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https://python.langchain.com/docs/guides/productionization/evaluation/comparison/pairwise_string/ | ## Pairwise string comparison
[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb)
Open In Colab
Often you will want to compare predictions of an LLM, Chain, or Agent f... | null | {
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Open In Colab
Often you will want to compare predictions of an LLM, Chain, or Agent for a given input. The StringComparison evaluators facilitate this so you can answer questions like:
Which LLM or prompt produces a preferred output for a given question?
Which examples should I include for fe... |
https://python.langchain.com/docs/guides/productionization/evaluation/examples/ | ## Examples
🚧 _Docs under construction_ 🚧
Below are some examples for inspecting and checking different chains.
[
## 📄️ Comparing Chain Outputs
Open In Colab
](https://python.langchain.com/docs/guides/productionization/evaluation/examples/comparisons/)
* * *
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🚧 Docs under construction 🚧
Below are some examples for inspecting and checking different chains.
📄️ Comparing Chain Outputs
Open In Colab
Help us out by providing feedback on this documentation page: |
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https://python.langchain.com/docs/guides/productionization/evaluation/examples/comparisons/ | ## Comparing Chain Outputs
[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/examples/comparisons.ipynb)
Open In Colab
Suppose you have two different prompts (or LLMs). How do you know which will gen... | null | {
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Open In Colab
Suppose you have two different prompts (or LLMs). How do you know which will generate “better” results?
One automated way to predict the preferred configuration is to use a PairwiseStringEvaluator like the PairwiseStringEvalChain[1]. This chain prompts an LLM to select which output... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/ | ## String Evaluators
A string evaluator is a component within LangChain designed to assess the performance of a language model by comparing its generated outputs (predictions) to a reference string or an input. This comparison is a crucial step in the evaluation of language models, providing a measure of the accuracy ... | null | {
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"description": "A string evaluator is a component within LangChain designed to assess the performance of a language model by comparing its generated outputs (predictions) to a reference string or an ... | String Evaluators
A string evaluator is a component within LangChain designed to assess the performance of a language model by comparing its generated outputs (predictions) to a reference string or an input. This comparison is a crucial step in the evaluation of language models, providing a measure of the accuracy or q... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/criteria_eval_chain/ | ## Criteria Evaluation
[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb)
Open In Colab
In scenarios where you wish to assess a model’s output using a specific rubric... | null | {
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Open In Colab
In scenarios where you wish to assess a model’s output using a specific rubric or criteria set, the criteria evaluator proves to be a handy tool. It allows you to verify if an LLM or Chain’s output complies with a defined set of criteria.
To understand its functionality and configurabi... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/embedding_distance/ | ## Embedding Distance
[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/embedding_distance.ipynb)
Open In Colab
To measure semantic similarity (or dissimilarity) between a prediction and a ref... | null | {
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Open In Colab
To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector distance metric the two embedded representations using the embedding_distance evaluator.[1]
Note: This returns a distance score, meaning that the lower the number,... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/custom/ | You can make your own custom string evaluators by inheriting from the `StringEvaluator` class and implementing the `_evaluate_strings` (and `_aevaluate_strings` for async support) methods.
In this example, you will create a perplexity evaluator using the HuggingFace [evaluate](https://huggingface.co/docs/evaluate/inde... | null | {
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"cache-control": "public, max-age=0... | You can make your own custom string evaluators by inheriting from the StringEvaluator class and implementing the _evaluate_strings (and _aevaluate_strings for async support) methods.
In this example, you will create a perplexity evaluator using the HuggingFace evaluate library. Perplexity is a measure of how well the g... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/exact_match/ | ## Exact Match
[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/exact_match.ipynb)
Open In Colab
Probably the simplest ways to evaluate an LLM or runnable’s string output against a reference ... | null | {
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Open In Colab
Probably the simplest ways to evaluate an LLM or runnable’s string output against a reference label is by a simple string equivalence.
This can be accessed using the exact_match evaluator.
from langchain.evaluation import ExactMatchStringEvaluator
evaluator = ExactMatchStringEvaluator()
Alter... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/json/ | ## JSON Evaluators
Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction/) and function calling applications often comes down to validation that the LLM’s string output can be parsed correctly and how it compares to a reference object. The following `JSON` validators provide functionality to c... | null | {
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Evaluating extraction and function calling applications often comes down to validation that the LLM’s string output can be parsed correctly and how it compares to a reference object. The following JSON validators provide functionality to check your model’s output consistently.
