Merge pull request #307 from weaviate/dev

Merge DEV to MAIN

.github/workflows/docker-image.yml

@@ -2,29 +2,29 @@ name: Docker Build and Push
 
 on:
   push:
-    branches: [ "main" ] 
+    branches: ["main"]
 
 jobs:
   build-and-push:
     runs-on: ubuntu-latest
-    
+
     steps:
-    - name: Checkout code
-      uses: actions/checkout@v4
-    
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
-    
-    - name: Login to DockerHub
-      uses: docker/login-action@v3
-      with:
-        username: ${{ secrets.DOCKERHUB_USERNAME }}
-        password: ${{ secrets.DOCKERHUB_TOKEN }}
-    
-    - name: Build and push
-      uses: docker/build-push-action@v4
-      with:
-        context: .
-        file: ./Dockerfile
-        push: true
-        tags: ${{ secrets.DOCKERHUB_USERNAME }}/verba:latest
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{secrets.DOCKER_USERNAME}}
+          password: ${{secrets.DOCKER_PASSWORD}}
+
+      - name: Build and push
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: ./Dockerfile
+          push: true
+          tags: semitechnologies/verba:latest

CHANGELOG.md

@@ -8,10 +8,13 @@ All notable changes to this project will be documented in this file.
 
 - Added new deployment type: Custom
 - Added new port configuration
+- Added Groq
 
 # Fixed
 
 - Catch Exception when trying to access the OpenAI API Embedding endpoint to retrieve model names
+- Fixed reading empty string as environment variables
+- Fixed default Unstructed URL
 
 ## [2.0.0] Importastic
 

README.md

@@ -24,6 +24,7 @@ pip install goldenverba
   - [AssemblyAI](#assemblyai)
   - [OpenAI](#openai)
   - [HuggingFace](#huggingface)
+  - [Groq](#groq)
 - [Quickstart: Deploy with pip](#how-to-deploy-with-pip)
 - [Quickstart: Build from Source](#how-to-build-from-source)
 - [Quickstart: Deploy with Docker](#how-to-install-verba-with-docker)
@@ -53,6 +54,7 @@ Verba is a fully-customizable personal assistant utilizing [Retrieval Augmented
 | Cohere (e.g. Command R+)          | ✅          | Embedding and Generation Models by Cohere               |
 | Anthrophic (e.g. Claude Sonnet)   | ✅          | Embedding and Generation Models by Anthrophic           |
 | OpenAI (e.g. GPT4)                | ✅          | Embedding and Generation Models by OpenAI               |
+| Groq (e.g. Llama3)                | ✅          | Generation Models by Groq (LPU inference)              |
 
 | 🤖 Embedding Support | Implemented | Description                              |
 | -------------------- | ----------- | ---------------------------------------- |
@@ -159,6 +161,7 @@ Below is a comprehensive list of the API keys and variables you may require:
 | OPENAI_API_KEY         | Your OpenAI Key                                            | Get Access to [OpenAI](https://openai.com/) Models                                |
 | OPENAI_BASE_URL        | URL to OpenAI instance                                     | Models                                                                            |
 | COHERE_API_KEY         | Your API Key                                               | Get Access to [Cohere](https://cohere.com/) Models                                |
+| GROQ_API_KEY         | Your Groq API Key                                               | Get Access to [Groq](https://groq.com/) Models
 | OLLAMA_URL             | URL to your Ollama instance (e.g. http://localhost:11434 ) | Get Access to [Ollama](https://ollama.com/) Models                                |
 | UNSTRUCTURED_API_KEY   | Your API Key                                               | Get Access to [Unstructured](https://docs.unstructured.io/welcome) Data Ingestion |
 | UNSTRUCTURED_API_URL   | URL to Unstructured Instance                               | Get Access to [Unstructured](https://docs.unstructured.io/welcome) Data Ingestion |
@@ -207,7 +210,7 @@ ollama run llama3
 
 Verba supports importing documents through Unstructured IO (e.g plain text, .pdf, .csv, and more). To use them you need the `UNSTRUCTURED_API_KEY` and `UNSTRUCTURED_API_URL` environment variable. You can get it from [Unstructured](https://unstructured.io/)
 
-> UNSTRUCTURED_API_URL is set to `https://api.unstructured.io/general/v0/general` by default
+> UNSTRUCTURED_API_URL is set to `https://api.unstructuredapp.io/general/v0/general` by default
 
 ## AssemblyAI
 
@@ -238,6 +241,13 @@ pip install `.[huggingface]`
 
 > If you're using Docker, modify the Dockerfile accordingly
 
+## Groq
+
+To use Groq LPUs as generation engine, you need to get an API key from [Groq](https://console.groq.com/keys).
+
+>Although you can provide it in the graphical interface when Verba is up, it is recommended to specify it as `GROQ_API_KEY` environment variable before you launch the application.  
+It will allow you to choose the generation model in an up-to-date available models list.
+
 # How to deploy with pip
 
