+    cross-fetch "^3.1.5"
+    extract-files "^9.0.0"
+    form-data "^3.0.0"
+
+graphql@^16.7.1:
+  version "16.7.1"
+  resolved "https://registry.yarnpkg.com/graphql/-/graphql-16.7.1.tgz#11475b74a7bff2aefd4691df52a0eca0abd9b642"
+  integrity sha512-DRYR9tf+UGU0KOsMcKAlXeFfX89UiiIZ0dRU3mR0yJfu6OjZqUcp68NnFLnqQU5RexygFoDy1EW+ccOYcPfmHg==
+
 gtoken@^6.1.0:
   version "6.1.2"
   resolved "https://registry.yarnpkg.com/gtoken/-/gtoken-6.1.2.tgz#aeb7bdb019ff4c3ba3ac100bbe7b6e74dce0e8bc"
@@ -2512,6 +2546,15 @@ vectordb@0.1.12:
     "@apache-arrow/ts" "^12.0.0"
     apache-arrow "^12.0.0"
 
+weaviate-ts-client@^1.4.0:
+  version "1.4.0"
+  resolved "https://registry.yarnpkg.com/weaviate-ts-client/-/weaviate-ts-client-1.4.0.tgz#e1adb670f2c1930a82601efb915b0131f6988b7e"
+  integrity sha512-G2V/IWMHXDjoJeATUYKkZXzAs7iRj4GE8B3AX59XDqMRW12X7VUkRgo4xWcHH1bjpLIHUYTzD5qZXcB8P9Hdmw==
+  dependencies:
+    graphql-request "^5.1.0"
+    isomorphic-fetch "^3.0.0"
+    uuid "^9.0.0"
+
 webidl-conversions@^3.0.0:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-3.0.1.tgz#24534275e2a7bc6be7bc86611cc16ae0a5654871"