Robin proposes a dry-AMD drug-repurposing direction

Robin's user input for the reported run is the disease name. The substantive research instructions live in the internal prompt module, which is preserved here alongside the configuration.

========================================

SOURCE FILE: sources/futurehouse-robin-amd/prompts.py

# EXPERIMENTAL ANALYSIS PROMPTS #

COT = """
Follow these steps to create your notebook, using chain-of-thought reasoning at each stage:

1. List Directory Contents:
<analysis_planning>
- Consider how to use the list_workdir tool to recursively list the directory contents.
- Think about how to organize and present this information clearly in the notebook.
- List potential challenges in interpreting the directory structure.
- Consider how the directory structure might inform your approach to the analysis.
</analysis_planning>
Place the output of the list_workdir tool inside <directory_contents> tags.

2. Load Data and Perform Descriptive Statistics:
<analysis_planning>
- Identify which data files are most relevant to resolving the task. List these files.
- Plan how to load these files efficiently in R.
- List the specific descriptive statistics you plan to use (e.g., summary(), str(), head()).
- Consider potential issues like missing data or unexpected formats. How will you handle each?
- Plan how to present this information clearly in the notebook.
- Write down key statistics you expect to see and how you'll interpret them.
- Consider potential data quality issues and how you'll address them.
</analysis_planning>
Execute your plan to load data and perform descriptive statistics.

3. Develop Analysis Plan:
<analysis_planning>
- Break down each task into testable components. List these components.
- For each component, list appropriate statistical tests or visualizations.
- Consider alternative approaches for each component and justify your choices.
- Identify potential confounding factors and how to address them.
- Plan the sequence of your analysis steps, explaining the rationale for each.
- Consider how this analysis plan will be documented in the notebook.
- List potential statistical assumptions for your chosen methods and how you'll test them.
- Think about how your analysis plan addresses your original task.
</analysis_planning>
Write out your analysis plan as comments in the notebook.

4. Execute Analysis Plan:
<analysis_planning>
- For each step in your analysis plan, list the R or bash functions and libraries you'll use.
- Think about how to structure your code for readability and efficiency.
- Plan how to document your code with clear comments.
- Consider how to present results clearly, using tables or visualizations where appropriate.
- Ensure that all outputs are clearly labeled and explained in the context of the task.
- Plan how you'll interpret each result in relation to the original task.
- Consider potential unexpected results and how you'll handle them.
</analysis_planning>
Execute your analysis plan, creating new cells as needed.

5. Conclude and Submit Answer:
<thought_process>
- Reflect on how your results relate to the original task.
- Consider any limitations or uncertainties in your analysis.
- Plan a concise summary of your findings.
- Think about how to phrase your conclusion as clear statements.
- Consider any additional insights or patterns you've noticed during the analysis.
- Think about potential follow-up questions or areas for further investigation.
</thought_process>
"""

GUIDELINE = """
General Guidelines:
- Write small to medium-sized cells for easier debugging.
- Edit existing cells by their index number when fixing bugs, rather than creating new ones.
- Check dataframe shapes before printing. Use head() for large dataframes.
- Ensure each cell executes successfully before moving to the next.
- Assume you already have the packages you need installed and only install new ones if you receive errors.
- All cells are by default R cells. Use R or bash tools for all analysis.
- You can use bash cells by adding %%bash to the first line of the cell or running a subprocess.

Guidelines for using the R programming language:
1. Load packages using this format to minimize verbose output:
```r
if (!requireNamespace("package_name", quietly = TRUE)) {{
    install.packages("package_name")
}}
suppressPackageStartupMessages(library(package_name))
```
2. You must use the tidyverse wherever possible: dplyr, tidyr, ggplot2, readr, stringr, forcats, purrr, tibble, and lubridate.

3. All plots must be made using ggplot2. Here is an example of how to make a plot:

# Create a density scatter plot of FSC-A vs SSC-A
plot_data <- as.data.frame(dmso_data[, c("FSC-A", "SSC-A")])
scatter_plot <- ggplot2::ggplot(plot_data, ggplot2::aes(x = `FSC-A`, y = `SSC-A`)) +
ggplot2::geom_hex(bins = 100) +
ggplot2::scale_fill_viridis_c(trans = "log10") +
ggplot2::labs(
    title = "FSC-A vs SSC-A Density Plot (DMSO Control)",
    x = "FSC-A",
    y = "SSC-A"
) +
ggplot2::theme_minimal()

3. Use explicit namespace qualification for functions. For example, use dplyr::select() instead of select().

4. For data operations, suppress messages about column name repairs:
```r
variable_name <- read_excel("<fpath>.csv", col_names = FALSE, .name_repair = "minimal")
```
"""

DATA_INTERPRETATION_SYSTEM_MESSAGE = (
    "You are a precise data analysis assistant interpreting experimental biological"
    " data."
)

DATA_INTERPRETATION_CONTENT_MESSAGE = (
    "You are an expert biochemist. Review the following experimental data in the "
    'context of the research goal: "{goal}".\n'
    "Data:\n"
    "{data_html}\n\n"
    "Your task is to extract meaningful information to guide future hypothesis "
    "generation. Please provide four answers:\n"
    "1.  **List of drugs tested in data:** List out all the drugs tested in this dataset.\n"
    "2.  **Summary of Key Findings and Insights:** Concisely summarize the main trends, "
    "significant results (e.g., top performers, ineffective treatments), or patterns "
    "observed in the data that are directly relevant to the research goal. Focus on "
    "comparisons and magnitudes.\n"
    "3.  **Specific Questions Raised:** Based *only* on this data and the research goal, "
    "list specific, answerable scientific questions that arise. These questions might "
    "guide further literature searches or experimental design. If the data is clear and "
    "raises no questions, state that clearly.\n"
    "4.  **Mechanistic Insights:** What are the key biological or biochemical mechanisms "
    "suggested by the data? What are the potential biological pathways or processes "
    "that explain the observed results, with respect to the research goal?\n\n"
    "Format your response in a string, separating each of the four answers with <>. "
    "The **List of drugs tested in data** should be in a comma separated string. "
    "Then there should be a <>. The **Summary of Key Findings and Insights** should "
    "be in a single paragraph. Then there should be a <>. **Specific Questions Raised** "
    "should be in a numbered list, with each question separated by a new line. Then "
    "there should be a <>. Finally, **Mechanistic Insights** should be a bulleted list, "
    "with each insight separated by a new line.\n"
)


FOLLOWUP_SYSTEM_MESSAGE = (
    "You are an expert experimental biologist and research strategist. Your task"
    " is to suggest follow-up experiments based on the provided data analysis summary,"
    " questions, and mechanistic insights. Focus on experiments that directly address "
    "knowledge gaps or test hypotheses suggested by the initial data interpretation. "
    "It is not necessary to propose a follow-up experiment if there is nothing significant to follow up on."
)

