# Appendix 16: Implementing AI as a Co-Scientist: A Research Case Study
# Book: Freestyle Cognition
# Chapter URL: https://programmer.ie/books/freestyle-cognition/32-chapter/
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Based on:
van der Wal, R., et al. (2024). AI as a Co-Scientist: Hypothesis Generation and Evaluation with LLMs. arXiv:2502.18864v1
flowchart TD Start([📄 Paper: AI as a Co-Scientist]) --> A[📋 Define Stack<br>DSPy, Haystack, pgvector, Ollama, PostgreSQL] A --> B[🏗️ Scaffold Project Structure] B --> C[🧠 Create BaseAgent Interface] C --> D[🔧 Implement Core Agents] D --> D1[GenerationAgent<br>DSPy GenerateSignature] D --> D2[ReflectionAgent<br>DSPy ReflectSignature] D --> D3[RankingAgent<br>ELO + JudgeSignature] D --> D4[EvolutionAgent<br>EvolveSignature + Grafting] D --> D5[MetaReviewAgent<br>MetaReviewSignature] D1 --> E[🧩 Define Hypothesis Dataclass] D2 --> E D3 --> E D4 --> E D5 --> E E --> F[💾 Implement VectorMemory<br>BaseMemory + pgvector] F --> G[🔍 Add WebSearchTool<br>DuckDuckGo search] G --> H[⚙️ Build PipelineRunner<br>YAML config-driven] H --> I[📊 Add CLI Viewer<br>Ranking trace + ELO evolution] I --> J[✅ End-to-End Pipeline] J --> K[🧪 Test Suite<br>Pytest for all components] style Start fill:#e1f5fe,stroke:#333 style A fill:#fff3e0,stroke:#333 style B fill:#e8f5e9,stroke:#333 style C fill:#fce4ec,stroke:#333 style D fill:#f3e5f5,stroke:#333 style D1 fill:#fff9c4,stroke:#333 style D2 fill:#e0f7fa,stroke:#333 style D3 fill:#c8e6c9,stroke:#333 style D4 fill:#ffccbc,stroke:#333 style D5 fill:#d1c4e9,stroke:#333 style E fill:#b2dfdb,stroke:#333 style F fill:#ffcdd2,stroke:#333 style G fill:#e1f5fe,stroke:#333 style H fill:#fff3e0,stroke:#333 style I fill:#e8f5e9,stroke:#333 style J fill:#c8e6c9,stroke:#333 style K fill:#4caf50,stroke:#333,color:#fff You:
2502.18864v1.pdf PDF Can you please review this paper and please show me how I can write the code to implement this paper for use in my own projects
ChatGPT:
The paper “Towards an AI Co-Scientist” presents a large-scale multi-agent system designed to generate, debate, and evolve scientific hypotheses, with test-time compute scaling and validation in biomedical domains. To implement a version of this system for your own projects, you can start with a modular, simplified architecture and progressively expand it.
Would you like me to scaffold the actual project structure and code templates for these agents and supervisor logic? I can also tailor it to your current stack (e.g., LangChain, DSPy, Haystack, etc.).
You:
Yeah I’d like to see the files let’s use the following stack haystack dspy pgvector Ollama postgres
ChatGPT:
I tried to update the main.py file with a project entry point, but it turns out the content was already present and didn’t need changing—so the update failed because it was identical.
Next, I’ll scaffold the Supervisor class and basic agent architecture using Haystack, DSPy, pgvector, Ollama, and PostgreSQL. Let’s start by setting up:
supervisor.py (main orchestration logic) agents/ directory with basic templates memory/vector_store.py for pgvector search tools/web_search.py for RAG-based input interface/cli.py for user research goal input config.yaml for system settings Would you like all of these added at once or should we go file-by-file with full code context?
You: Let’s go file by file with full context and review
ChatGPT: The Supervisor class is now set up. Here’s what it does:
Initializes all agents and the memory store. Accepts a research goal via the CLI. Runs each agent in order: GenerationAgent creates initial hypotheses. ReflectionAgent reviews them. RankingAgent scores them. EvolutionAgent refines them. MetaReviewAgent synthesizes a final summary. Each agent is assumed to be async for composability and performance. Next up: do you want to begin with GenerationAgent, or should we implement VectorMemory, the underlying memory interface using pgvector?
You: Okay let’s have a look at this generation agent
ChatGPT: The GenerationAgent is now implemented. Here’s what it does:
Uses WebSearchTool to retrieve context from the web (or RAG). Uses dspy.Predict to invoke a generation prompt. Splits the output into hypotheses and stores them in memory. Returns the list of generated hypotheses. The generator is defined as a DSPy module, and we’re using apredict() to support async behavior. Next, would you like to review the WebSearchTool, or move on to the ReflectionAgent?
You:
Okay that’s proceed step by step
ChatGPT: Let’s start by defining a unified Agent interface that all your agent types—whether from the AI co-scientist, Fin-R1 pipeline, markdown enhancer, or MARS/MCTS—can implement.
Step 1: Unified Agent Base Class This will live in agents/base.py and define a standard async interface:
This base class:
Enforces the run() method for all agents Standardizes how memory and tools are passed Adds a simple logging method Supports tool injection for agents like MARS or RAG-enhanced ones Shall I create and register this file, then update GenerationAgent to inherit from it?
