From 0a56efe940e308d664fb60158876526e4d5b67e3 Mon Sep 17 00:00:00 2001 From: Jieyu Zhang Date: Sun, 12 Nov 2023 12:28:10 -0800 Subject: [PATCH 1/6] add EcoAssistant blog --- website/blog/authors.yml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/website/blog/authors.yml b/website/blog/authors.yml index d2fefc887cc..cf77c02e363 100644 --- a/website/blog/authors.yml +++ b/website/blog/authors.yml @@ -39,3 +39,9 @@ beibinli: title: Senior Research Engineer at Microsoft url: https://github.com/beibinli image_url: https://github.com/beibinli.png + +jieyuz2: + name: Jieyu Zhang + title: PhD student at University of Washington + url: https://jieyuz2.github.io/ + image_url: https://github.com/jieyuz2.png From 67f87d5a7fd610f3579586cf1d419c2426d357ee Mon Sep 17 00:00:00 2001 From: Jieyu Zhang Date: Sun, 12 Nov 2023 12:30:22 -0800 Subject: [PATCH 2/6] add blog --- website/blog/2023-11-09-EcoAssistant/blog.md | 102 ++++++++++++++++++ .../blog/2023-11-09-EcoAssistant/img/chat.png | Bin 0 -> 315192 bytes .../2023-11-09-EcoAssistant/img/results.png | Bin 0 -> 28941 bytes .../2023-11-09-EcoAssistant/img/system.png | Bin 0 -> 286438 bytes .../img/template-demo.png | Bin 0 -> 61483 bytes .../2023-11-09-EcoAssistant/img/template.png | Bin 0 -> 335376 bytes 6 files changed, 102 insertions(+) create mode 100644 website/blog/2023-11-09-EcoAssistant/blog.md create mode 100644 website/blog/2023-11-09-EcoAssistant/img/chat.png create mode 100644 website/blog/2023-11-09-EcoAssistant/img/results.png create mode 100644 website/blog/2023-11-09-EcoAssistant/img/system.png create mode 100644 website/blog/2023-11-09-EcoAssistant/img/template-demo.png create mode 100644 website/blog/2023-11-09-EcoAssistant/img/template.png diff --git a/website/blog/2023-11-09-EcoAssistant/blog.md b/website/blog/2023-11-09-EcoAssistant/blog.md new file mode 100644 index 00000000000..ac4bafcd354 --- /dev/null +++ b/website/blog/2023-11-09-EcoAssistant/blog.md @@ -0,0 +1,102 @@ +--- +title: EcoAssistant: using LLM assistants more accurately and affordably +authors: jieyuz2 +tags: [LMM, RAG, cost-effectiveness] +--- + +![system](img/system.png) + +**TL;DR:** +* Introducing the **EcoAssistant**, which is designed to solve user queries more accurately and affordably. +* We show how to let the LLM assistant agent leverage external API to solve user query. +* We show how to reduce the cost of using GPT models via **Assistant Hierachy**. +* We show how to leverage the idea of Retrieval-augmented Generation (RAG) to improve the success rate via **Solution Demonstration**. + + +## EcoAssistant + +In this blog, we introduce the **EcoAssistant**, a system built upon AutoGen with the goal of solving user queries more accurately and affordably. + +### Problem setup + +Recently, users have been using conversational LLMs such as ChatGPT for various queries. +Reports indicate that 23% of ChatGPT user queries are for knowledge extraction purposes. +Many of these queries require knowledge that is external to the information stored within any pre-trained large language models (LLMs). +These tasks can only be completed by generating code to fetch necessary information via external APIs that contain the requested information. +In the table below, we show three types of user queries that we aim to address in this work. + +| Dataset | API | Example query | +|-------------|----------|----------| +| Places| [Google Places](https://developers.google.com/maps/documentation/places/web-service/overview) | I’m looking for a 24-hour pharmacy in Montreal, can you find one for me? | +| Weather | [Weather API](https://www.weatherapi.com) | What is the current cloud coverage in Mumbai, India? | +| Stock | [Alpha Vantage Stock API](https://www.alphavantage.co/documentation/) | Can you give me the opening price of Microsoft for the month of January 2023? | + + +### Leveraging external APIs + +To address