mirror of
https://github.com/hwchase17/langchain.git
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FEAT: Add azure cognitive health tool (#13448)
- **Description:** This change adds an agent to the Azure Cognitive Services toolkit for identifying healthcare entities - **Dependencies:** azure-ai-textanalytics (Optional) --------- Co-authored-by: James Beck <James.Beck@sa.gov.au> Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
jwbeck97
GitHub
parent
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commit
a93616e972
@ -12,7 +12,8 @@
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"- AzureCogsImageAnalysisTool: used to extract caption, objects, tags, and text from images. (Note: this tool is not available on Mac OS yet, due to the dependency on `azure-ai-vision` package, which is only supported on Windows and Linux currently.)\n",
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"- AzureCogsFormRecognizerTool: used to extract text, tables, and key-value pairs from documents.\n",
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"- AzureCogsSpeech2TextTool: used to transcribe speech to text.\n",
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"- AzureCogsText2SpeechTool: used to synthesize text to speech."
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"- AzureCogsText2SpeechTool: used to synthesize text to speech.\n",
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"- AzureCogsTextAnalyticsHealthTool: used to extract healthcare entities."
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]
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},
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{
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@ -32,6 +33,7 @@
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"source": [
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"# !pip install --upgrade azure-ai-formrecognizer > /dev/null\n",
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"# !pip install --upgrade azure-cognitiveservices-speech > /dev/null\n",
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"# !pip install --upgrade azure-ai-textanalytics > /dev/null\n",
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"\n",
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"# For Windows/Linux\n",
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"# !pip install --upgrade azure-ai-vision > /dev/null"
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@ -60,7 +62,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -101,7 +103,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -111,7 +113,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -240,6 +242,65 @@
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"display.display(audio)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mAction:\n",
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"```\n",
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"{\n",
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" \"action\": \"azure_cognitive_services_text_analyics_health\",\n",
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" \"action_input\": \"The patient is a 54-year-old gentleman with a history of progressive angina over the past several months. The patient had a cardiac catheterization in July of this year revealing total occlusion of the RCA and 50% left main disease, with a strong family history of coronary artery disease with a brother dying at the age of 52 from a myocardial infarction and another brother who is status post coronary artery bypass grafting. The patient had a stress echocardiogram done on July, 2001, which showed no wall motion abnormalities, but this was a difficult study due to body habitus. The patient went for six minutes with minimal ST depressions in the anterior lateral leads, thought due to fatigue and wrist pain, his anginal equivalent. Due to the patient's increased symptoms and family history and history left main disease with total occasional of his RCA was referred for revascularization with open heart surgery.\"\n",
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"}\n",
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"```\n",
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"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mThe text conatins the following healthcare entities: 54-year-old is a healthcare entity of type Age, gentleman is a healthcare entity of type Gender, progressive angina is a healthcare entity of type Diagnosis, past several months is a healthcare entity of type Time, cardiac catheterization is a healthcare entity of type ExaminationName, July of this year is a healthcare entity of type Time, total is a healthcare entity of type ConditionQualifier, occlusion is a healthcare entity of type SymptomOrSign, RCA is a healthcare entity of type BodyStructure, 50 is a healthcare entity of type MeasurementValue, % is a healthcare entity of type MeasurementUnit, left main is a healthcare entity of type BodyStructure, disease is a healthcare entity of type Diagnosis, family is a healthcare entity of type FamilyRelation, coronary artery disease is a healthcare entity of type Diagnosis, brother is a healthcare entity of type FamilyRelation, dying is a healthcare entity of type Diagnosis, 52 is a healthcare entity of type Age, myocardial infarction is a healthcare entity of type Diagnosis, brother is a healthcare entity of type FamilyRelation, coronary artery bypass grafting is a healthcare entity of type TreatmentName, stress echocardiogram is a healthcare entity of type ExaminationName, July, 2001 is a healthcare entity of type Time, wall motion abnormalities is a healthcare entity of type SymptomOrSign, body habitus is a healthcare entity of type SymptomOrSign, six minutes is a healthcare entity of type Time, minimal is a healthcare entity of type ConditionQualifier, ST depressions in the anterior lateral leads is a healthcare entity of type SymptomOrSign, fatigue is a healthcare entity of type SymptomOrSign, wrist pain is a healthcare entity of type SymptomOrSign, anginal equivalent is a healthcare entity of type SymptomOrSign, increased is a healthcare entity of type Course, symptoms is a healthcare entity of type SymptomOrSign, family is a healthcare entity of type FamilyRelation, left is a healthcare entity of type Direction, main is a healthcare entity of type BodyStructure, disease is a healthcare entity of type Diagnosis, occasional is a healthcare entity of type Course, RCA is a healthcare entity of type BodyStructure, revascularization is a healthcare entity of type TreatmentName, open heart surgery is a healthcare entity of type TreatmentName\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I know what to respond\n",
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"Action:\n",
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"```\n",
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"{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"The text contains the following diagnoses: progressive angina, coronary artery disease, myocardial infarction, and coronary artery bypass grafting.\"\n",
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"}\n",
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"```\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'The text contains the following diagnoses: progressive angina, coronary artery disease, myocardial infarction, and coronary artery bypass grafting.'"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\n",