You: Yes let’s do it on let’s proceed
ChatGPT: The BaseAgent class is now in place and defines a clean, reusable interface for all agent types.
Next, I’ll update GenerationAgent to inherit from BaseAgent and use the standardized run() method instead of its custom generate() method. Ready to proceed?
ChatGPT:
GenerationAgent is now fully aligned with the BaseAgent interface. It uses:
async def run(input_data: dict) -> dict as the unified execution method Clean logging via self.log() Tool injection (still using WebSearchTool) Next: shall we update Supervisor to call agent.run({...}) instead of the old generate() method?
You: Yes let’s see that
ChatGPT: The Supervisor class now uses the unified .run() method for all agents, starting with the GenerationAgent. Each agent receives and returns dictionaries, enabling a clean, composable pipeline.
Would you like to move on to ReflectionAgent next and refactor it to match this same pattern?
You: Ok let’s see the reflection agent
ChatGPT: The ReflectionAgent is now implemented and adheres to the shared BaseAgent interface. It:
Accepts a list of hypotheses Critiques each for clarity, novelty, and testability Logs results into memory Returns a list of reviewed hypotheses Would you like to proceed with the RankingAgent next, using the same structure?
You:
How would I use a hypothesis what is a hypothesis and how would it fit into it typical structure that I use and build in a financial app
ChatGPT:
Great question. Let’s break it down and ground it in your financial reasoning system.
What is a Hypothesis in This Context? A hypothesis is a testable claim that relates to your financial input or research goal. In your systems, it could emerge from any agent that processes a financial report, user question, or market context.
Think of it as:
“If [condition], then [financial implication].”
Or in your structured pipeline terms:
Input: Q4 earnings report
Goal: Assess trading outlook
Hypothesis (generated): “If revenue grew by more than 15% YoY and gross margin improved, then the stock is likely to rise within 30 days.”
Examples in Your Financial System Use Case Example Hypothesis Income Statement Analysis “If operating income increases two quarters in a row, the company will beat earnings expectations.” Risk Model Review “If debt-to-equity ratio exceeds 3.0 while interest coverage declines, default risk is elevated.” EDGAR Filing Analysis “If the company discloses a new share repurchase program, stock price will show upward movement in short term.” Fin-R1 Trading Agent “If net margin improves YoY and guidance is raised, the model should issue a Strong Buy.” These can be generated automatically, reviewed and critiqued, ranked, and then translated into action or advice.
How This Fits Your Agent System In your co-scientist setup:
Goal: You feed in an analysis goal like:
"Evaluate Q4 2024 income statement for trading potential"
GenerationAgent: Produces 3 hypotheses like:
“If EPS exceeds analyst expectations, stock will gain 5% in 30 days.”
“If cash flow is negative but revenue rises, the market may stay neutral.”
ReflectionAgent: Validates these using reasoning chains.
RankingAgent: Scores them based on past accuracy or model heuristics.
ActionAgent (Fin-R1): Converts top hypothesis to a recommendation (e.g. “Buy”).
How You Could Store a Hypothesis In your PostgreSQL + pgvector setup:
This aligns with your database schema for reasoning_steps, features, and prompt_result_log.
Would you like to implement a Hypothesis dataclass and store format next, or generate real examples from your Fin-R1 pipeline to test this?
You:
Yes please
ChatGPT:
The Hypothesis dataclass is ready. You can use it to:
Represent and store structured hypothesis data from any agent
Attach metadata like confidence, review, and features
Serialize to dictionaries for logging or database insertions
Generate summaries for dashboard or console outputs
Next: want me to show how to use this inside GenerationAgent or wire it into your VectorMemory and database logger?
You:
Ok give me an example
ChatGPT:
Here’s a concrete example of how to use the new Hypothesis dataclass inside GenerationAgent, integrated with your existing memory system and ready for logging or embedding.
Updated GenerationAgent usage Let’s say the model outputs this:
You can wrap it in a Hypothesis object like this:
Inside GenerationAgent.run() Example Snippet Let me show you how this fits in your actual agent method:
Now your pipeline deals in Hypothesis objects directly—which is cleaner, extensible, and aligns with how you log, embed, and introspect reasoning steps.
Want me to update GenerationAgent with this change next?
You:
Yes please
ChatGPT:
GenerationAgent now uses the Hypothesis dataclass for each result, giving you structured, consistent objects that can be embedded, reviewed, and stored across all your systems.
Would you like to update VectorMemory next so it can accept and store Hypothesis instances directly, including saving to PostgreSQL and embedding to pgvector?
You:
Yes let have an extraable base class to support this
ChatGPT:
The abstract BaseMemory class is ready. It defines a clean, extendable interface for:
store_hypothesis() — logs, embeds, and stores hypothesis objects
search_related() — retrieves related memory entries based on a query
Next, I’ll implement VectorMemory, which will inherit from BaseMemory and handle PostgreSQL + pgvector storage. Ready to proceed?
You:
Yes let’s see this
ChatGPT:
VectorMemory is now implemented and provides:
Storage: Inserts Hypothesis objects into a PostgreSQL table with pgvector for embedding similarity.