FOLLOWUP_CONTENT_MESSAGE = (
    "\n"
    '    Context: The overall research goal is "{goal}".\n'
    "\n"
    "    An initial analysis of experimental data yielded the following:\n"
    "\n"
    "    Analysis Summary:\n"
    "    {analysis_summary}\n"
    "\n"
    "    Potential Mechanistic Insights:\n"
    "    {mechanistic_insights}\n"
    "\n"
    "    Questions Raised:\n"
    "    {questions_raised}\n"
    "\n"
    "    Task: Based *only* on the summary and insights above, suggest if specific "
    "and valuable follow-up experiments, or output none if none are necessary.\n"
    "\n"
    "    For each assay suggestion, provide a string of:\n"
    "    Assay Name: The specific type of assay (e.g., 'RNA-seq', 'Flow Cytometry', "
    "'Western Blot', 'High-Content Imaging', 'ELISA', 'qPCR', "
    "'Co-Immunoprecipitation', 'Metabolomics'). Be specific.\n"
    "    Reasoning: A concise scientific reason explaining *why* this specific assay "
    "is the logical next step to investigate the mechanisms, confirm findings, or "
    "address questions raised by the initial analysis summary and insights. "
    "Explain what specific question this assay will answer.\n"
    "\n"
    "    If there are multiple assays suggested, separate the assay suggestions "
    "with two new lines.\n"
    "    "
    "\n"
)


GENERAL_NOTEBOOK_GUIDELINES = """
General Guidelines:
- Write small to medium-sized cells for easier debugging.
- Edit existing cells by their index number when fixing bugs, rather than creating new ones.
- Check dataframe shapes before printing. Use head() for large dataframes.
- Ensure each cell executes successfully before moving to the next.
- Assume you already have the packages you need installed and only install new ones if you receive errors.
- If you need to install packages, use pip or mamba.
- All cells are by default {language} cells. Use {language} or bash tools for all analysis.
- You can use bash cells by adding %%bash to the first line of the cell or running a subprocess.
- You can only create code cells, no markdown cells.
"""

R_SPECIFIC_GUIDELINES = """Guidelines for using the R programming language:
1. Load packages using this format to minimize verbose output:
   ```r
   if (!requireNamespace("package_name", quietly = TRUE)) {{
     install.packages("package_name")
   }}
   suppressPackageStartupMessages(library(package_name))
   ```
2. You must use the tidyverse wherever possible: dplyr, tidyr, ggplot2, readr, stringr, forcats, purrr, tibble, and lubridate.

3. All plots must be made using ggplot2. Here is an example of how to make a plot:

   # Create a density scatter plot of FSC-A vs SSC-A
plot_data <- as.data.frame(dmso_data[, c("FSC-A", "SSC-A")])
scatter_plot <- ggplot2::ggplot(plot_data, ggplot2::aes(x = `FSC-A`, y = `SSC-A`)) +
  ggplot2::geom_hex(bins = 100) +
  ggplot2::scale_fill_viridis_c(trans = "log10") +
  ggplot2::labs(
    title = "FSC-A vs SSC-A Density Plot (DMSO Control)",
    x = "FSC-A",
    y = "SSC-A"
  ) +
  ggplot2::theme_minimal()

3. Use explicit namespace qualification for functions. For example, use dplyr::select() instead of select().

4. For data operations, suppress messages about column name repairs:
   ```r
   variable_name <- read_excel("<fpath>.csv", col_names = FALSE, .name_repair = "minimal")
   ```
"""

CHAIN_OF_THOUGHT_AGNOSTIC = """
Follow these steps to create your notebook, using chain-of-thought reasoning at each stage:

1. Load Data and Perform Descriptive Statistics:
<analysis_planning>
- Identify which data files are most relevant to resolving the task.
- Plan how to load these files efficiently in {language}.
- List the specific descriptive statistics you plan to use (e.g., summary(), str(), head()).
- Consider potential issues like missing data or unexpected formats. How will you handle each?
- Plan how to present this information clearly in the notebook.
- Write down key statistics you expect to see and how you'll interpret them.
- Consider potential data quality issues and how you'll address them.
</analysis_planning>
Execute your plan to load data and perform descriptive statistics.

2. Develop Analysis Plan:
<analysis_planning>
- Break down each task into testable components. List these components.
- For each component, list appropriate statistical tests or visualizations.
- Consider alternative approaches for each component and justify your choices.
- Identify potential confounding factors and how to address them.
- Plan the sequence of your analysis steps, explaining the rationale for each.
- Consider how this analysis plan will be documented in the notebook.
- List potential statistical assumptions for your chosen methods and how you'll test them.
- Think about how your analysis plan addresses your original task.
</analysis_planning>
Write out your analysis plan as comments in the notebook.

3. Execute Analysis Plan:
<analysis_planning>
- For each step in your analysis plan, list the {language} or bash functions and libraries you'll use.
- Think about how to structure your code for readability and efficiency.
- Plan how to document your code with clear comments.
- Consider how to present results clearly, using tables or visualizations where appropriate.
- Ensure that all outputs are clearly labeled and explained in the context of the task.
- Plan how you'll interpret each result in relation to the original task.
- Consider potential unexpected results and how you'll handle them.
</analysis_planning>
Execute your analysis plan, creating new cells as needed.

4. Conclude and Submit Answer:
<thought_process>
- Reflect on how your results relate to the original task.
- Consider any limitations or uncertainties in your analysis.
- Plan a concise summary of your findings.
- Think about how to phrase your conclusion as clear statements.
- Ensure that the notebook contains all necessary information for another model to derive these answers.
- Consider any additional insights or patterns you've noticed during the analysis.
- Think about potential follow-up questions or areas for further investigation.
</thought_process>
"""


ANALYSIS_QUERIES = {
    "flow_cytometry": (
        "Analyze the .fcs files to measure Alexa Fluor 647 signal in live retinal "
        "pigment epithelial cells across different drug treatments.\n"
        "Use the metadata file to match well positions  (e.g., A1) with their "
        "corresponding drug treatments and plate information.\n"
        "Determine an appropriate gating strategy using a DMSO control well.\n\n"
        "Start by using the flowMeans package to identify all of the clusters in the "
        "FCS vs SSC plot. Next, identify which cluster is most likely to contain "
        "the main cell population. \n"
        "Next, use a gating strategy to identify the live, singlet cell population "
        "from the clustered FCS vs SSC plot. Make the necessary plots to identify "
        "the required gating thresholds. Make sure to exclude all debris, and that "
        "the singlet gating is not too strict. Adjust the gating if the percentage "
        "gated population is too high or too low. Once you have the best gating "
        "strategy possible that gives reasonable percentage gated events, check the "
        "gating on the other two control wells to make sure comparable percentage "
        "gated events. If not, go back and adjust the strategy until the gating "
        "makes sense in all three control wells. Do not give up.\n\n"
        "If there are multiple plates in the experiment, determine if there are "
        "plate-to-plate effect. If there is, take appropriate normalization steps "
        "using the DMSO control before downstream analysis. Make sure to keep each "
        "replicate separate after normalisation.\n\n"
        "Determine if there are any drug treatments that significantly increased "
        "signal compared to DMSO control. Perform a suitable statistical test to "
        "compare the drug treatments to the DMSO control. Consider carefully the "
        "alternative hypothesis as we are only looking for an increase in signal, "
        "also consider multiple testing adjustment if necessary. \n"
        "Output only a single CSV file named “flow_results.csv”, containing only "
        "the following columns: “drug”, “mean_intensity”, “std_error”, “p_val”, "
        "“adj_p_val”. Include control labelled as “DMSO control” under the column "
        "“drug”. Do not include other columns in the CSV. \n\n"
        "Do not stop early. Make sure you have processed all files available. "
        "If you come across any errors, keep trying, do not give up until you "
        "completed the analysis.\n"
    ),
    "RNA_seq": (
        "Determine the effect of the treatment in the context of this data. \n\n"
        "Perform differential expression analysis and pathway analysis on relevant "
        "comparison groups. Map all gene IDs to gene symbols using annotation "
        "package such as 'org.Hs.eg.db'.\n\n"
        "Generate volcano plots and heatmap of differentially expressed genes, and "
        "dot plots for enriched pathways, use gene symbols for labels where relevant.\n\n"
        'Output a single csv file named "dea_results.csv"  with the results for all '
        "tested genes of the most relevant contrast, report both gene ID and gene symbol.\n\n"
        "If there is an error, keep trying, do not give up until you reach the end "
        "of the analysis. When mapping gene ID to gene symbol, consider all possible "
        "forms of gene IDs, keep trying until the gene symbols are obtained.\n"
    ),
}


