fix: streamlit fetch data

app/helpers/_vectorstore.py

@@ -156,7 +156,7 @@ def get_collection_metadata(self, collection_names: List[str] = [], type: str =
 
         # sort by updated_at and remove duplicates collections with same names (keep the latest version), concerns only public collections
         sorted_data = sorted(metadata, key=lambda x: x.payload.get("updated_at", 0), reverse=False)
-        metadata = list({item.payload["name"]: item for item in sorted_data}.values())
+        metadata = list({item.payload["name"]: item for item in sorted_data if "name" in item.payload}.values())
 
         for i in range(len(metadata)):
             metadata[i] = CollectionMetadata(

app/tools/__init__.py

@@ -1,4 +1,4 @@
 from ._baserag import BaseRAG
-from ._sppfewshots import SPPFewShots
+from ._fewshots import FewShots
 
-__all__ = ["BaseRAG", "SPPFewShots"]
+__all__ = ["BaseRAG", "FewShots"]

app/tools/_sppfewshots.py → app/tools/_fewshots.py

@@ -7,12 +7,13 @@
 from app.schemas.tools import ToolOutput
 
 
-class SPPFewShots:
+class FewShots:
     """
-    Service Public Plus Fewshots RAG.
+    Fewshots RAG.
 
     Args:
         embeddings_model (str): OpenAI embeddings model
+        collection (str): Collection name. The collection must have question/answer pairs in metadata.
         k (int, optional): Top K per collection. Defaults to 4.
     """
 
@@ -56,7 +57,7 @@ async def get_prompt(
         results = vectorstore.search(collection_names=[collection.name], prompt=prompt, k=k, model=embeddings_model)
 
         context = "\n\n\n".join([
-            f"Question: {result.payload.get('description', 'N/A')}\n" f"Réponse: {result.payload.get('reponse', 'N/A')}" for result in results
+            f"Question: {result.payload.get('question', 'N/A')}\n" f"Réponse: {result.payload.get('answer', 'N/A')}" for result in results
         ])
 
         prompt = self.DEFAULT_PROMPT_TEMPLATE.format(context=context, prompt=prompt)

compose.yml

@@ -15,6 +15,15 @@ services:
     volumes:
      - .:/home/albert/conf # a config.yml file should be in this folder
 
+  streamlit:
+    image: ghcr.io/etalab-ia/albert-api/ui:latest
+    command: ["streamlit", "run", "chat.py", "--server.port=8501", "--browser.gatherUsageStats", "false", "--theme.base", "light"]
+    restart: always
+    environment:
+      - BASE_URL=http://fastapi:8000/v1
+    ports:
+      - 8501:8501
+
   qdrant:
     image: qdrant/qdrant:v1.9.7-unprivileged
     restart: always
@@ -54,17 +63,6 @@ services:
         timeout: 10s
         retries: 5
 
-  streamlit:
-    image: ghcr.io/etalab-ia/albert-api/ui:latest
-    command: ["streamlit", "run", "app.py", "--server.port=8501", "--browser.gatherUsageStats", "false", "--theme.base", "light"]
-    restart: always
-    environment:
-      - BASE_URL=http://fastapi:8000/v1
-    ports:
-      - 8501:8501
-    depends_on:
-      - fastapi
-
 volumes:
   qdrant:
   minio:

docs/deploiement.md

@@ -90,5 +90,5 @@ Voici les types de base de données supportées, à configurer dans le fichier d
 2. Exécutez l'application Streamlit
 
   ```bash
-  streamlit run ui/app.py
+  streamlit run ui/chat.py --server.port 8501 --browser.gatherUsageStats false --theme.base light
   ```