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community: update documentation and model IDs for FriendliAI provider (#28984)
### Description - In the example, remove `llama-2-13b-chat`, `mixtral-8x7b-instruct-v0-1`. - Fix llm friendli streaming implementation. - Update examples in documentation and remove duplicates. ### Issue N/A ### Dependencies None ### Twitter handle `@friendliai`
This commit is contained in:
minpeter
GitHub
parent
437ec53e29
commit
a873e0fbfb
@ -2,10 +2,14 @@
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"---\n",
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"sidebar_label: Friendli\n",
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"sidebar_label: ChatFriendli\n",
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"---"
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]
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},
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@ -37,7 +41,7 @@
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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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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -57,13 +61,13 @@
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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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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.chat_models.friendli import ChatFriendli\n",
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"\n",
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"chat = ChatFriendli(model=\"llama-2-13b-chat\", max_tokens=100, temperature=0)"
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"chat = ChatFriendli(model=\"meta-llama-3.1-8b-instruct\", max_tokens=100, temperature=0)"
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]
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},
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{
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@ -84,16 +88,16 @@
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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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"execution_count": 4,
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"metadata": {},
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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=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")"
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"AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-d47c1056-54e8-4ea9-ad63-07cf74b834b7-0')"
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]
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},
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -111,17 +115,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 5,
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"metadata": {},
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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=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"),\n",
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" AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")]"
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"[AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-36775b84-2a7a-48f0-8c68-df23ffffe4b2-0'),\n",
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" AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-b204be41-bc06-4d3a-9f74-e66ab1e60e4f-0')]"
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]
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},
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"execution_count": 4,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -132,16 +136,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LLMResult(generations=[[ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))], [ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))]], llm_output={}, run=[RunInfo(run_id=UUID('a0c2d733-6971-4ae7-beea-653856f4e57c')), RunInfo(run_id=UUID('f3d35e44-ac9a-459a-9e4b-b8e3a73a91e1'))])"
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"LLMResult(generations=[[ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-2e4cb949-8c51-40d5-92a0-cd0ac577db83-0'))], [ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-afcdd1be-463c-4e50-9731-7a9f5958e396-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('2e4cb949-8c51-40d5-92a0-cd0ac577db83')), RunInfo(run_id=UUID('afcdd1be-463c-4e50-9731-7a9f5958e396'))], type='LLMResult')"
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]
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},
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -152,18 +156,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 8,
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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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" Knock, knock!\n",
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"Who's there?\n",
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"Cows go.\n",
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"Cows go who?\n",
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"MOO!"
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"Why don't eggs tell jokes? They'd crack each other up."
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]
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}
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],
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@ -181,16 +181,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 9,
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"metadata": {},
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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=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")"
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"AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-ba8062fb-68af-47b8-bd7b-d1e01b914744-0')"
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]
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},
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"execution_count": 7,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -201,17 +201,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 10,
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"metadata": {},
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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=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"),\n",
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" AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")]"
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"[AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-5d2c77ab-2637-45da-8bbe-1b1f18a22369-0'),\n",
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" AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-f1338470-8b52-4d6e-9428-a694a08ae484-0')]"
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]
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},
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"execution_count": 8,
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -222,16 +222,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LLMResult(generations=[[ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))], [ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))]], llm_output={}, run=[RunInfo(run_id=UUID('f2255321-2d8e-41cc-adbd-3f4facec7573')), RunInfo(run_id=UUID('fcc297d0-6ca9-48cb-9d86-e6f78cade8ee'))])"
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"LLMResult(generations=[[ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-d4e44569-39cc-40cc-93fc-de53e599fd51-0'))], [ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-54647cc2-bee3-4154-ad00-2e547993e6d7-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('d4e44569-39cc-40cc-93fc-de53e599fd51')), RunInfo(run_id=UUID('54647cc2-bee3-4154-ad00-2e547993e6d7'))], type='LLMResult')"
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]
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},
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"execution_count": 9,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -242,18 +242,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 12,
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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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" Knock, knock!\n",
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"Who's there?\n",
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"Cows go.\n",
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"Cows go who?\n",
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"MOO!"
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"Why don't eggs tell jokes? They'd crack each other up."
