gerasdf commited on
Commit
20528a7
·
1 Parent(s): f5924e7

Revert "History with doc_ids. This version doesn't work. to do get the _ids for documents I'd have to impement either a new retriever that answers with documents and their _ids or manually implement the vector search in the pipeline. I'm dropping it for now, I'll just re-do the vector search when loading a history"

Browse files
Files changed (1) hide show
  1. query.py +5 -10
query.py CHANGED
@@ -13,7 +13,7 @@ from elevenlabs import VoiceSettings
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  from elevenlabs.client import ElevenLabs
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  from openai import OpenAI
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- from json import loads as json_loads, dumps as json_dumps
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  import itertools
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  import time
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  import os
@@ -49,9 +49,7 @@ def ai_setup():
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  retriever = vstore.as_retriever(search_kwargs={'k': 10})
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  prompt_template = os.environ.get("PROMPT_TEMPLATE")
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- prompt = ChatPromptTemplate.from_messages([
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- ('system', "{doc_ids}"),
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- ('system', prompt_template)])
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  prompt_chain = (
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  {"context": retriever, "question": RunnablePassthrough()}
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  | RunnableLambda(format_context)
@@ -101,9 +99,7 @@ def format_context(pipeline_state):
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  context += text
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  context += '\n\n---\n'
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- doc_ids = [1,2,3,4,5]
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  pipeline_state["context"] = context
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- pipeline_state["doc_ids"] = json_dumps(doc_ids)
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  return pipeline_state
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  def just_read(pipeline_state):
@@ -278,13 +274,12 @@ def chat(message, history, state, request:gr.Request):
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  else:
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  if AI:
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  if not history:
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- system_prompts = prompt_chain.invoke(message)
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- system_prompt = system_prompts.messages[1]
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  state["system"] = system_prompt
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  # Next is commented out because astra has a limit on document size
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- doc_ids = system_prompts.messages[0].content
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- add_history(state, request, "system", doc_ids, name=message)
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  else:
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  system_prompt = state["system"]
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  from elevenlabs.client import ElevenLabs
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  from openai import OpenAI
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+ from json import loads as json_loads
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  import itertools
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  import time
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  import os
 
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  retriever = vstore.as_retriever(search_kwargs={'k': 10})
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  prompt_template = os.environ.get("PROMPT_TEMPLATE")
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+ prompt = ChatPromptTemplate.from_messages([('system', prompt_template)])
 
 
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  prompt_chain = (
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  {"context": retriever, "question": RunnablePassthrough()}
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  | RunnableLambda(format_context)
 
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  context += text
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  context += '\n\n---\n'
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  pipeline_state["context"] = context
 
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  return pipeline_state
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  def just_read(pipeline_state):
 
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  else:
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  if AI:
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  if not history:
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+ system_prompt = prompt_chain.invoke(message)
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+ system_prompt = system_prompt.messages[0]
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  state["system"] = system_prompt
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  # Next is commented out because astra has a limit on document size
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+ # add_history(state, request, "system", system_prompt, name=message)
 
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  else:
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  system_prompt = state["system"]
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