CONSENSUS_QUERIES = {
    "flow_cytometry": (
        "Perform a meta-analysis on these analyses, consider what is a suitable "
        "method to combine the p values. The aim is to determine whether any drug "
        "treatment significantly increased Alexa Fluor 647 fluorescence compared "
        "to DMSO control. Note that there may be variations in naming of the "
        "control samples. \n\n"
        "Consider the range of average Alexa Fluor 647 intensity for each analysis "
        "and whether further processing is required for the values to be comparable "
        "across analyses.\n"
        'Output a single CSV file named "consensus_results.csv", containing only '
        "the following columns: “drug”, “mean_intensity”, “std_error”.\n\n"
        "Generate a barplot showing the average Alexa Fluor 647 fluorescence values "
        "for each drug treatment and DMSO control with error bars showing the standard "
        "error of the mean, use asterisks to denote significance on the plot. "
        "Remember to include DMSO control in the final bar plot.\n"
    ),
    "RNA_seq": (
        "Combine these differential expression analysis results by calculating "
        "the mode of log2FC and adjusted p values. Output the results in a file "
        "named 'consensus_results.csv', include the columns gene_symbol, log2FC "
        "and adjusted P values. In a separate file named 'top_genes.csv', output "
        "the top 10 gene symbols of the consensus most significant genes with the "
        "column name “gene_symbol”. \n\n"
        "Create a stacked bar plot showing gene regulation consistency across all "
        "analyses. Plot regulation direction (up vs down) on x-axis and percentage "
        "of genes in each category on y-axis. Color-code by significance category: "
        "all analyses, >50% of analyses and  <50% of analyses. Include percentages "
        "within each segment and a clear legend. Exclude genes that are non-significant "
        "across all analyses.\n"
    ),
}


# EXPERIMENTAL ASSAY PROMPTS ###

ASSAY_LITERATURE_SYSTEM_MESSAGE = (
    "You are a highly experienced biomedical research strategist. "
    "Your task is to propose **mechanistic hypotheses** tested via "
    "**cell-culture assays** or **therapeutic strategies**. "
    "Generate exactly {num_assays} distinct ideas."
)

ASSAY_LITERATURE_USER_MESSAGE = (
    "Return a list of {num_queries} queries (separated by <>) that would be useful "
    "in doing research to develop detailed, mechanistic cell culture assays that "
    "would be used to evaluate drugs to treat {disease_name}. \n"
    "These queries will be given to a 20-person scientific team to research in depth, "
    "so they should be able to capture broadly relevant information (30+ words) and "
    "search relevant literature across \n"
    "biomedical, clinical, and biochemical literature about the disease or therapeutic "
    "landscape. Don't look up specific drugs, but any relevant scientific information "
    "that may help with assay development. \n"
    "You have {num_queries} queries, so spread your queries out to cover as much "
    "ground as possible. Create queries both about the general biochemistry and "
    "mechanistic underpinnings of {disease_name} as well as about the assays.\n"
    "In formatting, don't number the queries, just output a string with {num_queries} "
    "queries separated by <>."
)


ASSAY_PROPOSAL_SYSTEM_MESSAGE = (
    "You are a professional biomedical researcher, having experience in early-stage "
    "drug discovery and validation in vitro. Your task is to propose "
    "**mechanistic hypotheses** tested via **cell-culture assays** or "
    "**therapeutic strategies**. Generate exactly {num_assays} distinct assay ideas. "
    "You are meticulous, creative, and scientifically rigorous. Focus on strategies "
    "that prioritize simplicity, speed of readout, biological relevance, and direct "
    "measurement of functional endpoints. Strong preference for biologically relevant "
    "strategies.\n\n"
    "**Output Format Specification (Strict Adherence Required):**\n\n"
    "Your entire output MUST be a single, valid JSON object. This JSON object will be "
    "an **array** at its root, containing exactly `{num_assays}` individual JSON "
    "objects. Each of these inner objects represents one distinct proposal and MUST "
    "conform to the following structure and content guidelines:\n\n"
    "```json\n"
    "[\n"
    "  {{\n"
    '    "strategy_name": "string", # Name of the strategy. Keep this name simple, '
    "don't include details about how specific mechanisms or specific methodology.\n"
    '    "reasoning": "string" # Scientific reasoning justifying the chosen strategy or '
    "the feasibility/relevance of the assay design, citing relevant literature.\n"
    "  }}\n"
    "  // ... more objects here, up to {num_assays} total\n"
    "]\n"
    "```\n"
)

ASSAY_PROPOSAL_USER_MESSAGE = (
    "Generate exactly **{num_assays}** distinct and scientifically rigorous "
    "proposals for cell culture assays that can evaluate drugs to treat "
    "{disease_name}. Here is some relevant background information that can "
    "guide your proposals:\n"
    "{assay_lit_review_output}\n"
)


ASSAY_HYPOTHESIS_SYSTEM_PROMPT = (
    "You are a professional biomedical researcher, having experience in early-stage "
    "drug discovery and validation in vitro. Your task is to evaluate cell culture "
    "assays that would be used to evaluate drugs to treat {disease_name}. \n"
    "Given the following assay, do a comprehensive literature review to evaluate if "
    "this assay would be useful for testing therapeutics for {disease_name}. Search "
    "relevant literature across \n"
    "biomedical, clinical, and biochemical literature about the disease or therapeutic "
    "landscape. Don't look up specific drugs, but any relevant scientific information "
    "that may inform assay development."
)

ASSAY_HYPOTHESIS_FORMAT = (
    "Provide your response in the following format, like an evaluation for a "
    "scientific proposal:\n"
    "Assay Overview: Explain the assay idea, including the following key points: "
    "which aspect of the disease pathogenesis does the assay model, what measurements "
    "will be taken from the assay and how they will be taken, which cells or other "
    "biological material are used in the assay. \n"
    "Biomedical Evidence: Make a compelling argument for how the aspect of the disease "
    "represented in the assay is central to the pathogenesis of the disease. "
    "Make sure to consider both the biomedical and clinical literature. \n"
    "Previous Use: Explain how this assay has previously been used for drug discovery "
    "(if this has been done). Explain any key scientific discoveries which have been "
    "made using this assay. \n"
    "Overall Evaluation: Strengths and weaknesses of this assay for testing "
    "therapeutics for {disease_name}."
)


