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Needle In A Haystack - Pressure Testing LLMs

A simple 'needle in a haystack' analysis to test in-context retrieval ability of long context LLMs.

Supported model providers: OpenAI, Anthropic, Cohere

Get the behind the scenes on the overview video.

GPT-4-128 Context Testing

The Test

  1. Place a random fact or statement (the 'needle') in the middle of a long context window (the 'haystack')
  2. Ask the model to retrieve this statement
  3. Iterate over various document depths (where the needle is placed) and context lengths to measure performance

This is the code that backed this OpenAI and Anthropic analysis.

The results from the original tests are in /original_results. The script has upgraded a lot since those test were ran so the data formats may not match your script results.

Getting Started

Setup Virtual Environment

We recommend setting up a virtual environment to isolate Python dependencies, ensuring project-specific packages without conflicting with system-wide installations.

python3 -m venv venv
source venv/bin/activate

Environment Variables

  • NIAH_MODEL_API_KEY - API key for interacting with the model. Depending on the provider, this gets used appropriately with the correct sdk.
  • NIAH_EVALUATOR_API_KEY - API key to use if openai evaluation strategy is used.

Install Package

Install the package from PyPi:

pip install needlehaystack

Run Test

Start using the package by calling the entry point needlehaystack.run_test from command line.

You can then run the analysis on OpenAI, Anthropic, or Cohere models with the following command line arguments:

  • provider - The provider of the model, available options are openai, anthropic, and cohere. Defaults to openai
  • evaluator - The evaluator, which can either be a model or LangSmith. See more on LangSmith below. If using a model, only openai is currently supported. Defaults to openai.
  • model_name - Model name of the language model accessible by the provider. Defaults to gpt-3.5-turbo-0125
  • evaluator_model_name - Model name of the language model accessible by the evaluator. Defaults to gpt-3.5-turbo-0125

Additionally, LLMNeedleHaystackTester parameters can also be passed as command line arguments, except model_to_test and evaluator.

Here are some example use cases.

Following command runs the test for openai model gpt-3.5-turbo-0125 for a single context length of 2000 and single document depth of 50%.

needlehaystack.run_test --provider openai --model_name "gpt-3.5-turbo-0125" --document_depth_percents "[50]" --context_lengths "[2000]"

Following command runs the test for anthropic model claude-2.1 for a single context length of 2000 and single document depth of 50%.

needlehaystack.run_test --provider anthropic --model_name "claude-2.1" --document_depth_percents "[50]" --context_lengths "[2000]"

Following command runs the test for cohere model command-r for a single context length of 2000 and single document depth of 50%.

needlehaystack.run_test --provider cohere --model_name "command-r" --document_depth_percents "[50]" --context_lengths "[2000]"

For Contributors

  1. Fork and clone the repository.
  2. Create and activate the virtual environment as described above.
  3. Set the environment variables as described above.
  4. Install the package in editable mode by running the following command from repository root:
pip install -e .

The package needlehaystack is available for import in your test cases. Develop, make changes and test locally.

LLMNeedleHaystackTester parameters:

  • model_to_test - The model to run the needle in a haystack test on. Default is None.
  • evaluator - An evaluator to evaluate the model's response. Default is None.
  • needle - The statement or fact which will be placed in your context ('haystack')
  • haystack_dir - The directory which contains the text files to load as background context. Only text files are supported
  • retrieval_question - The question with which to retrieve your needle in the background context
  • results_version - You may want to run your test multiple times for the same combination of length/depth, change the version number if so
  • num_concurrent_requests - Default: 1. Set higher if you'd like to run more requests in parallel. Keep in mind rate limits.
  • save_results - Whether or not you'd like to save your results to file. They will be temporarily saved in the object regardless. True/False. If save_results = True, then this script will populate a result/ directory with evaluation information. Due to potential concurrent requests each new test will be saved as a few file.
  • save_contexts - Whether or not you'd like to save your contexts to file. Warning these will get very long. True/False
  • final_context_length_buffer - The amount of context to take off each input to account for system messages and output tokens. This can be more intelligent but using a static value for now. Default 200 tokens.
  • context_lengths_min - The starting point of your context lengths list to iterate
  • context_lengths_max - The ending point of your context lengths list to iterate
  • context_lengths_num_intervals - The number of intervals between your min/max to iterate through
  • context_lengths - A custom set of context lengths. This will override the values set for context_lengths_min, max, and intervals if set
  • document_depth_percent_min - The starting point of your document depths. Should be int > 0
  • document_depth_percent_max - The ending point of your document depths. Should be int < 100
  • document_depth_percent_intervals - The number of iterations to do between your min/max points
  • document_depth_percents - A custom set of document depths lengths. This will override the values set for document_depth_percent_min, max, and intervals if set
  • document_depth_percent_interval_type - Determines the distribution of depths to iterate over. 'linear' or 'sigmoid
  • seconds_to_sleep_between_completions - Default: None, set # of seconds if you'd like to slow down your requests
  • print_ongoing_status - Default: True, whether or not to print the status of test as they complete

