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

.hadolint.yaml

@@ -0,0 +1,8 @@
+failure-threshold: warning
+ignored:
+  - DL3008
+  - DL3013
+format: tty
+trustedRegistries:
+  - docker.io
+  - gcr.io

.vscode/settings.json

@@ -4,16 +4,20 @@
     "Astra",
     "Dockerized",
     "Embeddable",
+    "GROQ",
     "hljs",
+    "inferencing",
     "Langchain",
     "Milvus",
     "Mintplex",
     "Ollama",
     "openai",
+    "openrouter",
     "Qdrant",
     "vectordbs",
     "Weaviate",
     "Zilliz"
   ],
-  "eslint.experimental.useFlatConfig": true
+  "eslint.experimental.useFlatConfig": true,
+  "docker.languageserver.formatter.ignoreMultilineInstructions": true
 }

collector/utils/files/index.js

@@ -1,28 +1,16 @@
 const fs = require("fs");
 const path = require("path");
-const { getType } = require("mime");
+const { MimeDetector } = require("./mime");
 
 function isTextType(filepath) {
-  if (!fs.existsSync(filepath)) return false;
-  // These are types of mime primary classes that for sure
-  // cannot also for forced into a text type.
-  const nonTextTypes = ["multipart", "image", "model", "audio", "video"];
-  // These are full-mimes we for sure cannot parse or interpret as text
-  // documents
-  const BAD_MIMES = [
-    "application/octet-stream",
-    "application/zip",
-    "application/pkcs8",
-    "application/vnd.microsoft.portable-executable",
-    "application/x-msdownload",
-  ];
-
   try {
-    const mime = getType(filepath);
+    if (!fs.existsSync(filepath)) return false;
-    if (BAD_MIMES.includes(mime)) return false;
+    const mimeLib = new MimeDetector();
+    const mime = mimeLib.getType(filepath);
+    if (mimeLib.badMimes.includes(mime)) return false;
 
     const type = mime.split("/")[0];
-    if (nonTextTypes.includes(type)) return false;
+    if (mimeLib.nonTextTypes.includes(type)) return false;
     return true;
   } catch {
     return false;

collector/utils/files/mime.js

@@ -0,0 +1,37 @@
+const MimeLib = require("mime");
+
+class MimeDetector {
+  nonTextTypes = ["multipart", "image", "model", "audio", "video"];
+  badMimes = [
+    "application/octet-stream",
+    "application/zip",
+    "application/pkcs8",
+    "application/vnd.microsoft.portable-executable",
+    "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", // XLSX are binaries and need to be handled explicitly.
+    "application/x-msdownload",
+  ];
+
+  constructor() {
+    this.lib = MimeLib;
+    this.setOverrides();
+  }
+
+  setOverrides() {
+    // the .ts extension maps to video/mp2t because of https://en.wikipedia.org/wiki/MPEG_transport_stream
+    // which has had this extension far before TS was invented. So need to force re-map this MIME map.
+    this.lib.define(
+      {
+        "text/plain": ["ts", "py", "opts", "lock", "jsonl"],
+      },
+      true
+    );
+  }
+
+  getType(filepath) {
+    return this.lib.getType(filepath);
+  }
+}
+
+module.exports = {
+  MimeDetector,
+};

docker/.env.example

@@ -61,6 +61,10 @@ GID='1000'
 # HUGGING_FACE_LLM_API_KEY=hf_xxxxxx
 # HUGGING_FACE_LLM_TOKEN_LIMIT=8000
 
+# LLM_PROVIDER='groq'
+# GROQ_API_KEY=gsk_abcxyz
+# GROQ_MODEL_PREF=llama2-70b-4096
+
 ###########################################
 ######## Embedding API SElECTION ##########
 ###########################################

docker/Dockerfile

@@ -1,12 +1,17 @@
 # Setup base image
-FROM ubuntu:jammy-20230522 AS base
+FROM ubuntu:jammy-20230916 AS base
 
+# Build arguments
 ARG ARG_UID=1000
 ARG ARG_GID=1000
 
 FROM base AS build-arm64
 RUN echo "Preparing build of AnythingLLM image for arm64 architecture"
 
+SHELL ["/bin/bash", "-o", "pipefail", "-c"]
+
+# Install system dependencies
+# hadolint ignore=DL3008,DL3013
 RUN DEBIAN_FRONTEND=noninteractive apt-get update && \
     DEBIAN_FRONTEND=noninteractive apt-get install -yq --no-install-recommends \
         unzip curl gnupg libgfortran5 libgbm1 tzdata netcat \
@@ -25,8 +30,8 @@ RUN DEBIAN_FRONTEND=noninteractive apt-get update && \
         && rm yarn_1.22.19_all.deb
 
 # Create a group and user with specific UID and GID
-RUN groupadd -g $ARG_GID anythingllm && \
+RUN groupadd -g "$ARG_GID" anythingllm && \
-    useradd -u $ARG_UID -m -d /app -s /bin/bash -g anythingllm anythingllm && \
+    useradd -l -u "$ARG_UID" -m -d /app -s /bin/bash -g anythingllm anythingllm && \
     mkdir -p /app/frontend/ /app/server/ /app/collector/ && chown -R anythingllm:anythingllm /app
 