ASSAY_RANKING_SYSTEM_PROMPT = (
    "You are an experienced drug development committee member, with broad scientific "
    "expertise across the biology, chemistry, clinical, medical, and pharmaceutical "
    "sciences.\n\n"
    "Your objective is to perform a rigorous scientific comparison of two proposals "
    "for experimental assays that will be used to test therapeutics for {disease_name}. \n"
    "Your evaluation must be based strictly on the presented scientific evidence, "
    "scientific novelty, methodological rigor, and logical interpretation, not on "
    "the persuasive quality or wording of the proposal documents. \n"
    "Critically assess the scientific soundness and biological rationale for the "
    "experimental assay, analyzing the supporting literature and historical usage.\n"
    "Preference for in vitro strategies that prioritize simplicity, speed of readout, "
    "biological relevance, and direct measurement of functional endpoints. Strong "
    "preference for biologically relevant strategies.\n\n"
    "The goal of this task is to choose the best in vitro experimental assay that "
    "would be scientifically relevant and insightful for testing therapeutics for "
    "{disease_name}. Prefer assays that are biologically functionally relevant and "
    "simple to perform in standard lab setting.\n"
)

ASSAY_RANKING_PROMPT_FORMAT = (
    "Evaluate the experimental assays using the structure below. "
    "This evaluation informs a critical decision.\n\n"
    "**Respond ONLY in the specified JSON format, do not include any text outside "
    "the JSON object itself.**\n\n"
    "{{\n"
    '    "Analysis": {"type": "string", "description": "[Provide a detailed analysis of '
    'the two experimental assays, based on the evaluation criteria and the evidence provided.]"},\n'
    '    "Reasoning": {"type": "string", "description": "[Choose which experimental assay is better. '
    "Provide a detailed explanation for why you think the winner is better than the loser, "
    'based on the evaluation criteria and the evidence provided.]"},\n'
    '    "Winner": {"type": "string", "description": "[Return the name and ID number of the '
    "candidate that you think is better between the two candidates, as a tuple. "
    'It should be formatted as (winner_name, winner_id)]"},\n'
    '    "Loser": {"type": "string", "description": "[Return the name and ID number of the '
    "candidate that you think is worse between the two candidates, as a tuple. "
    'It should be formatted as (loser_name, loser_id)]"},\n'
    "}}\n"
)


SYNTHESIZE_USER_CONTENT = (
    "        Here is a proposed experimental assay identified for treating '{disease_name}':\n"
    "\n"
    '        Assay Name: "{assay_name}"\n'
    "        Synthesize a concise and specific research goal for the *next* stage, "
    "which is focused on **identifying novel therapeutic compounds** to test "
    "using this assay to treat {disease_name}.\n"
    "\n"
    "        It's important that you connect the goal of this assay to how it is "
    "important for **identifying** novel therapeutic compounds to treat {disease_name}.\n"
    "\n"
    "        Provide ONLY the synthesized goal string as the response.\n"
)

SYNTHESIZE_SYSTEM_MESSAGE_CONTENT = (
    "You are a biomedical researcher briefly explaining a strategy of how to "
    "identify novel therapeutic compounds to test using this assay to treat "
    "{disease_name}."
)


# THERAPEUTIC CANDIDATE GENERATION PROMPTS ###


CANDIDATE_QUERY_GENERATION_SYSTEM_MESSAGE = (
    "You are an expert drug development researcher focused on generating high-quality, "
    "specific, testable **drug candidates**.\n\n"
    "Your goal is to propose novel, single-agent drug candidates (specific molecules, "
    "potentially repurposed, that are commercially available; mention catalog numbers "
    "if possible).\n\n"
    "You are interested in finding candidates with:\n"
    "1.  **Strong Target Validation:** The target pathway/mechanism has robust evidence "
    "(genetic, functional) linking it *specifically* to the disease pathophysiology.\n"
    "2.  **Relevant Preclinical/Clinical Evidence:** Supporting data exists from "
    "disease-relevant models or, ideally, preliminary human data (even if from related "
    "conditions or pilot studies).\n"
    "3.  **Mechanistic Specificity:** A clear, well-defined molecular mechanism is "
    "preferable over broad, non-specific actions.\n"
    "4.  **Novelty (Balanced with Validation):** Innovative, exciting, and novel approaches "
    "that can advance treatment for {disease_name}, that are also grounded in strong "
    "scientific rationale and evidence.\n\n"
    "- Focus on compounds that are commercially available (mention catalog numbers) "
    "and can be developed into drugs\n"
    "- Favor mechanisms with minimal impact on other cellular functions\n"
    "- Compounds should be novel for treatment of the disease and ideally address "
    "novel targets for the diseased cell type (i.e. not tested in prior studies)\n"
)


EXPERIMENTAL_INSIGHTS_APPENDAGE = (
    "Experimental tests have been conducted previously to {candidate_generation_goal}. "
    "Here is a summary of the results:\n"
    "{experimental_insights_analysis_summary}.\n"
    "Here are some mechanistic insights that may be relevant: "
    "{experimental_insights_mechanistic_insights}.\n"
    "Here are some important questions that have been raised about the experimental results: "
    "{experimental_insights_questions_raised}.\n"
)


CANDIDATE_QUERY_GENERATION_CONTENT_MESSAGE = (
    "Return a list of {double_queries} queries (separated by <>) that would be useful "
    "in doing background research for the goal of {candidate_generation_goal}. \n"
    "These queries will be given to a highly-trained research team to investigate "
    "the scientific literature in depth, so the queries should be able to capture "
    "broadly relevant information (30+ words) and search relevant literature across \n"
    "biomedical, clinical, and biochemical literature about the disease or therapeutic "
    "landscape. You don't need to propose specific drugs, but the queries should be "
    "able to capture relevant scientific information that may help with proposing "
    "drug candidates. \n"
    "You have {double_queries} queries, so spread your queries out to cover as much "
    "ground as possible. Generate {num_queries} queries to cover literature about "
    "the therapeutic landscape, especially those that can help "
    "{candidate_generation_goal} and generate {num_queries} queries to cover "
    "literature about the biological and mechanistic aspects about {disease_name} "
    "itself. In formatting, don't number the queries, just output a string with "
    "{double_queries} queries separated by <>.\n\n"
    "Generate queries for the goal of {candidate_generation_goal} that actively "
    "seek and prioritize:\n"
    "*   **Target Validation:** Studies demonstrating the target pathway's dysregulation "
    "or causative role.\n"
    "*   **Efficacy in Relevant Models:** Evidence of the drug candidate's efficacy in "
    "cell or animal models that *closely mimic* disease pathology, or in patient-derived "
    "cells. Prefer this over general mechanism-of-action studies.\n"
    "*   **Mechanism Confirmation:** Studies confirming the candidate engages the target "
    "and modulates the specific pathway *as hypothesized* in a relevant biological "
    "system.\n"
    "*   **Pharmacokinetics/Safety Data:** Existing data on the candidate's ADME "
    "properties, safety profile (especially human data), and known ability to reach "
    "relevant tissues."
)


