Update lawchain.py

lawchain.py

@@ -20,9 +20,9 @@ persist_directory = 'db'
 instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base") 
 embedding = instructor_embeddings
 #tokenizer = AutoTokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0")
-tokenizer = AutoTokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0", update_special_tokens=True)
+tokenizer = AutoTokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0")
 model = AutoModelForSeq2SeqLM.from_pretrained("lmsys/fastchat-t5-3b-v1.0")
-pipe = pipeline("text2text-generation",model=model, tokenizer=tokenizer, max_length=256)
+pipe = pipeline("text2text-generation",model=model, tokenizer=tokenizer)
 local_llm = HuggingFacePipeline(pipeline=pipe)
 vectordb = Chroma(persist_directory=persist_directory,embedding_function=embedding)
 retriever = vectordb.as_retriever(search_kwargs={"k": 3})