these queries, we first build a **two-agent system** based on AutoGen, +where the first agent is a **LLM assistant agent** (`AssistantAgent` in AutoGen) that is responsible for proposing and refining the code and +the second agent is a **code executor agent** (`UserProxyAgent` in AutoGen) that would extract the generated code and execute it, forwarding the output back to the LLM assistant agent. +A visualization of the two-agent system is shown below. + +![chat](img/chat.png) + +To instruct the assistant agent to leverage external APIs, we only need to add the API name/key dictionary at the beginning of the initial message. +The template is shown below, where the red part is the information of APIs and black part is user query. + +![template](img/template.png) + +Importantly, we don't want to reveal our real API key to the assistant agent for safety concerns. +Therefore, we use a **fake API key** to replace the real API key in the initial message. +In particular, we generate a random token (e.g., `181dbb37`) for each API key and replace the real API key with the token in the initial message. +Then, when the code executor execute the code, the fake API key would be automatically replaced by the real API key. + + +### Solution Demonstration +In most practical scenarios, queries from users would appear sequentially over time. +Our **EcoAssistant** leverages past success to help the LLM assistants address future queries via **Solution Demonstration**. +Specifically, whenever a query is deemed successfully resolved by user feedback, we capture and store the query and the final generated code snippet. +These query-code pairs are saved in a specialized vector database. When new queries appear, **EcoAssistant** retrieves the most similar query from the database, which is then appended with the associated code to the initial prompt for the new query, serving as a demonstration. +The new template of initial message is shown below, where the blue part corresponds to the solution demonstration. + +![template](img/template-demo.png) + +We found that this utilization of past successful query-code pairs improves the query resolution process with fewer iterations and enhances the system's performance. + + +### Assistant Hierarchy +LLMs usually have different prices and performance, for example, GPT-3.5-turbo is much cheaper than GPT-4 but also less accurate. +Thus, we propose the **Assistant Hierarchy** to reduce the cost of using LLMs. +The core idea is that we use the cheaper LLMs first and only use the more expensive LLMs when necessary. +By this way, we are able to reduce the reliance on expensive LLMs and thus reduce the cost. +In particular, given multiple LLMs, we initiate one assistant agent for each and start the conversation with the most cost-effective LLM assistant. +If the conversation between the current LLM assistant and the code executor concludes without successfully resolving the query, **EcoAssistant** would then restart the conversation with the next more expensive LLM assistant in the hierarchy. +We found that this strategy significantly reduces costs while still effectively addressing queries. + +### A Synergistic Effect +We found that the **Assistant Hierarchy** and **Solution Demonstration** of **EcoAssistant** have a synergistic effect. +Because the query-code database is shared by all LLM assistants, even without specialized design, +the solution from more powerful LLM assistant (e.g., GPT-4) could be later retrieved to guide weaker LLM assistant (e.g., GPT-3.5-turbo). +Such a synergistic effect further improves the performance and reduce the cost of **EcoAssistant**. + +### Experimental Results + +We evaluate **EcoAssistant** on three datasets: Places, Weather, and Stock. When comparing it with a single GPT-4 assistant, we found that **EcoAssistant** achieves a higher success rate with a lower cost as shown