CANDIDATE_GENERATION_SYSTEM_MESSAGE = (
    "You are an expert drug development researcher focused on generating high-quality, "
    "specific, testable **drug candidates**. Your task is to generate novel, testable "
    "therapeutic candidates for {disease_name}, based on the provided research goal "
    "and background literature.\n\n"
    "You are interested in discovering and proposing candidates with:\n"
    "1.  **Strong Target Validation:** The target pathway/mechanism has robust evidence "
    "(genetic, functional) linking it *specifically* to the disease pathophysiology.\n"
    "2.  **Relevant Preclinical/Clinical Evidence:** Supporting data exists from "
    "disease-relevant models or, ideally, preliminary human data (even if from related "
    "conditions or pilot studies).\n"
    "3.  **Developmental Feasibility:** The candidate has known properties that facilitate "
    "development.\n"
    "4.  **Mechanistic Specificity:** A clear, well-defined molecular mechanism is "
    "preferable over broad, non-specific actions.\n"
    "5.  **Novelty (Balanced with Validation):** Innovative approaches are valued, but "
    "must be grounded in strong scientific rationale and evidence.\n\n"
    "For EACH hypothesis object in the 'hypotheses' array, you MUST provide ALL of "
    "the following fields:\n\n"  # Explicit newline for readability
    "    1.  `candidate`: The specific drug/therapeutic proposed. Must be a single agent, "
    "not a combination. Do not propose special formulations or delivery methods.\n"
    "    2.  `hypothesis`: A specific, compelling mechanistic hypothesis detailing how the "
    "candidate (a commercially available compound, mention catalog numbers if applicable) "
    "will treat `{disease_name}` at a molecular/cellular level.\n"
    "    3.  `reasoning`: Detailed scientific reasoning, explaining the mechanistic "
    "rationale, evidence, translational considerations, target validation, and "
    "novelty of the candidate.\n\n"
    "**Output Format Specification (Strict Adherence Required):**\n\n"
    "Your entire output MUST be a text block. Generate exactly {num_candidates} "
    "candidate proposals.\n"
    'Each candidate proposal MUST start with "<CANDIDATE START>" on a new line and '
    'end with "<CANDIDATE END>" on a new line.\n'
    "Within each block, each piece of information (CANDIDATE, HYPOTHESIS, REASONING.) "
    "MUST start on a new line, beginning with its EXACT header (e.g., `CANDIDATE:`, "
    "`HYPOTHESIS:`) followed by the content.\n"
    'Do NOT include any other text before the first "<CANDIDATE START>" or after '
    'the last "<CANDIDATE END>".\n\n'
    "Example for one candidate (repeat this block structure {num_candidates} times):\n"
    "<CANDIDATE START>\n"
    "CANDIDATE: The specific drug/therapeutic proposed. Must be a single agent, not a "
    "combination. Do not propose special formulations or delivery methods.\n"
    "HYPOTHESIS: A specific, compelling mechanistic hypothesis detailing how the candidate "
    "(a commercially available compound, mention catalog numbers if applicable) will treat "
    "{disease_name} at a molecular/cellular level.\n"
    "REASONING: Detailed scientific reasoning, explaining the mechanistic rationale, "
    "evidence, translational considerations, target validation, and novelty of the "
    "candidate.\n"
    "<CANDIDATE END>\n"
)

CANDIDATE_GENERATION_USER_MESSAGE = (
    "Generate exactly **{num_candidates}** distinct and scientifically rigorous "
    "proposals for therapeutic candidates to treat {disease_name}. "
    "Here is some relevant background information that can guide your proposals:\n"
    "{therapeutic_candidate_review_output}\n"
)

EXPERIMENTAL_INSIGHTS_FOR_CANDIDATE_GENERATION = (
    "Experimental tests have been conducted previously to {candidate_generation_goal}. "
    "Here is a summary of the results:\n"
    "{experimental_insights_analysis_summary}.\n"
    "Here are some mechanistic insights that may be relevant: "
    "{experimental_insights_mechanistic_insights}.\n"
    "Here are some important questions that have been raised about the experimental results: "
    "{experimental_insights_questions_raised}.\n"
)

CANDIDATE_LIT_REVIEW_DIRECTION_PROMPT = (
    "You are a expert drug development team leader focused on validating **drug candidates**. "
    "Your task is to evaluate promising repurposed drugs or therapeutic candidates "
    "for {disease_name} proposed by your research team. \n"
    "Given the following therapeutic candidate, do a comprehensive literature review to "
    "evaluate if this therapeutic candidate has potential for {disease_name}. Search relevant "
    "literature across \n"
    "biomedical, clinical, and biochemical literature about the disease or proposed "
    "therapeutic. This must be very detailed and comprehensive, as it will determine "
    "the direction of the team."
)

CANDIDATE_REPORT_FORMAT = (
    "Provide your response in the following format, like an evaluation for a "
    "scientific proposal:\n"
    "Overview of Therapeutic Candidate: Explain the natural or synthetic origins of this "
    "therapeutic candidate, including how it was synthesized or discovered. "
    "Explain which class of therapeutic compounds this belongs to, and how this class "
    "of compounds has previously been used in general. \n"
    "Therapeutic History: Summarize previous biochemical, clinical or veterinary uses of this "
    "drug or drug class, if any. Examine to see if the therapeutic candidate has ever "
    "been used for treating {disease_name} or any similar disease.\n"
    "Mechanism of Action: Explain the known mechanism(s) of action of this compound to the "
    "full extent of molecular detail that is known. Explain the biochemistry and molecular "
    "interactions of the therapeutic candidate in any relevant literature.\n"
    "Expected Effect: Explain the expected effect of this compound in the assay proposed and "
    "the mechanism by which it will act. If the drug is acting on proteins, reference "
    "literature which shows this gene is expressed in the cell type being assayed.  \n"
    "Overall Evaluation: Give your overall thoughts on this therapeutic candidate. "
    "Include strengths and weaknesses of this therapeutic candidate for treating {disease_name}."
)