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]
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}
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],
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@ -265,7 +261,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "langchain",
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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@ -279,7 +275,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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"version": "3.12.2"
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}
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},
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"nbformat": 4,
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@ -29,7 +29,7 @@
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"Ensure the `langchain_community` and `friendli-client` are installed.\n",
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"\n",
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"```sh\n",
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"pip install -U langchain-community friendli-client.\n",
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"pip install -U langchain-community friendli-client\n",
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"```\n",
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"\n",
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"Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment."
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@ -40,13 +40,20 @@
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": ["import getpass\nimport os\n\nif \"FRIENDLI_TOKEN\" not in os.environ:\n os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"]
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"if \"FRIENDLI_TOKEN\" not in os.environ:\n",
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" os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
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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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"You can initialize a Friendli chat model with selecting the model you want to use. The default model is `mixtral-8x7b-instruct-v0-1`. You can check the available models at [docs.friendli.ai](https://docs.periflow.ai/guides/serverless_endpoints/pricing#text-generation-models)."
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"You can initialize a Friendli chat model with selecting the model you want to use. \n",
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"The default model is `meta-llama-3.1-8b-instruct`. You can check the available models at [friendli.ai/docs](https://friendli.ai/docs/guides/serverless_endpoints/pricing#text-generation-models)."
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]
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},
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{
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@ -54,7 +61,11 @@
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_community.llms.friendli import Friendli\n\nllm = Friendli(model=\"mixtral-8x7b-instruct-v0-1\", max_tokens=100, temperature=0)"]
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"source": [
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"from langchain_community.llms.friendli import Friendli\n",
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"\n",
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"llm = Friendli(model=\"meta-llama-3.1-8b-instruct\", max_tokens=100, temperature=0)"
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]
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},
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{
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"cell_type": "markdown",
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@ -80,7 +91,7 @@
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{
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"data": {
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"text/plain": [
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"'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"'"
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"\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\""
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]
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},
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"execution_count": 3,
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@ -88,7 +99,9 @@
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"output_type": "execute_result"
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}
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],
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"source": ["llm.invoke(\"Tell me a joke.\")"]
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"source": [
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"llm.invoke(\"Tell me a joke.\")"
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]
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},
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{
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"cell_type": "code",
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@ -98,8 +111,8 @@
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{
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"data": {
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"text/plain": [
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"['Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"',\n",
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" 'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"']"
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"[\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\",\n",
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" \" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\"]"
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]
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},
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"execution_count": 4,
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@ -107,7 +120,9 @@
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"output_type": "execute_result"
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}
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],
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"source": ["llm.batch([\"Tell me a joke.\", \"Tell me a joke.\"])"]
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"source": [
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"llm.batch([\"Tell me a joke.\", \"Tell me a joke.\"])"
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]
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},
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{
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"cell_type": "code",
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@ -117,7 +132,7 @@
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{