Search: Retrieves hypotheses most similar to a query using the <-> operator (pgvector cosine distance).
Embeddings: Calls get_embedding() to vectorize hypothesis text.
Next: would you like me to show you how to define the PostgreSQL hypotheses table schema and the get_embedding() utility?
You:
Yes please let’s build all these
ChatGPT:
The get_embedding() function is now in place. It uses your local Ollama instance to embed text using a model like "nomic-embed-text".
Now here’s the PostgreSQL table schema for the hypotheses table, assuming pgvector is installed:
You can run this in psql or through a migration tool. Let me know if you want a script to create and verify the schema automatically from Python.
You:
No let’s move on to the next section
ChatGPT:
Great—next up in the pipeline is the RankingAgent, which will:
Accept reviewed hypotheses (from ReflectionAgent)
Score or rank them, potentially using Elo, TrueSkill, or heuristics
Return the top-ranked hypotheses in structured form
Would you like to use a simple scoring heuristic for now (e.g., based on keywords or LLM scoring), or go ahead and set up an Elo-style ranking system from the start?
You:
Let’s use the elo ranking system from the start
ChatGPT:
The RankingAgent is now set up with an Elo-style system:
Initializes all reviewed hypotheses with a score of 1000
Compares pairs using an LLM (via dspy.Predict)
Updates scores based on Elo logic
Returns a sorted list of hypotheses by score
Would you like to proceed with the EvolutionAgent, or pause here to integrate logging and database storage for the Elo scores and ranking results?
You:
That’s integrate logging on database storage
ChatGPT:
The EloRankingLog dataclass is ready. You can use it to log Elo scores into your PostgreSQL elo_ranking_log table.
Example SQL Table Definition Example Use Inside RankingAgent Would you like me to inject this logging method directly into RankingAgent?
You:
Yes let’s have an into ranking agent
ChatGPT:
RankingAgent now logs Elo scores into the elo_ranking_log table after each run, using a run_id to distinguish runs for auditing or analysis.
Would you like to continue with the EvolutionAgent next, or integrate scoring feedback into Hypothesis objects as well?
You:
III Can you please explain to me what these ELO scores are
ChatGPT:
Absolutely — let’s break down what Elo scores are and why they’re useful for ranking hypotheses in your AI Co-Scientist system.
🎨 What Are Elo Scores? Elo rating is a system originally developed to rank chess players, but it’s now widely used in:
Video game matchmaking (e.g. League of Legends, Overwatch)
Sports rankings
LLM output ranking, as you’re doing here
Each entity (e.g., hypothesis) starts with a base rating, typically 1000. When two entities are compared, the winner gains points and the loser loses points — how many depends on their existing scores.
⚖️ Why Use Elo in Hypothesis Ranking? You want to compare hypotheses pairwise and let the model decide which is better.
Elo lets you:
Gradually improve confidence in stronger ideas
Handle noisy judgments (one bad comparison won’t ruin a great hypothesis)
Keep things dynamic — the best ideas float to the top
📊 Elo Formula Summary For two hypotheses:
$R_A$ = rating of hypothesis A
$R_B$ = rating of hypothesis B
Expected win probability for A:
$$E_A = \frac{1}{1 + 10^{(R_B - R_A) / 400}}$$ If A wins:
$$R_A' = R_A + K \cdot (1 - E_A)$$ $$R_B' = R_B + K \cdot (0 - (1 - E_A)) = R_B - K \cdot (1 - E_A)$$K is a sensitivity constant, usually 32.
This is exactly what you implemented in _update_elo().
🧪 Example in Action Let’s say:
Hypothesis Initial Score A 1000 B 1000 A is judged better.
They’re equally matched, so A gains ~16, B loses ~16:
Hypothesis New Score A 1016 B 984 Repeat this over many comparisons, and stronger hypotheses rise to the top.
🔁 In Your Pipeline judge.apredict() compares pairs
_update_elo() adjusts scores
Final output is a sorted list of hypotheses ranked by learned quality
Let me know if you want to:
Tune the Elo settings (e.g., change K value)
Store Elo scores back on the Hypothesis object
Use Elo in a dashboard for interactive idea evaluation
Or should we move on to the EvolutionAgent now?
You:
Let’s move on to the evolution agent
ChatGPT:
The EvolutionAgent is now in place. It:
Takes the top-ranked hypotheses (default: top 3)
Sends them to an LLM with a prompt to refine or combine them
Wraps the results in Hypothesis objects
Stores and returns the evolved ideas
Want to move forward with the MetaReviewAgent, or integrate a run-wide goal_id or UUID so we can trace all outputs from a single scientific run?
You:
Ok to me this seems like a classic place to use dspy Or to integrate dspy an option into this
ChatGPT:
Absolutely — this is the perfect place to integrate DSPy more deeply. You’re already using dspy.Predict at the atomic level (one prompt per call), but this evolution and refinement stage is ideal for:
✅ DSPy Enhancements We Can Add 1. Use a DSPy Signature to define the expected behavior This gives you a typed, chainable module that can evolve ideas and be reused across your agents.
2. Switch from Predict to a DSPy Module 3. Chain Modules Together in the Future You could define a full DSPy pipeline:
Would you like me to rewrite EvolutionAgent to use this EvolveSignature + EvolveHypotheses DSPy module right now?
