mirror of https://github.com/hwchase17/langchain
notebook fmt (#12498)
Bagatur
7 months ago
committed by
GitHub
parent
56cc5b847c
commit
2424fff3f1
@ -1,243 +1,243 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2da95378",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Regex Match\n",
|
||||
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/regex_match.ipynb)\n",
|
||||
"\n",
|
||||
"To evaluate chain or runnable string predictions against a custom regex, you can use the `regex_match` evaluator."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "0de44d01-1fea-4701-b941-c4fb74e521e7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.evaluation import RegexMatchStringEvaluator\n",
|
||||
"\n",
|
||||
"evaluator = RegexMatchStringEvaluator()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "fe3baf5f-bfee-4745-bcd6-1a9b422ed46f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Alternatively via the loader:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "f6790c46",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.evaluation import load_evaluator\n",
|
||||
"\n",
|
||||
"evaluator = load_evaluator(\"regex_match\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "49ad9139",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check for the presence of a YYYY-MM-DD string.\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The delivery will be made on 2024-01-05\",\n",
|
||||
" reference=\".*\\\\b\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\b.*\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "1f5e82a3-247e-45a8-85fc-6af53bf7ff82",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0}"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check for the presence of a MM-DD-YYYY string.\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The delivery will be made on 2024-01-05\",\n",
|
||||
" reference=\".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "168fcd92-dffb-4345-b097-02d0fedf52fd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1}"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check for the presence of a MM-DD-YYYY string.\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The delivery will be made on 01-05-2024\",\n",
|
||||
" reference=\".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1d82dab5-6a49-4fe7-b3fb-8bcfb27d26e0",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Match against multiple patterns\n",
|
||||
"\n",
|
||||
"To match against multiple patterns, use a regex union \"|\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "b87b915e-b7c2-476b-a452-99688a22293a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1}"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check for the presence of a MM-DD-YYYY string or YYYY-MM-DD\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The delivery will be made on 01-05-2024\",\n",
|
||||
" reference=\"|\".join([\".*\\\\b\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\b.*\", \".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\"])\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configure the RegexMatchStringEvaluator\n",
|
||||
"\n",
|
||||
"You can specify any regex flags to use when matching."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import re\n",
|
||||
"\n",
|
||||
"evaluator = RegexMatchStringEvaluator(\n",
|
||||
" flags=re.IGNORECASE\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Alternatively\n",
|
||||
"# evaluator = load_evaluator(\"exact_match\", flags=re.IGNORECASE)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1}"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"I LOVE testing\",\n",
|
||||
" reference=\"I love testing\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "82de8d3e-c829-440e-a582-3fb70cecad3b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2da95378",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Regex Match\n",
|
||||
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/regex_match.ipynb)\n",
|
||||
"\n",
|
||||
"To evaluate chain or runnable string predictions against a custom regex, you can use the `regex_match` evaluator."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "0de44d01-1fea-4701-b941-c4fb74e521e7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.evaluation import RegexMatchStringEvaluator\n",
|
||||
"\n",
|
||||
"evaluator = RegexMatchStringEvaluator()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "fe3baf5f-bfee-4745-bcd6-1a9b422ed46f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Alternatively via the loader:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "f6790c46",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.evaluation import load_evaluator\n",
|
||||
"\n",
|
||||
"evaluator = load_evaluator(\"regex_match\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "49ad9139",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check for the presence of a YYYY-MM-DD string.\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The delivery will be made on 2024-01-05\",\n",
|
||||
" reference=\".*\\\\b\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\b.*\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "1f5e82a3-247e-45a8-85fc-6af53bf7ff82",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0}"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check for the presence of a MM-DD-YYYY string.\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The delivery will be made on 2024-01-05\",\n",
|
||||
" reference=\".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "168fcd92-dffb-4345-b097-02d0fedf52fd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1}"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check for the presence of a MM-DD-YYYY string.\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The delivery will be made on 01-05-2024\",\n",
|
||||
" reference=\".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1d82dab5-6a49-4fe7-b3fb-8bcfb27d26e0",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Match against multiple patterns\n",
|
||||
"\n",
|
||||
"To match against multiple patterns, use a regex union \"|\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "b87b915e-b7c2-476b-a452-99688a22293a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1}"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check for the presence of a MM-DD-YYYY string or YYYY-MM-DD\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The delivery will be made on 01-05-2024\",\n",
