mirror of https://github.com/hwchase17/langchain
merge from upstream
olgavrou
1 year ago
commit
00d56fb0fc
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---
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sidebar_class_name: hidden
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---
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# LangChain Expression Language (LCEL)
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LangChain Expression Language or LCEL is a declarative way to easily compose chains together.
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Any chain constructed this way will automatically have full sync, async, and streaming support.
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#### [Interface](/docs/expression_language/interface)
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The base interface shared by all LCEL objects
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#### [Cookbook](/docs/expression_language/cookbook)
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Examples of common LCEL usage patterns
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@ -1,9 +0,0 @@
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# LangChain Expression Language
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import DocCardList from "@theme/DocCardList";
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LangChain Expression Language is a declarative way to easily compose chains together.
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Any chain constructed this way will automatically have full sync, async, and streaming support.
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See guides below for how to interact with chains constructed this way as well as cookbook examples.
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<DocCardList />
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# Removing logical fallacies from model output
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Logical fallacies are flawed reasoning or false arguments that can undermine the validity of a model's outputs. Examples include circular reasoning, false
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dichotomies, ad hominem attacks, etc. Machine learning models are optimized to perform well on specific metrics like accuracy, perplexity, or loss. However,
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optimizing for metrics alone does not guarantee logically sound reasoning.
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Language models can learn to exploit flaws in reasoning to generate plausible-sounding but logically invalid arguments. When models rely on fallacies, their outputs become unreliable and untrustworthy, even if they achieve high scores on metrics. Users cannot depend on such outputs. Propagating logical fallacies can spread misinformation, confuse users, and lead to harmful real-world consequences when models are deployed in products or services.
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Monitoring and testing specifically for logical flaws is challenging unlike other quality issues. It requires reasoning about arguments rather than pattern matching.
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Therefore, it is crucial that model developers proactively address logical fallacies after optimizing metrics. Specialized techniques like causal modeling, robustness testing, and bias mitigation can help avoid flawed reasoning. Overall, allowing logical flaws to persist makes models less safe and ethical. Eliminating fallacies ensures model outputs remain logically valid and aligned with human reasoning. This maintains user trust and mitigates risks.
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```python
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# Imports
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from langchain.llms import OpenAI
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from langchain.prompts import PromptTemplate
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from langchain.chains.llm import LLMChain
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from langchain_experimental.fallacy_removal.base import FallacyChain
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```
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```python
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# Example of a model output being returned with a logical fallacy
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misleading_prompt = PromptTemplate(
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template="""You have to respond by using only logical fallacies inherent in your answer explanations.
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Question: {question}
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Bad answer:""",
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input_variables=["question"],
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)
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llm = OpenAI(temperature=0)
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misleading_chain = LLMChain(llm=llm, prompt=misleading_prompt)
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misleading_chain.run(question="How do I know the earth is round?")
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```
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<CodeOutputBlock lang="python">
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```
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'The earth is round because my professor said it is, and everyone believes my professor'
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```
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</CodeOutputBlock>
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```python
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fallacies = FallacyChain.get_fallacies(["correction"])
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fallacy_chain = FallacyChain.from_llm(
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chain=misleading_chain,
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logical_fallacies=fallacies,
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llm=llm,
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verbose=True,
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)
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fallacy_chain.run(question="How do I know the earth is round?")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new FallacyChain chain...
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Initial response: The earth is round because my professor said it is, and everyone believes my professor.
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Applying correction...
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Fallacy Critique: The model's response uses an appeal to authority and ad populum (everyone believes the professor). Fallacy Critique Needed.
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Updated response: You can find evidence of a round earth due to empirical evidence like photos from space, observations of ships disappearing over the horizon, seeing the curved shadow on the moon, or the ability to circumnavigate the globe.
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> Finished chain.
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'You can find evidence of a round earth due to empirical evidence like photos from space, observations of ships disappearing over the horizon, seeing the curved shadow on the moon, or the ability to circumnavigate the globe.'
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```
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</CodeOutputBlock>
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@ -1,11 +1,11 @@
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# LLM
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An LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.
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An `LLMChain` is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.
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An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.
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An `LLMChain` consists of a `PromptTemplate` and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.