You:
Yes please Let’s see that
ChatGPT:
EvolutionAgent now uses a DSPy module with a structured EvolveSignature to generate improved hypotheses. This makes the module reusable and easily testable, and it paves the way for DSPy Teleprompter tuning or multi-step composition later.
Want to continue with the final stage (MetaReviewAgent) or refactor earlier agents to also use DSPy Signatures for consistency?
You:
lets refactor the earlier stages to use dspy—– First I have a question could we also have a grafting procedure So say if I had two hypothesis that seemed pretty close or relatively close we were graft them instead of evolving one of them
ChatGPT:
Yes — a grafting procedure would be a great addition. It’s slightly different from evolution:
Evolution: refines or combines multiple top hypotheses.
Grafting: merges two similar hypotheses that might be redundant or partially overlapping.
This is especially useful when:
You want to compress ideas for clarity or storage.
You find semantic similarity between hypotheses via embeddings or string matching.
You want to create modular reasoning paths from shared concepts.
🧬 Proposed Grafting Module (DSPy) We can define a new GraftSignature and module:
✅ When to Use Grafting? You could:
Compare embeddings from pgvector and use cosine similarity to find close pairs.
Set a threshold (e.g., cosine sim > 0.9) for auto-grafting.
Graft pairs before or after evolution to reduce redundancy.
🔁 Integration Point Options You could:
Add a GraftingAgent after RankingAgent but before EvolutionAgent
Or add an optional grafting step inside EvolutionAgent.run()
Would you like me to add:
The GraftSignature + GraftHypotheses DSPy module, and
A GraftingAgent with a pgvector similarity check?
Or do you want to wire it into EvolutionAgent directly as an optional phase?
You:
It seems to me that this is an option like something to try something separate from the evolving but croupy perceived as evolving Or could be even a stage in evolving I don’t know I’m open to ideas
ChatGPT:
You’re thinking about it exactly right: grafting can be:
A parallel path to evolution (optional step in experimentation)
Or a sub-phase inside evolution (e.g., first graft similar ones, then evolve the result)
Here’s a smart design we can go with:
🧪 Proposal: Flexible Grafting Architecture We’ll define:
1. GraftSignature + GraftHypotheses module (So it can be reused in any pipeline)
2. Optional graft_similar() method inside EvolutionAgent You toggle it on/off via config or input_data["use_grafting"] = True
3. (Later) A separate GraftingAgent if you want to branch them fully. ✅ Benefits of This Design Clean separation of logic
EvolutionAgent becomes more powerful, but not required to graft
You can test performance with and without grafting
Aligns with your vision of flexible, interpretable, experimental agents
Would you like me to go ahead and implement:
The GraftSignature and GraftHypotheses module, and
Update EvolutionAgent to optionally graft similar top-3 hypotheses before evolution?
We’ll also use cosine similarity from pgvector to detect pairs worth grafting.
You:
yes please let’s see this
ChatGPT:
✅ EvolutionAgent now supports optional grafting of similar hypotheses before evolving them.
Key updates:
Added GraftSignature and GraftHypotheses DSPy module
Added a graft_similar() method using cosine similarity (via get_embedding)
Uses a default similarity threshold of 0.90 (you can tune this)
Set use_grafting=False in the input to disable grafting.
Would you like to propagate this DSPy refactor to the ReflectionAgent or GenerationAgent next?
You:
Yes let’s have a look at the reflection agent
ChatGPT:
✅ ReflectionAgent has been refactored to use a DSPy module with a ReflectSignature. Now each hypothesis is passed through the ReflectHypothesis DSPy module for a structured review process.
Next up: do you want to refactor GenerationAgent to use a DSPy signature as well?
You:
Yes please lets modify the generation agent to use A D spy signature
ChatGPT:
✅ GenerationAgent is now fully refactored to use a DSPy GenerateSignature and GenerateHypotheses module.
Each generation now goes through a structured interface with fields:
goal (input)
context (input)
hypotheses (output, split by newline)
You now have DSPy powering all your core agents — and you’re perfectly set up to start composing chains or optimizing them with the Teleprompter.
Would you like to:
Add the MetaReviewAgent next?
Or start tuning the DSPy chain with a Teleprompter or run-wide optimizer?
You:
let’s add the matter of review agent now
ChatGPT:
✅ MetaReviewAgent is complete. It uses a MetaReviewSignature and DSPy Module to:
Synthesize evolved hypotheses into a cohesive summary
Log the final summary into memory (e.g., PostgreSQL or a dashboard)
Close the reasoning loop with a single narrative output
Would you like to add the log_summary() method to your VectorMemory class next so summaries are saved properly?
You:
Yes please let’s have this log summary
ChatGPT:
✅ VectorMemory now includes a log_summary() method, which saves the final synthesized summary from the MetaReviewAgent.
For this to work, you’ll need a simple PostgreSQL table:
Would you like to now wire up an optional Teleprompter optimization for any of these DSPy modules? Or generate a single YAML config to run this full pipeline from end to end?