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"data": {
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"text/plain": [
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"LLMResult(generations=[[Generation(text='Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"')], [Generation(text='Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"')]], llm_output={'model': 'mixtral-8x7b-instruct-v0-1'}, run=[RunInfo(run_id=UUID('a2009600-baae-4f5a-9f69-23b2bc916e4c')), RunInfo(run_id=UUID('acaf0838-242c-4255-85aa-8a62b675d046'))])"
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"LLMResult(generations=[[Generation(text=\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\")], [Generation(text=\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\")]], llm_output={'model': 'meta-llama-3.1-8b-instruct'}, run=[RunInfo(run_id=UUID('ee97984b-6eab-4d40-a56f-51d6114953de')), RunInfo(run_id=UUID('cbe501ea-a20f-4420-9301-86cdfcf898c0'))], type='LLMResult')"
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]
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},
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"execution_count": 5,
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@ -125,25 +140,19 @@
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"output_type": "execute_result"
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}
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],
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"source": ["llm.generate([\"Tell me a joke.\", \"Tell me a joke.\"])"]
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"source": [
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"llm.generate([\"Tell me a joke.\", \"Tell me a joke.\"])"
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]
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},
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{
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"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Username checks out.\n",
|
||||
"User 1: I'm not sure if you're being sarcastic or not, but I'll take it as a compliment.\n",
|
||||
"User 0: I'm not being sarcastic. I'm just saying that your username is very fitting.\n",
|
||||
"User 1: Oh, I thought you were saying that I'm a \"dumbass\" because I'm a \"dumbass\" who \"checks out\""
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["for chunk in llm.stream(\"Tell me a joke.\"):\n print(chunk, end=\"\", flush=True)"]
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for chunk in llm.stream(\"Tell me a joke.\"):\n",
|
||||
" print(chunk, end=\"\", flush=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -152,6 +161,26 @@
|
||||
"You can also use all functionality of async APIs: `ainvoke`, `abatch`, `agenerate`, and `astream`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\""
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"await llm.ainvoke(\"Tell me a joke.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
@ -160,7 +189,8 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"'"
|
||||
"[\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\",\n",
|
||||
" \" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\"]"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
@ -168,7 +198,9 @@
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": ["await llm.ainvoke(\"Tell me a joke.\")"]
|
||||
"source": [
|
||||
"await llm.abatch([\"Tell me a joke.\", \"Tell me a joke.\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@ -178,8 +210,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"',\n",
|
||||
" 'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"']"
|
||||
"LLMResult(generations=[[Generation(text=\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\")], [Generation(text=\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\")]], llm_output={'model': 'meta-llama-3.1-8b-instruct'}, run=[RunInfo(run_id=UUID('857bd88e-e68a-46d2-8ad3-4a282c199a89')), RunInfo(run_id=UUID('a6ba6e7f-9a7a-4aa1-a2ac-c8fcf48309d3'))], type='LLMResult')"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
@ -187,48 +218,24 @@
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": ["await llm.abatch([\"Tell me a joke.\", \"Tell me a joke.\"])"]
|
||||
"source": [
|
||||
"await llm.agenerate([\"Tell me a joke.\", \"Tell me a joke.\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"LLMResult(generations=[[Generation(text=\"Username checks out.\\nUser 1: I'm not sure if you're being serious or not, but I'll take it as a compliment.\\nUser 0: I'm being serious. I'm not sure if you're being serious or not.\\nUser 1: I'm being serious. I'm not sure if you're being serious or not.\\nUser 0: I'm being serious. I'm not sure\")], [Generation(text=\"Username checks out.\\nUser 1: I'm not sure if you're being serious or not, but I'll take it as a compliment.\\nUser 0: I'm being serious. I'm not sure if you're being serious or not.\\nUser 1: I'm being serious. I'm not sure if you're being serious or not.\\nUser 0: I'm being serious. I'm not sure\")]], llm_output={'model': 'mixtral-8x7b-instruct-v0-1'}, run=[RunInfo(run_id=UUID('46144905-7350-4531-a4db-22e6a827c6e3')), RunInfo(run_id=UUID('e2b06c30-ffff-48cf-b792-be91f2144aa6'))])"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": ["await llm.agenerate([\"Tell me a joke.\", \"Tell me a joke.\"])"]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Username checks out.\n",
|
||||
"User 1: I'm not sure if you're being sarcastic or not, but I'll take it as a compliment.\n",
|
||||
"User 0: I'm not being sarcastic. I'm just saying that your username is very fitting.\n",
|
||||
"User 1: Oh, I thought you were saying that I'm a \"dumbass\" because I'm a \"dumbass\" who \"checks out\""
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["async for chunk in llm.astream(\"Tell me a joke.\"):\n print(chunk, end=\"\", flush=True)"]
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"async for chunk in llm.astream(\"Tell me a joke.\"):\n",
|
||||
" print(chunk, end=\"\", flush=True)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "langchain",
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -242,7 +249,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.7"
|
||||
"version": "3.12.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
# Friendli AI
|
||||
|
||||
>[FriendliAI](https://friendli.ai/) enhances AI application performance and optimizes
|
||||
> [FriendliAI](https://friendli.ai/) enhances AI application performance and optimizes
|
||||
> cost savings with scalable, efficient deployment options, tailored for high-demand AI workloads.
|
||||
|
||||
## Installation and setup
|
||||
@ -8,10 +8,11 @@
|
||||
Install the `friendli-client` python package.
|
||||
|
||||
```bash
|
||||
pip install friendli-client
|
||||
pip install -U langchain_community friendli-client
|
||||
```
|
||||
|
||||
Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token,
|
||||
and set it as the `FRIENDLI_TOKEN` environment variable.
|
||||
and set it as the `FRIENDLI_TOKEN` environment variabzle.