 # Copy docker helper scripts
@@ -61,6 +66,10 @@ RUN echo "Done running arm64 specific installtion steps"
 FROM base AS build-amd64
 RUN echo "Preparing build of AnythingLLM image for non-ARM architecture"
 
+SHELL ["/bin/bash", "-o", "pipefail", "-c"]
+
+# Install system dependencies
+# hadolint ignore=DL3008,DL3013
 RUN DEBIAN_FRONTEND=noninteractive apt-get update && \
     DEBIAN_FRONTEND=noninteractive apt-get install -yq --no-install-recommends \
         curl gnupg libgfortran5 libgbm1 tzdata netcat \
@@ -79,8 +88,8 @@ RUN DEBIAN_FRONTEND=noninteractive apt-get update && \
         && rm yarn_1.22.19_all.deb
 
 # Create a group and user with specific UID and GID
-RUN groupadd -g $ARG_GID anythingllm && \
+RUN groupadd -g "$ARG_GID" anythingllm && \
-    useradd -u $ARG_UID -m -d /app -s /bin/bash -g anythingllm anythingllm && \
+    useradd -l -u "$ARG_UID" -m -d /app -s /bin/bash -g anythingllm anythingllm && \
     mkdir -p /app/frontend/ /app/server/ /app/collector/ && chown -R anythingllm:anythingllm /app
 
 # Copy docker helper scripts
@@ -95,6 +104,8 @@ RUN chmod +x /usr/local/bin/docker-entrypoint.sh && \
 #############################################
 # COMMON BUILD FLOW FOR ALL ARCHS
 #############################################
+
+# hadolint ignore=DL3006
 FROM build-${TARGETARCH} AS build
 RUN echo "Running common build flow of AnythingLLM image for all architectures"
 
@@ -102,43 +113,54 @@ USER anythingllm
 WORKDIR /app
 
 # Install frontend dependencies
-FROM build as frontend-deps
+FROM build AS frontend-deps
 
 COPY ./frontend/package.json ./frontend/yarn.lock ./frontend/
-RUN cd ./frontend/ && yarn install --network-timeout 100000 && yarn cache clean
+WORKDIR /app/frontend
+RUN yarn install --network-timeout 100000 && yarn cache clean
+WORKDIR /app
 
 # Install server dependencies
-FROM build as server-deps
+FROM build AS server-deps
 COPY ./server/package.json ./server/yarn.lock ./server/
-RUN cd ./server/ && yarn install --production --network-timeout 100000 && yarn cache clean
+WORKDIR /app/server
+RUN yarn install --production --network-timeout 100000 && yarn cache clean
+WORKDIR /app
 
 # Compile Llama.cpp bindings for node-llama-cpp for this operating system.
 USER root
-RUN cd ./server && npx --no node-llama-cpp download
+WORKDIR /app/server
+RUN npx --no node-llama-cpp download
+WORKDIR /app
 USER anythingllm
 
 # Build the frontend
-FROM frontend-deps as build-stage
+FROM frontend-deps AS build-stage
 COPY ./frontend/ ./frontend/
-RUN cd ./frontend/ && yarn build && yarn cache clean
+WORKDIR /app/frontend
+RUN yarn build && yarn cache clean
+WORKDIR /app
 
 # Setup the server
-FROM server-deps as production-stage
+FROM server-deps AS production-stage
 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 --chown=anythingllm:anythingllm --from=build-stage /app/frontend/dist ./server/public
 
 # Copy the collector
 COPY --chown=anythingllm:anythingllm ./collector/ ./collector/
 
 # Install collector dependencies
+WORKDIR /app/collector
 ENV PUPPETEER_DOWNLOAD_BASE_URL=https://storage.googleapis.com/chrome-for-testing-public 
-RUN cd /app/collector && yarn install --production --network-timeout 100000 && yarn cache clean
+RUN yarn install --production --network-timeout 100000 && yarn cache clean
 
 # Migrate and Run Prisma against known schema
-RUN cd ./server && npx prisma generate --schema=./prisma/schema.prisma
+WORKDIR /app/server
-RUN cd ./server && npx prisma migrate deploy --schema=./prisma/schema.prisma
+RUN npx prisma generate --schema=./prisma/schema.prisma && \
+    npx prisma migrate deploy --schema=./prisma/schema.prisma
+WORKDIR /app
 
 # Setup the environment
 ENV NODE_ENV=production

docker/docker-entrypoint.sh

@@ -1,7 +1,8 @@
 #!/bin/bash
-{ cd /app/server/ &&\
+{
-  npx prisma generate --schema=./prisma/schema.prisma &&\
+  cd /app/server/ &&
-  npx prisma migrate deploy --schema=./prisma/schema.prisma &&\
+    npx prisma generate --schema=./prisma/schema.prisma &&
+    npx prisma migrate deploy --schema=./prisma/schema.prisma &&
     node /app/server/index.js
 } &
 { node /app/collector/index.js; } &

docker/docker-healthcheck.sh

@@ -4,7 +4,7 @@
 response=$(curl --write-out '%{http_code}' --silent --output /dev/null http://localhost:3001/api/ping)
 