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" \"\"\"The patient is a 54-year-old gentleman with a history of progressive angina over the past several months.\n",
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"The patient had a cardiac catheterization in July of this year revealing total occlusion of the RCA and 50% left main disease ,\n",
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"with a strong family history of coronary artery disease with a brother dying at the age of 52 from a myocardial infarction and\n",
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"another brother who is status post coronary artery bypass grafting. The patient had a stress echocardiogram done on July , 2001 ,\n",
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"which showed no wall motion abnormalities , but this was a difficult study due to body habitus. The patient went for six minutes with\n",
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"minimal ST depressions in the anterior lateral leads , thought due to fatigue and wrist pain , his anginal equivalent. Due to the patient's\n",
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"increased symptoms and family history and history left main disease with total occasional of his RCA was referred for revascularization with open heart surgery.\n",
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"\n",
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"List all the diagnoses.\n",
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"\"\"\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -264,7 +325,7 @@
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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": "3.10.12"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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@ -9,6 +9,7 @@ from langchain.tools.azure_cognitive_services import (
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AzureCogsImageAnalysisTool,
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AzureCogsSpeech2TextTool,
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AzureCogsText2SpeechTool,
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AzureCogsTextAnalyticsHealthTool,
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)
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from langchain.tools.base import BaseTool
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@ -23,6 +24,7 @@ class AzureCognitiveServicesToolkit(BaseToolkit):
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AzureCogsFormRecognizerTool(),
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AzureCogsSpeech2TextTool(),
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AzureCogsText2SpeechTool(),
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AzureCogsTextAnalyticsHealthTool(),
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]
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# TODO: Remove check once azure-ai-vision supports MacOS.
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@ -84,6 +84,14 @@ def _import_azure_cognitive_services_AzureCogsText2SpeechTool() -> Any:
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return AzureCogsText2SpeechTool
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def _import_azure_cognitive_services_AzureCogsTextAnalyticsHealthTool() -> Any:
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from langchain.tools.azure_cognitive_services import (
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AzureCogsTextAnalyticsHealthTool,
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)
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return AzureCogsTextAnalyticsHealthTool
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def _import_bing_search_tool_BingSearchResults() -> Any:
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from langchain.tools.bing_search.tool import BingSearchResults
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@ -691,6 +699,8 @@ def __getattr__(name: str) -> Any:
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return _import_azure_cognitive_services_AzureCogsSpeech2TextTool()
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elif name == "AzureCogsText2SpeechTool":
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return _import_azure_cognitive_services_AzureCogsText2SpeechTool()
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elif name == "AzureCogsTextAnalyticsHealthTool":
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return _import_azure_cognitive_services_AzureCogsTextAnalyticsHealthTool()
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elif name == "BingSearchResults":
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return _import_bing_search_tool_BingSearchResults()
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elif name == "BingSearchRun":
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@ -900,6 +910,7 @@ __all__ = [
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"AzureCogsImageAnalysisTool",
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"AzureCogsSpeech2TextTool",
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"AzureCogsText2SpeechTool",
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"AzureCogsTextAnalyticsHealthTool",
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"BaseGraphQLTool",
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"BaseRequestsTool",
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"BaseSQLDatabaseTool",
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@ -12,10 +12,14 @@ from langchain.tools.azure_cognitive_services.speech2text import (
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from langchain.tools.azure_cognitive_services.text2speech import (
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AzureCogsText2SpeechTool,
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)
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from langchain.tools.azure_cognitive_services.text_analytics_health import (
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AzureCogsTextAnalyticsHealthTool,
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)
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__all__ = [
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"AzureCogsImageAnalysisTool",
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"AzureCogsFormRecognizerTool",
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"AzureCogsSpeech2TextTool",
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"AzureCogsText2SpeechTool",
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"AzureCogsTextAnalyticsHealthTool",
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]
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@ -0,0 +1,104 @@
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from __future__ import annotations
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import logging
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from typing import Any, Dict, Optional
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from langchain.callbacks.manager import CallbackManagerForToolRun
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from langchain.pydantic_v1 import root_validator
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from langchain.tools.base import BaseTool
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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class AzureCogsTextAnalyticsHealthTool(BaseTool):
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"""Tool that queries the Azure Cognitive Services Text Analytics for Health API.