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## Get started
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import Example from "@snippets/modules/chains/foundational/llm_chain.mdx"
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<Example/>
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<Example/>
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bf733a38-db84-4363-89e2-de6735c37230",
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"metadata": {},
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"source": [
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"# Bedrock Chat\n",
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"\n",
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"[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"
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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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"id": "d51edc81",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"%pip install boto3"
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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": 1,
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"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import BedrockChat\n",
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"from langchain.schema import HumanMessage"
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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": 2,
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"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat = BedrockChat(model_id=\"anthropic.claude-v2\", model_kwargs={\"temperature\":0.1})"
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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": 3,
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"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\" Voici la traduction en français : J'adore programmer.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 3,
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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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"messages = [\n",
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" HumanMessage(\n",
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" content=\"Translate this sentence from English to French. I love programming.\"\n",
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" )\n",
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"]\n",
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"chat(messages)"
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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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"id": "c253883f",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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||||
"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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||||
"language_info": {
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||||
"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.11.4"
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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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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Azure Document Intelligence"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning \n",
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"based service that extracts text (including handwriting), tables or key-value-pairs from\n",
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"scanned documents or images.\n",
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"\n",
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||||
"This current implementation of a loader using Document Intelligence is able to incorporate content page-wise and turn it into LangChain documents.\n",
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"\n",
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"Document Intelligence supports PDF, JPEG, PNG, BMP, or TIFF.\n",
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"\n",
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"Further documentation is available at https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/?view=doc-intel-3.1.0.\n"
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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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"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"%pip install langchain azure-ai-formrecognizer -q"
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||||
]
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||||
},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||||
"## Example 1"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The first example uses a local file which will be sent to Azure Document Intelligence.\n",
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||||
"\n",
|
||||
"First, an instance of a DocumentAnalysisClient is created with endpoint and key for the Azure service. "
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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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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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||||
