Building applications with LLMs through composability
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upgrade chroma to 0.4.0 (#7749)
** This should land Monday the 17th ** 

Chroma is upgrading from `0.3.29` to `0.4.0`. `0.4.0` is easier to
build, more durable, faster, smaller, and more extensible. This comes
with a few changes:

1. A simplified and improved client setup. Instead of having to remember
weird settings, users can just do `EphemeralClient`, `PersistentClient`
or `HttpClient` (the underlying direct `Client` implementation is also
still accessible)

2. We migrated data stores away from `duckdb` and `clickhouse`. This
changes the api for the `PersistentClient` that used to reference
`chroma_db_impl="duckdb+parquet"`. Now we simply set
`is_persistent=true`. `is_persistent` is set for you to `true` if you
use `PersistentClient`.

3. Because we migrated away from `duckdb` and `clickhouse` - this also
means that users need to migrate their data into the new layout and
schema. Chroma is committed to providing extension notification and
tooling around any schema and data migrations (for example - this PR!).

After upgrading to `0.4.0` - if users try to access their data that was
stored in the previous regime, the system will throw an `Exception` and
instruct them how to use the migration assistant to migrate their data.
The migration assitant is a pip installable CLI: `pip install
chroma_migrate`. And is runnable by calling `chroma_migrate`

-- TODO ADD here is a short video demonstrating how it works. 

Please reference the readme at
[chroma-core/chroma-migrate](https://github.com/chroma-core/chroma-migrate)
to see a full write-up of our philosophy on migrations as well as more
details about this particular migration.

Please direct any users facing issues upgrading to our Discord channel
called
[#get-help](https://discord.com/channels/1073293645303795742/1129200523111841883).
We have also created a [email
listserv](https://airtable.com/shrHaErIs1j9F97BE) to notify developers
directly in the future about breaking changes.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
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🦜🔗 LangChain

Building applications with LLMs through composability

Release Notes lint test Downloads License: MIT Twitter Open in Dev Containers Open in GitHub Codespaces GitHub star chart Dependency Status Open Issues

Looking for the JS/TS version? Check out LangChain.js.

Production Support: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.

Quick Install

pip install langchain or pip install langsmith && conda install langchain -c conda-forge

🤔 What is this?

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

This library aims to assist in the development of those types of applications. Common examples of these applications include:

Question Answering over specific documents

💬 Chatbots

🤖 Agents

📖 Documentation

Please see here for full documentation on:

  • Getting started (installation, setting up the environment, simple examples)
  • How-To examples (demos, integrations, helper functions)
  • Reference (full API docs)
  • Resources (high-level explanation of core concepts)

🚀 What can this help with?

There are six main areas that LangChain is designed to help with. These are, in increasing order of complexity:

📃 LLMs and Prompts:

This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.

🔗 Chains:

Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.

📚 Data Augmented Generation:

Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.

🤖 Agents:

Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.

🧠 Memory:

Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.

🧐 Evaluation:

[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.

For more information on these concepts, please see our full documentation.

💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see here.