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.
- Place a random fact or statement (the 'needle') in the middle of a long context window (the 'haystack')
- Ask the model to retrieve this statement
- 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.
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
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 ifopenai
evaluation strategy is used.
Install the package from PyPi:
pip install needlehaystack
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 areopenai
,anthropic
, andcohere
. Defaults toopenai
evaluator
- The evaluator, which can either be amodel
oderLangSmith
. See more onLangSmith
below. If using amodel
, onlyopenai
is currently supported. Defaults toopenai
.model_name
- Model name of the language model accessible by the provider. Defaults togpt-3.5-turbo-0125
evaluator_model_name
- Model name of the language model accessible by the evaluator. Defaults togpt-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]"
- Fork and clone the repository.
- Create and activate the virtual environment as described above.
- Set the environment variables as described above.
- 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.
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 supportedretrieval_question
- The question with which to retrieve your needle in the background contextresults_version
- You may want to run your test multiple times for the same combination of length/depth, change the version number if sonum_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. Ifsave_results = True
, then this script will populate aresult/
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/Falsefinal_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 iteratecontext_lengths_max
- The ending point of your context lengths list to iteratecontext_lengths_num_intervals
- The number of intervals between your min/max to iterate throughcontext_lengths
- A custom set of context lengths. This will override the values set forcontext_lengths_min
, max, and intervals if setdocument_depth_percent_min
- The starting point of your document depths. Should be int > 0document_depth_percent_max
- The ending point of your document depths. Should be int < 100document_depth_percent_intervals
- The number of iterations to do between your min/max pointsdocument_depth_percents
- A custom set of document depths lengths. This will override the values set fordocument_depth_percent_min
, max, and intervals if setdocument_depth_percent_interval_type
- Determines the distribution of depths to iterate over. 'linear' or 'sigmoidseconds_to_sleep_between_completions
- Default: None, set # of seconds if you'd like to slow down your requestsprint_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-needleneedles
- 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 begpt-3.5-turbo-0125
.
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.
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
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"
(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."]'
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.