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from transformers import pipeline
# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"j
repo_id = "jonruida/model-IC"
query_pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.float16,
device_map="auto", max_new_tokens=200)
def test_rag(pipeline, input_text):
docs = chroma_db/chroma.sqlite3.similarity_search_with_score(query)
context = []
for doc,score in docs:
if(score<7):
doc_details = doc.to_json()['kwargs']
context.append( doc_details['page_content'])
if(len(context)!=0):
messages = [{"role": "user", "content": "Basándote en la siguiente información: " + "\n".join(context) + "\n Responde en castellano a la pregunta: " + query}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
answer = outputs[0]["generated_text"]
return answer[answer.rfind("[/INST]")+8:],docs
else:
return "No tengo información para responder a esta pregunta",docs
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