Ensure simple program works

README.md

@@ -60,6 +60,9 @@ LaSR uses the same interface as [SymbolicRegression.jl](https://github.com/Miles
 
 For example, we can modify the `example.jl` from the SymbolicRegression.jl documentation to use LaSR as follows:
 
+> [!NOTE]
+> LaSR searches for the LLM query prompts in a  a directory called `prompts/` at the location you start Julia. You can download and extract the `prompts.zip` folder from [here](https://github.com/trishullab/LibraryAugmentedSymbolicRegression.jl/raw/refs/heads/master/prompts.zip) to the desired location. If you wish to use a different location, you can pass a different `prompts_dir` argument to the `LLMOptions` object.
+
 ```julia
 import LibraryAugmentedSymbolicRegression: LaSRRegressor, LLMOptions, LLMWeights
 import MLJ: machine, fit!, predict, report
@@ -80,17 +83,22 @@ model = LaSRRegressor(
     llm_options=LLMOptions(
         active=true,
         weights=LLMWeights(llm_mutate=0.1, llm_crossover=0.1, llm_gen_random=0.1),
-        promtp_evol=true,
+        prompt_evol=true,
         prompt_concepts=true,
         api_key="token-abc123",
+        prompts_dir="prompts/",
+        llm_recorder_dir="lasr_runs/debug_0/",
         model="meta-llama/Meta-Llama-3-8B-Instruct",
         api_kwargs=Dict("url" => "http://localhost:11440/v1"),
-        var_order=Dict("a" => "angle", "b" => "bias")
+        var_order=Dict("a" => "angle", "b" => "bias"),
+        llm_context="We believe the function to be a trigonometric function of the angle and a quadratic function of the bias.",
     )
 )
 mach = machine(model, X, y)
 
+# ensure ./prompts/ exists. If not, download and extract the prompts.zip file from the repository.
 fit!(mach)
+# open ./lasr_runs/debug_0/llm_calls.txt to see the LLM interactions.
 report(mach)
 predict(mach, X)
 ```
@@ -113,10 +121,12 @@ llm_options = LLMOptions(
     model="meta-llama/Meta-Llama-3-8B-Instruct",                                # LLM model to use.
     api_kwargs=Dict("url" => "http://localhost:11440/v1"),                      # Keyword arguments passed to server.
     http_kwargs=Dict("retries" => 3, "readtimeout" => 3600),                    # Keyword arguments passed to HTTP requests.
-    llm_recorder_dir="lasr_runs/debug_0",                                       # Directory to log LLM interactions.
+    prompts_dir="prompts/",                                                      # Directory to look for zero shot prompts to the LLM.
+    llm_recorder_dir="lasr_runs/debug_0/",                                       # Directory to log LLM interactions.
     llm_context="",                                                             # Natural language concept to start with. You should also be able to initialize with a list of concepts.
     var_order=nothing,                                                          # Dict(variable_name => new_name).
     idea_threshold=30                                                           # Number of concepts to keep track of.
+    is_parametric=false,                                                        # This is a special flag to allow sampling parametric equations from LaSR. This won't be needed for most users.
 )
 ```
 
@@ -135,3 +145,101 @@ LibraryAugmentedSymbolicRegression.jl development is kept independent from the m
 
 > [!NOTE]  
 > The `ext/SymbolicRegressionLaSRExt` module is not yet available in the released version of SymbolicRegression.jl. It will be available in the release `vX.X.X` of SymbolicRegression.jl.
+
+
+
+## Running with Ollama
+
+LaSR can be paired with any LLM server that is compatible with OpenAI's API. Ollama is a free and open-source LLM server geared towards running LLMs on commodity laptops. You can download and setup Ollama from [here](https://ollama.com/download). After this, run:
+
+```bash
+$ ollama help
+Large language model runner
+
+Usage:
+  ollama [flags]
+  ollama [command]
+
+Available Commands:
+  serve       Start ollama
+  create      Create a model from a Modelfile
+  show        Show information for a model
+  run         Run a model
+  stop        Stop a running model
+  pull        Pull a model from a registry
+  push        Push a model to a registry
+  list        List models
+  ps          List running models
+  cp          Copy a model
+  rm          Remove a model
+  help        Help about any command
+
+Flags:
+  -h, --help      help for ollama
+  -v, --version   Show version information
+
+Use "ollama [command] --help" for more information about a command.
+$ ollama pull llama3.1
+# This downloads a 4GB-ish file that contains the Llama3.1 8B model.
+# Ollama, by default, runs on port 11434 of your local machine. Let's try a debug query to make sure we can connect to Ollama.
+$ curl http://localhost:11434/v1/models
+{"object":"list","data":[{"id":"llama3.1:latest","object":"model","created":1730973855,"owned_by":"library"},{"id":"mistral:latest","object":"model","created":1697556753,"owned_by":"library"},{"id":"wizard-math:latest","object":"model","created":1697556753,"owned_by":"library"},{"id":"codellama:latest","object":"model","created":1693414395,"owned_by":"library"},{"id":"nous-hermes-llama2:latest","object":"model","created":1691000950,"owned_by":"library"}]}