"from azure.ai.formrecognizer import DocumentAnalysisClient\n",
|
||||
"from azure.core.credentials import AzureKeyCredential\n",
|
||||
"\n",
|
||||
"document_analysis_client = DocumentAnalysisClient(\n",
|
||||
" endpoint=\"<service_endpoint>\", credential=AzureKeyCredential(\"<service_key>\")\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
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"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
|
||||
"With the initialized document analysis client, we can proceed to create an instance of the DocumentIntelligenceLoader:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
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||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"from langchain.document_loaders.pdf import DocumentIntelligenceLoader\n",
|
||||
"loader = DocumentIntelligenceLoader(\n",
|
||||
" \"<Local_filename>\",\n",
|
||||
" client=document_analysis_client,\n",
|
||||
" model=\"<model_name>\") # e.g. prebuilt-document\n",
|
||||
"\n",
|
||||
"documents = loader.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The output contains each page of the source document as a LangChain document: "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='...', metadata={'source': '...', 'page': 1})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"documents"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.9.5"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
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{
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||||
"cells": [
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||||
{
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||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# SQL Chat Message History\n",
|
||||
"\n",
|
||||
"This notebook goes over a **SQLChatMessageHistory** class that allows to store chat history in any database supported by SQLAlchemy.\n",
|
||||
"\n",
|
||||
"Please note that to use it with databases other than SQLite, you will need to install the corresponding database driver."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "f22eab3f84cbeb37"
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Basic Usage\n",
|
||||
"\n",
|
||||
"To use the storage you need to provide only 2 things:\n",
|
||||
"\n",
|
||||
"1. Session Id - a unique identifier of the session, like user name, email, chat id etc.\n",
|
||||
"2. Connection string - a string that specifies the database connection. It will be passed to SQLAlchemy create_engine function."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "f8f2830ee9ca1e01"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.memory.chat_message_histories import SQLChatMessageHistory\n",
|
||||
"\n",
|
||||
"chat_message_history = SQLChatMessageHistory(\n",
|
||||
"\tsession_id='test_session',\n",
|
||||
"\tconnection_string='sqlite:///sqlite.db'\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chat_message_history.add_user_message('Hello')\n",
|
||||
"chat_message_history.add_ai_message('Hi')"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-08-28T10:04:38.077748Z",
|
||||
"start_time": "2023-08-28T10:04:36.105894Z"
|
||||
}
|
||||
},
|
||||
"id": "4576e914a866fb40"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]"
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chat_message_history.messages"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-08-28T10:04:38.929396Z",
|
||||
"start_time": "2023-08-28T10:04:38.915727Z"
|
||||
}
|
||||
},
|
||||
"id": "b476688cbb32ba90"
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Custom Storage Format\n",
|
||||
"\n",
|
||||
"By default, only the session id and message dictionary are stored in the table.\n",
|
||||
"\n",
|
||||
"However, sometimes you might want to store some additional information, like message date, author, language etc.\n",
|
||||
"\n",
|
||||
"To do that, you can create a custom message converter, by implementing **BaseMessageConverter** interface."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "2e5337719d5614fd"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime\n",
|
||||
"from langchain.schema import BaseMessage, HumanMessage, AIMessage, SystemMessage\n",
|
||||
"from typing import Any\n",
|
||||
"from sqlalchemy import Column, Integer, Text, DateTime\n",
|
||||
"from sqlalchemy.orm import declarative_base\n",
|
||||
"from langchain.memory.chat_message_histories.sql import BaseMessageConverter\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Base = declarative_base()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class CustomMessage(Base):\n",
|
||||
"\t__tablename__ = 'custom_message_store'\n",
|
||||
"\n",
|
||||
"\tid = Column(Integer, primary_key=True)\n",
|
||||
"\tsession_id = Column(Text)\n",
|
||||
"\ttype = Column(Text)\n",
|
||||
"\tcontent = Column(Text)\n",
|
||||
"\tcreated_at = Column(DateTime)\n",
|
||||
"\tauthor_email = Column(Text)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class CustomMessageConverter(BaseMessageConverter):\n",
|
||||
"\tdef __init__(self, author_email: str):\n",
|
||||
"\t\tself.author_email = author_email\n",
|
||||
"\t\n",
|
||||
"\tdef from_sql_model(self, sql_message: Any) -> BaseMessage:\n",
|
||||
"\t\tif sql_message.type == 'human':\n",
|
||||
"\t\t\treturn HumanMessage(\n",
|
||||
"\t\t\t\tcontent=sql_message.content,\n",