 # If the HTTP response code is 200 (OK), the server is up
-if [ $response -eq 200 ]; then
+if [ "$response" -eq 200 ]; then
   echo "Server is up"
   exit 0
 else

frontend/src/components/LLMSelection/AnthropicAiOptions/index.jsx

@@ -48,7 +48,13 @@ export default function AnthropicAiOptions({ settings, showAlert = false }) {
             required={true}
             className="bg-zinc-900 border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
           >
-            {["claude-2", "claude-instant-1"].map((model) => {
+            {[
+              "claude-instant-1.2",
+              "claude-2.0",
+              "claude-2.1",
+              "claude-3-opus-20240229",
+              "claude-3-sonnet-20240229",
+            ].map((model) => {
               return (
                 <option key={model} value={model}>
                   {model}

frontend/src/components/LLMSelection/GroqAiOptions/index.jsx

@@ -0,0 +1,41 @@
+export default function GroqAiOptions({ settings }) {
+  return (
+    <div className="flex gap-x-4">
+      <div className="flex flex-col w-60">
+        <label className="text-white text-sm font-semibold block mb-4">
+          Groq API Key
+        </label>
+        <input
+          type="password"
+          name="GroqApiKey"
+          className="bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:border-white block w-full p-2.5"
+          placeholder="Groq API Key"
+          defaultValue={settings?.GroqApiKey ? "*".repeat(20) : ""}
+          required={true}
+          autoComplete="off"
+          spellCheck={false}
+        />
+      </div>
+
+      <div className="flex flex-col w-60">
+        <label className="text-white text-sm font-semibold block mb-4">
+          Chat Model Selection
+        </label>
+        <select
+          name="GroqModelPref"
+          defaultValue={settings?.GroqModelPref || "llama2-70b-4096"}
+          required={true}
+          className="bg-zinc-900 border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
+        >
+          {["llama2-70b-4096", "mixtral-8x7b-32768"].map((model) => {
+            return (
+              <option key={model} value={model}>
+                {model}
+              </option>
+            );
+          })}
+        </select>
+      </div>
+    </div>
+  );
+}

frontend/src/components/Modals/MangeWorkspace/Documents/UploadFile/index.jsx

@@ -105,7 +105,7 @@ export default function UploadFile({ workspace, fetchKeys, setLoading }) {
             </div>
           </div>
         ) : (
-          <div className="grid grid-cols-2 gap-2 overflow-auto max-h-[400px] p-1 overflow-y-auto">
+          <div className="grid grid-cols-2 gap-2 overflow-auto max-h-[180px] p-1 overflow-y-scroll no-scroll">
             {files.map((file) => (
               <FileUploadProgress
                 key={file.uid}

frontend/src/hooks/useGetProvidersModels.js

@@ -13,12 +13,19 @@ const PROVIDER_DEFAULT_MODELS = {
     "gpt-4-32k",
   ],
   gemini: ["gemini-pro"],
-  anthropic: ["claude-2", "claude-instant-1"],
+  anthropic: [
+    "claude-instant-1.2",
+    "claude-2.0",
+    "claude-2.1",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+  ],
   azure: [],
   lmstudio: [],
   localai: [],
   ollama: [],
   togetherai: [],
+  groq: ["llama2-70b-4096", "mixtral-8x7b-32768"],
   native: [],
 };
 

frontend/src/pages/Admin/Workspaces/WorkspaceRow/EditWorkspaceUsersModal/index.jsx

@@ -52,7 +52,7 @@ export default function EditWorkspaceUsersModal({
         </div>
         <form onSubmit={handleUpdate}>
           <div className="p-6 space-y-6 flex h-full w-full">
-            <div className="w-full flex flex-col gap-y-4">
+            <div className="w-full flex flex-col gap-y-4 max-h-[350px] overflow-y-scroll">
               {users
                 .filter((user) => user.role !== "admin")
                 .map((user) => {

frontend/src/pages/GeneralSettings/LLMPreference/index.jsx

@@ -16,6 +16,7 @@ import MistralLogo from "@/media/llmprovider/mistral.jpeg";
 import HuggingFaceLogo from "@/media/llmprovider/huggingface.png";
 import PerplexityLogo from "@/media/llmprovider/perplexity.png";
 import OpenRouterLogo from "@/media/llmprovider/openrouter.jpeg";
+import GroqLogo from "@/media/llmprovider/groq.png";
 import PreLoader from "@/components/Preloader";
 import OpenAiOptions from "@/components/LLMSelection/OpenAiOptions";
 import AzureAiOptions from "@/components/LLMSelection/AzureAiOptions";
@@ -28,11 +29,12 @@ import OllamaLLMOptions from "@/components/LLMSelection/OllamaLLMOptions";
 import TogetherAiOptions from "@/components/LLMSelection/TogetherAiOptions";
 import MistralOptions from "@/components/LLMSelection/MistralOptions";
 import HuggingFaceOptions from "@/components/LLMSelection/HuggingFaceOptions";
+import PerplexityOptions from "@/components/LLMSelection/PerplexityOptions";
+import OpenRouterOptions from "@/components/LLMSelection/OpenRouterOptions";
+import GroqAiOptions from "@/components/LLMSelection/GroqAiOptions";
 