+
+$ curl http://localhost:11434/v1/completions -H "Content-Type: application/json"   -H "Authorization: Bearer token-abc123"   -d '{
+    "model": "llama3.1:latest",
+    "prompt": "Once upon a time,",
+    "max_tokens": 50,
+    "temperature": 0.7
+  }'
+
+{"id":"cmpl-626","object":"text_completion","created":1730977391,"model":"llama3.1:latest","system_fingerprint":"fp_ollama","choices":[{"text":"...in a far-off kingdom, hidden behind a veil of sparkling mist and whispering leaves, there existed a magical realm unlike any other.","index":0,"finish_reason":"stop"}],"usage":{"prompt_tokens":15,"completion_tokens":29,"total_tokens":44}}
+```
+
+Now, we can run the simple example in Julia with model_name as `llama3.1:latest` and the HTTP URL as `http://localhost:11434/v1`:
+
+```julia
+import LibraryAugmentedSymbolicRegression: LaSRRegressor, LLMOptions, LLMWeights
+import MLJ: machine, fit!, predict, report
+
+# Dataset with two named features:
+X = (a = rand(500), b = rand(500))
+
+# and one target:
+y = @. 2 * cos(X.a * 23.5) - X.b ^ 2
+
+# with some noise:
+y = y .+ randn(500) .* 1e-3
+
+model = LaSRRegressor(
+    niterations=50,
+    binary_operators=[+, -, *],
+    unary_operators=[cos],
+    llm_options=LLMOptions(
+        active=true,
+        weights=LLMWeights(llm_mutate=0.1, llm_crossover=0.1, llm_gen_random=0.1),
+        prompt_evol=true,
+        prompt_concepts=true,
+        api_key="token-abc123",
+        prompts_dir="prompts/",
+        llm_recorder_dir="lasr_runs/debug_0/",
+        model="llama3.1:latest",
+        api_kwargs=Dict("url" => "http://127.0.0.1:11434/v1"),
+        var_order=Dict("a" => "angle", "b" => "bias"),
+        llm_context="We believe the function to be a trigonometric function of the angle and a quadratic function of the bias."
+    )
+)
+
+mach = machine(model, X, y)
+fit!(mach)
+# julia> fit!(mach)
+# [ Info: Training machine(LaSRRegressor(binary_operators = Function[+, -, *], …), …).
+# ┌ Warning: You are using multithreading mode, but only one thread is available. Try starting julia with `--threads=auto`.
+# └ @ LibraryAugmentedSymbolicRegression ~/Desktop/projects/004_scientific_discovery/LibraryAugmentedSymbolicRegression.jl/src/Configure.jl:55
+# [ Info: Tokens: 476 in 22.4 seconds
+# [ Info: Started!
+# [ Info: Tokens: 542 in 49.2 seconds
+# [ Info: Tokens: 556 in 51.1 seconds
+# [ Info: Tokens: 573 in 53.2 seconds
+report(mach)
+predict(mach, X)
+```

src/LLMFunctions.jl

@@ -180,7 +180,7 @@ function gen_llm_random_tree(
             http_kwargs=convertDict(options.llm_options.http_kwargs),
         )
     catch e
-        llm_recorder(options.llm_options, "None", "gen_random|failed")
+        llm_recorder(options.llm_options, "None " * string(e), "gen_random|failed")
         return gen_random_tree_fixed_size(node_count, options, nfeatures, T)
     end
     llm_recorder(options.llm_options, string(msg.content), "llm_output|gen_random")
@@ -499,7 +499,7 @@ function prompt_evol(idea_database, options::Options)
             http_kwargs=convertDict(options.llm_options.http_kwargs),
         )
     catch e
-        llm_recorder(options.llm_options, "None", "ideas|failed")
+        llm_recorder(options.llm_options, "None " * string(e), "ideas|failed")
         return nothing
     end
     llm_recorder(options.llm_options, string(msg.content), "llm_output|ideas")
@@ -648,7 +648,7 @@ function update_idea_database(idea_database, dominating, worst_members, options:
             http_kwargs=convertDict(options.llm_options.http_kwargs),
         )
     catch e
-        llm_recorder(options.llm_options, "None", "ideas|failed")
+        llm_recorder(options.llm_options, "None " * string(e), "ideas|failed")
         return nothing
     end
 
@@ -754,7 +754,8 @@ function llm_mutate_op(
             http_kwargs=convertDict(options.llm_options.http_kwargs),
         )
     catch e
-        llm_recorder(options.llm_options, "None", "mutate|failed")
+        llm_recorder(options.llm_options, "None " * string(e), "mutate|failed")
+        # log error in llm_recorder
         return tree
     end
 
@@ -869,7 +870,7 @@ function llm_crossover_trees(
             http_kwargs=convertDict(options.llm_options.http_kwargs),
         )
     catch e
-        llm_recorder(options.llm_options, "None", "crossover|failed")
+        llm_recorder(options.llm_options, "None " * string(e), "crossover|failed")
         return tree1, tree2
     end
 

src/LLMOptions.jl

@@ -88,7 +88,7 @@ function validate_llm_options(options::LLMOptions)
             throw(ArgumentError("api_kwargs must have a 'url' key."))
         end
         if !isdir(options.prompts_dir)
-            throw(ArgumentError("prompts_dir must be a valid directory."))
+            throw(ArgumentError("options.prompts_dir not found."))
         end
     end
 end