mirror of
https://github.com/hwchase17/langchain
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8eea46ed0e
## Description This PR adds the `aembed_query` and `aembed_documents` async methods for improving the embeddings generation for large documents. The implementation uses asyncio tasks and gather to achieve concurrency as there is no bedrock async API in boto3. ### Maintainers @agola11 @aarora79 ### Open questions To avoid throttling from the Bedrock API, should there be an option to limit the concurrency of the calls?
88 lines
1.9 KiB
Plaintext
88 lines
1.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Bedrock"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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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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"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": null,
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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.llms import Bedrock\n",
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"\n",
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"llm = Bedrock(\n",
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" credentials_profile_name=\"bedrock-admin\",\n",
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" model_id=\"amazon.titan-tg1-large\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Using in a conversation chain"
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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 langchain.chains import ConversationChain\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"\n",
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"conversation = ConversationChain(\n",
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" llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
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")\n",
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"\n",
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"conversation.predict(input=\"Hi there!\")"
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]
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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": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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