 import LLMItem from "@/components/LLMSelection/LLMItem";
 import { MagnifyingGlass } from "@phosphor-icons/react";
-import PerplexityOptions from "@/components/LLMSelection/PerplexityOptions";
-import OpenRouterOptions from "@/components/LLMSelection/OpenRouterOptions";
 
 export default function GeneralLLMPreference() {
   const [saving, setSaving] = useState(false);
@@ -173,6 +175,14 @@ export default function GeneralLLMPreference() {
       options: <OpenRouterOptions settings={settings} />,
       description: "A unified interface for LLMs.",
     },
+    {
+      name: "Groq",
+      value: "groq",
+      logo: GroqLogo,
+      options: <GroqAiOptions settings={settings} />,
+      description:
+        "The fastest LLM inferencing available for real-time AI applications.",
+    },
     {
       name: "Native",
       value: "native",

frontend/src/pages/OnboardingFlow/Steps/DataHandling/index.jsx

@@ -13,6 +13,7 @@ import MistralLogo from "@/media/llmprovider/mistral.jpeg";
 import HuggingFaceLogo from "@/media/llmprovider/huggingface.png";
 import PerplexityLogo from "@/media/llmprovider/perplexity.png";
 import OpenRouterLogo from "@/media/llmprovider/openrouter.jpeg";
+import GroqLogo from "@/media/llmprovider/groq.png";
 import ZillizLogo from "@/media/vectordbs/zilliz.png";
 import AstraDBLogo from "@/media/vectordbs/astraDB.png";
 import ChromaLogo from "@/media/vectordbs/chroma.png";
@@ -127,6 +128,14 @@ const LLM_SELECTION_PRIVACY = {
     ],
     logo: OpenRouterLogo,
   },
+  groq: {
+    name: "Groq",
+    description: [
+      "Your chats will not be used for training",
+      "Your prompts and document text used in response creation are visible to Groq",
+    ],
+    logo: GroqLogo,
+  },
 };
 
 const VECTOR_DB_PRIVACY = {

frontend/src/pages/OnboardingFlow/Steps/LLMPreference/index.jsx

@@ -13,6 +13,7 @@ import MistralLogo from "@/media/llmprovider/mistral.jpeg";
 import HuggingFaceLogo from "@/media/llmprovider/huggingface.png";
 import PerplexityLogo from "@/media/llmprovider/perplexity.png";
 import OpenRouterLogo from "@/media/llmprovider/openrouter.jpeg";
+import GroqLogo from "@/media/llmprovider/groq.png";
 import OpenAiOptions from "@/components/LLMSelection/OpenAiOptions";
 import AzureAiOptions from "@/components/LLMSelection/AzureAiOptions";
 import AnthropicAiOptions from "@/components/LLMSelection/AnthropicAiOptions";
@@ -25,12 +26,13 @@ import MistralOptions from "@/components/LLMSelection/MistralOptions";
 import HuggingFaceOptions from "@/components/LLMSelection/HuggingFaceOptions";
 import TogetherAiOptions from "@/components/LLMSelection/TogetherAiOptions";
 import PerplexityOptions from "@/components/LLMSelection/PerplexityOptions";
+import OpenRouterOptions from "@/components/LLMSelection/OpenRouterOptions";
+import GroqAiOptions from "@/components/LLMSelection/GroqAiOptions";
 import LLMItem from "@/components/LLMSelection/LLMItem";
 import System from "@/models/system";
 import paths from "@/utils/paths";
 import showToast from "@/utils/toast";
 import { useNavigate } from "react-router-dom";
-import OpenRouterOptions from "@/components/LLMSelection/OpenRouterOptions";
 
 const TITLE = "LLM Preference";
 const DESCRIPTION =
@@ -147,6 +149,14 @@ export default function LLMPreference({
       options: <OpenRouterOptions settings={settings} />,
       description: "A unified interface for LLMs.",
     },
+    {
+      name: "Groq",
+      value: "groq",
+      logo: GroqLogo,
+      options: <GroqAiOptions settings={settings} />,
+      description:
+        "The fastest LLM inferencing available for real-time AI applications.",
+    },
     {
       name: "Native",
       value: "native",

server/.env.example

@@ -58,6 +58,10 @@ JWT_SECRET="my-random-string-for-seeding" # Please generate random string at lea
 # HUGGING_FACE_LLM_API_KEY=hf_xxxxxx
 # HUGGING_FACE_LLM_TOKEN_LIMIT=8000
 