|
||||
|
||||
|
||||
## Chat models
|
||||
@ -20,6 +21,11 @@ See a [usage example](/docs/integrations/chat/friendli).
|
||||
|
||||
```python
|
||||
from langchain_community.chat_models.friendli import ChatFriendli
|
||||
|
||||
chat = ChatFriendli(model='meta-llama-3.1-8b-instruct')
|
||||
|
||||
for m in chat.stream("Tell me fun things to do in NYC"):
|
||||
print(m.content, end="", flush=True)
|
||||
```
|
||||
|
||||
## LLMs
|
||||
@ -28,4 +34,8 @@ See a [usage example](/docs/integrations/llms/friendli).
|
||||
|
||||
```python
|
||||
from langchain_community.llms.friendli import Friendli
|
||||
|
||||
llm = Friendli(model='meta-llama-3.1-8b-instruct')
|
||||
|
||||
print(llm.invoke("def bubble_sort(): "))
|
||||
```
|
||||
@ -1,32 +0,0 @@
|
||||
# Friendli AI
|
||||
|
||||
>[Friendli AI](https://friendli.ai/) is a company that fine-tunes, deploys LLMs,
|
||||
> and serves a wide range of Generative AI use cases.
|
||||
|
||||
|
||||
## Installation and setup
|
||||
|
||||
- Install the integration package:
|
||||
|
||||
```
|
||||
pip install friendli-client
|
||||
```
|
||||
|
||||
- Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token,
|
||||
and set it as the `FRIENDLI_TOKEN` environment.
|
||||
|
||||
## Chat models
|
||||
|
||||
See a [usage example](/docs/integrations/chat/friendli).
|
||||
|
||||
```python
|
||||
from langchain_community.chat_models.friendli import ChatFriendli
|
||||
```
|
||||
|
||||
## LLMs
|
||||
|
||||
See a [usage example](/docs/integrations/llms/friendli).
|
||||
|
||||
```python
|
||||
from langchain_community.llms.friendli import Friendli
|
||||
```
|
||||
@ -75,12 +75,12 @@ class ChatFriendli(BaseChatModel, BaseFriendli):
|
||||
from langchain_community.chat_models import FriendliChat
|
||||
|
||||
chat = Friendli(
|
||||
model="llama-2-13b-chat", friendli_token="YOUR FRIENDLI TOKEN"
|
||||
model="meta-llama-3.1-8b-instruct", friendli_token="YOUR FRIENDLI TOKEN"
|
||||
)
|
||||
chat.invoke("What is generative AI?")
|
||||
"""
|
||||
|
||||
model: str = "llama-2-13b-chat"
|
||||
model: str = "meta-llama-3.1-8b-instruct"
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
|
||||
@ -16,14 +16,19 @@ from langchain_core.utils.utils import convert_to_secret_str
|
||||
from pydantic import Field, SecretStr
|
||||
|
||||
|
||||
def _stream_response_to_generation_chunk(stream_response: Any) -> GenerationChunk:
|
||||
def _stream_response_to_generation_chunk(
|
||||
stream_response: Any,
|
||||
) -> GenerationChunk:
|
||||
"""Convert a stream response to a generation chunk."""
|
||||
if stream_response.event == "token_sampled":
|
||||
return GenerationChunk(
|
||||
text=stream_response.text,
|
||||
generation_info={"token": str(stream_response.token)},
|
||||
)
|
||||
return GenerationChunk(text="")
|
||||
if not stream_response.get("choices", None):
|
||||
return GenerationChunk(text="")
|
||||
return GenerationChunk(
|
||||
text=stream_response.choices[0].text,
|
||||
# generation_info=dict(
|
||||
# finish_reason=stream_response.choices[0].get("finish_reason", None),
|
||||
# logprobs=stream_response.choices[0].get("logprobs", None),
|
||||
# ),
|
||||
)
|
||||
|
||||
|
||||
class BaseFriendli(Serializable):
|
||||
@ -34,7 +39,7 @@ class BaseFriendli(Serializable):
|
||||
# Friendli Async client.
|
||||
async_client: Any = Field(default=None, exclude=True)
|
||||
# Model name to use.
|
||||
model: str = "mixtral-8x7b-instruct-v0-1"
|
||||
model: str = "meta-llama-3.1-8b-instruct"