|
||||
" reference=\"|\".join(\n",
|
||||
" [\".*\\\\b\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\b.*\", \".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\"]\n",
|
||||
" ),\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configure the RegexMatchStringEvaluator\n",
|
||||
"\n",
|
||||
"You can specify any regex flags to use when matching."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import re\n",
|
||||
"\n",
|
||||
"evaluator = RegexMatchStringEvaluator(flags=re.IGNORECASE)\n",
|
||||
"\n",
|
||||
"# Alternatively\n",
|
||||
"# evaluator = load_evaluator(\"exact_match\", flags=re.IGNORECASE)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 1}"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"I LOVE testing\",\n",
|
||||
" reference=\"I love testing\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "82de8d3e-c829-440e-a582-3fb70cecad3b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,223 +1,221 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2da95378",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# String Distance\n",
|
||||
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
|
||||
"\n",
|
||||
"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",
|
||||
"\n",
|
||||
"This can be accessed using the `string_distance` evaluator, which uses distance metric's from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
|
||||
"\n",
|
||||
"**Note:** The returned scores are _distances_, meaning lower is typically \"better\".\n",
|
||||
"\n",
|
||||
"For more information, check out the reference docs for the [StringDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string_distance.base.StringDistanceEvalChain) for more info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "8b47b909-3251-4774-9a7d-e436da4f8979",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install rapidfuzz"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "f6790c46",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.evaluation import load_evaluator\n",
|
||||
"\n",
|
||||
"evaluator = load_evaluator(\"string_distance\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "49ad9139",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0.11555555555555552}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The job is completely done.\",\n",
|
||||
" reference=\"The job is done\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "c06a2296",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0.0724999999999999}"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# The results purely character-based, so it's less useful when negation is concerned\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The job is done.\",\n",
|
||||
" reference=\"The job isn't done\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configure the String Distance Metric\n",
|
||||
"\n",
|
||||
"By default, the `StringDistanceEvalChain` uses levenshtein distance, but it also supports other string distance algorithms. Configure using the `distance` argument."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "a88bc7d7-62d3-408d-b0e0-43abcecf35c8",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<StringDistance.DAMERAU_LEVENSHTEIN: 'damerau_levenshtein'>,\n",
|
||||
" <StringDistance.LEVENSHTEIN: 'levenshtein'>,\n",
|
||||
" <StringDistance.JARO: 'jaro'>,\n",
|
||||
" <StringDistance.JARO_WINKLER: 'jaro_winkler'>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain.evaluation import StringDistance\n",
|
||||
"\n",
|
||||
"list(StringDistance)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"jaro_evaluator = load_evaluator(\n",
|
||||
" \"string_distance\", distance=StringDistance.JARO\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0.19259259259259254}"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"jaro_evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The job is completely done.\",\n",
|
||||
" reference=\"The job is done\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "7020b046-0ef7-40cc-8778-b928e35f3ce1",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0.12083333333333324}"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"jaro_evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The job is done.\",\n",
|
||||
" reference=\"The job isn't done\",\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2da95378",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# String Distance\n",
|
||||
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
|
||||
"\n",
|
||||
"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",
|
||||
"\n",
|
||||
"This can be accessed using the `string_distance` evaluator, which uses distance metric's from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
|
||||
"\n",
|
||||
"**Note:** The returned scores are _distances_, meaning lower is typically \"better\".\n",
|
||||
"\n",
|
||||
"For more information, check out the reference docs for the [StringDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string_distance.base.StringDistanceEvalChain) for more info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "8b47b909-3251-4774-9a7d-e436da4f8979",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install rapidfuzz"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "f6790c46",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.evaluation import load_evaluator\n",
|
||||
"\n",
|
||||
"evaluator = load_evaluator(\"string_distance\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "49ad9139",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0.11555555555555552}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The job is completely done.\",\n",
|
||||
" reference=\"The job is done\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "c06a2296",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0.0724999999999999}"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# The results purely character-based, so it's less useful when negation is concerned\n",
|
||||
"evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The job is done.\",\n",
|
||||
" reference=\"The job isn't done\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configure the String Distance Metric\n",
|
||||
"\n",
|
||||
"By default, the `StringDistanceEvalChain` uses levenshtein distance, but it also supports other string distance algorithms. Configure using the `distance` argument."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "a88bc7d7-62d3-408d-b0e0-43abcecf35c8",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<StringDistance.DAMERAU_LEVENSHTEIN: 'damerau_levenshtein'>,\n",