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In order to set this up, follow instructions at:
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https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/quickstart?tabs=windows&pivots=programming-language-python
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"""
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azure_cogs_key: str = "" #: :meta private:
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azure_cogs_endpoint: str = "" #: :meta private:
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text_analytics_client: Any #: :meta private:
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name: str = "azure_cognitive_services_text_analyics_health"
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description: str = (
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"A wrapper around Azure Cognitive Services Text Analytics for Health. "
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"Useful for when you need to identify entities in healthcare data. "
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"Input should be text."
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)
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and endpoint exists in environment."""
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azure_cogs_key = get_from_dict_or_env(
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values, "azure_cogs_key", "AZURE_COGS_KEY"
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)
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azure_cogs_endpoint = get_from_dict_or_env(
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values, "azure_cogs_endpoint", "AZURE_COGS_ENDPOINT"
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)
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try:
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import azure.ai.textanalytics as sdk
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from azure.core.credentials import AzureKeyCredential
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values["text_analytics_client"] = sdk.TextAnalyticsClient(
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endpoint=azure_cogs_endpoint,
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credential=AzureKeyCredential(azure_cogs_key),
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)
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except ImportError:
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raise ImportError(
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"azure-ai-textanalytics is not installed. "
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"Run `pip install azure-ai-textanalytics` to install."
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)
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return values
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def _text_analysis(self, text: str) -> Dict:
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poller = self.text_analytics_client.begin_analyze_healthcare_entities(
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[{"id": "1", "language": "en", "text": text}]
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)
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result = poller.result()
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res_dict = {}
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docs = [doc for doc in result if not doc.is_error]
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if docs is not None:
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res_dict["entities"] = [
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f"{x.text} is a healthcare entity of type {x.category}"
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for y in docs
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for x in y.entities
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]
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return res_dict
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def _format_text_analysis_result(self, text_analysis_result: Dict) -> str:
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formatted_result = []
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if "entities" in text_analysis_result:
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formatted_result.append(
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f"""The text contains the following healthcare entities: {
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', '.join(text_analysis_result['entities'])
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}""".replace("\n", " ")
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)
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return "\n".join(formatted_result)
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def _run(
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self,
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query: str,
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run_manager: Optional[CallbackManagerForToolRun] = None,
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) -> str:
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"""Use the tool."""
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try:
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text_analysis_result = self._text_analysis(query)
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return self._format_text_analysis_result(text_analysis_result)
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except Exception as e:
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raise RuntimeError(
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f"Error while running AzureCogsTextAnalyticsHealthTool: {e}"
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)
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26
libs/langchain/poetry.lock
generated
26
libs/langchain/poetry.lock
generated
@ -693,6 +693,23 @@ azure-core = ">=1.23.0,<2.0.0"
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msrest = ">=0.6.21"
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typing-extensions = ">=4.0.1"
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[[package]]
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name = "azure-ai-textanalytics"
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version = "5.3.0"
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description = "Microsoft Azure Text Analytics Client Library for Python"
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optional = true
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python-versions = ">=3.7"
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files = [
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{file = "azure-ai-textanalytics-5.3.0.zip", hash = "sha256:4f7d067d5bb08422599ca6175510d39b0911c711301647e5f18e904a5027bf58"},