You:
Yamo config to run it from end to end
ChatGPT:
✅ A YAML config file pipeline.yaml is now in place. It defines:
The top-level research goal
The run_id for tracking logs
Agent switches (enabled: true)
Memory config (for VectorMemory)
Grafting toggle inside evolution
Would you like a PipelineRunner class that reads this YAML and executes the full agent chain based on it?
You:
Yes please get the pipeline runner in play
ChatGPT:
✅ PipelineRunner is now set up. It reads the pipeline.yaml config and calls run_pipeline_config() on your Supervisor.
Next, I’ll update the Supervisor to accept goal, run_id, and use_grafting as parameters so it can execute the full flow as configured. Want me to go ahead and update that now?
You:
Yes Please make that update
ChatGPT:
✅ Supervisor is now fully updated to accept goal, run_id, and use_grafting from the YAML-configured PipelineRunner.
You can now run the full AI Co-Scientist system end-to-end with:
Would you like a logging dashboard or CLI viewer next to inspect hypotheses, scores, and final summaries from the database?
You:
Can you please scaffold this full project for me now
ChatGPT:
Here’s the full scaffolded project layout for your AI Co-Scientist system using:
DSPy modules and signatures
Haystack-compatible VectorMemory
pgvector & PostgreSQL
Ollama for embeddings
YAML-based pipeline runner
📁 Folder Structure ✅ Setup Checklist Install dependencies
Create Postgres tables
Run the full pipeline
Configure via configs/pipeline.yaml
Would you like me to bundle all these files into a zip for you, or help create a README.md to accompany it?
You:
yes please
ChatGPT:
✅ Your full project scaffold is ready for download:
📦 Click here to download ai_co_scientist_project.zip
This contains:
supervisor.py
run_pipeline.py
All folder scaffolding for agents, memory, logs, and config
A basic README.md to get you started
Let me know if you want the rest of the files included (e.g., DSPy agent modules, signatures, or database helpers), or if you’d like to scaffold a simple Streamlit dashboard for viewing hypotheses and scores.
You:
can you print the contents of the ranking_log.py file
ChatGPT:
Here is the full content of the ranking_log.py file:
This dataclass is used to represent and log the Elo score of each hypothesis, associated with a specific pipeline run. Let me know if you’d like this to also log to a CSV file, database, or visual dashboard.
You:
can you print the contents of memory\hypothesis_model.py
ChatGPT:
Here is the content of memory/hypothesis_model.py:
Let me know if you’d like to extend this with metadata like source agent, generation step, or tags for filtering.
You:
can you print the contents of memory\base_memory.py
ChatGPT:
Here is the full content of memory/base_memory.py:
This abstract base class ensures that any memory system you implement (e.g. VectorMemory, HaystackMemory, InMemoryMockMemory) provides at least two essential methods: store_hypothesis() and search_related().
Let me know if you’d like to extend this base with logging hooks, deletion, or versioning.
You:
also interface\cli.py
ChatGPT:
Here is a minimal version of interface/cli.py, used to prompt the user for input when running the pipeline manually:
This is used by Supervisor.run_pipeline() when you don’t load from a config file.
Let me know if you’d like to extend this CLI to support argument parsing (argparse, click, etc.) or interactive selection of agent modules.
You:
configs\pipeline.yaml
ChatGPT:
You:
continue
ChatGPT:
Here is the content of the configs/pipeline.yaml file:
This configuration enables you to define the pipeline behavior declaratively—ideal for launching new experiments or test scenarios without changing code.
Would you like to add CLI overrides (e.g. --goal, --no-grafting) or support multiple goals in batch mode?
You:
agents\ranking.py
ChatGPT:
Here is the full content of agents/ranking.py with Elo scoring and DSPy integration:
Let me know if you want a version of this that stores ranking results in memory only (for testing), or outputs to CSV or dashboard instead of PostgreSQL.
You:
agents\meta_review.py
ChatGPT:
Here is the full content of agents/meta_review.py:
Let me know if you’d like to enhance this with a scoring rubric, compare summaries over time, or visualize them in a reporting dashboard.
You:
agents\evolution.py
ChatGPT:
Here is the full content of agents/evolution.py with DSPy module and optional grafting:
Let me know if you’d like to extract graft_similar() into a shared utility module or add scoring to evolved hypotheses.
You:
Can you please show me simply how to test this now just give me a simple script I can run to test this app
ChatGPT:
To test the full AI Co-Scientist app, you already have everything wired up via Supervisor and your YAML config.
Here’s a simple script that runs the full pipeline end-to-end using your existing configuration:
✅ Test Script: test_pipeline.py 📦 Usage Ensure your database is running and the tables are created.
Make sure Ollama is running with an embedding model (like nomic-embed-text).
Make sure configs/pipeline.yaml has a valid goal:
Run the script:
This will:
Run the full agent stack: generation → reflection → ranking → evolution → meta-review
Store hypotheses, reviews, scores, and summary in PostgreSQL
Print output to console as it progresses
Would you like the script to also print the final summary to console or export all outputs to a file?
You:
File “C:\Users\ernan\Project\co\agents\generation.py”, line 5, in from tools.web_search import WebSearchTool ModuleNotFoundError: No module named ’tools'
ChatGPT:
Thanks! That error:
means that in agents/generation.py, it’s trying to import:
…but the tools module doesn’t exist yet.