|
||||
# Friendli personal access token to run as.
|
||||
friendli_token: Optional[SecretStr] = None
|
||||
# Friendli team ID to run as.
|
||||
@ -107,7 +112,7 @@ class Friendli(LLM, BaseFriendli):
|
||||
from langchain_community.llms import Friendli
|
||||
|
||||
friendli = Friendli(
|
||||
model="mixtral-8x7b-instruct-v0-1", friendli_token="YOUR FRIENDLI TOKEN"
|
||||
model="meta-llama-3.1-8b-instruct", friendli_token="YOUR FRIENDLI TOKEN"
|
||||
)
|
||||
"""
|
||||
|
||||
|
||||
@ -114,14 +114,14 @@ async def test_friendli_ainvoke(
|
||||
@pytest.mark.requires("friendli")
|
||||
def test_friendli_stream(mock_friendli_client: Mock, friendli_llm: Friendli) -> None:
|
||||
"""Test stream with friendli."""
|
||||
mock_choice_0 = Mock()
|
||||
mock_choice_0.text = "Hello "
|
||||
mock_choice_1 = Mock()
|
||||
mock_choice_1.text = "langchain"
|
||||
mock_chunk_0 = Mock()
|
||||
mock_chunk_0.event = "token_sampled"
|
||||
mock_chunk_0.text = "Hello "
|
||||
mock_chunk_0.token = 0
|
||||
mock_chunk_0.choices = [mock_choice_0]
|
||||
mock_chunk_1 = Mock()
|
||||
mock_chunk_1.event = "token_sampled"
|
||||
mock_chunk_1.text = "Friendli"
|
||||
mock_chunk_1.token = 1
|
||||
mock_chunk_1.choices = [mock_choice_1]
|
||||
mock_stream = MagicMock()
|
||||
mock_chunks = [mock_chunk_0, mock_chunk_1]
|
||||
mock_stream.__iter__.return_value = mock_chunks
|
||||
@ -129,7 +129,7 @@ def test_friendli_stream(mock_friendli_client: Mock, friendli_llm: Friendli) ->
|
||||
mock_friendli_client.completions.create.return_value = mock_stream
|
||||
stream = friendli_llm.stream("Hello langchain")
|
||||
for i, chunk in enumerate(stream):
|
||||
assert chunk == mock_chunks[i].text
|
||||
assert chunk == mock_chunks[i].choices[0].text
|
||||
|
||||
mock_friendli_client.completions.create.assert_called_once_with(
|
||||
model=friendli_llm.model,
|
||||
@ -149,22 +149,22 @@ async def test_friendli_astream(
|
||||
mock_friendli_async_client: AsyncMock, friendli_llm: Friendli
|
||||
) -> None:
|
||||
"""Test async stream with friendli."""
|
||||
mock_choice_0 = Mock()
|
||||
mock_choice_0.text = "Hello "
|
||||
mock_choice_1 = Mock()
|
||||
mock_choice_1.text = "langchain"
|
||||
mock_chunk_0 = Mock()
|
||||
mock_chunk_0.event = "token_sampled"
|
||||
mock_chunk_0.text = "Hello "
|
||||
mock_chunk_0.token = 0
|
||||
mock_chunk_0.choices = [mock_choice_0]
|
||||
mock_chunk_1 = Mock()
|
||||
mock_chunk_1.event = "token_sampled"
|
||||
mock_chunk_1.text = "Friendli"
|
||||
mock_chunk_1.token = 1
|
||||
mock_chunk_1.choices = [mock_choice_1]
|
||||
mock_stream = AsyncMock()
|
||||
mock_chunks = [mock_chunk_0, mock_chunk_1]
|
||||
mock_stream.__aiter__.return_value = mock_chunks
|
||||
mock_stream.__aiter__.return_value = iter(mock_chunks)
|
||||
|
||||
mock_friendli_async_client.completions.create.return_value = mock_stream
|
||||
stream = friendli_llm.astream("Hello langchain")
|
||||
async for i, chunk in aenumerate(stream):
|
||||
assert chunk == mock_chunks[i].text
|
||||
assert chunk == mock_chunks[i].choices[0].text
|
||||
|
||||
mock_friendli_async_client.completions.create.assert_awaited_once_with(
|
||||
model=friendli_llm.model,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user