in the figure below. +For more details about the experimental results and other experiments, please refer to our [paper](https://arxiv.org/abs/2310.03046). + +![exp](img/results.png) + +## Further reading + +Please refer to our [paper](https://arxiv.org/abs/2310.03046) and [codebase](https://github.com/JieyuZ2/EcoAssistant) for more details about **EcoAssistant**. + +If you find this blog useful, please consider citing: + +```bibtex +@article{zhang2023ecoassistant, + title={EcoAssistant: Using LLM Assistant More Affordably and Accurately}, + author={Zhang, Jieyu and Krishna, Ranjay and Awadallah, Ahmed H and Wang, Chi}, + journal={arXiv preprint arXiv:2310.03046}, + year={2023} +} +``` diff --git a/website/blog/2023-11-09-EcoAssistant/img/chat.png b/website/blog/2023-11-09-EcoAssistant/img/chat.png new file mode 100644 index 0000000000000000000000000000000000000000..7b08b22f2ee321c21127b601b98da04c6846dd46 GIT binary patch literal 315192 zcmeFZcU+U_-Up1eYFjI49jFK>R8SG9AR;rCA)*2*GQzBdfDj=<2pAxVtpj8#2nYe9 zY#{{M8?1vB5_TdhsEiN-L>8OG%Z+ zZx`Iy0{s2$?F(SIl$6$M$>Z(}$xcuv+=ZAkf z{N3-H_Mc06{^ORtJD=#_!B-M;jvRZQu-8)h=W{!sUG@s}`eFC8L%)2Ju&doCWMC9G z&j+zGoE=w|oJaRsBOF$Q=uA2v-x=g!=a4Z2hdKCliMIhV{P+4V0{>q`p!*T|wk1A5 z<6m?;_g;v-1Y3<7lgeY@vcv0eLR1OE3i&Vh-2JZHU({Q(39l!BHAjDT{_-!MK7W(o zFNy-*?P%R`2l&LVO?=P;eP{DuH-#!Pahb8L)gP1M&HWsedgz$NJ>18MBH}5F0$-}}p! zM9rTO8eNgmMO4bf?UlOjqV0d}b4!k(W|L;K*Vx^EB%}MDuB=5arX;{h5$uip!>@%$x}3J@f*{^=7XCSw5prL;ox#mFwU;-z-_)d35`Gko zS9rPE_9Ga}lzM5`smdt(5>U=G;>X5)LjILjijzfoj-Hp3U7T~{oL@_o6LX7H#bZtj zo`H+VOS}>;Zsq%ms&}8`BOJ`Sc1C5h%_o-@s>(M+e7SJz0RL zYnmNdk(XKuwcKr5pm;mMEb$m@W)R#OBYYOedFwprAz$znD*l&$aHW-@DJ+nink@yJ zAXws>SNIAkq0z4hOwVE($Ag*XQ_$;%`EKE0c2pcTve|`Mxo0q>E(2V`bok`BN$TK* zw0;w-E5{-@n_Vc6>Z;4o=RR%QzOO@xnn@e1Bzf>?s4wSCq0UWNqQi8dZ?xCE_lc!4>m1JUzkujAaMx-ubz6e}DHQcp>Ji#OP%l!N8}n=S-{lu&m5^r>R_c z3|cF7SEWn^MvEDa(0Q_t`zc4aw53UOpqqhrH`&ksD}1(zwb6}k&+L0+FCMAj@l5Rq znEN%m3Qy?TCe_OKl$p8?R}qB1NH8%fWwL<{CaUS%7ioD(J*Sq@BBL+wvwN|-()wB& z=U_JVW##8>B^N4Clr!gt?O74hOympnh0^|EH+9k(hX?-o%|f2{g_4f(BO-;z6@ER- zoHKPWfv_h^xD#}q({d##&bER?3ATsGZA51Vkr$)Sr1y!~l{=|{c)#tXBNa}KqDTZu zSxwBX>$Lx7!6~hMh}Pio+TFS#tM%={BJ-3%e;}9Y$B9+&5aE&Hi$Pyiy4j`$UGr^$ zQvE7aUWYQqixs8ullMhGUt6)H%*B4!sD1AKS3;F|jtokXn^syrR)jXWe(X*bS32 z9qibeZ%6&9sLZCXjPPh`G(;3wVMyJT=*X{X3-GT7+*SU4=x%8}pVQGCeUm+b?$a_C zUsP!~xl*)DM+7&7x35(cjz0Ziaoek%2`v^^Et}0CdJ1-mGL|K!7r<3rq3CtNzz;&j z?C562sYy+3k?~{iTL1zZf|=o&I)K~uM4-DO=)Re8kfs;gpW@knUgKEISo zp}DFkl>ehom|eCXCCcCV0;OsOR~a=ghY2&em54AbKUqFAe#EEQhf`%EZlFQ?dQU8l zR4n4NdzeJ$_eRE10P3G7x$4G8DM!_F8FZbCS+-@u>ocyy*$XSs!pqWtY2sg{AMGaXfoY;dG4~vCyHw5{#)dRMkO+L}WLaFW zA>gRHI#yp4rY4~8p3pjebZ)mgOUCO3y1TE{IWfh zzo?(-Q~CDJmlV4oq-Z(U)n1@?^~D312*fSoe*WB|@tmebwE@d?MfBv<_6F?C!%zzjS%8X5**RBJkWgo{^QeI%l)wu(*sfdhd$cr z>H;-mnbSg5-0ReaaP&C2^ugv}>yef#y+HC6Kvq3K@6K$L@hfHmIP(h5RTV{ZGwbEZ zsV`N2a|y`H0_OKl3L>qnATE+nJh^M8@^oTzBeE;$3h|}tg#OS1&<$Cd zU*(0oD6w2EtPvR|5FVH@n8)}B&lga8FN>CEpl!dK`AI)1XGC##&6>trVzFE1kl97#k#2< zY^V+tPK;Kon=&u$w2^h4yieTABxB9^KOw{mH%s08in*hvQ!&uQXDXP_Y%kM5rHe

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z1nR#b*T33Nd=yi-ek%564MQ87&Sl~Ie}@}Dg8Am&3Vgi#h9eyALrNT@y=JOEPwtR{ zsvkZc<;YfkPrp!BPW+|?WK>^ZkYuUBQp(I26T~$4o$+sG!F#2e`@B&+Ra2dke&uGclba;aei-O$GoM z_)l++^13bOa5avS8i{Lfuyf4*sEB>tasyp;CibhJ&g^q0(K)W2!()|>*1cX;A$xSd O*R7j+Hwvy Date: Sun, 12 Nov 2023 12:43:15 -0800 Subject: [PATCH 3/6] Rename blog.md to blog.mdx --- website/blog/2023-11-09-EcoAssistant/{blog.md => blog.mdx} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename website/blog/2023-11-09-EcoAssistant/{blog.md => blog.mdx} (100%) diff --git a/website/blog/2023-11-09-EcoAssistant/blog.md b/website/blog/2023-11-09-EcoAssistant/blog.mdx similarity index 100% rename from website/blog/2023-11-09-EcoAssistant/blog.md rename to website/blog/2023-11-09-EcoAssistant/blog.mdx From 87e05588098119c3dffa21a411b2ab2733ec6856 Mon Sep 17 00:00:00 2001 From: Jieyu Zhang Date: Sun, 12 