|
||||
" <StringDistance.LEVENSHTEIN: 'levenshtein'>,\n",
|
||||
" <StringDistance.JARO: 'jaro'>,\n",
|
||||
" <StringDistance.JARO_WINKLER: 'jaro_winkler'>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain.evaluation import StringDistance\n",
|
||||
"\n",
|
||||
"list(StringDistance)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"jaro_evaluator = load_evaluator(\"string_distance\", distance=StringDistance.JARO)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0.19259259259259254}"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"jaro_evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The job is completely done.\",\n",
|
||||
" reference=\"The job is done\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "7020b046-0ef7-40cc-8778-b928e35f3ce1",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'score': 0.12083333333333324}"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"jaro_evaluator.evaluate_strings(\n",
|
||||
" prediction=\"The job is done.\",\n",
|
||||
" reference=\"The job isn't done\",\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,139 +1,139 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bf733a38-db84-4363-89e2-de6735c37230",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bedrock Chat\n",
|
||||
"\n",
|
||||
"[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d51edc81",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install boto3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chat_models import BedrockChat\n",
|
||||
"from langchain.schema import HumanMessage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chat = BedrockChat(model_id=\"anthropic.claude-v2\", model_kwargs={\"temperature\":0.1})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content=\" Voici la traduction en français : J'adore programmer.\", additional_kwargs={}, example=False)"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"messages = [\n",
|
||||
" HumanMessage(\n",
|
||||
" content=\"Translate this sentence from English to French. I love programming.\"\n",
|
||||
" )\n",
|
||||
"]\n",
|
||||
"chat(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"id": "a4a4f4d4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### For BedrockChat with Streaming"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c253883f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
|
||||
"\n",
|
||||
"chat = BedrockChat(\n",
|
||||
" model_id=\"anthropic.claude-v2\",\n",
|
||||
" streaming=True,\n",
|
||||
" callbacks=[StreamingStdOutCallbackHandler()],\n",
|
||||
" model_kwargs={\"temperature\": 0.1},\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d9e52838",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"messages = [\n",
|
||||
" HumanMessage(\n",
|
||||
" content=\"Translate this sentence from English to French. I love programming.\"\n",
|
||||
" )\n",
|
||||
"]\n",
|
||||
"chat(messages)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bf733a38-db84-4363-89e2-de6735c37230",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bedrock Chat\n",
|
||||
"\n",
|
||||
"[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d51edc81",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install boto3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chat_models import BedrockChat\n",
|
||||
"from langchain.schema import HumanMessage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chat = BedrockChat(model_id=\"anthropic.claude-v2\", model_kwargs={\"temperature\": 0.1})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content=\" Voici la traduction en français : J'adore programmer.\", additional_kwargs={}, example=False)"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"messages = [\n",
|
||||
" HumanMessage(\n",
|
||||
" content=\"Translate this sentence from English to French. I love programming.\"\n",
|
||||
" )\n",
|
||||
"]\n",
|
||||
"chat(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"id": "a4a4f4d4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### For BedrockChat with Streaming"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c253883f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
|
||||
"\n",
|
||||
"chat = BedrockChat(\n",
|
||||
" model_id=\"anthropic.claude-v2\",\n",
|
||||
" streaming=True,\n",
|
||||
" callbacks=[StreamingStdOutCallbackHandler()],\n",
|
||||
" model_kwargs={\"temperature\": 0.1},\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d9e52838",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"messages = [\n",
|
||||
" HumanMessage(\n",
|
||||
" content=\"Translate this sentence from English to French. I love programming.\"\n",
|
||||
" )\n",
|
||||
"]\n",
|
||||
"chat(messages)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
||||
@ -1,159 +1,161 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a634365e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# AWS S3 Directory\n",
|
||||
"\n",
|
||||
">[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service\n",
|
||||
"\n",
|
||||
">[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)\n",
|
||||
"\n",
|
||||
"This covers how to load document objects from an `AWS S3 Directory` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "49815096",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install boto3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "2f0cd6a5",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.document_loaders import S3DirectoryLoader"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "321cc7f1",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3DirectoryLoader(\"testing-hwc\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2b11d155",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0690c40a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Specifying a prefix\n",
|
||||
"You can also specify a prefix for more finegrained control over what files to load."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "72d44781",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3DirectoryLoader(\"testing-hwc\", prefix=\"fake\")"
|
||||
]
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a634365e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# AWS S3 Directory\n",
|
||||
"\n",
|
||||
">[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service\n",
|
||||
"\n",
|
||||