+# LLM_PROVIDER='groq'
+# GROQ_API_KEY=gsk_abcxyz
+# GROQ_MODEL_PREF=llama2-70b-4096
+
 ###########################################
 ######## Embedding API SElECTION ##########
 ###########################################

server/models/systemSettings.js

@@ -219,12 +219,25 @@ const SystemSettings = {
             AzureOpenAiEmbeddingModelPref: process.env.EMBEDDING_MODEL_PREF,
           }
         : {}),
+
+      ...(llmProvider === "groq"
+        ? {
+            GroqApiKey: !!process.env.GROQ_API_KEY,
+            GroqModelPref: process.env.GROQ_MODEL_PREF,
+
+            // For embedding credentials when groq is selected.
+            OpenAiKey: !!process.env.OPEN_AI_KEY,
+            AzureOpenAiEndpoint: process.env.AZURE_OPENAI_ENDPOINT,
+            AzureOpenAiKey: !!process.env.AZURE_OPENAI_KEY,
+            AzureOpenAiEmbeddingModelPref: process.env.EMBEDDING_MODEL_PREF,
+          }
+        : {}),
       ...(llmProvider === "native"
         ? {
             NativeLLMModelPref: process.env.NATIVE_LLM_MODEL_PREF,
             NativeLLMTokenLimit: process.env.NATIVE_LLM_MODEL_TOKEN_LIMIT,
 
-            // For embedding credentials when ollama is selected.
+            // For embedding credentials when native is selected.
             OpenAiKey: !!process.env.OPEN_AI_KEY,
             AzureOpenAiEndpoint: process.env.AZURE_OPENAI_ENDPOINT,
             AzureOpenAiKey: !!process.env.AZURE_OPENAI_KEY,

server/package.json

@@ -20,7 +20,7 @@
     "seed": "node prisma/seed.js"
   },
   "dependencies": {
-    "@anthropic-ai/sdk": "^0.8.1",
+    "@anthropic-ai/sdk": "^0.16.1",
     "@azure/openai": "1.0.0-beta.10",
     "@datastax/astra-db-ts": "^0.1.3",
     "@google/generative-ai": "^0.1.3",

server/utils/AiProviders/anthropic/index.js

@@ -1,6 +1,6 @@
 const { v4 } = require("uuid");
 const { chatPrompt } = require("../../chats");
-
+const { writeResponseChunk } = require("../../helpers/chat/responses");
 class AnthropicLLM {
   constructor(embedder = null, modelPreference = null) {
     if (!process.env.ANTHROPIC_API_KEY)
@@ -13,7 +13,7 @@ class AnthropicLLM {
     });
     this.anthropic = anthropic;
     this.model =
-      modelPreference || process.env.ANTHROPIC_MODEL_PREF || "claude-2";
+      modelPreference || process.env.ANTHROPIC_MODEL_PREF || "claude-2.0";
     this.limits = {
       history: this.promptWindowLimit() * 0.15,
       system: this.promptWindowLimit() * 0.15,
@@ -35,17 +35,29 @@ class AnthropicLLM {
 
   promptWindowLimit() {
     switch (this.model) {
-      case "claude-instant-1":
+      case "claude-instant-1.2":
-        return 72_000;
-      case "claude-2":
         return 100_000;
+      case "claude-2.0":
+        return 100_000;
+      case "claude-2.1":
+        return 200_000;
+      case "claude-3-opus-20240229":
+        return 200_000;
+      case "claude-3-sonnet-20240229":
+        return 200_000;
       default:
-        return 72_000; // assume a claude-instant-1 model
+        return 100_000; // assume a claude-instant-1.2 model
     }
   }
 
   isValidChatCompletionModel(modelName = "") {
-    const validModels = ["claude-2", "claude-instant-1"];
+    const validModels = [
+      "claude-instant-1.2",
+      "claude-2.0",
+      "claude-2.1",
+      "claude-3-opus-20240229",
+      "claude-3-sonnet-20240229",
+    ];
     return validModels.includes(modelName);
   }
 
@@ -62,36 +74,43 @@ class AnthropicLLM {
     chatHistory = [],
     userPrompt = "",
   }) {
-    return `\n\nHuman: Please read question supplied within the <question> tags. Using all information generate an answer to the question and output it within <${
+    const prompt = {
-      this.answerKey
+      role: "system",
-    }> tags. Previous conversations can be used within the <history> tags and can be used to influence the output. Content between the <system> tag is additional information and instruction that will impact how answers are formatted or responded to. Additional contextual information retrieved to help answer the users specific query is available to use for answering and can be found between <context> tags. When no <context> tags may are present use the knowledge available and in the conversation to answer. When one or more <context> tags are available you will use those to help answer the question or augment pre-existing knowledge. You should never say "Based on the provided context" or other phrasing that is not related to the user question.
+      content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
-    <system>${systemPrompt}</system>
+    };
-    ${contextTexts
+
-      .map((text, i) => {
+    return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
-        return `<context>${text}</context>\n`;
-      })
-      .join("")}
-    <history>${chatHistory.map((history) => {
-      switch (history.role) {
-        case "assistant":
-          return `\n\nAssistant: ${history.content}`;
-        case "user":
-          return `\n\nHuman: ${history.content}`;
-        default:
-          return "\n";
-      }
-    })}</history>
-    <question>${userPrompt}</question>
-    \n\nAssistant:`;
   }
 