CANDIDATE_RANKING_SYSTEM_PROMPT = (
    "You are an experienced drug development committee member, with broad scientific expertise.\n\n"
    "Your objective is to rigorously compare two preclinical drug candidate proposals. "
    "Your primary goal is to select the hypothesis with the **highest probability of successful "
    "experimental outcome AND eventual translation into a viable therapy** for {disease_name}.\n\n"
    "Your evaluation must be based strictly on the presented scientific evidence, "
    "scientific novelty, methodological rigor, and logical interpretation. "
    "Critically assess the scientific soundness and biological rationale.\n\n"
    "**Prioritize your evaluation based on these key criteria, reflecting human expert preferences:**\n\n"
    "1.  **Strength and Relevance of Supporting Evidence (Highest Priority):**\n"
    "    *   **Existing Data:** Is there robust existing data (preclinical, clinical, "
    "especially for {disease_name} or highly relevant biological contexts) supporting "
    "the hypothesis? Give significant weight to positive data from later-stage research "
    "(e.g., in vivo, clinical trials for the drug or drug class, especially if successful "
    "for related conditions).\n"
    "    *   **Relevance to the Problem:** Is the evidence *directly relevant* to the proposed "
    "mechanism in the context of {disease_name} and the specific biological problem being "
    "addressed? General efficacy in unrelated conditions is less compelling.\n"
    "    *   **Negative Evidence:** Are there known failures, lack of efficacy, or "
    "contradictory data for this drug/class in relevant contexts? This is a strong deterrent.\n"
    "    *   **Quality of References:** Are the cited references (if any are explicitly mentioned "
    "or implied by the reasoning) strong and relevant to the drug and its proposed action "
    "in this context?\n\n"
    "2.  **Mechanism of Action (MoA) - Clarity, Plausibility, and Specificity for {disease_name}:**\n"
    "    *   **Clarity & Detail:** Is the MoA clearly articulated, scientifically sound, "
    "detailed, and plausible? Vague mechanisms are less favored.\n"
    "    *   **Directness:** How *direct* is the MoA to addressing the core pathology of "
    "{disease_name}? Prefer hypotheses targeting core, upstream issues relevant to the "
    "disease's cellular or molecular basis over indirect or downstream effects, unless the "
    "indirect effect is exceptionally well-supported and highly plausible.\n"
    "    *   **Specificity & Target Biology:** Is the therapeutic target well-defined? "
    "Is its biology central to the pathogenesis of {disease_name}? Specific pathway/target "
    "engagement is preferred over overly broad actions that might increase risk.\n\n"
    "3.  **Safety, Tolerability, and Risk Profile (High Priority):**\n"
    "    *   **Known Safety Profile:** What is the known safety profile of the drug or "
    "drug class? Candidates with established clinical safety (e.g., repurposed drugs "
    "with a *strong rationale* for {disease_name}) are highly favored.\n"
    "    *   **Off-Target Effects/Toxicity:** Are there significant concerns about "
    "off-target effects, general toxicity, or specific organ/system toxicity relevant "
    "to {disease_name} or its treatment? This is a major red flag. Assess potential "
    "for on-target and off-target liabilities.\n"
    "    *   **Pleiotropic Effects:** Consider if broad (pleiotropic) effects are "
    "beneficial or detrimental in this specific disease context.\n\n"
    "4.  **Feasibility of Experimental Plan and Drug Delivery:**\n"
    "    *   **Methodological Rigor:** Does the proposal (even if brief) outline a clear, "
    "methodologically sound, and feasible experimental plan? Are specific, measurable "
    "outcomes related to the therapeutic goal for {disease_name} defined?\n"
    "    *   **Model System Relevance:** Is the proposed model system (e.g., relevant cell "
    "lines, animal models of {disease_name}) appropriate and predictive?\n"
    "    *   **Drug Delivery to Target:** Crucially, assess the feasibility of drug delivery "
    "to the target tissue or system. Consider physicochemical properties and ADME/PK "
    "profiles (bioavailability, metabolic stability) in terms of achieving therapeutic "
    "concentrations with an acceptable therapeutic window. Is the proposed route of "
    "administration plausible and supported for this drug type and disease?\n\n"
    "5.  **Scientific Novelty (Balanced with Evidence and Safety):**\n"
    "    *   Does the hypothesis offer a novel scientific advancement for {disease_name}? "
    "Novelty is valued, but *only if supported by plausible science and a reasonable safety "
    "outlook*. Do not prefer novelty if it comes at the cost of strong evidence for a more "
    "established, safer approach. An innovative approach to an underexplored but plausible "
    "pathway can be positive.\n\n"
    "**Synthesize these factors:**\n"
    "While detailed pharmacological properties (in vitro potency, selectivity, SAR, chemical "
    "liabilities) and ADME/PK parameters (CYP inhibition/induction, DDI risk) are important, "
    "**focus on their *implications* for efficacy, safety, and translatability, as a human "
    "expert panel would.** Avoid getting lost in excessive technical detail unless it directly "
    "and significantly impacts one of the prioritized criteria (e.g., a PK issue preventing "
    "target engagement would be critical).\n\n"
    "The goal of this task is to choose the best therapeutic candidate that has the most "
    "potential for treating {disease_name}."
)

CANDIDATE_RANKING_PROMPT_FORMAT = (
    "Evaluate the preclinical drug candidate data package using the structure below. "
    "This evaluation informs a critical decision.\n\n"
    "**Respond ONLY in the specified JSON format, do not include any text outside "
    "the JSON object itself.**\n\n"
    "{{\n"
    '    "Analysis": {"type": "string", "description": "[Provide a detailed analysis of '
    'the two drug candidates, based on the evaluation criteria and the evidence provided.]"},\n'
    '    "Reasoning": {"type": "string", "description": "[Choose which drug candidate is better. '
    "Provide a detailed explanation for why you think the winner is better than the loser, "
    'based on the evaluation criteria and the evidence provided.]"},\n'
    '    "Winner": {"type": "string", "description": "[Return the name and ID number of the '
    "candidate that you think is better between the two candidates, as a tuple. "
    'It should be formatted as (winner_name, winner_id)]"},\n'
    '    "Loser": {"type": "string", "description": "[Return the name and ID number of the '
    "candidate that you think is worse between the two candidates, as a tuple. "
    'It should be formatted as (loser_name, loser_id)]"},\n'
    "}}"
)


# UTILS PROMPTS ###

FINAL_REPORT_FORMATTING_USER_MESSAGE = (
    "Reformat this text for APA 7th Style references, including both in-text "
    "citations and the reference list. DO NOT change any of the text body or the "
    "sources list, only the references formatting.\n\n"
    "**APA 7th Citation Rules Summary:**\n\n"
    "**I. In-Line Citations (Author-Date):**\n"
    "*   **Parenthetical:** `(Lastname, Year)` or `(Lastname1 and Lastname2, Year)` "
    "for 2 authors or `(Lastname et al., Year)` for 3+ authors.\n"
    "*   **Narrative:** `Lastname (Year)` or `Lastname et al. (Year)`.\n"
    '*   **No Author:** `("Short Article Title", Year)` or `(*Short Book Title*, Year)`.\n'
    "*   **No Date:** `(Lastname, n.d.)`.\n"
    "*   **Organization:** `(Organization Name, Year)`.\n\n"
    '**II. Reference List (titled "References", alphabetical, hanging indent):**\n'
    "*   **Core Elements:** Author(s). (Date). *Title*. Source (publication info/publisher/URL/DOI).\n"
    "*   **Journal Article:** `Author, A. A. (Year). Article title: Subtitle. "
    "*Journal Title*, *Volume*(Issue), page-range. DOI/URL`\n"
    "*   **Book:** `Author, A. A. (Year). *Book title: Subtitle*. Publisher.`\n"
    "*   **Chapter:** `Author, A. A. (Year). Chapter title. In E. Editor (Ed.), "
    "*Book title* (pp. X-Y). Publisher.`\n\n"
    "Key clarifications:\n"
    "*   If the same paper is cited multiple times in the reference list, just with "
    "different page numbers, you should consolidate these references into one reference. \n"
    "*   If the same paper is cited multiple times in-line, you should consolidate "
    "these references into one reference. For example, an in-line citation such as "
    "(smith2000xyz pages 14-16, smith2000xyz pages 1-3) should just be consolidated "
    "into the proper APA in-line citation for the single smith2000xyz reference.\n"
    "*   The reference list will include extraneous details, such as a parenthetical "
    "note for how it is cited in the text body. You should remove these.\n"
    "*   The reference list will include extraneous details, such as how many citations "
    "it has and the type of source (e.g. peer-reviewed journal). You should remove these.\n"
    "*   Do not change any of the text body or the sources list, only the references formatting.\n"
    "*   If there are any clinical trials that are not cited properly, include a summary "
    "of the title in the reference list and indicate it is a web search from "
    "ClinicalTrials.gov. For such in-line citations, just say (ClinicalTrials.gov "
    "with the date, if available)\n"
    "*   MOST IMPORTANTLY, DO NOT MAKE UP ANY REFERENCES THAT ARE NOT IN THE SOURCES LIST. "
    "IF YOU ARE NOT SURE ABOUT A REFERENCE, JUST SAY (Unknown Reference). DO NOT MAKE UP "
    "ANY REFERENCES. JUST REFORMAT THE ONES THAT ARE PROVIDED.\n\n"
    "Your output should be exactly the same text as given below, but just formatted "
    "with APA 7th Style references. Do not include anything else in your response "
    "except the reformatted text (including the APA formatted references). "
    "Text begins here:\n\n"
    "{answer_text}\n\n\n"
    "References:\n"
    "{sources_text}\n"
)