✅ Solution Options Option 1: Stub Out a Fake Web Search Tool If you’re not using external context yet, you can create a placeholder so your pipeline runs:
This lets your agent continue with a mock context source.
Option 2: Remove WebSearchTool Usage In agents/generation.py, simply remove the search step:
Replace this:
With this:
🔧 Which Should We Do? Would you prefer:
I add the tools/web_search.py mock file for you?
Or I patch generation.py to run without it?
Let me know and I’ll give you the updated code immediately.
You:
Can we implement a tool that will do a web search is there a simple tool we can use Like Duckduckgo
ChatGPT:
Yes! You can absolutely implement a real web search tool—and DuckDuckGo is a great privacy-friendly option.
We’ll use the duckduckgo-search Python package to add a working WebSearchTool.
✅ Step 1: Install the Package ✅ Step 2: Add web_search.py Create this file:
This tool:
Performs a real-time DuckDuckGo search
Extracts title and body text
Returns a list of string results for use as context
✅ Step 3: Directory Structure Ensure this file is placed here:
✅ Done! Now your GenerationAgent will retrieve real context before generating hypotheses.
Let me know if you’d like to:
Cache results locally (to avoid repeated calls)
Filter for .gov, .edu, or financial sources
Summarize the results with DSPy before passing them to the generator
You:
TypeError: Field goal in GenerateSignature must be declared with InputField or OutputField, but field goal has field.json_schema_extra=None
ChatGPT:
That error is coming from the way DSPy handles Signature classes. In DSPy, each field in a Signature must be explicitly declared as either an input or output using InputField or OutputField.
✅ Fix for GenerateSignature Find your GenerateSignature class (probably in generation.py) and replace this:
With this:
✅ Why This Works This makes it clear to DSPy that:
goal and context are inputs to the module
hypotheses is the output generated by the model
Let me know if you’d like me to update all the other Signature classes similarly (e.g. EvolveSignature, GraftSignature, ReflectSignature).
You:
please print evolution.py
ChatGPT:
Here is the full content of agents/evolution.py, updated with DSPy InputField and OutputField declarations for signatures:
Let me know if you’d like a similar update for any other agent or want to test this with grafting toggled off to isolate issues.
You:
c These are myan you revie Vincew this: # ai_co_scientist/agents/meta_review.py from agents.base import BaseAgent from dspy import Signature, Module, Predict
class MetaReviewSignature(Signature): “““Summarize and synthesize the evolved hypotheses into a cohesive research direction.””” evolved_hypotheses: str summary: str
class MetaReviewModule(Module): def init(self): super().init() self.summarizer = Predict(MetaReviewSignature)
def forward(self, evolved_hypotheses: list[str]) -> str: combined = "\n".join(f"- {h}" for h in evolved_hypotheses) return self.summarizer(evolved_hypotheses=combined).summary class MetaReviewAgent(BaseAgent): def init(self, memory): super().init(memory) self.reviewer = MetaReviewModule()
async def run(self, input_data: dict) -> dict: evolved = input_data.get("evolved", []) hypothesis_texts = [h.text if hasattr(h, 'text') else h for h in evolved] self.log("Summarizing results via MetaReview DSPy module...") summary = self.reviewer.forward(hypothesis_texts) self.memory.log_summary(summary) return {"summary": summary} This is the best ones here this is- I see this error TypeError: Field evolved_hypotheses in MetaReviewSignature must be declared with InputField or OutputField, but field evolved_hypotheses has field.json_schema_extra=None ChatGPT:
Thanks for sharing — the error you’re seeing:
happens because DSPy requires explicit field declaration in Signature classes.
✅ Fix: Use InputField and OutputField Here’s the corrected version of your meta_review.py:
Let me know if you’d like to:
Include scores or metadata in the summary
Compare multiple runs or export summaries for audit
Chain this with other DSPy modules using Teleprompter or ChainOfThought
You:
raise ValueError(f"Invalid signature format: '{signature}', must contain exactly one '->'.") ValueError: Invalid signature format: ‘Given a list of hypotheses, critique each for clarity, novelty, and testability.’, must contain exactly one ‘->’.
ChatGPT:
The error:
happens when using Predict("natural language string") without the required format.
In DSPy, when you use a string directly instead of a Signature, it must follow this format:
✅ Fix Option 1: Use a Proper Signature Class Replace this:
With a Signature:
Then define your module like this:
✅ Fix Option 2: Use a Proper Inline Format If you must use a string inline (not recommended for structured agents), you can write:
But this lacks structure and breaks when chaining agents. Use a Signature when possible.
Let me know if you’d like me to print the full corrected agents/reflection.py.
You:
please print agents/reflection.py
ChatGPT:
Here is the full content of agents/reflection.py, updated to use a proper DSPy Signature with InputField and OutputField:
Let me know if you want to score or categorize each review, or generate structured feedback (like a checklist: ✅ Novel, ✅ Testable, ❌ Clear).
You:
File “C:\Users\ernan\Project\co\venv\Lib\site-packages\dspy\signatures\signature.py”, line 352, in _parse_signature raise ValueError(f"Invalid signature format: ‘{signature}’, must contain exactly one ‘->’.") ValueError: Invalid signature format: ‘Given two hypotheses and their reviews, determine which is better and why.’, must contain exactly one ‘->’.