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{file = "azure_ai_textanalytics-5.3.0-py3-none-any.whl", hash = "sha256:69bb736d93de81060e9075d42b6f0b92c25be0fb106da5cb6a6d30e772168221"},
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]
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[package.dependencies]
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azure-common = ">=1.1,<2.0"
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azure-core = ">=1.24.0,<2.0.0"
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isodate = ">=0.6.1,<1.0.0"
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typing-extensions = ">=4.0.1"
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[[package]]
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name = "azure-ai-vision"
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version = "0.11.1b1"
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@ -2889,7 +2906,7 @@ files = [
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{file = "greenlet-3.0.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:527cd90ba3d8d7ae7dceb06fda619895768a46a1b4e423bdb24c1969823b8362"},
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{file = "greenlet-3.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:37f60b3a42d8b5499be910d1267b24355c495064f271cfe74bf28b17b099133c"},
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{file = "greenlet-3.0.0-cp311-universal2-macosx_10_9_universal2.whl", hash = "sha256:c3692ecf3fe754c8c0f2c95ff19626584459eab110eaab66413b1e7425cd84e9"},
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{file = "greenlet-3.0.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:1482fba7fbed96ea7842b5a7fc11d61727e8be75a077e603e8ab49d24e234383"},
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{file = "greenlet-3.0.0-cp312-cp312-macosx_13_0_arm64.whl", hash = "sha256:be557119bf467d37a8099d91fbf11b2de5eb1fd5fc5b91598407574848dc910f"},
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{file = "greenlet-3.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:73b2f1922a39d5d59cc0e597987300df3396b148a9bd10b76a058a2f2772fc04"},
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{file = "greenlet-3.0.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d1e22c22f7826096ad503e9bb681b05b8c1f5a8138469b255eb91f26a76634f2"},
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@ -2899,7 +2916,6 @@ files = [
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{file = "greenlet-3.0.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:952256c2bc5b4ee8df8dfc54fc4de330970bf5d79253c863fb5e6761f00dda35"},
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{file = "greenlet-3.0.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:269d06fa0f9624455ce08ae0179430eea61085e3cf6457f05982b37fd2cefe17"},
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{file = "greenlet-3.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:9adbd8ecf097e34ada8efde9b6fec4dd2a903b1e98037adf72d12993a1c80b51"},
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{file = "greenlet-3.0.0-cp312-universal2-macosx_10_9_universal2.whl", hash = "sha256:553d6fb2324e7f4f0899e5ad2c427a4579ed4873f42124beba763f16032959af"},
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{file = "greenlet-3.0.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c6b5ce7f40f0e2f8b88c28e6691ca6806814157ff05e794cdd161be928550f4c"},
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{file = "greenlet-3.0.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ecf94aa539e97a8411b5ea52fc6ccd8371be9550c4041011a091eb8b3ca1d810"},
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{file = "greenlet-3.0.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:80dcd3c938cbcac986c5c92779db8e8ce51a89a849c135172c88ecbdc8c056b7"},
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@ -11030,8 +11046,8 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
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cffi = ["cffi (>=1.11)"]
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[extras]
|
||||
all = ["O365", "aleph-alpha-client", "amadeus", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jinja2", "jq", "lancedb", "langkit", "lark", "librosa", "lxml", "manifest-ml", "marqo", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "python-arango", "pyvespa", "qdrant-client", "rdflib", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
|
||||
azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"]
|
||||
all = ["O365", "aleph-alpha-client", "amadeus", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-textanalytics", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jinja2", "jq", "lancedb", "langkit", "lark", "librosa", "lxml", "manifest-ml", "marqo", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "python-arango", "pyvespa", "qdrant-client", "rdflib", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
|
||||
azure = ["azure-ai-formrecognizer", "azure-ai-textanalytics", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"]
|
||||
clarifai = ["clarifai"]
|
||||
cli = ["typer"]
|
||||
cohere = ["cohere"]
|
||||
@ -11047,4 +11063,4 @@ text-helpers = ["chardet"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "964150ae1b32757b20d1eafa4cdf46f1dc0273fd8879f2ee3229642339df2cd5"
|
||||
content-hash = "db9fcf70a90810fa88259ed5b7c8fd878d18c2979282591c2a2fd83a6476ae8a"
|
||||
|
||||
@ -138,6 +138,7 @@ anthropic = {version = "^0.3.11", optional = true}
|
||||
aiosqlite = {version = "^0.19.0", optional = true}
|
||||
rspace_client = {version = "^2.5.0", optional = true}
|
||||
upstash-redis = {version = "^0.15.0", optional = true}
|
||||
azure-ai-textanalytics = {version = "^5.3.0", optional = true}
|
||||
google-cloud-documentai = {version = "^2.20.1", optional = true}
|
||||
fireworks-ai = {version = "^0.6.0", optional = true, python = ">=3.9,<4.0"}
|
||||
javelin-sdk = {version = "^0.1.8", optional = true}
|
||||
@ -233,6 +234,7 @@ azure = [
|
||||
"azure-ai-vision",
|
||||
"azure-cognitiveservices-speech",
|
||||
"azure-search-documents",
|
||||
"azure-ai-textanalytics",
|
||||
]
|
||||
all = [
|
||||
"clarifai",
|
||||
@ -297,6 +299,7 @@ all = [
|
||||
"azure-ai-formrecognizer",
|
||||
"azure-ai-vision",
|
||||
"azure-cognitiveservices-speech",
|
||||
"azure-ai-textanalytics",
|
||||
"momento",
|
||||
"singlestoredb",
|
||||
"tigrisdb",
|
||||
|
||||
@ -13,6 +13,7 @@ EXPECTED_ALL = [
|
||||
"AzureCogsImageAnalysisTool",
|
||||
"AzureCogsSpeech2TextTool",
|
||||
"AzureCogsText2SpeechTool",
|
||||
"AzureCogsTextAnalyticsHealthTool",
|
||||
"BaseGraphQLTool",
|
||||
"BaseRequestsTool",
|
||||
"BaseSQLDatabaseTool",
|
||||
|
||||
@ -14,6 +14,7 @@ _EXPECTED = [
|
||||
"AzureCogsImageAnalysisTool",
|
||||
"AzureCogsSpeech2TextTool",
|
||||
"AzureCogsText2SpeechTool",
|
||||
"AzureCogsTextAnalyticsHealthTool",
|
||||
"BaseGraphQLTool",
|
||||
"BaseRequestsTool",
|
||||
"BaseSQLDatabaseTool",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user