 `Python >=3.10.0`

frontend/app/components/Chat/ChatInterface.tsx

@@ -94,6 +94,8 @@ const ChatInterface: React.FC<ChatInterfaceProps> = ({
 
   const [userInput, setUserInput] = useState("");
   const [messages, setMessages] = useState<Message[]>([]);
+  const [isComposing, setIsComposing] = useState(false);
+
   const currentEmbedding = RAGConfig
     ? (RAGConfig["Embedder"].components[RAGConfig["Embedder"].selected].config[
         "Model"
@@ -317,8 +319,16 @@ const ChatInterface: React.FC<ChatInterfaceProps> = ({
     }
   };
 
+  const handleCompositionStart = () => {
+    setIsComposing(true);
+  }
+
+  const handleCompositionEnd = () => {
+    setIsComposing(false);
+  }
+
   const handleKeyDown = (e: any) => {
-    if (e.key === "Enter" && !e.shiftKey) {
+    if (e.key === "Enter" && !e.shiftKey && !isComposing) {
       e.preventDefault(); // Prevent new line
       sendUserMessage(); // Submit form
     }
@@ -591,6 +601,8 @@ const ChatInterface: React.FC<ChatInterfaceProps> = ({
                     : `No documents detected...`
                 }
                 onKeyDown={handleKeyDown}
+                onCompositionStart={handleCompositionStart}
+                onCompositionEnd={handleCompositionEnd}
                 value={userInput}
                 onChange={(e) => {
                   const newValue = e.target.value;

goldenverba/.env.example

@@ -16,7 +16,7 @@
 # FIRECRAWL_API_KEY=
 
 # UNSTRUCTURED_API_KEY=
-# UNSTRUCTURED_API_URL=
+# UNSTRUCTURED_API_URL=https://api.unstructuredapp.io/general/v0/general
 
 # GITHUB_TOKEN=
 # GITLAB_TOKEN=

goldenverba/components/embedding/CohereEmbedder.py

@@ -5,7 +5,7 @@
 
 from goldenverba.components.interfaces import Embedding
 from goldenverba.components.types import InputConfig
-from goldenverba.components.util import get_environment
+from goldenverba.components.util import get_environment, get_token
 
 from wasabi import msg
 
@@ -20,16 +20,16 @@ def __init__(self):
         self.name = "Cohere"
         self.description = "Vectorizes documents and queries using Cohere"
         self.url = os.getenv("COHERE_BASE_URL", "https://api.cohere.com/v1")
-        models = get_models(self.url, os.getenv("COHERE_API_KEY", None), "embed")
+        models = get_models(self.url, get_token("COHERE_API_KEY", None), "embed")
 
         self.config["Model"] = InputConfig(
             type="dropdown",
-            value=models[0],
+            value=models[0] if models else "",
             description="Select a Cohere Embedding Model",
-            values=models,
+            values=models if models else [],
         )
 
-        if os.getenv("COHERE_API_KEY") is None:
+        if get_token("COHERE_API_KEY") is None:
             self.config["API Key"] = InputConfig(
                 type="password",
                 value="",
@@ -71,7 +71,7 @@ def chunks(lst, n):
 
 def get_models(url: str, token: str, model_type: str):
     try:
-        if token is None:
+        if token is None or token == "":
             return [
                 "embed-english-v3.0",
                 "embed-multilingual-v3.0",

goldenverba/components/embedding/OpenAIEmbedder.py

@@ -8,7 +8,7 @@
 
 from goldenverba.components.interfaces import Embedding
 from goldenverba.components.types import InputConfig
-from goldenverba.components.util import get_environment
+from goldenverba.components.util import get_environment, get_token
 
 
 class OpenAIEmbedder(Embedding):
@@ -20,7 +20,7 @@ def __init__(self):
         self.description = "Vectorizes documents and queries using OpenAI"
 
         # Fetch available models
-        api_key = os.getenv("OPENAI_API_KEY")
+        api_key = get_token("OPENAI_API_KEY")
         base_url = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
         models = self.get_models(api_key, base_url)
 

goldenverba/components/generation/CohereGenerator.py

@@ -6,7 +6,7 @@
 from goldenverba.components.interfaces import Generator
 from goldenverba.components.types import InputConfig
 from goldenverba.components.embedding.CohereEmbedder import get_models
-from goldenverba.components.util import get_environment
+from goldenverba.components.util import get_environment, get_token
 
 
 class CohereGenerator(Generator):
@@ -21,16 +21,16 @@ def __init__(self):
         self.url = os.getenv("COHERE_BASE_URL", "https://api.cohere.com/v1")
         self.context_window = 10000
 
-        models = get_models(self.url, os.getenv("COHERE_API_KEY", None), "chat")
+        models = get_models(self.url, get_token("COHERE_API_KEY", None), "chat")
 
         self.config["Model"] = InputConfig(
             type="dropdown",
-            value=models[0],
+            value=models[0] if models else "",
             description="Select a Cohere Embedding Model",
-            values=models,
+            values=models if models else [],
         )
 
-        if os.getenv("COHERE_API_KEY") is None:
+        if get_token("COHERE_API_KEY") is None:
             self.config["API Key"] = InputConfig(
                 type="password",
                 value="",