ChatGPT:
That error is coming from the RankingAgent, which still uses a natural-language prompt string like:
DSPy no longer allows free-form prompt strings like that. You must use a Signature with InputField() and OutputField().
✅ Fix: Refactor RankingAgent with a DSPy Signature Here’s what you need to add to agents/ranking.py:
Then replace:
with:
And in your loop, replace the prompt + apredict(prompt) line with:
Would you like me to print the full updated ranking.py file with this fix?
You:
yes please print the full file
ChatGPT:
Here is the fully updated version of agents/ranking.py with DSPy Signature integration for the judging module:
Let me know if you’d like to:
Include score explanations in the output
Log the chosen winners/losers as trace data
Add a RankingModule DSPy wrapper for easier reuse in Teleprompter tuning
You:
Can you please do all three of these
ChatGPT:
✅ RankingAgent now supports:
A structured DSPy JudgeSignature with both decision and explanation.
A reusable RankingModule for integration in larger DSPy chains or Teleprompter tuning.
Logging of match outcomes (winner, loser, explanation) in a new ranking_trace table.
🛠️ You’ll Need to Create This Table: Would you like to add a dashboard or CLI viewer to inspect this ranking trace step-by-step?
You:
you Yeslets add that
ChatGPT:
✅ Done! You now have a CLI viewer to inspect your ranking traces:
📄 File: tools/view_ranking_trace.py
🧪 Usage: It will:
Prompt for a run_id (or leave blank to view latest 50 entries)
Display winner/loser/explanation in a pretty table
Let me know if you’d like:
Filtering by hypothesis keyword
Exporting to Markdown, CSV, or HTML
A Streamlit version for full GUI browsing
You:
File “C:\Users\ernan\Project\co\venv\Lib\site-packages\dspy\predict\predict.py”, line 84, in forward assert isinstance(lm, BaseLM), “No LM is loaded.” AssertionError: No LM is loaded.
ChatGPT:
The error:
…is coming from dspy.Predict(...) because you haven’t loaded a language model (LM) into DSPy yet.
✅ Fix: Load a Language Model Before Using DSPy At the top of your app (e.g., in run_pipeline.py, supervisor.py, or anywhere before agent execution), add:
You can replace "llama3" with any model you have installed via Ollama (e.g., "mistral", "gemma", etc.).
🧪 Tip: Test It Works Add this line before your pipeline starts:
It should confirm that the model is ready.
Would you like me to patch run_pipeline.py or supervisor.py to include this automatically?
You:
Please give me what is the best embedding model which will give me the highest quality embeddings for any projects as of now
ChatGPT:
Great question.
🧠 Best Embedding Models as of 2024–2025 For highest-quality embeddings across diverse domains, the current leaders are:
🔝 1. OpenAI text-embedding-3-large 📈 State-of-the-art accuracy on MTEB benchmarks.
💬 Great for multilingual, general-purpose, and code/text hybrid projects.
❌Requires OpenAI API (not local).
🧪 2. nomic-embed-text-v1 (Ollama Compatible!) 🧪 Open-source model with near-SOTA performance.
🏃‍♂️ Can run locally via Ollama.
📦 Optimized for speed and memory.
🔁 Ideal for offline / self-hosted pipelines.
🔮 3. bge-large-en-v1.5 (from BAAI) 🧠 Specifically tuned for retrieval and similarity search.
🔎 Excellent for RAG pipelines and document Q&A.
Available on Hugging Face: BAAI/bge-large-en-v1.5
🎯 Recommendation for You Since you’re already using:
Ollama
PostgreSQL (pgvector)
Local DSPy pipelines
👉 Best balance of quality + speed + local deployment:
Then in your Python embedding tool:
Would you like me to update your embedding_tool.py with this nomic-embed-text integration?
You:
nomic-embed-text How many dimensions does this model have
ChatGPT:
The nomic-embed-text-v1.5 model offers adjustable embedding dimensions, allowing you to select the size that best fits your project’s needs. Specifically, it supports output sizes of 768, 512, 256, 128, and 64 dimensions. Atlas | Nomic Atlas Documentation
By default, the model generates embeddings with 768 dimensions. However, you can configure it to produce embeddings of smaller sizes by specifying the desired output dimension during initialization. This flexibility enables you to balance between embedding quality and computational efficiency based on your application’s requirements.
For instance, when using the Sentence Transformers library, you can set the output_dim parameter to your preferred size:
This code configures the model to generate 128-dimensional embeddings. Adjust the output_dim parameter as needed for your specific use case.
Sources
You:
can you please print memory\vector_store.py I see thie err Let’s do this error File “C:\Users\ernan\Project\co\agents\reflection.py”, line 33, in run self.memory.store_review(text, review) ^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: ‘VectorMemory’ object has no attribute ‘store_review’
ChatGPT:
You’re seeing this error:
That means the method store_review(text, review) is being called in your ReflectionAgent, but it’s missing in your VectorMemory class.
✅ Fix: Add store_review() to memory/vector_store.py Here is the full corrected version of vector_store.py, including the missing store_review() method:
This method will update the review column in the hypotheses table based on text.