|
||||
"\t\t\t)\n",
|
||||
"\t\telif sql_message.type == 'ai':\n",
|
||||
"\t\t\treturn AIMessage(\n",
|
||||
"\t\t\t\tcontent=sql_message.content,\n",
|
||||
"\t\t\t)\n",
|
||||
"\t\telif sql_message.type == 'system':\n",
|
||||
"\t\t\treturn SystemMessage(\n",
|
||||
"\t\t\t\tcontent=sql_message.content,\n",
|
||||
"\t\t\t)\n",
|
||||
"\t\telse:\n",
|
||||
"\t\t\traise ValueError(f'Unknown message type: {sql_message.type}')\n",
|
||||
"\t\n",
|
||||
"\tdef to_sql_model(self, message: BaseMessage, session_id: str) -> Any:\n",
|
||||
"\t\tnow = datetime.now()\n",
|
||||
"\t\treturn CustomMessage(\n",
|
||||
"\t\t\tsession_id=session_id,\n",
|
||||
"\t\t\ttype=message.type,\n",
|
||||
"\t\t\tcontent=message.content,\n",
|
||||
"\t\t\tcreated_at=now,\n",
|
||||
"\t\t\tauthor_email=self.author_email\n",
|
||||
"\t\t)\n",
|
||||
"\t\n",
|
||||
"\tdef get_sql_model_class(self) -> Any:\n",
|
||||
"\t\treturn CustomMessage\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chat_message_history = SQLChatMessageHistory(\n",
|
||||
"\tsession_id='test_session',\n",
|
||||
"\tconnection_string='sqlite:///sqlite.db',\n",
|
||||
"\tcustom_message_converter=CustomMessageConverter(\n",
|
||||
"\t\tauthor_email='test@example.com'\n",
|
||||
" )\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chat_message_history.add_user_message('Hello')\n",
|
||||
"chat_message_history.add_ai_message('Hi')"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-08-28T10:04:41.510498Z",
|
||||
"start_time": "2023-08-28T10:04:41.494912Z"
|
||||
}
|
||||
},
|
||||
"id": "fdfde84c07d071bb"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]"
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chat_message_history.messages"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-08-28T10:04:43.497990Z",
|
||||
"start_time": "2023-08-28T10:04:43.492517Z"
|
||||
}
|
||||
},
|
||||
"id": "4a6a54d8a9e2856f"
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"You also might want to change the name of session_id column. In this case you'll need to specify `session_id_field_name` parameter."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "622aded629a1adeb"
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -0,0 +1,513 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Eden AI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent.\n",
|
||||
"\n",
|
||||
"Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/ )\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"By including an Edenai tool in the list of tools provided to an Agent, you can grant your Agent the ability to do multiple tasks, such as:\n",
|
||||
"\n",
|
||||
"- speech to text\n",
|
||||
"- text to speech\n",
|
||||
"- text explicit content detection \n",
|
||||
"- image explicit content detection\n",
|
||||
"- object detection\n",
|
||||
"- OCR invoice parsing\n",
|
||||
"- OCR ID parsing\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"In this example, we will go through the process of utilizing the Edenai tools to create an Agent that can perform some of the tasks listed above."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---------------------------------------------------------------------------\n",
|
||||
"Accessing the EDENAI's API requires an API key, \n",
|
||||
"\n",
|
||||
"which you can get by creating an account https://app.edenai.run/user/register and heading here https://app.edenai.run/admin/account/settings\n",
|
||||
"\n",
|
||||
"Once we have a key we'll want to set it as the environment variable ``EDENAI_API_KEY`` or you can pass the key in directly via the edenai_api_key named parameter when initiating the EdenAI tools, e.g. ``EdenAiTextModerationTool(edenai_api_key=\"...\")``"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.tools.edenai import (\n",
|
||||
" EdenAiSpeechToTextTool,\n",
|
||||
" EdenAiTextToSpeechTool,\n",
|
||||
" EdenAiExplicitImageTool,\n",
|
||||
" EdenAiObjectDetectionTool,\n",
|
||||
" EdenAiParsingIDTool,\n",
|
||||
" EdenAiParsingInvoiceTool,\n",
|
||||
" EdenAiTextModerationTool,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.llms import EdenAI\n",
|
||||
"from langchain.agents import initialize_agent, AgentType\n",
|
||||
"\n",
|
||||
"llm=EdenAI(feature=\"text\",provider=\"openai\", params={\"temperature\" : 0.2,\"max_tokens\" : 250})\n",
|
||||
"\n",
|
||||
"tools = [\n",
|
||||
" EdenAiTextModerationTool(providers=[\"openai\"],language=\"en\"),\n",
|
||||
" EdenAiObjectDetectionTool(providers=[\"google\",\"api4ai\"]),\n",
|
||||
" EdenAiTextToSpeechTool(providers=[\"amazon\"],language=\"en\",voice=\"MALE\"),\n",
|
||||
" EdenAiExplicitImageTool(providers=[\"amazon\",\"google\"]),\n",
|
||||
" EdenAiSpeechToTextTool(providers=[\"amazon\"]),\n",
|
||||
" EdenAiParsingIDTool(providers=[\"amazon\",\"klippa\"],language=\"en\"),\n",
|
||||
" EdenAiParsingInvoiceTool(providers=[\"amazon\",\"google\"],language=\"en\"),\n",
|
||||
"]\n",
|
||||
"agent_chain = initialize_agent(\n",
|
||||
" tools,\n",
|
||||
" llm,\n",
|
||||
" agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
|
||||
" verbose=True,\n",
|
||||
" return_intermediate_steps=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Example with text"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I need to scan the text for explicit content and then convert it to speech\n",
|
||||
"Action: edenai_explicit_content_detection_text\n",
