Merge pull request #34 from joshuasundance-swca/summarize

.idea/.name

@@ -1 +1 @@
-langchain-streamlit-demo
+langchain-streamlit-demo

.idea/inspectionProfiles/Project_Default.xml

@@ -18,4 +18,4 @@
  </inspection_tool>
  <inspection_tool class="PyShadowingNamesInspection" enabled="false" level="WEAK WARNING" enabled_by_default="false" />
  </profile>
-</component>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -3,4 +3,4 @@
  <option name="USE_PROJECT_PROFILE" value="false" />
  <version value="1.0" />
  </settings>
-</component>
+</component>

.idea/kubernetes-settings.xml

@@ -3,4 +3,4 @@
  <component name="KubernetesSettings">
  <option name="contextName" value="swca-aks" />
  </component>
-</project>
+</project>

.idea/langchain-streamlit-demo.iml

@@ -5,4 +5,4 @@
  <orderEntry type="jdk" jdkName="Remote Python 3.11.4 Docker (&lt;none&gt;:&lt;none&gt;) (5)" jdkType="Python SDK" />
  <orderEntry type="sourceFolder" forTests="false" />
  </component>
-</module>
+</module>

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="ProjectRootManager" version="2" project-jdk-name="Remote Python 3.11.4 Docker (&lt;none&gt;:&lt;none&gt;) (5)" project-jdk-type="Python SDK" />
-</project>
+</project>

.idea/modules.xml

@@ -5,4 +5,4 @@
  <module fileurl="file://$PROJECT_DIR$/.idea/langchain-streamlit-demo.iml" filepath="$PROJECT_DIR$/.idea/langchain-streamlit-demo.iml" />
  </modules>
  </component>
-</project>
+</project>

.idea/vcs.xml

@@ -3,4 +3,4 @@
  <component name="VcsDirectoryMappings">
  <mapping directory="$PROJECT_DIR$" vcs="Git" />
  </component>
-</project>
+</project>

langchain-streamlit-demo/app.py

@@ -1,29 +1,33 @@
 import os
 from datetime import datetime
 from tempfile import NamedTemporaryFile
-from typing import Union
+from typing import Tuple, List, Dict, Any, Union
 
 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
 from langchain.memory import ConversationBufferMemory, StreamlitChatMessageHistory
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.schema.document import Document
 from langchain.schema.retriever import BaseRetriever
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import FAISS
 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"
 
 # --- Initialization ---
@@ -46,6 +50,7 @@ st_init_null(
  "document_chat_chain_type",
  "llm",
  "ls_tracer",
+ "provider",
  "retriever",
  "run",
  "run_id",
@@ -120,11 +125,11 @@ DEFAULT_CHUNK_OVERLAP = 0
 
 
 @st.cache_data
-def get_retriever(
+def get_texts_and_retriever(
  uploaded_file_bytes: bytes,
  chunk_size: int = DEFAULT_CHUNK_SIZE,
  chunk_overlap: int = DEFAULT_CHUNK_OVERLAP,
-) -> BaseRetriever:
+) -> Tuple[List[Document], BaseRetriever]:
  with NamedTemporaryFile() as temp_file:
  temp_file.write(uploaded_file_bytes)
  temp_file.seek(0)
@@ -138,7 +143,7 @@ def get_retriever(
  texts = text_splitter.split_documents(documents)
  embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
  db = FAISS.from_documents(texts, embeddings)
- return db.as_retriever()
+ return texts, db.as_retriever()
 
 
 # --- Sidebar ---
@@ -152,10 +157,12 @@ with sidebar:
  index=SUPPORTED_MODELS.index(DEFAULT_MODEL),
  )
 
- provider = MODEL_DICT[model]
+ st.session_state.provider = MODEL_DICT[model]
 
- provider_api_key = PROVIDER_KEY_DICT.get(provider) or st.text_input(
- f"{provider} API key",
+ provider_api_key = PROVIDER_KEY_DICT.get(
+ st.session_state.provider,
+ ) or st.text_input(
+ f"{st.session_state.provider} API key",
  type="password",
  )
 