LLMMultiNeedleHaystackTester parameters:

  • multi_needle - True or False, whether to run multi-needle
  • needles - List of needles to insert in the context

Other Parameters:

  • model_name - The name of the model you'd like to use. Should match the exact value which needs to be passed to the api. Ex: For OpenAI inference and evaluator models it would be gpt-3.5-turbo-0125.

Results Visualization

LLMNeedleInHaystackVisualization.ipynb holds the code to make the pivot table visualization. The pivot table was then transferred to Google Slides for custom annotations and formatting. See the google slides version. See an overview of how this viz was created here.

OpenAI's GPT-4-128K (Run 11/8/2023)

GPT-4-128 Context Testing

Anthropic's Claude 2.1 (Run 11/21/2023)

GPT-4-128 Context Testing

Multi Needle Evaluator

To enable multi-needle insertion into our context, use --multi_needle True.

This inserts the first needle at the specified depth_percent, then evenly distributes subsequent needles through the remaining context after this depth.

For even spacing, it calculates the depth_percent_interval as:

depth_percent_interval = (100 - depth_percent) / len(self.needles)

So, the first needle is placed at a depth percent of depth_percent, the second at depth_percent + depth_percent_interval, the third at depth_percent + 2 * depth_percent_interval, and so on.

Following example shows the depth percents for the case of 10 needles and depth_percent of 40%.

depth_percent_interval = (100 - 40) / 10 = 6

Needle 1: 40
Needle 2: 40 + 6 = 46
Needle 3: 40 + 2 * 6 = 52
Needle 4: 40 + 3 * 6 = 58
Needle 5: 40 + 4 * 6 = 64
Needle 6: 40 + 5 * 6 = 70
Needle 7: 40 + 6 * 6 = 76
Needle 8: 40 + 7 * 6 = 82
Needle 9: 40 + 8 * 6 = 88
Needle 10: 40 + 9 * 6 = 94

LangSmith Evaluator

You can use LangSmith to orchestrate evals and store results.

(1) Sign up for LangSmith (2) Set env variables for LangSmith as specified in the setup. (3) In the Datasets + Testing tab, use + Dataset to create a new dataset, call it multi-needle-eval-sf to start. (4) Populate the dataset with a test question:

question: What are the 5 best things to do in San Franscisco?
answer: "The 5 best things to do in San Francisco are: 1) Go to Dolores Park. 2) Eat at Tony's Pizza Napoletana. 3) Visit Alcatraz. 4) Hike up Twin Peaks. 5) Bike across the Golden Gate Bridge"

Screenshot 2024-03-05 at 4 54 15 PM (5) Run with --evaluator langsmith and --eval_set multi-needle-eval-sf to run against our recently created eval set.

Let's see all these working together on a new dataset, multi-needle-eval-pizza.

Here is the multi-needle-eval-pizza eval set, which has a question and reference answer. You can also and resulting runs: https://smith.langchain.com/public/74d2af1c-333d-4a73-87bc-a837f8f0f65c/d

Here is the command to run this using multi-needle eval and passing the relevant needles:

needlehaystack.run_test --evaluator langsmith --context_lengths_num_intervals 3 --document_depth_percent_intervals 3 --provider openai --model_name "gpt-4-0125-preview" --multi_needle True --eval_set multi-needle-eval-pizza --needles '["Figs are one of the three most delicious pizza toppings.", "Prosciutto is one of the three most delicious pizza toppings.", "Goat cheese is one of the three most delicious pizza toppings."]'

License

This project is licensed under the MIT License - see the LICENSE file for details. Use of this software requires attribution to the original author and project, as detailed in the license.