updated tldr chain

app.py

@@ -49,7 +49,7 @@ Here's a quick guide to getting started with me:
 | `/paper-synopsis <list of snippet ids>` | Generate a synopsis of the paper. |
 | `/deep-dive [<list of snippet ids>] <query>` | Query me with a specific context. |
 | `/summarise-section [<list of snippet ids>] <section name>` | Summarize a specific section of the paper. |
-| `/tldr <list of snippet ids>` | Generate a tldr summary of the paper. |
+| `/tldr [<list of snippet ids>] <query>` | Generate a tldr summary of the paper. |
 
 
 <br>
@@ -79,7 +79,7 @@ def process_documents_wrapper(inputs):
     return index_documents_wrapper(None, f"/add-papers {inputs}")
 
 
-def index_documents_wrapper(inputs=None, arg='/library'):
+def index_documents_wrapper(inputs=None, arg="/library"):
     response = pd.DataFrame(
         st.session_state.index, columns=["id", "reference", "tokens"]
     )
@@ -251,12 +251,14 @@ def synopsis_wrapper(inputs):
 
 
 def tldr_wrapper(inputs):
-    if inputs == []:
-        raise InvalidArgumentError("Please provide snippet ids")
-    document = "\n\n".join([st.session_state.documents[c].page_content for c in inputs])
+    print(inputs)
+    context, query = parse_context_and_question(inputs)
+    document = "\n\n".join(
+        [st.session_state.documents[c].page_content for c in context]
+    )
     llm = ChatOpenAI(model=st.session_state.model, temperature=0)
     with get_openai_callback() as cb:
-        summary = tldr_chain(llm).invoke({"paper": document})
+        summary = tldr_chain(llm).invoke({"title": query, "paper": document})
         stats = cb
     st.session_state.messages.append(("/tldr", summary, "identity"))
     st.session_state.costing.append(
@@ -401,7 +403,7 @@ if __name__ == "__main__":
         ("/insight-mind-map", list, insights_mind_map_wrapper),
         ("/paper-synopsis", list, synopsis_wrapper),
         ("/summarise-section", str, summarise_wrapper),
-        ("/tldr", list, tldr_wrapper),
+        ("/tldr", str, tldr_wrapper),
     ]
     command_center = CommandCenter(
         default_input_type=str,

tldr_chain.py

@@ -3,12 +3,9 @@ from langchain_core.output_parsers import StrOutputParser
 
 tldr_prompt_template = """
 Create a mind map of the given research paper along the given lines:
-1. Background: A brief overview of what's being studied in what context
-2. Justification: Why the researchers conducted the study
-3. Method: How the researchers arrived at the result
-4. Major Findings: The main findings of the study
-5. Key Results: More details about the results of the study
-6. Conclusion: Significance of the findings and what they mean for future research
+what is {title}?
+what this paper brings to the research community including how it is different from existing work?
+what novel ideas/techniques are presented in this paper?
 
 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.
 
@@ -21,6 +18,6 @@ Ensure that the outline is structured in Markdown format for clarity, facilitati
 tldr_output_parser = StrOutputParser()
 tldr_prompt = PromptTemplate(
     template=tldr_prompt_template,
-    input_variables=["paper"],
+    input_variables=["title", "paper"],
 )
 tldr_chain = lambda model: tldr_prompt | model | tldr_output_parser