Upload 2 files

app.py

@@ -18,6 +18,7 @@ from chat_chains import (
 from autoqa_chains import auto_qa_chain, auto_qa_output_parser
 from chain_of_density import chain_of_density_chain
 from insights_bullet_chain import insights_bullet_chain
+from insights_mind_map_chain import insights_mind_map_chain
 from synopsis_chain import synopsis_chain
 from custom_exceptions import InvalidArgumentError, InvalidCommandError
 from openai_configuration import openai_parser
@@ -43,6 +44,7 @@ Here's a quick guide to getting started with me:
 | `/auto-insight <list of snippet ids>` | Automatically generate questions and answers for the paper. |
 | `/condense-summary <list of snippet ids>` | Generate increasingly concise, entity-dense summaries of the paper. |
 | `/insight-bullets <list of snippet ids>` | Extract and summarize key insights, methods, results, and conclusions. |
+| `/insight-mind-map <list of snippet ids>` | Create a structured outline of the key insights in Markdown format. |
 | `/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. |
@@ -264,6 +266,25 @@ def insights_bullet_wrapper(inputs):
     return (insights, "identity")
 
 
+def insights_mind_map_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])
+    llm = ChatOpenAI(model=st.session_state.model, temperature=0)
+    with get_openai_callback() as cb:
+        insights = insights_mind_map_chain(llm).invoke({"paper": document})
+        stats = cb
+    st.session_state.messages.append(("/insight-mind-map", insights, "identity"))
+    st.session_state.costing.append(
+        {
+            "prompt tokens": stats.prompt_tokens,
+            "completion tokens": stats.completion_tokens,
+            "cost": stats.total_cost,
+        }
+    )
+    return (insights, "identity")
+
+
 def auto_qa_chain_wrapper(inputs):
     if inputs == []:
         raise InvalidArgumentError("Please provide snippet ids")
@@ -346,6 +367,7 @@ if __name__ == "__main__":
         ("/deep-dive", str, query_llm_wrapper),
         ("/condense-summary", list, chain_of_density_wrapper),
         ("/insight-bullets", list, insights_bullet_wrapper),
+        ("/insight-mind-map", list, insights_mind_map_wrapper),
         ("/paper-synopsis", list, synopsis_wrapper),
         ("/summarise-section", str, summarise_wrapper),
     ]

insights_mind_map_chain.py

@@ -0,0 +1,37 @@
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+insights_mind_map_prompt_template = """
+Based on the provided excerpt, create a detailed outline in Markdown format that organizes the content into distinct sections for the experiment setup and separate branches for each analysis mentioned. 
+This structure should provide a clear and comprehensive overview of the study, emphasizing the setup, methodologies, findings, and implications of each analysis independently. 
+Structure the outline as follows:
+
+- **Experiment Setup**:
+    - Description of the general setup for the experiments, including any datasets and baselines used.
+
+For each analysis mentioned in the excerpt, create individual branches as follows:
+
+- **Analysis [Name/Number]**:
+    1. **Objectives**: 
+        - The main goal or purpose of this specific analysis.
+    2. **Methods**: 
+        - Detailed overview of the approach, techniques, or methodologies employed in this analysis.
+    3. **Results and Conclusions**: 
+        - Key findings, data interpretation, and the implications drawn from this analysis.
+        - Combine this section if the results and conclusions are closely aligned to streamline content.
+    4. **Figures and Tables**:
+        - Mention and describe any figures or tables referenced, linking them to their corresponding insights.
+
+Ensure that the outline is structured in Markdown format for clarity, facilitating its integration into documents or presentations. This structured approach aims to provide a comprehensive view of the experimental framework and the individual analyses, highlighting their unique contributions to the overall study."
+
+excerpt: {paper}
+"""
+
+insights_mind_map_output_parser = StrOutputParser()
+insights_mind_map_prompt = PromptTemplate(
+    template=insights_mind_map_prompt_template,
+    input_variables=["paper"],
+)
+insights_mind_map_chain = (
+    lambda model: insights_mind_map_prompt | model | insights_mind_map_output_parser
+)