JsonValidityEvaluator
The... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/regex_match/ | ## Regex Match
[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/regex_match.ipynb)
Open In Colab
To evaluate chain or runnable string predictions against a custom regex, you can use the `rege... | null | {
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Open In Colab
To evaluate chain or runnable string predictions against a custom regex, you can use the regex_match evaluator.
from langchain.evaluation import RegexMatchStringEvaluator
evaluator = RegexMatchStringEvaluator()
Alternatively via the loader:
from langchain.evaluation import load_evaluator
eva... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/string_distance/ | ## String Distance
[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)
Open In Colab
> In information theory, linguistics, and computer science, the [Levenshtein distance ... | null | {
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Open In Colab
In information theory, linguistics, and computer science, the Levenshtein distance (Wikipedia) is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletion... |
https://python.langchain.com/docs/guides/productionization/evaluation/trajectory/ | ## Trajectory Evaluators
Trajectory Evaluators in LangChain provide a more holistic approach to evaluating an agent. These evaluators assess the full sequence of actions taken by an agent and their corresponding responses, which we refer to as the "trajectory". This allows you to better measure an agent's effectivenes... | null | {
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"description": "Trajectory Evaluators in LangChain provide a more holistic approach to evaluating an agent. These evaluators assess the full sequence of actions taken by an agent and their corres... | Trajectory Evaluators
Trajectory Evaluators in LangChain provide a more holistic approach to evaluating an agent. These evaluators assess the full sequence of actions taken by an agent and their corresponding responses, which we refer to as the "trajectory". This allows you to better measure an agent's effectiveness an... |
https://python.langchain.com/docs/guides/productionization/evaluation/string/scoring_eval_chain/ | ## Scoring Evaluator
The Scoring Evaluator instructs a language model to assess your model’s predictions on a specified scale (default is 1-10) based on your custom criteria or rubric. This feature provides a nuanced evaluation instead of a simplistic binary score, aiding in evaluating models against tailored rubrics ... | null | {
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The Scoring Evaluator instructs a language model to assess your model’s predictions on a specified scale (default is 1-10) based on your custom criteria or rubric. This feature provides a nuanced evaluation instead of a simplistic binary score, aiding in evaluating models against tailored rubrics and ... |
https://python.langchain.com/docs/guides/productionization/evaluation/trajectory/custom/ | You can make your own custom trajectory evaluators by inheriting from the [AgentTrajectoryEvaluator](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.AgentTrajectoryEvaluator.html#langchain.evaluation.schema.AgentTrajectoryEvaluator) class and overwriting the `_evaluate_agent_trajectory... | null | {
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"cache-control": "public, max-a... | You can make your own custom trajectory evaluators by inheriting from the AgentTrajectoryEvaluator class and overwriting the _evaluate_agent_trajectory (and _aevaluate_agent_action) method.
In this example, you will make a simple trajectory evaluator that uses an LLM to determine if any actions were unnecessary.
from t... |
https://python.langchain.com/docs/guides/productionization/evaluation/trajectory/trajectory_eval/ | ## Agent Trajectory
[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb)
Open In Colab
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Open In Colab
Agents can be difficult to holistically evaluate due to the breadth of actions and generation they can make. We recommend using multiple evaluation techniques appropriate to your use case. One way to evaluate an agent is to look at the whole trajectory of actions taken along with their re... |
https://python.langchain.com/docs/guides/productionization/fallbacks/ | ## Fallbacks
When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safeguard against these. That’s why we’ve introduced the concept ... | null | {
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When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safeguard against these. That’s why we’ve introduced the concept of f... |
https://python.langchain.com/docs/guides/productionization/safety/ | ## Privacy & Safety
One of the key concerns with using LLMs is that they may misuse private data or generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.
* [Amazon Com... | null | {
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"description": "One of the key concerns with using LLMs is that they may misuse private data or generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in... | Privacy & Safety
One of the key concerns with using LLMs is that they may misuse private data or generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.
Amazon Comprehend mo... |
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