">[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)\n",
|
||||
"\n",
|
||||
"This covers how to load document objects from an `AWS S3 Directory` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "49815096",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install boto3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "2f0cd6a5",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.document_loaders import S3DirectoryLoader"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "321cc7f1",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3DirectoryLoader(\"testing-hwc\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2b11d155",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0690c40a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Specifying a prefix\n",
|
||||
"You can also specify a prefix for more finegrained control over what files to load."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "72d44781",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3DirectoryLoader(\"testing-hwc\", prefix=\"fake\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "2d3c32db",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "2d3c32db",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 's3://testing-hwc/fake.docx'}, lookup_index=0)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 's3://testing-hwc/fake.docx'}, lookup_index=0)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Configuring the AWS Boto3 client\n",
|
||||
"You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n",
|
||||
"named arguments when creating the S3DirectoryLoader.\n",
|
||||
"This is useful for instance when AWS credentials can't be set as environment variables.\n",
|
||||
"See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured."
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "91a7ac07"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3DirectoryLoader(\"testing-hwc\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\")"
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "f485ec8c"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "c0fa76ae"
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Configuring the AWS Boto3 client\n",
|
||||
"You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n",
|
||||
"named arguments when creating the S3DirectoryLoader.\n",
|
||||
"This is useful for instance when AWS credentials can't be set as environment variables.\n",
|
||||
"See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured."
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "91a7ac07"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3DirectoryLoader(\n",
|
||||
" \"testing-hwc\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\"\n",
|
||||
")"
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "f485ec8c"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "c0fa76ae"
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,122 +1,124 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "66a7777e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# AWS S3 File\n",
|
||||
"\n",
|
||||
">[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.\n",
|
||||
"\n",
|
||||
">[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)\n",
|
||||
"\n",
|
||||
"This covers how to load document objects from an `AWS S3 File` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "9ec8a3b3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.document_loaders import S3FileLoader"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "43128d8d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install boto3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "35d6809a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3FileLoader(\"testing-hwc\", \"fake.docx\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "efd6be84",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 's3://testing-hwc/fake.docx'}, lookup_index=0)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
]
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "66a7777e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# AWS S3 File\n",
|
||||
"\n",
|
||||
">[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.\n",
|
||||
"\n",
|
||||
">[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)\n",
|
||||
"\n",
|
||||
"This covers how to load document objects from an `AWS S3 File` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "9ec8a3b3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.document_loaders import S3FileLoader"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "43128d8d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install boto3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "35d6809a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3FileLoader(\"testing-hwc\", \"fake.docx\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "efd6be84",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "93689594",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configuring the AWS Boto3 client\n",
|
||||
"You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n",
|
||||
"named arguments when creating the S3DirectoryLoader.\n",
|
||||
"This is useful for instance when AWS credentials can't be set as environment variables.\n",
|
||||
"See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured."
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 's3://testing-hwc/fake.docx'}, lookup_index=0)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3FileLoader(\"testing-hwc\", \"fake.docx\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\")"
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "43106ee8"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "1764a727"
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "93689594",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configuring the AWS Boto3 client\n",
|
||||
"You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n",
|
||||
"named arguments when creating the S3DirectoryLoader.\n",
|
||||
"This is useful for instance when AWS credentials can't be set as environment variables.\n",
|
||||
"See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = S3FileLoader(\n",
|
||||
" \"testing-hwc\", \"fake.docx\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\"\n",
|
||||
")"
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "43106ee8"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
],
|
||||
"metadata": {},
|
||||
"id": "1764a727"
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue