qagen & summarize

langchain-streamlit-demo/app.py

@@ -7,12 +7,12 @@ import anthropic
 import langsmith.utils
 import openai
 import streamlit as st
-from langchain import LLMChain
 from langchain.callbacks import StreamlitCallbackHandler
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.tracers.langchain import LangChainTracer, wait_for_all_tracers
 from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
 from langchain.chains import RetrievalQA
+from langchain.chains.llm import LLMChain
 from langchain.chat_models import ChatOpenAI, ChatAnyscale, ChatAnthropic
 from langchain.document_loaders import PyPDFLoader
 from langchain.embeddings import OpenAIEmbeddings
@@ -26,6 +26,7 @@ from langsmith.client import Client
 from streamlit_feedback import streamlit_feedback
 
 from qagen import get_qa_gen_chain, combine_qa_pair_lists
+from summarize import get_summarization_chain
 
 __version__ = "0.0.6"
 
@@ -216,7 +217,14 @@ with sidebar:
  )
  document_chat_chain_type = st.selectbox(
  label="Document Chat Chain Type",
- options=["stuff", "refine", "map_reduce", "map_rerank", "Q&A Generation"],
+ options=[
+ "stuff",
+ "refine",
+ "map_reduce",
+ "map_rerank",
+ "Q&A Generation",
+ "Summarization",
+ ],
  index=0,
  help=chain_type_help,
  disabled=not document_chat,
@@ -331,13 +339,7 @@ if st.session_state.llm:
  # --- Document Chat ---
  if st.session_state.retriever:
  if document_chat_chain_type == "Summarization":
- raise NotImplementedError
- # st.session_state.doc_chain = RetrievalQA.from_chain_type(
- # llm=st.session_state.llm,
- # chain_type=chain_type,
- # retriever=st.session_state.retriever,
- # memory=MEMORY,
- # )
+ st.session_state.doc_chain = "summarization"
  elif document_chat_chain_type == "Q&A Generation":
  st.session_state.doc_chain = get_qa_gen_chain(st.session_state.llm)
 
@@ -393,7 +395,17 @@ if st.session_state.llm:
  full_response: Union[str, None]
  if use_document_chat:
  if document_chat_chain_type == "Summarization":
- raise NotImplementedError
+ st.session_state.doc_chain = get_summarization_chain(
+ st.session_state.llm,
+ prompt,
+ )
+ full_response = st.session_state.doc_chain.run(
+ st.session_state.texts,
+ callbacks=callbacks,
+ tags=["Streamlit Chat"],
+ )
+
+ st.markdown(full_response)
  elif document_chat_chain_type == "Q&A Generation":
  config: Dict[str, Any] = dict(
  callbacks=callbacks,
@@ -409,14 +421,21 @@ if st.session_state.llm:
  config,
  )
  results = combine_qa_pair_lists(raw_results).QuestionAnswerPairs
- full_response = "\n".join(
- f"**Q:** {result.question}\n**A:** {result.answer}\n"
- for result in results
+
+ def _to_str(idx, qap):
+ question_piece = f"{idx}. **Q:** {qap.question}"
+ whitespace = " " * (len(str(idx)) + 2)
+ answer_piece = f"{whitespace}**A:** {qap.answer}"
+ return f"{question_piece}\n{answer_piece}"
+
+ output_text = "\n\n".join(
+ [
+ _to_str(idx, qap)
+ for idx, qap in enumerate(results, start=1)
+ ],
  )
- for idx, result in enumerate(results, start=1):
- st.markdown(f"{idx}. **Q:** {result.question}")
- st.markdown(f"{idx}. **A:** {result.answer}")
- st.markdown("\n")
+
+ st.markdown(output_text)
 
  else:
  st_handler = StreamlitCallbackHandler(st.container())

langchain-streamlit-demo/summarize.py

@@ -0,0 +1,51 @@
+from langchain.chains.base import Chain
+from langchain.chains.summarize import load_summarize_chain
+from langchain.prompts import PromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
+
+prompt_template = """Write a concise summary of the following text, based on the user input.
+User input: {query}
+Text:
+```
+{text}
+```
+CONCISE SUMMARY:"""
+
+refine_template = (
+ "You are iteratively crafting a summary of the text below based on the user input\n"
+ "User input: {query}"
+ "We have provided an existing summary up to a certain point: {existing_answer}\n"
+ "We have the opportunity to refine the existing summary"
+ "(only if needed) with some more context below.\n"
+ "------------\n"
+ "{text}\n"
+ "------------\n"
+ "Given the new context, refine the original summary.\n"
+ "If the context isn't useful, return the original summary.\n"
+ "If the context is useful, refine the summary to include the new context.\n"
+ "Your contribution is helping to build a comprehensive summary of a large body of knowledge.\n"
+ "You do not have the complete context, so do not discard pieces of the original summary."
+)
+
+
+def get_summarization_chain(
+ llm: BaseLanguageModel,
+ prompt: str,
+) -> Chain:
+ _prompt = PromptTemplate.from_template(
+ prompt_template,
+ partial_variables={"query": prompt},
+ )
+ refine_prompt = PromptTemplate.from_template(
+ refine_template,
+ partial_variables={"query": prompt},
+ )
+ return load_summarize_chain(
+ llm=llm,
+ chain_type="refine",
+ question_prompt=_prompt,
+ refine_prompt=refine_prompt,
+ return_intermediate_steps=False,
+ input_key="input_documents",
+ output_key="output_text",
+ )