import os, sys, json, re import pandas as pd import numpy as np os.environ['LAV_DIR'] = '/home/gmare/lav/' dL = os.listdir(os.environ['LAV_DIR']+'/src/') sys.path = list(set(sys.path + [os.environ['LAV_DIR']+'/src/'+x for x in dL])) import kotoba.chatbot_utils as c_t import kotoba.chatbot_unify as c_u import kotoba.chatbot_prompt as c_p import kotoba.pdf_tools as p_t import kotoba.table_chat as t_c import importlib
importlib.reload(c_t) importlib.reload(c_p) baseDir = os.environ['HOME'] + '/lav/soft/raw/' pdf_doc = baseDir + 'Policies.pdf' pdf_doc = baseDir + 'data_proc.pdf' headers_split = [("####","Chapter"),("######","Section"),('########','Subsection')] pdf_doc = baseDir + 'panasonic_airconditioner_manual.pdf' headers_split = [('#',"Title"),("##","Chapter"),("####","Section"),("###","Subsection")] pdf_doc = baseDir + 'leipzig/kompendium.pdf'
- pdf_doc = baseDir + 'BaroneLamberto2.pdf'
collN = re.sub(".pdf","",pdf_doc).split("/")[-1]
with open(baseDir + 'leipzig/kompendium.md','r') as f:
md_text = f.read()
importlib.reload(p_t) docL = p_t.split_text(md_text,headers_split) print(len(docL))
text = docL[2].page_content llm = c_t.get_llm_bedrock() insD = t_c.md2df(text,llm) respL = [] qL = ["Was ist versichert?","Was ist in compact Tariff versichert dass nicht in classic Tariff versichert ist?","Was ist in classic Tariff versichert dass nicht in compact Tariff versichert ist?"] for q in qL:
resp = insD.chat(q)
respL.append({"question":q,"answer":resp})
if False: #caption images
importlib.reload(c_t) docL = c_t.pdf2md(pdf_doc,headers_split) collT = c_t.create_collection(docL,collN,baseDir) vectT = c_t.faiss_vector_storage(docL,collN,baseDir) vectT = c_t.create_neo4j(docL,collN,baseDir,os.environ['NEO4J_PASS'])
if False: #caption images
importlib.reload(c_t) fL = os.listdir(baseDir + collN) imgL = c_t.image_description(baseDir + collN,fL) collI = c_t.create_collection(imgL,collN + "_img",baseDir)
if False: # create summaries
importlib.reload(c_t) llm = c_t.get_llm_bedrock() summL = c_t.section_summary(docL,llm) collS = c_t.create_collection(summL,collN + "_summary",baseDir) vectS = c_t.faiss_vector_storage(summL,collN + "_summary",baseDir)
if False:
#docL = c_t.pdf_page([pdf_doc]) #docL = c_t.pdf2tree(pdf_doc) docL = c_t.pdf2md(pdf_doc) collT, collS = c_t.create_collection_summary(docL,collN,baseDir,llm)
else:
importlib.reload(c_t) c_t.list_collection(baseDir) collT = c_t.load_chroma(collN,baseDir) collS = c_t.load_chroma(collN + "_summary",baseDir) collI = c_t.load_chroma(collN + "_img",baseDir) vectT = c_t.get_vectorstore(collN,baseDir) retrT = c_t.get_chroma_retriever(collN,baseDir)
q = "Where is the error code table"
resL = vectT.similarity_search(query=q,k=5)
print("\n".join([str(x.metadata) for x in resL]))
resL = retrT.invoke(q)
print("\n".join([str(x.metadata) for x in resL]))
llm = c_t.get_llm_bedrock() res = c_t.search_neo4j(q,llm,collN,os.environ['NEO4J_PASS'])
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
retriever = vectT.as_retriever(search_type="mmr", search_kwargs={"k": 1, "fetch_k": 5})
#retriever.invoke("Error code 53", filter={"source": "news"})
print(retriever.invoke("Error code 53"))
# index = c_t.load_faiss(pdf_doc,baseDir)
# query_engine = index.as_query_engine()
# response = query_engine.query(q)
# print(response.response)
# n = response.source_nodes[0]
if False: #langchain
importlib.reload(c_p)
importlib.reload(c_t)
llm = c_t.get_llm()
chain = c_t.get_chain_confidence(llm,collN,baseDir)
resL = []
for i, aud in audD.iterrows():
print("%0.2f" % (100.*i/audD.shape[0]),end="\r")
q = aud['audit_question_en']
if q == or q != q:
continue
try:
ans = c_t.format_confidence(chain.invoke(q))
except:
continue
res['question'] = q
res['pred_answer'] = ans['answer']
res['pred_justification'] = ans['confidence']
res['pred_context'] =
res["ref_justification"] = aud['exp_reference_en']
res['ref_context'] = aud['Content of BAIT Chapter (all)']
res['ref_answer'] = aud['exp_result']
resL.append(res)
evalDf = pd.DataFrame(resL) evalDf.to_csv(baseDir + "pred_" + modN + ".csv",index=False)
resp = requests.get('https://api.unify.ai/v0/models',headers={"Authorization":"Bearer " + os.environ['UNIFY_KEY']})
modL = resp.text
modL = ["gpt-4o@openai","gpt-3.5-turbo@openai","mixtral-8x7b-instruct-v0.1@aws-bedrock","claude-3-haiku@anthropic","claude-3-opus@anthropic","claude-3-sonnet@anthropic"]
- selL = collT.get(include=[],limit=5,offset=1)
db = c_t.get_vectorstore(collN,baseDir) importlib.reload(c_u) for j, m in enumerate(modL): # unify
try:
unify = c_u.get_unify(modL[j])
except:
continue
modN = modL[j].split("@")[0]
print(modN)
resL = []
for i, aud in audD.iterrows():
print("%0.2f" % (100.*i/audD.shape[0]),end="\r")
q = aud['audit_question_en']
if q == or q != q:
continue
retL = db.similarity_search_with_relevance_scores(q)
retS = "\n".join([x[0].metadata['s'] for x in retL])
ansS = c_u.ask_rag(q,retS,unify)
ansD = eval("{"+ans+"}")
res = {}
yes = False
try:
if re.search(c_u.yesRe,ansD['Answer'].split(",")[0]):
yes = True
except:
if re.search(c_u.yesRe,ansS):
yes = True
res['pred_answer'] = yes
res['pred_justification'] = ans
res['pred_context'] = retS
res['question'] = q
res["ref_justification"] = aud['exp_reference_en']
res['ref_context'] = aud['Content of BAIT Chapter (all)']
res['ref_answer'] = aud['exp_result']
resL.append(res)
evalDf = pd.DataFrame(resL) evalDf.to_csv(baseDir + "pred_" + modN + ".csv",index=False)
print("te se qe te ve be te ne?")