|
||||
"Action Input: 'i want to slap you'\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mnsfw_likelihood: 3\n",
|
||||
"\"sexual\": 1\n",
|
||||
"\"hate\": 1\n",
|
||||
"\"harassment\": 1\n",
|
||||
"\"self-harm\": 1\n",
|
||||
"\"sexual/minors\": 1\n",
|
||||
"\"hate/threatening\": 1\n",
|
||||
"\"violence/graphic\": 1\n",
|
||||
"\"self-harm/intent\": 1\n",
|
||||
"\"self-harm/instructions\": 1\n",
|
||||
"\"harassment/threatening\": 1\n",
|
||||
"\"violence\": 3\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now need to convert the text to speech\n",
|
||||
"Action: edenai_text_to_speech\n",
|
||||
"Action Input: 'i want to slap you'\u001b[0m\n",
|
||||
"Observation: \u001b[38;5;200m\u001b[1;3mhttps://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"input_ = \"\"\"i have this text : 'i want to slap you' \n",
|
||||
"first : i want to know if this text contains explicit content or not .\n",
|
||||
"second : if it does contain explicit content i want to know what is the explicit content in this text, \n",
|
||||
"third : i want to make the text into speech .\n",
|
||||
"if there is URL in the observations , you will always put it in the output (final answer) .\n",
|
||||
"\"\"\"\n",
|
||||
"result = agent_chain(input_)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"you can have more details of the execution by printing the result "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn'"
|
||||
]
|
||||
},
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result['output']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'input': \" i have this text : 'i want to slap you' \\n first : i want to know if this text contains explicit content or not .\\n second : if it does contain explicit content i want to know what is the explicit content in this text, \\n third : i want to make the text into speech .\\n if there is URL in the observations , you will always put it in the output (final answer) .\\n\\n \",\n",
|
||||
" 'output': 'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn',\n",
|
||||
" 'intermediate_steps': [(AgentAction(tool='edenai_explicit_content_detection_text', tool_input=\"'i want to slap you'\", log=\" I need to scan the text for explicit content and then convert it to speech\\nAction: edenai_explicit_content_detection_text\\nAction Input: 'i want to slap you'\"),\n",
|
||||
" 'nsfw_likelihood: 3\\n\"sexual\": 1\\n\"hate\": 1\\n\"harassment\": 1\\n\"self-harm\": 1\\n\"sexual/minors\": 1\\n\"hate/threatening\": 1\\n\"violence/graphic\": 1\\n\"self-harm/intent\": 1\\n\"self-harm/instructions\": 1\\n\"harassment/threatening\": 1\\n\"violence\": 3'),\n",
|
||||
" (AgentAction(tool='edenai_text_to_speech', tool_input=\"'i want to slap you'\", log=\" I now need to convert the text to speech\\nAction: edenai_text_to_speech\\nAction Input: 'i want to slap you'\"),\n",
|
||||
" 'https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK')]}"
|
||||
]
|
||||
},
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Example with images"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech.\n",
|
||||
"Action: edenai_object_detection\n",
|
||||
"Action Input: https://static.javatpoint.com/images/objects.jpg\u001b[0m\n",
|
||||
"Observation: \u001b[33;1m\u001b[1;3mApple - Confidence 0.94003654\n",
|
||||
"Apple - Confidence 0.94003654\n",
|
||||
"Apple - Confidence 0.94003654\n",
|
||||
"Backpack - Confidence 0.7481894\n",
|
||||
"Backpack - Confidence 0.7481894\n",
|
||||
"Backpack - Confidence 0.7481894\n",
|
||||
"Luggage & bags - Confidence 0.70691586\n",
|
||||
"Luggage & bags - Confidence 0.70691586\n",
|
||||
"Luggage & bags - Confidence 0.70691586\n",
|
||||
"Container - Confidence 0.654727\n",
|
||||
"Container - Confidence 0.654727\n",
|
||||
"Container - Confidence 0.654727\n",
|
||||
"Luggage & bags - Confidence 0.5871518\n",
|
||||
"Luggage & bags - Confidence 0.5871518\n",
|
||||
"Luggage & bags - Confidence 0.5871518\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I need to check if any of the objects are harmful.\n",
|
||||
"Action: edenai_explicit_content_detection_text\n",
|
||||
"Action Input: Apple, Backpack, Luggage & bags, Container\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mnsfw_likelihood: 2\n",
|
||||
"\"sexually explicit\": 1\n",
|
||||
"\"sexually suggestive\": 2\n",
|
||||
"\"offensive\": 1\n",
|
||||
"nsfw_likelihood: 1\n",
|
||||
"\"sexual\": 1\n",
|
||||
"\"hate\": 1\n",
|
||||
"\"harassment\": 1\n",
|
||||
"\"self-harm\": 1\n",
|
||||
"\"sexual/minors\": 1\n",
|
||||
"\"hate/threatening\": 1\n",
|
||||
"\"violence/graphic\": 1\n",
|
||||
"\"self-harm/intent\": 1\n",
|
||||
"\"self-harm/instructions\": 1\n",
|
||||
"\"harassment/threatening\": 1\n",
|
||||
"\"violence\": 1\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m None of the objects are harmful.\n",
|
||||
"Action: edenai_text_to_speech\n",
|
||||
"Action Input: 'this item is safe'\u001b[0m\n",
|
||||
"Observation: \u001b[38;5;200m\u001b[1;3mhttps://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
|
||||
"Final Answer: The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"input_ = \"\"\"i have this url of an image : \"https://static.javatpoint.com/images/objects.jpg\"\n",
|
||||
"first : i want to know if the image contain objects .\n",
|
||||
"second : if it does contain objects , i want to know if any of them is harmful, \n",