-  async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
+  async getChatCompletion(messages = null, { temperature = 0.7 }) {
     if (!this.isValidChatCompletionModel(this.model))
       throw new Error(
         `Anthropic chat: ${this.model} is not valid for chat completion!`
       );
 
-    const compressedPrompt = await this.compressMessages(
+    try {
+      const response = await this.anthropic.messages.create({
+        model: this.model,
+        max_tokens: 4096,
+        system: messages[0].content, // Strip out the system message
+        messages: messages.slice(1), // Pop off the system message
+        temperature: Number(temperature ?? this.defaultTemp),
+      });
+
+      return response.content[0].text;
+    } catch (error) {
+      console.log(error);
+      return error;
+    }
+  }
+
+  async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
+    if (!this.isValidChatCompletionModel(this.model))
+      throw new Error(
+        `Anthropic chat: ${this.model} is not valid for chat completion!`
+      );
+
+    const messages = await this.compressMessages(
       {
         systemPrompt: chatPrompt(workspace),
         userPrompt: prompt,
@@ -99,58 +118,85 @@ class AnthropicLLM {
       },
       rawHistory
     );
-    const { content, error } = await this.anthropic.completions
-      .create({
-        model: this.model,
-        max_tokens_to_sample: 300,
-        prompt: compressedPrompt,
-      })
-      .then((res) => {
-        const { completion } = res;
-        const re = new RegExp(
-          "(?:<" + this.answerKey + ">)([\\s\\S]*)(?:</" + this.answerKey + ">)"
-        );
-        const response = completion.match(re)?.[1]?.trim();
-        if (!response)
-          throw new Error("Anthropic: No response could be parsed.");
-        return { content: response, error: null };
-      })
-      .catch((e) => {
-        return { content: null, error: e.message };
-      });
 
-    if (error) throw new Error(error);
+    const streamRequest = await this.anthropic.messages.stream({
-    return content;
+      model: this.model,
+      max_tokens: 4096,
+      system: messages[0].content, // Strip out the system message
+      messages: messages.slice(1), // Pop off the system message
+      temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
+    });
+    return streamRequest;
   }
 
-  async getChatCompletion(prompt = "", _opts = {}) {
+  async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
     if (!this.isValidChatCompletionModel(this.model))
       throw new Error(
-        `Anthropic chat: ${this.model} is not valid for chat completion!`
+        `OpenAI chat: ${this.model} is not valid for chat completion!`
       );
 
-    const { content, error } = await this.anthropic.completions
+    const streamRequest = await this.anthropic.messages.stream({
-      .create({
       model: this.model,
-        max_tokens_to_sample: 300,
+      max_tokens: 4096,
-        prompt,
+      system: messages[0].content, // Strip out the system message
-      })
+      messages: messages.slice(1), // Pop off the system message
-      .then((res) => {
+      temperature: Number(temperature ?? this.defaultTemp),
-        const { completion } = res;
-        const re = new RegExp(
-          "(?:<" + this.answerKey + ">)([\\s\\S]*)(?:</" + this.answerKey + ">)"
-        );
-        const response = completion.match(re)?.[1]?.trim();
-        if (!response)
-          throw new Error("Anthropic: No response could be parsed.");
-        return { content: response, error: null };
-      })
-      .catch((e) => {
-        return { content: null, error: e.message };
     });
+    return streamRequest;
+  }
 
-    if (error) throw new Error(error);
+  handleStream(response, stream, responseProps) {
-    return content;
+    return new Promise((resolve) => {
+      let fullText = "";
+      const { uuid = v4(), sources = [] } = responseProps;
+
+      stream.on("streamEvent", (message) => {
+        const data = message;
+        if (
+          data.type === "content_block_delta" &&
+          data.delta.type === "text_delta"
+        ) {
+          const text = data.delta.text;
+          fullText += text;
+
+          writeResponseChunk(response, {
+            uuid,
+            sources,
+            type: "textResponseChunk",
+            textResponse: text,
+            close: false,
+            error: false,
+          });
+        }
+
+        if (
+          message.type === "message_stop" ||
+          (data.stop_reason && data.stop_reason === "end_turn")
+        ) {
+          writeResponseChunk(response, {
+            uuid,
+            sources,
+            type: "textResponseChunk",
+            textResponse: "",
+            close: true,
+            error: false,
+          });
+          resolve(fullText);
+        }
+      });
+    });
+  }
+
+  #appendContext(contextTexts = []) {
+    if (!contextTexts || !contextTexts.length) return "";
+    return (
+      "\nContext:\n" +
+      contextTexts
+        .map((text, i) => {
+          return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
+        })
+        .join("")
+    );
   }
 