@@ -170,7 +177,7 @@ with sidebar:
 
  openai_api_key = (
  provider_api_key
- if provider == "OpenAI"
+ if st.session_state.provider == "OpenAI"
  else OPENAI_API_KEY
  or st.sidebar.text_input("OpenAI API Key: ", type="password")
  )
@@ -210,7 +217,14 @@ with sidebar:
  )
  document_chat_chain_type = st.selectbox(
  label="Document Chat Chain Type",
- options=["stuff", "refine", "map_reduce", "map_rerank"],
+ options=[
+ "stuff",
+ "refine",
+ "map_reduce",
+ "map_rerank",
+ "Q&A Generation",
+ "Summarization",
+ ],
  index=0,
  help=chain_type_help,
  disabled=not document_chat,
@@ -218,7 +232,10 @@ with sidebar:
 
  if uploaded_file:
  if openai_api_key:
- st.session_state.retriever = get_retriever(
+ (
+ st.session_state.texts,
+ st.session_state.retriever,
+ ) = get_texts_and_retriever(
  uploaded_file_bytes=uploaded_file.getvalue(),
  chunk_size=chunk_size,
  chunk_overlap=chunk_overlap,
@@ -280,7 +297,7 @@ with sidebar:
 
 # --- LLM Instantiation ---
 if provider_api_key:
- if provider == "OpenAI":
+ if st.session_state.provider == "OpenAI":
  st.session_state.llm = ChatOpenAI(
  model=model,
  openai_api_key=provider_api_key,
@@ -288,7 +305,7 @@ if provider_api_key:
  streaming=True,
  max_tokens=max_tokens,
  )
- elif provider == "Anthropic":
+ elif st.session_state.provider == "Anthropic":
  st.session_state.llm = ChatAnthropic(
  model_name=model,
  anthropic_api_key=provider_api_key,
@@ -296,7 +313,7 @@ if provider_api_key:
  streaming=True,
  max_tokens_to_sample=max_tokens,
  )
- elif provider == "Anyscale Endpoints":
+ elif st.session_state.provider == "Anyscale Endpoints":
  st.session_state.llm = ChatAnyscale(
  model=model,
  anyscale_api_key=provider_api_key,
@@ -321,18 +338,18 @@ for msg in STMEMORY.messages:
 if st.session_state.llm:
  # --- Document Chat ---
  if st.session_state.retriever:
- # st.session_state.doc_chain = ConversationalRetrievalChain.from_llm(
- # st.session_state.llm,
- # st.session_state.retriever,
- # memory=MEMORY,
- # )
-
- st.session_state.doc_chain = RetrievalQA.from_chain_type(
- llm=st.session_state.llm,
- chain_type=document_chat_chain_type,
- retriever=st.session_state.retriever,
- memory=MEMORY,
- )
+ if document_chat_chain_type == "Summarization":
+ 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)
+
+ else:
+ st.session_state.doc_chain = RetrievalQA.from_chain_type(
+ llm=st.session_state.llm,
+ chain_type=document_chat_chain_type,
+ retriever=st.session_state.retriever,
+ memory=MEMORY,
+ )
 
  else:
  # --- Regular Chat ---
@@ -375,17 +392,62 @@ if st.session_state.llm:
  )
 
