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docs[patch]: link and description cleanup (#14471)
Fixed inconsistencies; added links and descriptions --------- Co-authored-by: Erick Friis <erickfriis@gmail.com>
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Leonid Ganeline
GitHub
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@ -5,13 +5,13 @@
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"id": "465cfbef-5bba-4b3b-b02d-fe2eba39db17",
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"metadata": {},
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"source": [
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"# Evaluating Structured Output: JSON Evaluators\n",
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"# JSON Evaluators\n",
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"\n",
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"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 provide functionality to check your model's output in a consistent way.\n",
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"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 check your model's output consistently.\n",
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"\n",
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"## JsonValidityEvaluator\n",
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"\n",
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"The `JsonValidityEvaluator` is designed to check the validity of a JSON string prediction.\n",
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"The `JsonValidityEvaluator` is designed to check the validity of a `JSON` string prediction.\n",
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"\n",
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"### Overview:\n",
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"- **Requires Input?**: No\n",
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@ -377,7 +377,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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@ -8,9 +8,12 @@
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"# String Distance\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
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"\n",
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"One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance. This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
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">In information theory, linguistics, and computer science, the [Levenshtein distance (Wikipedia)](https://en.wikipedia.org/wiki/Levenshtein_distance) 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, deletions or substitutions) required to change one word into the other. It is named after the Soviet mathematician Vladimir Levenshtein, who considered this distance in 1965.\n",
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"\n",
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"This can be accessed using the `string_distance` evaluator, which uses distance metric's from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
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"\n",
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"One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as `Levenshtein` or `postfix` distance. This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
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"\n",
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"This can be accessed using the `string_distance` evaluator, which uses distance metrics from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
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"\n",
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"**Note:** The returned scores are _distances_, meaning lower is typically \"better\".\n",
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"\n",
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@ -213,9 +216,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}
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@ -115,6 +115,17 @@ See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).
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from langchain.embeddings import HuggingFaceInstructEmbeddings
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```
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#### HuggingFaceBgeEmbeddings
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>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
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>BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://www.baai.ac.cn/english.html). `BAAI` is a private non-profit organization engaged in AI research and development.
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See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
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```python
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from langchain.embeddings import HuggingFaceBgeEmbeddings
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```
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## Tools
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@ -1,14 +1,23 @@
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# DataForSEO
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>[DataForSeo](https://dataforseo.com/) provides comprehensive SEO and digital marketing data solutions via API.
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This page provides instructions on how to use the DataForSEO search APIs within LangChain.
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## Installation and Setup
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- Get a DataForSEO API Access login and password, and set them as environment variables (`DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` respectively). You can find it in your dashboard.
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Get a [DataForSEO API Access login and password](https://app.dataforseo.com/register), and set them as environment variables
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(`DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` respectively).
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## Wrappers
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```python
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import os
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### Utility
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os.environ["DATAFORSEO_LOGIN"] = "your_login"
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os.environ["DATAFORSEO_PASSWORD"] = "your_password"
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```
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## Utility
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The DataForSEO utility wraps the API. To import this utility, use:
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@ -18,7 +27,7 @@ from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapper
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For a detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/dataforseo).
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### Tool
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## Tool
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You can also load this wrapper as a Tool to use with an Agent:
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@ -34,18 +43,3 @@ dataforseo = DataForSeoAPIWrapper(api_login="your_login", api_password="your_pas
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result = dataforseo.run("Bill Gates")
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print(result)
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```
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## Environment Variables
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You can store your DataForSEO API Access login and password as environment variables. The wrapper will automatically check for these environment variables if no values are provided:
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```python
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import os
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os.environ["DATAFORSEO_LOGIN"] = "your_login"
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os.environ["DATAFORSEO_PASSWORD"] = "your_password"
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dataforseo = DataForSeoAPIWrapper()
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result = dataforseo.run("weather in Los Angeles")
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print(result)
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```
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@ -4,9 +4,13 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# DataForSeo\n",
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"# DataForSEO\n",
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"\n",
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"This notebook demonstrates how to use the `DataForSeo API` to obtain search engine results. The `DataForSeo API` retrieves `SERP` from most popular search engines like `Google`, `Bing`, `Yahoo`. It also allows to get SERPs from different search engine types like `Maps`, `News`, `Events`, etc.\n"
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">[DataForSeo](https://dataforseo.com/) provides comprehensive SEO and digital marketing data solutions via API.\n",
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">\n",
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">The `DataForSeo API` retrieves `SERP` from the most popular search engines like `Google`, `Bing`, `Yahoo`. It also allows to >get SERPs from different search engine types like `Maps`, `News`, `Events`, etc.\n",
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"\n",
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"This notebook demonstrates how to use the [DataForSeo API](https://dataforseo.com/apis) to obtain search engine results. "
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]
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},
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{
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