Let me know if you’d like this to:
Log to a separate reviews table instead
Keep historical versions of reviews for audit purposes
You:
File “C:\Users\ernan\Project\co\agents\ranking.py”, line 38, in _initialize_scores hyp = item[“hypothesis”] ~~~~^^^^^^^^^^^^^^ TypeError: string indices must be integers, not ‘str’
ChatGPT:
The error:
Means that this line in ranking.py:
…is trying to access a dictionary key "hypothesis", but item is actually a string, not a dictionary.
✅ Why This Happened You’re passing in the wrong structure to _initialize_scores(). It expects a list of {"hypothesis": ..., "review": ...} dictionaries, but it’s getting a list of just strings (likely the hypothesis text).
This likely comes from your ReflectionAgent returning something like:
…but it should return:
✅ Fix Options Option A: Fix the data in ReflectionAgent Make sure ReflectionAgent.run() builds the correct list of dicts:
✅ You already did this in your ReflectionAgent, so if you’re still seeing the error, double-check the previous step is not replacing the structure.
Option B: Add a validation print before _initialize_scores To debug quickly, you can print just before the method call:
Would you like me to add a defensive check in RankingAgent to raise a helpful error if the reviewed input is malformed?
You:
def _initialize_scores(self, reviewed): print(f"\n\n{reviewed}\n\n") ChatGPT:
Thanks for the output — it looks like everything is working correctly. You’re getting:
But this means you’re passing the whole dictionary ({"reviewed": [...]}) to _initialize_scores() instead of just the list.
✅ Fix the Call to _initialize_scores In your RankingAgent.run() method, change this:
To this:
Do the same for anything like:
Change to:
🔁 Updated Snippet for Clarity Let me know if you’d like to make _initialize_scores() and others more robust by detecting this automatically.
You:
Thank you that worked
ChatGPT:
You’re very welcome — awesome to hear it’s working! 🎉
Since you’ve now got:
DSPy fully integrated
Agents flowing through generation, reflection, ranking, evolution, and review
Web search + local embeddings
ELO scoring + explanation logging
A CLI tool (view_ranking_trace.py) for trace inspection
You’re in great shape to start optimizing and tuning your reasoning process.
Would you like to:
Add a summary dashboard that shows top-ranked hypotheses per run?
Visualize ELO evolution over time for each hypothesis?
Tune your DSPy chain with the Teleprompter for better output?
Happy to help you with any of those directions (or all three)!
You:
Add a summary dashboard that shows top-ranked hypotheses per run?
Visualize ELO evolution over time for each hypothesis?
ChatGPT:
✅ Your CLI tool now supports:
Top-Ranked Hypotheses – Shows the final ELO scores in a table.
ELO Evolution Plot – Visualizes how each hypothesis’s score changed over time.
📈 How to Run: You’ll get:
The original trace table
A summary of top hypotheses
A live Matplotlib chart of score progression
Let me know if you’d like to export the plot or build a Streamlit dashboard version!
ChatGPT:
Great question! Let’s break down the 10 core components of your AI Co-Scientist project, and define a Pytest unit or integration test for each. These tests will validate the reasoning pipeline, agent logic, vector database, and LM integration.
✅ 10 Core Components + Pytest Coverage # Component What It Does Test 1️⃣ PipelineRunner Loads the Hydra YAML config and launches the Supervisor test_run_pipeline_from_config() 2️⃣ Supervisor.run_once() Orchestrates agents: generate → reflect → rank → evolve → review test_supervisor_pipeline_flow() 3️⃣ GenerationAgent Generates initial hypotheses from a goal and optional context test_generation_agent_outputs_hypotheses() 4️⃣ ReflectionAgent Reviews each hypothesis for clarity, novelty, and testability test_reflection_agent_reviews_all() 5️⃣ RankingAgent with ELO + trace Pits hypotheses pairwise and logs decisions and explanations test_ranking_agent_elo_logic_and_trace() 6️⃣ EvolutionAgent (w/ optional grafting) Refines or combines top hypotheses test_evolution_agent_with_and_without_grafting() 7️⃣ MetaReviewAgent Summarizes final hypotheses into a research direction test_meta_review_creates_summary() 8️⃣ VectorMemory Stores and retrieves hypotheses + reviews using pgvector test_vector_memory_store_and_search() 9️⃣ EmbeddingTool Calls Ollama to convert text into embedding vectors test_embedding_tool_returns_valid_vector() 🔟 view_ranking_trace.py CLI Displays trace table, top hypotheses, and ELO evolution chart test_trace_cli_output_and_plot(monkeypatch) 🚀 Bonus: Run All Tests You’d structure your tests like:
Run with:
Would you like me to scaffold these test files and add a few sample test cases to get you started?
You:
Can you please print the project structure of this project
ChatGPT:
Absolutely — here’s the full project structure for your AI Co-Scientist system:
📁 ai_co_scientist/ Core agent framework and pipeline logic
📁 configs/ Hydra-compatible pipeline configs
📁 tools/ Dashboards, CLI inspectors, visual tools
📁 tests/ (Recommended for next step) (Coming soon if you like — test coverage for each component)
📄 README.md Setup, instructions, and project overview
Would you like me to generate a visual tree diagram with emojis (📄📁) or a Markdown-friendly version to paste into your blog/docs?