   async compressMessages(promptArgs = {}, rawHistory = []) {

server/utils/AiProviders/groq/index.js

@@ -0,0 +1,207 @@
+const { NativeEmbedder } = require("../../EmbeddingEngines/native");
+const { chatPrompt } = require("../../chats");
+const { handleDefaultStreamResponse } = require("../../helpers/chat/responses");
+
+class GroqLLM {
+  constructor(embedder = null, modelPreference = null) {
+    const { Configuration, OpenAIApi } = require("openai");
+    if (!process.env.GROQ_API_KEY) throw new Error("No Groq API key was set.");
+
+    const config = new Configuration({
+      basePath: "https://api.groq.com/openai/v1",
+      apiKey: process.env.GROQ_API_KEY,
+    });
+
+    this.openai = new OpenAIApi(config);
+    this.model =
+      modelPreference || process.env.GROQ_MODEL_PREF || "llama2-70b-4096";
+    this.limits = {
+      history: this.promptWindowLimit() * 0.15,
+      system: this.promptWindowLimit() * 0.15,
+      user: this.promptWindowLimit() * 0.7,
+    };
+
+    this.embedder = !embedder ? new NativeEmbedder() : embedder;
+    this.defaultTemp = 0.7;
+  }
+
+  #appendContext(contextTexts = []) {
+    if (!contextTexts || !contextTexts.length) return "";
+    return (
+      "\nContext:\n" +
+      contextTexts
+        .map((text, i) => {
+          return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
+        })
+        .join("")
+    );
+  }
+
+  streamingEnabled() {
+    return "streamChat" in this && "streamGetChatCompletion" in this;
+  }
+
+  promptWindowLimit() {
+    switch (this.model) {
+      case "llama2-70b-4096":
+        return 4096;
+      case "mixtral-8x7b-32768":
+        return 32_768;
+      default:
+        return 4096;
+    }
+  }
+
+  async isValidChatCompletionModel(modelName = "") {
+    const validModels = ["llama2-70b-4096", "mixtral-8x7b-32768"];
+    const isPreset = validModels.some((model) => modelName === model);
+    if (isPreset) return true;
+
+    const model = await this.openai
+      .retrieveModel(modelName)
+      .then((res) => res.data)
+      .catch(() => null);
+    return !!model;
+  }
+
+  constructPrompt({
+    systemPrompt = "",
+    contextTexts = [],
+    chatHistory = [],
+    userPrompt = "",
+  }) {
+    const prompt = {
+      role: "system",
+      content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
+    };
+    return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
+  }
+
+  async isSafe(_input = "") {
+    // Not implemented so must be stubbed
+    return { safe: true, reasons: [] };
+  }
+
+  async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
+    if (!(await this.isValidChatCompletionModel(this.model)))
+      throw new Error(
+        `Groq chat: ${this.model} is not valid for chat completion!`
+      );
+
+    const textResponse = await this.openai
+      .createChatCompletion({
+        model: this.model,
+        temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
+        n: 1,
+        messages: await this.compressMessages(
+          {
+            systemPrompt: chatPrompt(workspace),
+            userPrompt: prompt,
+            chatHistory,
+          },
+          rawHistory
+        ),
+      })
+      .then((json) => {
+        const res = json.data;
+        if (!res.hasOwnProperty("choices"))
+          throw new Error("GroqAI chat: No results!");
+        if (res.choices.length === 0)
+          throw new Error("GroqAI chat: No results length!");
+        return res.choices[0].message.content;
+      })
+      .catch((error) => {
+        throw new Error(
+          `GroqAI::createChatCompletion failed with: ${error.message}`
+        );
+      });
+
+    return textResponse;
+  }
+
+  async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
+    if (!(await this.isValidChatCompletionModel(this.model)))
+      throw new Error(
+        `GroqAI:streamChat: ${this.model} is not valid for chat completion!`
+      );
+
+    const streamRequest = await this.openai.createChatCompletion(
+      {
+        model: this.model,
+        stream: true,
+        temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
+        n: 1,
+        messages: await this.compressMessages(
+          {
+            systemPrompt: chatPrompt(workspace),
+            userPrompt: prompt,
+            chatHistory,
+          },
+          rawHistory
+        ),
+      },
+      { responseType: "stream" }
+    );
+    return streamRequest;
+  }
+
+  async getChatCompletion(messages = null, { temperature = 0.7 }) {
+    if (!(await this.isValidChatCompletionModel(this.model)))
+      throw new Error(
+        `GroqAI:chatCompletion: ${this.model} is not valid for chat completion!`
+      );
+
+    const { data } = await this.openai
+      .createChatCompletion({
+        model: this.model,
+        messages,
+        temperature,
+      })
+      .catch((e) => {
+        throw new Error(e.response.data.error.message);
+      });
+
+    if (!data.hasOwnProperty("choices")) return null;
+    return data.choices[0].message.content;
+  }
+
+  async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
+    if (!(await this.isValidChatCompletionModel(this.model)))
+      throw new Error(
+        `GroqAI:streamChatCompletion: ${this.model} is not valid for chat completion!`
+      );
+
+    const streamRequest = await this.openai.createChatCompletion(
+      {
+        model: this.model,
+        stream: true,
+        messages,
+        temperature,
+      },
+      { responseType: "stream" }
+    );
+    return streamRequest;
+  }
+
+  handleStream(response, stream, responseProps) {
+    return handleDefaultStreamResponse(response, stream, responseProps);
+  }
+
+  // Simple wrapper for dynamic embedder & normalize interface for all LLM implementations
+  async embedTextInput(textInput) {
+    return await this.embedder.embedTextInput(textInput);
+  }
+  async embedChunks(textChunks = []) {
+    return await this.embedder.embedChunks(textChunks);
+  }
+
+  async compressMessages(promptArgs = {}, rawHistory = []) {
+    const { messageArrayCompressor } = require("../../helpers/chat");
+    const messageArray = this.constructPrompt(promptArgs);
+    return await messageArrayCompressor(this, messageArray, rawHistory);
+  }
+}
+
+module.exports = {
+  GroqLLM,
+};

server/utils/helpers/chat/convertTo.js

@@ -21,12 +21,8 @@ async function convertToCSV(preparedData) {
   return rows.join("\n");
 }
 
