added EvalDataset Generation
Browse files
app.py
CHANGED
@@ -127,12 +127,13 @@ class BSIChatbot:
|
|
127 |
#db = Qdrant(client=client, collection_name=self.collection_name, embeddings=embeddings, )
|
128 |
|
129 |
#Embedding, Vector generation and storing:
|
130 |
-
self.embedding_model
|
131 |
-
|
132 |
-
|
133 |
-
|
134 |
-
|
135 |
-
|
|
|
136 |
|
137 |
#index_cpu = faiss.IndexFlatL2(1024)
|
138 |
#res = faiss.StandardGpuResources()
|
@@ -554,9 +555,9 @@ class BSIChatbot:
|
|
554 |
print(data["Frage"])
|
555 |
#def ragPromptNew(self, query, rerankingStep, history, stepBackPrompt)
|
556 |
try:
|
557 |
-
print(using("PreRag"))
|
558 |
data["Answer"],data["Context"] = self.ragPromptNew(data["Frage"],True,None,True, True)
|
559 |
-
print(using("AfterRag"))
|
560 |
data["Answer"]=data["Answer"].choices[0].message.content
|
561 |
except Exception as e:
|
562 |
print(f"Fehler bei Eintrag {i}: {e}")
|
|
|
127 |
#db = Qdrant(client=client, collection_name=self.collection_name, embeddings=embeddings, )
|
128 |
|
129 |
#Embedding, Vector generation and storing:
|
130 |
+
if self.embedding_model is None:
|
131 |
+
self.embedding_model = HuggingFaceEmbeddings(
|
132 |
+
model_name=self.word_and_embed_model_path,
|
133 |
+
multi_process=False,
|
134 |
+
model_kwargs={"device": "cuda"},
|
135 |
+
encode_kwargs={"normalize_embeddings": True}, # Set `True` for cosine similarity
|
136 |
+
)
|
137 |
|
138 |
#index_cpu = faiss.IndexFlatL2(1024)
|
139 |
#res = faiss.StandardGpuResources()
|
|
|
555 |
print(data["Frage"])
|
556 |
#def ragPromptNew(self, query, rerankingStep, history, stepBackPrompt)
|
557 |
try:
|
558 |
+
print(self.using("PreRag"))
|
559 |
data["Answer"],data["Context"] = self.ragPromptNew(data["Frage"],True,None,True, True)
|
560 |
+
print(self.using("AfterRag"))
|
561 |
data["Answer"]=data["Answer"].choices[0].message.content
|
562 |
except Exception as e:
|
563 |
print(f"Fehler bei Eintrag {i}: {e}")
|