Nov 2023 12:47:24 -0800 Subject: [PATCH 4/6] Rename blog.mdx to index.mdx --- website/blog/2023-11-09-EcoAssistant/{blog.mdx => index.mdx} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename website/blog/2023-11-09-EcoAssistant/{blog.mdx => index.mdx} (100%) diff --git a/website/blog/2023-11-09-EcoAssistant/blog.mdx b/website/blog/2023-11-09-EcoAssistant/index.mdx similarity index 100% rename from website/blog/2023-11-09-EcoAssistant/blog.mdx rename to website/blog/2023-11-09-EcoAssistant/index.mdx From c8a9084f85552bf41f44f1f2080fab0742c7e836 Mon Sep 17 00:00:00 2001 From: Jieyu Zhang Date: Sun, 12 Nov 2023 12:50:16 -0800 Subject: [PATCH 5/6] Update index.mdx --- website/blog/2023-11-09-EcoAssistant/index.mdx | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/website/blog/2023-11-09-EcoAssistant/index.mdx b/website/blog/2023-11-09-EcoAssistant/index.mdx index ac4bafcd354..c7e4084c5f3 100644 --- a/website/blog/2023-11-09-EcoAssistant/index.mdx +++ b/website/blog/2023-11-09-EcoAssistant/index.mdx @@ -65,19 +65,19 @@ We found that this utilization of past successful query-code pairs improves the ### Assistant Hierarchy -LLMs usually have different prices and performance, for example, GPT-3.5-turbo is much cheaper than GPT-4 but also less accurate. +LLMs usually have different prices and performance, for example, GPT-3.5-turbo is much cheaper than GPT-4 but also less accurate. Thus, we propose the **Assistant Hierarchy** to reduce the cost of using LLMs. The core idea is that we use the cheaper LLMs first and only use the more expensive LLMs when necessary. By this way, we are able to reduce the reliance on expensive LLMs and thus reduce the cost. -In particular, given multiple LLMs, we initiate one assistant agent for each and start the conversation with the most cost-effective LLM assistant. -If the conversation between the current LLM assistant and the code executor concludes without successfully resolving the query, **EcoAssistant** would then restart the conversation with the next more expensive LLM assistant in the hierarchy. +In particular, given multiple LLMs, we initiate one assistant agent for each and start the conversation with the most cost-effective LLM assistant. +If the conversation between the current LLM assistant and the code executor concludes without successfully resolving the query, **EcoAssistant** would then restart the conversation with the next more expensive LLM assistant in the hierarchy. We found that this strategy significantly reduces costs while still effectively addressing queries. ### A Synergistic Effect We found that the **Assistant Hierarchy** and **Solution Demonstration** of **EcoAssistant** have a synergistic effect. Because the query-code database is shared by all LLM assistants, even without specialized design, the solution from more powerful LLM assistant (e.g., GPT-4) could be later retrieved to guide weaker LLM assistant (e.g., GPT-3.5-turbo). -Such a synergistic effect further improves the performance and reduce the cost of **EcoAssistant**. +Such a synergistic effect further improves the performance and reduces the cost of **EcoAssistant**. ### Experimental Results From 3f7f42c302945740a259f15bce929c7ce78d5dd5 Mon Sep 17 00:00:00 2001 From: Jieyu Zhang Date: Sun, 12 Nov 2023 12:52:59 -0800 Subject: [PATCH 6/6] Update index.mdx --- website/blog/2023-11-09-EcoAssistant/index.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2023-11-09-EcoAssistant/index.mdx b/website/blog/2023-11-09-EcoAssistant/index.mdx index c7e4084c5f3..4882261d511 100644 --- a/website/blog/2023-11-09-EcoAssistant/index.mdx +++ b/website/blog/2023-11-09-EcoAssistant/index.mdx @@ -1,5 +1,5 @@ --- -title: EcoAssistant: using LLM assistants more accurately and affordably +title: EcoAssistant - Using LLM Assistants More Accurately and Affordably authors: jieyuz2 tags: [LMM, RAG, cost-effectiveness] ---