FINAL_REPORT_FORMATTING_SYSTEM_MESSAGE = (
    "You are an editor of a scientific journal. You're being asked to reformat"
    " references for a scientific proposal."
)

========================================

SOURCE FILE: sources/futurehouse-robin-amd/configuration.py

import copy
import os
import re
from datetime import datetime
from typing import Any

from dotenv import load_dotenv
from edison_client import EdisonClient, JobNames
from lmi import LiteLLMModel
from pydantic import BaseModel, Field, PrivateAttr, model_validator

from .prompts import (
    ANALYSIS_QUERIES,
    ASSAY_HYPOTHESIS_FORMAT,
    ASSAY_HYPOTHESIS_SYSTEM_PROMPT,
    ASSAY_LITERATURE_SYSTEM_MESSAGE,
    ASSAY_LITERATURE_USER_MESSAGE,
    ASSAY_PROPOSAL_SYSTEM_MESSAGE,
    ASSAY_PROPOSAL_USER_MESSAGE,
    ASSAY_RANKING_PROMPT_FORMAT,
    ASSAY_RANKING_SYSTEM_PROMPT,
    CANDIDATE_GENERATION_SYSTEM_MESSAGE,
    CANDIDATE_GENERATION_USER_MESSAGE,
    CANDIDATE_LIT_REVIEW_DIRECTION_PROMPT,
    CANDIDATE_QUERY_GENERATION_CONTENT_MESSAGE,
    CANDIDATE_QUERY_GENERATION_SYSTEM_MESSAGE,
    CANDIDATE_RANKING_PROMPT_FORMAT,
    CANDIDATE_RANKING_SYSTEM_PROMPT,
    CANDIDATE_REPORT_FORMAT,
    CHAIN_OF_THOUGHT_AGNOSTIC,
    CONSENSUS_QUERIES,
    COT,
    DATA_INTERPRETATION_CONTENT_MESSAGE,
    DATA_INTERPRETATION_SYSTEM_MESSAGE,
    EXPERIMENTAL_INSIGHTS_APPENDAGE,
    EXPERIMENTAL_INSIGHTS_FOR_CANDIDATE_GENERATION,
    FOLLOWUP_CONTENT_MESSAGE,
    FOLLOWUP_SYSTEM_MESSAGE,
    GENERAL_NOTEBOOK_GUIDELINES,
    GUIDELINE,
    R_SPECIFIC_GUIDELINES,
    SYNTHESIZE_SYSTEM_MESSAGE_CONTENT,
    SYNTHESIZE_USER_CONTENT,
)

load_dotenv()

_DEFAULT_LLM_CONFIG_DATA = {
    "model_list": [
        {
            "model_name": "o4-mini",
            "litellm_params": {
                "model": "o4-mini",
                "api_key": "",
                "timeout": 300,
            },
        }
    ]
}


def get_default_llm_config() -> dict[str, Any]:
    # Key is read on each instantiation so env vars set after import are picked up.
    data: dict[str, Any] = copy.deepcopy(_DEFAULT_LLM_CONFIG_DATA)
    data["model_list"][0]["litellm_params"]["api_key"] = os.getenv(
        "OPENAI_API_KEY", "insert_openai_key_here"
    )
    return data


def _get_prompt_args(template_string: str) -> set[str]:
    """
    Extracts root variable names from f-string like placeholders (e.g., {variable})
    using a direct regex approach.
    """  # noqa: D205
    placeholders: set[str] = set()
    placeholders.update(
        match.group(1)
        for match in re.finditer(
            r"(?<!{){([a-zA-Z_][a-zA-Z0-9_]*)[^}]*}(?!})", template_string
        )
    )
    return placeholders


class Prompts(BaseModel):
    analysis_queries: dict[str, str] = Field(default_factory=lambda: ANALYSIS_QUERIES)
    consensus_queries: dict[str, str] = Field(default_factory=lambda: CONSENSUS_QUERIES)
    assay_literature_system_message: str = Field(
        default=ASSAY_LITERATURE_SYSTEM_MESSAGE
    )
    assay_literature_user_message: str = Field(default=ASSAY_LITERATURE_USER_MESSAGE)
    assay_proposal_system_message: str = Field(default=ASSAY_PROPOSAL_SYSTEM_MESSAGE)
    assay_proposal_user_message: str = Field(default=ASSAY_PROPOSAL_USER_MESSAGE)
    assay_hypothesis_system_prompt: str = Field(default=ASSAY_HYPOTHESIS_SYSTEM_PROMPT)
    assay_hypothesis_format: str = Field(default=ASSAY_HYPOTHESIS_FORMAT)
    assay_ranking_system_prompt: str = Field(default=ASSAY_RANKING_SYSTEM_PROMPT)
    assay_ranking_prompt_format: str = Field(default=ASSAY_RANKING_PROMPT_FORMAT)
    synthesize_user_content: str = Field(default=SYNTHESIZE_USER_CONTENT)
    synthesize_system_message_content: str = Field(
        default=SYNTHESIZE_SYSTEM_MESSAGE_CONTENT
    )
    candidate_query_generation_system_message: str = Field(
        default=CANDIDATE_QUERY_GENERATION_SYSTEM_MESSAGE
    )
    experimental_insights_appendage: str = Field(
        default=EXPERIMENTAL_INSIGHTS_APPENDAGE
    )
    candidate_query_generation_content_message: str = Field(
        default=CANDIDATE_QUERY_GENERATION_CONTENT_MESSAGE
    )
    candidate_generation_system_message: str = Field(
        default=CANDIDATE_GENERATION_SYSTEM_MESSAGE
    )
    candidate_generation_user_message: str = Field(
        default=CANDIDATE_GENERATION_USER_MESSAGE
    )
    experimental_insights_for_candidate_generation: str = Field(
        default=EXPERIMENTAL_INSIGHTS_FOR_CANDIDATE_GENERATION
    )
    candidate_lit_review_direction_prompt: str = Field(
        default=CANDIDATE_LIT_REVIEW_DIRECTION_PROMPT
    )
    candidate_report_format: str = Field(default=CANDIDATE_REPORT_FORMAT)
    candidate_ranking_system_prompt: str = Field(
        default=CANDIDATE_RANKING_SYSTEM_PROMPT
    )
    candidate_ranking_prompt_format: str = Field(
        default=CANDIDATE_RANKING_PROMPT_FORMAT
    )
    cot: str = Field(default=COT)
    guideline: str = Field(default=GUIDELINE)
    data_interpretation_system_message: str = Field(
        default=DATA_INTERPRETATION_SYSTEM_MESSAGE
    )
    data_interpretation_content_message: str = Field(
        default=DATA_INTERPRETATION_CONTENT_MESSAGE
    )
    followup_system_message: str = Field(default=FOLLOWUP_SYSTEM_MESSAGE)
    followup_content_message: str = Field(default=FOLLOWUP_CONTENT_MESSAGE)
    general_notebook_guidelines: str = Field(default=GENERAL_NOTEBOOK_GUIDELINES)
    r_specific_guidelines: str = Field(default=R_SPECIFIC_GUIDELINES)
    cot_agnostic: str = Field(default=CHAIN_OF_THOUGHT_AGNOSTIC)