-async function convertToJSON(workspaceChatsMap) {
+async function convertToJSON(preparedData) {
-  const allMessages = [].concat.apply(
+  return JSON.stringify(preparedData, null, 4);
-    [],
-    Object.values(workspaceChatsMap).map((workspace) => workspace.messages)
-  );
-  return JSON.stringify(allMessages, null, 4);
 }
 
 // ref: https://raw.githubusercontent.com/gururise/AlpacaDataCleaned/main/alpaca_data.json
@@ -48,7 +44,7 @@ async function prepareWorkspaceChatsForExport(format = "jsonl") {
     id: "asc",
   });
 
-  if (format === "csv") {
+  if (format === "csv" || format === "json") {
     const preparedData = chats.map((chat) => {
       const responseJson = JSON.parse(chat.response);
       return {

server/utils/helpers/index.js

@@ -73,6 +73,9 @@ function getLLMProvider(modelPreference = null) {
     case "huggingface":
       const { HuggingFaceLLM } = require("../AiProviders/huggingface");
       return new HuggingFaceLLM(embedder, modelPreference);
+    case "groq":
+      const { GroqLLM } = require("../AiProviders/groq");
+      return new GroqLLM(embedder, modelPreference);
     default:
       throw new Error("ENV: No LLM_PROVIDER value found in environment!");
   }

server/utils/helpers/updateENV.js

@@ -259,6 +259,16 @@ const KEY_MAPPING = {
     checks: [isNotEmpty],
   },
 
+  // Groq Options
+  GroqApiKey: {
+    envKey: "GROQ_API_KEY",
+    checks: [isNotEmpty],
+  },
+  GroqModelPref: {
+    envKey: "GROQ_MODEL_PREF",
+    checks: [isNotEmpty],
+  },
+
   // System Settings
   AuthToken: {
     envKey: "AUTH_TOKEN",
@@ -336,6 +346,7 @@ function supportedLLM(input = "") {
     "huggingface",
     "perplexity",
     "openrouter",
+    "groq",
   ].includes(input);
   return validSelection ? null : `${input} is not a valid LLM provider.`;
 }
@@ -348,7 +359,13 @@ function validGeminiModel(input = "") {
 }
 
 function validAnthropicModel(input = "") {
-  const validModels = ["claude-2", "claude-instant-1"];
+  const validModels = [
+    "claude-instant-1.2",
+    "claude-2.0",
+    "claude-2.1",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+  ];
   return validModels.includes(input)
     ? null
     : `Invalid Model type. Must be one of ${validModels.join(", ")}.`;

server/utils/vectorDbProviders/chroma/index.js

@@ -7,6 +7,7 @@ const {
   getLLMProvider,
   getEmbeddingEngineSelection,
 } = require("../../helpers");
+const { parseAuthHeader } = require("../../http");
 
 const Chroma = {
   name: "Chroma",

server/yarn.lock

@@ -7,10 +7,10 @@
   resolved "https://registry.yarnpkg.com/@aashutoshrathi/word-wrap/-/word-wrap-1.2.6.tgz#bd9154aec9983f77b3a034ecaa015c2e4201f6cf"
   integrity sha512-1Yjs2SvM8TflER/OD3cOjhWWOZb58A2t7wpE2S9XfBYTiIl+XFhQG2bjy4Pu1I+EAlCNUzRDYDdFwFYUKvXcIA==
 
-"@anthropic-ai/sdk@^0.8.1":
+"@anthropic-ai/sdk@^0.16.1":
-  version "0.8.1"
+  version "0.16.1"
-  resolved "https://registry.yarnpkg.com/@anthropic-ai/sdk/-/sdk-0.8.1.tgz#7c7c6cb262abe3e6d0bb8bd1179b4589edd7a6ad"
+  resolved "https://registry.yarnpkg.com/@anthropic-ai/sdk/-/sdk-0.16.1.tgz#7472c42389d9a5323c20afa53995e1c3b922b95d"
-  integrity sha512-59etePenCizVx1O8Qhi1T1ruE04ISfNzCnyhZNcsss1QljsLmYS83jttarMNEvGYcsUF7rwxw2lzcC3Zbxao7g==
+  integrity sha512-vHgvfWEyFy5ktqam56Nrhv8MVa7EJthsRYNi+1OrFFfyrj9tR2/aji1QbVbQjYU/pPhPFaYrdCEC/MLPFrmKwA==
   dependencies:
     "@types/node" "^18.11.18"
     "@types/node-fetch" "^2.6.4"