|
||||
"third : if none of them is harmfull , make this text into a speech : 'this item is safe' .\n",
|
||||
"if there is URL in the observations , you will always put it in the output (final answer) .\n",
|
||||
"\"\"\"\n",
|
||||
"result = agent_chain(input_)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt\""
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result['output']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"you can have more details of the execution by printing the result "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'input': ' i have this url of an image : \"https://static.javatpoint.com/images/objects.jpg\"\\n first : i want to know if the image contain objects .\\n second : if it does contain objects , i want to know if any of them is harmful, \\n third : if none of them is harmfull , make this text into a speech : \\'this item is safe\\' .\\n if there is URL in the observations , you will always put it in the output (final answer) .\\n ',\n",
|
||||
" 'output': \"The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt\",\n",
|
||||
" 'intermediate_steps': [(AgentAction(tool='edenai_object_detection', tool_input='https://static.javatpoint.com/images/objects.jpg', log=' I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech.\\nAction: edenai_object_detection\\nAction Input: https://static.javatpoint.com/images/objects.jpg'),\n",
|
||||
" 'Apple - Confidence 0.94003654\\nApple - Confidence 0.94003654\\nApple - Confidence 0.94003654\\nBackpack - Confidence 0.7481894\\nBackpack - Confidence 0.7481894\\nBackpack - Confidence 0.7481894\\nLuggage & bags - Confidence 0.70691586\\nLuggage & bags - Confidence 0.70691586\\nLuggage & bags - Confidence 0.70691586\\nContainer - Confidence 0.654727\\nContainer - Confidence 0.654727\\nContainer - Confidence 0.654727\\nLuggage & bags - Confidence 0.5871518\\nLuggage & bags - Confidence 0.5871518\\nLuggage & bags - Confidence 0.5871518'),\n",
|
||||
" (AgentAction(tool='edenai_explicit_content_detection_text', tool_input='Apple, Backpack, Luggage & bags, Container', log=' I need to check if any of the objects are harmful.\\nAction: edenai_explicit_content_detection_text\\nAction Input: Apple, Backpack, Luggage & bags, Container'),\n",
|
||||
" 'nsfw_likelihood: 2\\n\"sexually explicit\": 1\\n\"sexually suggestive\": 2\\n\"offensive\": 1\\nnsfw_likelihood: 1\\n\"sexual\": 1\\n\"hate\": 1\\n\"harassment\": 1\\n\"self-harm\": 1\\n\"sexual/minors\": 1\\n\"hate/threatening\": 1\\n\"violence/graphic\": 1\\n\"self-harm/intent\": 1\\n\"self-harm/instructions\": 1\\n\"harassment/threatening\": 1\\n\"violence\": 1'),\n",
|
||||
" (AgentAction(tool='edenai_text_to_speech', tool_input=\"'this item is safe'\", log=\" None of the objects are harmful.\\nAction: edenai_text_to_speech\\nAction Input: 'this item is safe'\"),\n",
|
||||
" 'https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK')]}"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Example with OCR images"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I need to extract the information from the ID and then convert it to text and then to speech\n",
|
||||
"Action: edenai_identity_parsing\n",
|
||||
"Action Input: \"https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg\"\u001b[0m\n",
|
||||
"Observation: \u001b[38;5;200m\u001b[1;3mlast_name : \n",
|
||||
" value : ANGELA\n",
|
||||
"given_names : \n",
|
||||
" value : GREENE\n",
|
||||
"birth_place : \n",
|
||||
"birth_date : \n",
|
||||
" value : 2000-11-09\n",
|
||||
"issuance_date : \n",
|
||||
"expire_date : \n",
|
||||
"document_id : \n",
|
||||
"issuing_state : \n",
|
||||
"address : \n",
|
||||
"age : \n",
|
||||
"country : \n",
|
||||
"document_type : \n",
|
||||
" value : DRIVER LICENSE FRONT\n",
|
||||
"gender : \n",
|
||||
"image_id : \n",
|
||||
"image_signature : \n",
|
||||
"mrz : \n",
|
||||
"nationality : \u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now need to convert the information to text and then to speech\n",
|
||||
"Action: edenai_text_to_speech\n",
|
||||
"Action Input: \"Welcome Angela Greene!\"\u001b[0m\n",
|
||||
"Observation: \u001b[38;5;200m\u001b[1;3mhttps://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5yHAJjf657u7Z1lFTBMoXGBuw1VYmyno-3TAiPeUcVlQXPueJ-ymZXmwaITmGOfH7HipZngZBziofRAFdhMYbIjYhegu5jS7TxHwRuox32A__&Key-Pair-Id=K1F55BTI9AHGIK\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"input_ = \"\"\"i have this url of an id: \"https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg\"\n",
|
||||
"i want to extract the information in it.\n",
|
||||
"create a text welcoming the person by his name and make it into speech .\n",
|
||||
"if there is URL in the observations , you will always put it in the output (final answer) .\n",
|
||||
"\"\"\"\n",
|
||||
"result = agent_chain(input_)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y'"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result['output']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I need to extract information from the invoice document\n",
|
||||
"Action: edenai_invoice_parsing\n",
|
||||
"Action Input: \"https://app.edenai.run/assets/img/data_1.72e3bdcc.png\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mcustomer_information : \n",
|
||||
" customer_name : Damita J Goldsmith\n",
|
||||
" customer_address : 201 Stan Fey Dr,Upper Marlboro, MD 20774\n",
