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Lance Martin Profile picture
@RLanceMartin
Aug 8, 2023 • 8 tweets • 4 min read • Read on X
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Text-to-SQL 📒

LLMs unlock a natural language interface with structured data. Part 4 of our initiative to improve @LangChainAI docs shows how to use LLMs to write / execute SQL queries w/ chains and agents. Thanks @manuelsoria_ for work on the docs:
https://t.co/CyOqp5I3TMpython.langchain.com/docs/use_cases…
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1/ Text-to-SQL is an excellent LLM use-case: many ppl can describe what they want in natural language, but have difficultly mapping that to a specific SQL queries. LLMs can bridge this gap, e.g., see:
https://t.co/b0NMkHPe9xarxiv.org/pdf/2204.00498…
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2/ create_sql_query_chain( ) maps from natural language to a SQL query: pass the question and the database into the chain, and get SQL out. Run the query on the database easily: Image
3/ The LangSmith trace is a great way to see that the chain employs ideas from the paper above: give LLM a CREATE TABLE description for each table and and three example rows in a SELECT statement. This gives the LLM context about the db structure:
https://t.co/Pqu86RFcJPsmith.langchain.com/public/c8fa52e…
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4/ Extending this, SQLDatabaseChain will generate the query, execute if, and also synthesize the result in natural language. This creates a natural language wrapper around a SQL DB w/ input and output:
https://t.co/avT4kRVIiosmith.langchain.com/public/7f202a0…
Image
5/ Finally, SQL agents can be used for more complex tasks (multi-query) and can recover from errors. The trace shows how a ReAct agent can use a toolkit of SQL operations (read table, write query, run query) :
https://t.co/zUrXja5bzVsmith.langchain.com/public/a86dbe1…
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6/ For more, see blog post and webinar w/ @fpingham and @JonZLuo:

https://t.co/dtZQY4gAMLblog.langchain.dev/llms-and-sql/
7/ Finally, for more on the community initiative to improve the docs, see Part 3 on extraction:

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More from @RLanceMartin

Lance Martin Profile picture
May 11
self-verification (Outcomes) + self-learning (Dreaming) are two of the most interesting new features we shared at Code With Claude last week.

a few notes + video links to the talks ... Image
1/ Outcomes in Claude Managed Agents is just a "Ralph loop" to verify output vs a user provided rubric. it uses a grader sub-agent for the verification. some interesting points on the benefits of an isolated verifier here:
anthropic.com/engineering/ha…Image
2/ @jess__yan + i showed a toy example of Outcomes: i had an Managed Agent make a generative UI w/ metrics (charts, graphs) rendered as svg. i used an Outcomes loop to improve the render timing - Claude figured out various tricks (prompting, etc) to speed it up.
Read 7 tweets
Lance Martin Profile picture
Apr 10
i co-wrote the Anthropic engineering blog on Claude Managed Agents, and wanted to share some thoughts on agent harnesses + infrastructure for long-horizon tasks ... 🧵
anthropic.com/engineering/ma…Image
1/ Claude can perform tasks over increasingly long time-horizons. newer models (Mythos) will push this further. with Claude Managed Agents, we needed to design infrastructure that can support long-horizon work. Image
2/ we also needed a way to update the agent harness: each harness encodes assumptions about what Claude can't do. those assumptions go stale as Claude gets more capable, and can even hobble the model. Image
Read 11 tweets
Lance Martin Profile picture
Mar 22, 2024
Gave this short talk on RAG vs long context LLMs at a few meetups recently. Tries to pull together threads from a few recent projects + papers I really like.

Just put on YT, a few highlights w papers below ...
1/ Can long context LLMs retrieve & reason over multiple facts as a RAG system does? @GregKamradt and I dug into this w/ multi-needle-in-a-haystack on GPT4. Retrieval is not guaranteed: worse for more needles, worse at doc start, worse w/ reasoning.
Image
2/ Nice paper (@adamlerer & @alex_peys) suggests this may be due to recency bias from training: recent tokens are typically most informative for predicting the next one. Not good for context augmented generation.
arxiv.org/pdf/2310.01427…
Image
Read 11 tweets
Lance Martin Profile picture
Aug 25, 2023
Check out these new guides for 13 popular LLM use-cases. Part of a major community effort to improve the @LangChainAI docs + add CoLabs prototyping.

1/13: Open source LLMs
How to use many open source LLMs on your device
python.langchain.com/docs/guides/lo…
Image
2/13: Agents
How to quickly test various types of agents
python.langchain.com/docs/use_cases…
Image
3/13: RAG (retrieval augmented generation)
How to do RAG at multiple levels of abstraction
python.langchain.com/docs/use_cases…
Image
Read 14 tweets
Lance Martin Profile picture
Aug 23, 2023
GPT-3.5 and LLaMA2 fine-tuning guides 🪄

Considering LLM fine-tuning? Here's two new CoLab guides for fine-tuning GPT-3.5 & LLaMA2 on your data using LangSmith for dataset management and eval. We also share our lessons learned in a blog post here:

blog.langchain.dev/using-langsmit…
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... 1/ When to fine-tune? Fine-tuning is not advised for teaching an LLM new knowledge (see references from @OpenAI and others in our blog post). It's best for tasks (e.g., extraction) focused on "form, not facts":
anyscale.com/blog/fine-tuni…
... 2/ With this in mind, we fine-tuned LLaMA-7b-chat & GPT-3.5-turbo for knowledge graph triple extraction (see details in blog post and CoLab). Notebooks here:
LLaMA CoLab:
GPT-3.5-turbo CoLab:

colab.research.google.com/drive/1tpywvzw…
colab.research.google.com/drive/1YCyDHPS…
Read 9 tweets
Lance Martin Profile picture
Aug 12, 2023
Code understanding 🖥️🧠

LLMs excel at code analysis / completion (e.g., Co-Pilot, Code Interpreter, etc). Part 6 of our initiative to improve @LangChainAI docs covers code analysis, building on contributions of @cristobal_dev + others:
https://t.co/2DsxdjbYeypython.langchain.com/docs/use_cases…
Image
1/ Copilot and related tools (e.g., @codeiumdev) have dramatically accelerated dev productivity and shown that LLMs excel at code understanding / completion
2/ But, RAG for QA/chat on codebases is challenging b/c text splitters may break up elements (e.g., fxns, classes) and fail to preserve context about which element each code chunk comes from.
Read 6 tweets

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