rag beta release

crazy_functions/Rag_Interface.py

@@ -1,13 +1,18 @@
-from toolbox import CatchException, update_ui, get_conf, get_log_folder
+from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
+from crazy_functions.crazy_utils import input_clipping
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
 
 RAG_WORKER_REGISTER = {}
 
+MAX_HISTORY_ROUND = 5
+MAX_CONTEXT_TOKEN_LIMIT = 4096
+REMEMBER_PREVIEW = 1000
+
 @CatchException
 def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
 
-    # first, we retrieve rag worker from global context
+    # 1. we retrieve rag worker from global context
     user_name = chatbot.get_user()
     if user_name in RAG_WORKER_REGISTER:
         rag_worker = RAG_WORKER_REGISTER[user_name]
@@ -18,22 +23,53 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
             checkpoint_dir=get_log_folder(user_name, plugin_name='experimental_rag'), 
             auto_load_checkpoint=True)
 
-    # second, we search vector store and build prompts
-    i_say = txt
+    chatbot.append([txt, '正在召回知识 ...'])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 2. clip history to reduce token consumption
+    #   2-1. reduce chat round
+    txt_origin = txt
+
+    if len(history) > MAX_HISTORY_ROUND * 2:
+        history = history[-(MAX_HISTORY_ROUND * 2):]
+    txt_clip, history, flags = input_clipping(txt, history, max_token_limit=MAX_CONTEXT_TOKEN_LIMIT, return_clip_flags=True)
+    input_is_clipped_flag = (flags["original_input_len"] != flags["clipped_input_len"])
+
+    #   2-2. if input is clipped, add input to vector store before retrieve
+    if input_is_clipped_flag:
+        yield from update_ui_lastest_msg('检测到长输入, 正在向量化 ...', chatbot, history, delay=0) # 刷新界面
+        # save input to vector store
+        rag_worker.add_text_to_vector_store(txt_origin)
+        yield from update_ui_lastest_msg('向量化完成 ...', chatbot, history, delay=0) # 刷新界面
+        if len(txt_origin) > REMEMBER_PREVIEW:
+            HALF = REMEMBER_PREVIEW//2
+            i_say_to_remember = txt[:HALF] + f" ...\n...(省略{len(txt_origin)-REMEMBER_PREVIEW}字)...\n... " + txt[-HALF:]
+            if (flags["original_input_len"] - flags["clipped_input_len"]) > HALF:
+                txt_clip = txt_clip  + f" ...\n...(省略{len(txt_origin)-len(txt_clip)-HALF}字)...\n... " + txt[-HALF:]
+            else:
+                pass
+            i_say = txt_clip
+        else:
+            i_say_to_remember = i_say = txt_clip
+    else:
+        i_say_to_remember = i_say = txt_clip
+
+    # 3. we search vector store and build prompts
     nodes = rag_worker.retrieve_from_store_with_query(i_say)
     prompt = rag_worker.build_prompt(query=i_say, nodes=nodes)
 
-    # third, it is time to query llms
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+    # 4. it is time to query llms
+    if len(chatbot) != 0: chatbot.pop(-1) # pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`
+    model_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
         inputs=prompt, inputs_show_user=i_say,
         llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
         sys_prompt=system_prompt,
         retry_times_at_unknown_error=0
     )
 
-    # finally, remember what has been asked / answered
-    rag_worker.remember_qa(i_say, gpt_say)
-    history.extend([i_say, gpt_say])
+    # 5. remember what has been asked / answered
+    yield from update_ui_lastest_msg(model_say + '</br></br>' + '对话记忆中, 请稍等 ...', chatbot, history, delay=0.5) # 刷新界面
+    rag_worker.remember_qa(i_say_to_remember, model_say)
+    history.extend([i_say, model_say])
 
-    # yield, see you next time
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+    yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0) # 刷新界面

crazy_functions/crazy_utils.py

@@ -4,7 +4,7 @@ import threading
 import os
 import logging
 
-def input_clipping(inputs, history, max_token_limit):
+def input_clipping(inputs, history, max_token_limit, return_clip_flags=False):
     """
     当输入文本 + 历史文本超出最大限制时，采取措施丢弃一部分文本。
     输入：
@@ -20,17 +20,20 @@ def input_clipping(inputs, history, max_token_limit):
     enc = model_info["gpt-3.5-turbo"]['tokenizer']
     def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
 
+
     mode = 'input-and-history'
     # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
     input_token_num = get_token_num(inputs)
+    original_input_len = len(inputs)
     if input_token_num < max_token_limit//2:
         mode = 'only-history'
         max_token_limit = max_token_limit - input_token_num
 
     everything = [inputs] if mode == 'input-and-history' else ['']
     everything.extend(history)
-    n_token = get_token_num('\n'.join(everything))
+    full_token_num = n_token = get_token_num('\n'.join(everything))
     everything_token = [get_token_num(e) for e in everything]
+    everything_token_num = sum(everything_token)
     delta = max(everything_token) // 16 # 截断时的颗粒度
 
     while n_token > max_token_limit:
@@ -43,10 +46,24 @@ def input_clipping(inputs, history, max_token_limit):
 
     if mode == 'input-and-history':
         inputs = everything[0]
+        full_token_num = everything_token_num
     else:
-        pass
+        full_token_num = everything_token_num + input_token_num
+
     history = everything[1:]
-    return inputs, history
+
+    flags = {
+        "mode": mode,
+        "original_input_token_num": input_token_num,
+        "original_full_token_num": full_token_num,
+        "original_input_len": original_input_len,
+        "clipped_input_len": len(inputs),
+    }
+
+    if not return_clip_flags:
+        return inputs, history
+    else:
+        return inputs, history, flags
 
 def request_gpt_model_in_new_thread_with_ui_alive(
         inputs, inputs_show_user, llm_kwargs,