|
||||
" customer_shipping_address : 201 Stan Fey Drive,Upper Marlboro\n",
|
||||
"merchant_information : \n",
|
||||
" merchant_name : SNG Engineering Inc\n",
|
||||
" merchant_address : 344 Main St #200 Gaithersburg, MD 20878 USA\n",
|
||||
" merchant_phone : +1 301 548 0055\n",
|
||||
"invoice_number : 014-03\n",
|
||||
"taxes : \n",
|
||||
"payment_term : on receipt of service\n",
|
||||
"date : 2003-01-20\n",
|
||||
"po_number : \n",
|
||||
"locale : \n",
|
||||
"bank_informations : \n",
|
||||
"item_lines : \n",
|
||||
" description : Field inspection of construction on 1/19/2003 deficiencies in house,construction, Garage drive way & legal support to Attorney to\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the answer to the questions\n",
|
||||
"Final Answer: The customer is Damita J Goldsmith and the company name is SNG Engineering Inc.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"input_ = \"\"\"i have this url of an invoice document: \"https://app.edenai.run/assets/img/data_1.72e3bdcc.png\"\n",
|
||||
"i want to extract the information in it.\n",
|
||||
"and answer these questions :\n",
|
||||
"who is the customer ?\n",
|
||||
"what is the company name ? \n",
|
||||
"\"\"\"\n",
|
||||
"result=agent_chain()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The customer is Damita J Goldsmith and the company name is SNG Engineering Inc.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result['output']"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.11.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,215 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Google Drive\n",
|
||||
"\n",
|
||||
"This notebook walks through connecting a LangChain to the `Google Drive API`.\n",
|
||||
"\n",
|
||||
"## Prerequisites\n",
|
||||
"\n",
|
||||
"1. Create a Google Cloud project or use an existing project\n",
|
||||
"1. Enable the [Google Drive API](https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com)\n",
|
||||
"1. [Authorize credentials for desktop app](https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application)\n",
|
||||
"1. `pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib`\n",
|
||||
"\n",
|
||||
"## Instructions for retrieving your Google Docs data\n",
|
||||
"By default, the `GoogleDriveTools` and `GoogleDriveWrapper` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `GOOGLE_ACCOUNT_FILE` environment variable. \n",
|
||||
"The location of `token.json` use the same directory (or use the parameter `token_path`). Note that `token.json` will be created automatically the first time you use the tool.\n",
|
||||
"\n",
|
||||
"`GoogleDriveSearchTool` can retrieve a selection of files with some requests. \n",
|
||||
"\n",
|
||||
"By default, If you use a `folder_id`, all the files inside this folder can be retrieved to `Document`, if the name match the query.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can obtain your folder and document id from the URL:\n",
|
||||
"* Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is `\"1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5\"`\n",
|
||||
"* Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is `\"1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw\"`\n",
|
||||
"\n",
|
||||
"The special value `root` is for your personal home."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"folder_id=\"root\"\n",
|
||||
"#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"By default, all files with these mime-type can be converted to `Document`.\n",
|
||||
"- text/text\n",
|
||||
"- text/plain\n",
|
||||
"- text/html\n",
|
||||
"- text/csv\n",
|
||||
"- text/markdown\n",
|
||||
"- image/png\n",
|
||||
"- image/jpeg\n",
|
||||
"- application/epub+zip\n",
|
||||
"- application/pdf\n",
|
||||
"- application/rtf\n",
|
||||
"- application/vnd.google-apps.document (GDoc)\n",
|
||||
"- application/vnd.google-apps.presentation (GSlide)\n",
|
||||
"- application/vnd.google-apps.spreadsheet (GSheet)\n",
|
||||
"- application/vnd.google.colaboratory (Notebook colab)\n",
|
||||
"- application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)\n",
|
||||
"- application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)\n",
|
||||
"\n",
|
||||
"It's possible to update or customize this. See the documentation of `GoogleDriveAPIWrapper`.\n",
|
||||
"\n",
|
||||
"But, the corresponding packages must installed."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install unstructured"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.utilities.google_drive import GoogleDriveAPIWrapper\n",
|
||||
"from langchain.tools.google_drive.tool import GoogleDriveSearchTool\n",
|
||||
"\n",
|
||||
"# By default, search only in the filename.\n",
|
||||
"tool = GoogleDriveSearchTool(\n",
|
||||
" api_wrapper=GoogleDriveAPIWrapper(\n",
|
||||
" folder_id=folder_id,\n",
|
||||
" num_results=2,\n",
|
||||
" template=\"gdrive-query-in-folder\", # Search in the body of documents\n",
|
||||
" )\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"logging.basicConfig(level=logging.INFO)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tool.run(\"machine learning\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tool.description"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.agents import load_tools\n",
|
||||
"tools = load_tools([\"google-drive-search\"],\n",
|
||||
" folder_id=folder_id,\n",