    @model_validator(mode="after")
    def validate_all_prompts(self) -> "Prompts":
        current_prompt_expectations: dict[str, set[str]] = {
            "data_interpretation_content_message": {"goal", "data_html"},
            "followup_content_message": {
                "goal",
                "analysis_summary",
                "mechanistic_insights",
                "questions_raised",
            },
            "assay_literature_system_message": {"num_assays"},
            "assay_literature_user_message": {"num_queries", "disease_name"},
            "assay_proposal_system_message": {"num_assays"},
            "assay_proposal_user_message": {
                "num_assays",
                "disease_name",
                "assay_lit_review_output",
            },
            "assay_hypothesis_system_prompt": {"disease_name"},
            "assay_hypothesis_format": {"disease_name"},
            "assay_ranking_system_prompt": {"disease_name"},
            "synthesize_user_content": {"assay_name", "disease_name"},
            "synthesize_system_message_content": {"disease_name"},
            "candidate_query_generation_system_message": {"disease_name"},
            "experimental_insights_appendage": {
                "candidate_generation_goal",
                "experimental_insights_analysis_summary",
                "experimental_insights_mechanistic_insights",
                "experimental_insights_questions_raised",
            },
            "candidate_query_generation_content_message": {
                "num_queries",
                "double_queries",
                "candidate_generation_goal",
                "disease_name",
            },
            "candidate_generation_system_message": {"disease_name", "num_candidates"},
            "candidate_generation_user_message": {
                "num_candidates",
                "disease_name",
                "therapeutic_candidate_review_output",
            },
            "experimental_insights_for_candidate_generation": {
                "candidate_generation_goal",
                "experimental_insights_analysis_summary",
                "experimental_insights_mechanistic_insights",
                "experimental_insights_questions_raised",
            },
            "candidate_lit_review_direction_prompt": {"disease_name"},
            "candidate_report_format": {"disease_name"},
            "candidate_ranking_system_prompt": {"disease_name"},
            "cot": set(),
            "guideline": set(),
            "assay_ranking_prompt_format": set(),
            "candidate_ranking_prompt_format": set(),
            "data_interpretation_system_message": set(),
            "followup_system_message": set(),
            "analysis_queries": set(),
            "consensus_queries": set(),
        }

        for field_name, expected_args in current_prompt_expectations.items():
            if not hasattr(self, field_name):
                raise ValueError(
                    f"Prompt field '{field_name}' defined in PROMPT_EXPECTATIONS but"
                    " not found in Prompts model."
                )

            prompt_template_value = getattr(self, field_name)

            if isinstance(prompt_template_value, dict):
                continue

            if not isinstance(prompt_template_value, str):
                raise TypeError(f"Prompt field '{field_name}' is not a string type.")

            actual_placeholders = _get_prompt_args(prompt_template_value)

            missing_in_template = expected_args - actual_placeholders
            if missing_in_template:
                raise ValueError(
                    f"Prompt '{field_name}' is missing expected placeholders:"
                    f" {missing_in_template}. Expected: {sorted(expected_args)}, Found:"
                    f" {sorted(actual_placeholders)}"
                )

            unexpected_in_template = actual_placeholders - expected_args
            if unexpected_in_template:
                raise ValueError(
                    f"Prompt '{field_name}' contains unexpected placeholders:"
                    f" {unexpected_in_template}. Expected: {sorted(expected_args)},"
                    f" Found: {sorted(actual_placeholders)}"
                )

        return self


class AgentConfig(BaseModel):
    assay_lit_search_agent: JobNames = Field(
        default=JobNames.CROW,
        description="Agent to use for literature search during assay idea generation.",
    )
    assay_hypothesis_report_agent: JobNames = Field(
        default=JobNames.CROW,
        description="Agent to use for generating detailed reports on assay hypotheses.",
    )
    candidate_lit_search_agent: JobNames = Field(
        default=JobNames.CROW,
        description=(
            "Agent to use for literature search during therapeutic candidate idea"
            " generation."
        ),
    )
    candidate_hypothesis_report_agent: JobNames = Field(
        default=JobNames.FALCON,
        description=(
            "Agent to use for generating detailed reports on therapeutic candidates."
        ),
    )


class RobinConfiguration(BaseModel):

    class Config:
        arbitrary_types_allowed = True

    prompts: Prompts = Field(default_factory=Prompts)
    num_queries: int = Field(
        default=3,
        description=(
            "Number of queries to generate for each step, more means more data but also"
            " more cost."
        ),
    )
    num_assays: int = Field(default=3, description="Number of assay to generate.")
    num_candidates: int = Field(
        default=5, description="Number of candidates to generate for each query."
    )
    disease_name: str = Field(
        default="input_disease", description="Name of the disease to focus on."
    )
    run_folder_name: str | None = Field(
        default=None,
        description=(
            "Name of the folder where results will be stored. "
            "If not provided or None, it will be auto-generated "
            "using the disease_name and the timestamp."
        ),
    )
    edison_api_key: str = "insert_edison_api_key_here"
    llm_name: str = "o4-mini"
    llm_config: dict | None = Field(default_factory=get_default_llm_config)
    agent_settings: AgentConfig = Field(default_factory=AgentConfig)
    _edison_client: EdisonClient | None = PrivateAttr(default=None)
    _llm_client: LiteLLMModel | None = PrivateAttr(default=None)

    @model_validator(mode="after")
    def set_run_folder_name_default(self) -> "RobinConfiguration":
        if self.run_folder_name is None:
            disease_part = self.disease_name[:70].replace(" ", "_")
            timestamp_part = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
            self.run_folder_name = f"{disease_part}_{timestamp_part}"
        return self

    @property
    def edison_client(self) -> EdisonClient:
        if self._edison_client is None:
            api_key = os.getenv("EDISON_API_KEY") or self.edison_api_key
            if not api_key:
                raise ValueError(
                    "Edison API key is not set. Please provide it in the"
                    " configuration or set EDISON_API_KEY env variable."
                )
            self._edison_client = EdisonClient(api_key=api_key)
        return self._edison_client

    @property
    def llm_client(self) -> LiteLLMModel:
        if self._llm_client is None:
            self._llm_client = LiteLLMModel(name=self.llm_name, config=self.llm_config)
        return self._llm_client

    def get_da_client(self):
        from .multitrajectory_runner import MultiTrajectoryRunner

        return MultiTrajectoryRunner(configuration=self)

Retrieved

2026-07-17

Original format

Python source files

Completeness

full

Transcription

Verbatim source files from the cited repository commit.

Permission basis

Redistribution under the repository's Apache-2.0 License, preserved with the local source bundle.