add features to ease hackathon

app.py

@@ -47,7 +47,10 @@ synthesis_prompt_template = get_synthesis_prompt(config)
 
 ## Building LLM
 print("Building LLM")
-llm = get_llm_api()
+groq_model_name = (
+    config["groq_model_name"] if not os.getenv("EKI_OPENAI_LLM_DEPLOYMENT_NAME") else ""
+)
+llm = get_llm_api(groq_model_name)
 
 ## Loading BDDs
 print("Loading Databases")
@@ -58,7 +61,7 @@ if os.getenv("EKI_OPENAI_LLM_DEPLOYMENT_NAME"):
     bdd_afp = get_vectorstore_api("afp")
 
 else:
-    qdrants_public = get_qdrants_public(config, "your_database_hf")
+    qdrants_public = get_qdrants_public(config)
     qdrants = {**qdrants, **qdrants_public}
     bdd_presse = None
     bdd_afp = None

spinoza_project/config_public.yaml

@@ -24,6 +24,9 @@ prompt_naming:
   ADEME: "ADEME"
   Presse: "Presse"
 
+groq_model_name: "llama3-groq-70b-8192-tool-use-preview" # llama-3.1-8b-instant / llama3-groq-70b-8192-tool-use-preview / llama-3.2-90b-text-preview / llama-3.2-3b-preview
+database_hf: "your_database_hf"
+
 query_preprompt: "query: "
 passage_preprompt: "passage: "
 embedding_model: "intfloat/multilingual-e5-base"

spinoza_project/source/backend/llm_utils.py

@@ -42,7 +42,7 @@ class LLM:
         return predictions
 
 
-def get_llm_api():
+def get_llm_api(groq_model_name):
     if os.getenv("EKI_OPENAI_LLM_DEPLOYMENT_NAME"):
         print("Using Azure OpenAI API")
         return LLM(
@@ -62,7 +62,7 @@ def get_llm_api():
         print("Using GROQ API")
         return LLM(
             ChatGroq(
-                model="llama3-groq-70b-8192-tool-use-preview",  # llama-3.1-8b-instant / llama3-groq-70b-8192-tool-use-preview / llama-3.2-90b-text-preview / llama-3.2-3b-preview
+                model=groq_model_name,
                 temperature=0,
                 max_tokens=2048,
             )

spinoza_project/source/frontend/gradio_utils.py

@@ -64,11 +64,11 @@ def get_qdrants(config):
     return qdrants
 
 
-def get_qdrants_public(config, repo_id):
+def get_qdrants_public(config):
     qdrants = {
         tab: pickle_to_document_store(
             hf_hub_download(
-                repo_id=repo_id,
+                repo_id=config["prompt_naming"],
                 filename=f"database_{tab}.pickle",
                 repo_type="dataset",
             )