updated tldr chain
Browse files- app.py +9 -7
- tldr_chain.py +4 -7
app.py
CHANGED
@@ -49,7 +49,7 @@ Here's a quick guide to getting started with me:
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| `/paper-synopsis <list of snippet ids>` | Generate a synopsis of the paper. |
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| `/deep-dive [<list of snippet ids>] <query>` | Query me with a specific context. |
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| `/summarise-section [<list of snippet ids>] <section name>` | Summarize a specific section of the paper. |
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| `/tldr <list of snippet ids>` | Generate a tldr summary of the paper. |
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<br>
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@@ -79,7 +79,7 @@ def process_documents_wrapper(inputs):
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return index_documents_wrapper(None, f"/add-papers {inputs}")
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def index_documents_wrapper(inputs=None, arg=
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response = pd.DataFrame(
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st.session_state.index, columns=["id", "reference", "tokens"]
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)
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@@ -251,12 +251,14 @@ def synopsis_wrapper(inputs):
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def tldr_wrapper(inputs):
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document = "\n\n".join(
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llm = ChatOpenAI(model=st.session_state.model, temperature=0)
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with get_openai_callback() as cb:
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summary = tldr_chain(llm).invoke({"paper": document})
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stats = cb
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st.session_state.messages.append(("/tldr", summary, "identity"))
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st.session_state.costing.append(
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@@ -401,7 +403,7 @@ if __name__ == "__main__":
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("/insight-mind-map", list, insights_mind_map_wrapper),
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("/paper-synopsis", list, synopsis_wrapper),
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("/summarise-section", str, summarise_wrapper),
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("/tldr",
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]
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command_center = CommandCenter(
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default_input_type=str,
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| `/paper-synopsis <list of snippet ids>` | Generate a synopsis of the paper. |
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| `/deep-dive [<list of snippet ids>] <query>` | Query me with a specific context. |
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| `/summarise-section [<list of snippet ids>] <section name>` | Summarize a specific section of the paper. |
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| `/tldr [<list of snippet ids>] <query>` | Generate a tldr summary of the paper. |
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<br>
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return index_documents_wrapper(None, f"/add-papers {inputs}")
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def index_documents_wrapper(inputs=None, arg="/library"):
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response = pd.DataFrame(
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st.session_state.index, columns=["id", "reference", "tokens"]
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)
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def tldr_wrapper(inputs):
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print(inputs)
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context, query = parse_context_and_question(inputs)
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document = "\n\n".join(
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[st.session_state.documents[c].page_content for c in context]
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)
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llm = ChatOpenAI(model=st.session_state.model, temperature=0)
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with get_openai_callback() as cb:
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summary = tldr_chain(llm).invoke({"title": query, "paper": document})
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stats = cb
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st.session_state.messages.append(("/tldr", summary, "identity"))
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st.session_state.costing.append(
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("/insight-mind-map", list, insights_mind_map_wrapper),
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("/paper-synopsis", list, synopsis_wrapper),
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("/summarise-section", str, summarise_wrapper),
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("/tldr", str, tldr_wrapper),
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]
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command_center = CommandCenter(
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default_input_type=str,
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tldr_chain.py
CHANGED
@@ -3,12 +3,9 @@ from langchain_core.output_parsers import StrOutputParser
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tldr_prompt_template = """
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Create a mind map of the given research paper along the given lines:
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4. Major Findings: The main findings of the study
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5. Key Results: More details about the results of the study
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6. Conclusion: Significance of the findings and what they mean for future research
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The above sections may differ from paper to paper, hence you may need to adjust the structure accordingly by dropping / merging one or more sections.
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@@ -21,6 +18,6 @@ Ensure that the outline is structured in Markdown format for clarity, facilitati
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tldr_output_parser = StrOutputParser()
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tldr_prompt = PromptTemplate(
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template=tldr_prompt_template,
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input_variables=["paper"],
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)
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tldr_chain = lambda model: tldr_prompt | model | tldr_output_parser
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tldr_prompt_template = """
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Create a mind map of the given research paper along the given lines:
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what is {title}?
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what this paper brings to the research community including how it is different from existing work?
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what novel ideas/techniques are presented in this paper?
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The above sections may differ from paper to paper, hence you may need to adjust the structure accordingly by dropping / merging one or more sections.
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tldr_output_parser = StrOutputParser()
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tldr_prompt = PromptTemplate(
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template=tldr_prompt_template,
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input_variables=["title", "paper"],
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)
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tldr_chain = lambda model: tldr_prompt | model | tldr_output_parser
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