  try:
+ full_response: Union[str, None]
  if use_document_chat:
- st_handler = StreamlitCallbackHandler(st.container())
- callbacks.append(st_handler)
- full_response = st.session_state.doc_chain(
- {"query": prompt},
- callbacks=callbacks,
- tags=["Streamlit Chat"],
- return_only_outputs=True,
- )[st.session_state.doc_chain.output_key]
- st_handler._complete_current_thought()
- st.markdown(full_response)
+ if document_chat_chain_type == "Summarization":
+ 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,
+ tags=["Streamlit Chat"],
+ )
+ if st.session_state.provider == "Anthropic":
+ config["max_concurrency"] = 5
+ raw_results = st.session_state.doc_chain.batch(
+ [
+ {"input": doc.page_content, "prompt": prompt}
+ for doc in st.session_state.texts
+ ],
+ config,
+ )
+ results = combine_qa_pair_lists(raw_results).QuestionAnswerPairs
+
+ 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)
+ ],
+ )
+
+ st.markdown(output_text)
+
+ else:
+ st_handler = StreamlitCallbackHandler(st.container())
+ callbacks.append(st_handler)
+ full_response = st.session_state.doc_chain(
+ {"query": prompt},
+ callbacks=callbacks,
+ tags=["Streamlit Chat"],
+ return_only_outputs=True,
+ )[st.session_state.doc_chain.output_key]
+ st_handler._complete_current_thought()
+ st.markdown(full_response)
  else:
  message_placeholder = st.empty()
  stream_handler = StreamHandler(message_placeholder)
@@ -399,7 +461,7 @@ if st.session_state.llm:
  message_placeholder.markdown(full_response)
  except (openai.error.AuthenticationError, anthropic.AuthenticationError):
  st.error(
- f"Please enter a valid {provider} API key.",
+ f"Please enter a valid {st.session_state.provider} API key.",
  icon="❌",
  )
  full_response = None
@@ -468,4 +530,4 @@ if st.session_state.llm:
  st.warning("Invalid feedback score.")
 
 else:
- st.error(f"Please enter a valid {provider} API key.", icon="❌")
+ st.error(f"Please enter a valid {st.session_state.provider} API key.", icon="❌")

langchain-streamlit-demo/qagen.py

@@ -0,0 +1,75 @@
+from functools import reduce
+from typing import List
+
+from langchain.output_parsers import PydanticOutputParser, OutputFixingParser
+from langchain.prompts.chat import (
+ ChatPromptTemplate,
+)
+from langchain.schema.language_model import BaseLanguageModel
+from langchain.schema.runnable import RunnableSequence
+from pydantic import BaseModel, field_validator, Field
+
+
+class QuestionAnswerPair(BaseModel):
+ question: str = Field(..., description="The question that will be answered.")
+ answer: str = Field(..., description="The answer to the question that was asked.")
+
+ @field_validator("question")
+ def validate_question(cls, v: str) -> str:
+ if not v.endswith("?"):
+ raise ValueError("Question must end with a question mark.")
+ return v
+
+
+class QuestionAnswerPairList(BaseModel):
+ QuestionAnswerPairs: List[QuestionAnswerPair]
+
+
+PYDANTIC_PARSER: PydanticOutputParser = PydanticOutputParser(
+ pydantic_object=QuestionAnswerPairList,
+)
+
+
+templ1 = """You are a smart assistant designed to help college professors come up with reading comprehension questions.
+Given a piece of text, you must come up with question and answer pairs that can be used to test a student's reading comprehension abilities.
+Generate as many question/answer pairs as you can.
+When coming up with the question/answer pairs, you must respond in the following format:
+{format_instructions}
+
+Do not provide additional commentary and do not wrap your response in Markdown formatting. Return RAW, VALID JSON.
+"""
+templ2 = """{prompt}
+Please create question/answer pairs, in the specified JSON format, for the following text:
+----------------
+{input}"""
+CHAT_PROMPT = ChatPromptTemplate.from_messages(
+ [
+ ("system", templ1),
+ ("human", templ2),
+ ],
+).partial(format_instructions=PYDANTIC_PARSER.get_format_instructions)
+
+
+def combine_qa_pair_lists(
+ qa_pair_lists: List[QuestionAnswerPairList],
+) -> QuestionAnswerPairList:
+ def reducer(
+ accumulator: QuestionAnswerPairList,
+ current: QuestionAnswerPairList,
+ ) -> QuestionAnswerPairList:
+ return QuestionAnswerPairList(
+ QuestionAnswerPairs=accumulator.QuestionAnswerPairs
+ + current.QuestionAnswerPairs,
+ )
+
+ return reduce(
+ reducer,
+ qa_pair_lists,
+ QuestionAnswerPairList(QuestionAnswerPairs=[]),
+ )
+
+
+def get_qa_gen_chain(llm: BaseLanguageModel) -> RunnableSequence:
+ return (
+ CHAT_PROMPT | llm | OutputFixingParser.from_llm(llm=llm, parser=PYDANTIC_PARSER)
+ )

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",
+ )