|
||||
" template=\"gdrive-query-in-folder\",\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use within an Agent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain import OpenAI\n",
|
||||
"from langchain.agents import initialize_agent, AgentType\n",
|
||||
"llm = OpenAI(temperature=0)\n",
|
||||
"agent = initialize_agent(\n",
|
||||
" tools=tools,\n",
|
||||
" llm=llm,\n",
|
||||
" agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"agent.run(\n",
|
||||
" \"Search in google drive, who is 'Yann LeCun' ?\"\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": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -0,0 +1,248 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "71406a01-e5e5-4fe0-a1fa-49216871779e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Yahoo Finance News\n",
|
||||
"\n",
|
||||
"This notebook goes over how to use the `yahoo_finance_news` tool with an agent. \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Setting up\n",
|
||||
"\n",
|
||||
"First, you need to install `yfinance` python package."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "38717a85-2c3c-4452-a1c7-1ed4dea3da86",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install yfinance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4527b5f9-b496-45d8-8147-7a4ebb89734b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Example with Chain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "d137dd6c-d3d3-4813-af65-59eaaa6b3d76",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = \"...\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"id": "fc42f766-9ce6-4ba3-be6c-5ba8a345b0d3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chat_models import ChatOpenAI\n",
|
||||
"from langchain.agents import initialize_agent, AgentType\n",
|
||||
"from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"llm = ChatOpenAI(temperature=0.0)\n",
|
||||
"tools = [YahooFinanceNewsTool()]\n",
|
||||
"agent_chain = initialize_agent(\n",
|
||||
" tools,\n",
|
||||
" llm,\n",
|
||||
" agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
|
||||
" verbose=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"id": "3d1614b4-508e-4689-84b1-2a387f80aeb1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3mI should check the latest financial news about Microsoft stocks.\n",
|
||||
"Action: yahoo_finance_news\n",
|
||||
"Action Input: MSFT\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mMicrosoft (MSFT) Gains But Lags Market: What You Should Know\n",
|
||||
"In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day.\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3mI have the latest information on Microsoft stocks.\n",
|
||||
"Final Answer: Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent_chain.run(\n",
|
||||
" \"What happens today with Microsoft stocks?\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"id": "c899b64d-86a5-452c-b576-e94f485c27ea",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3mI should compare the current sentiment of Microsoft and Nvidia.\n",
|
||||
"Action: yahoo_finance_news\n",
|
||||
"Action Input: MSFT\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mMicrosoft (MSFT) Gains But Lags Market: What You Should Know\n",
|
||||
"In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day.\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3mI need to find the current sentiment of Nvidia as well.\n",
|
||||
"Action: yahoo_finance_news\n",
|
||||
"Action Input: NVDA\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3mI now know the current sentiment of both Microsoft and Nvidia.\n",
|
||||
"Final Answer: I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent_chain.run(\n",
|
||||
" \"How does Microsoft feels today comparing with Nvidia?\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "366b57dc-5292-4011-9b99-7b7c86237def",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How YahooFinanceNewsTool works?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"id": "7879b79c-b5c7-4a5d-8338-edda53ff41a6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tool = YahooFinanceNewsTool()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"id": "ac989456-33bc-4478-874e-98b9cb24d113",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'No news found for company that searched with NVDA ticker.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.run(\"NVDA\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"id": "46c697aa-102e-48d4-9834-081671aad40a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Top Research Reports for Apple, Broadcom & Caterpillar\n",
|
||||
"Today's Research Daily features new research reports on 16 major stocks, including Apple Inc. (AAPL), Broadcom Inc. (AVGO) and Caterpillar Inc. (CAT).\n",
|
||||
"\n",
|
||||
"Apple Stock on Pace for Worst Month of the Year\n",
|
||||
"Apple (AAPL) shares are on pace for their worst month of the year, according to Dow Jones Market Data. The stock is down 4.8% so far in August, putting it on pace for its worst month since December 2022, when it fell 12%.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"res = tool.run(\"AAPL\")\n",
|
||||
"print(res)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fa415bf7-dd92-43f5-89aa-de7b7deaaf2b",
|
||||
"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": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue