From 859684c1ec08b129f788e518223fc77e6cfa7b4a Mon Sep 17 00:00:00 2001 From: lbeurerkellner Date: Sun, 29 Oct 2023 10:36:46 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20web=20from=20@=20eth-sri/lmql@?= =?UTF-8?q?ef703b8d7210da908e84f1e16458f0497de1faf9=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 404.html | 2 +- README.html | 2 +- ...s => docs_latest_models_hf.md.db4e71b2.js} | 8 +++++--- ...docs_latest_models_hf.md.db4e71b2.lean.js} | 2 +- blog/index.html | 2 +- blog/posts/release-0.0.5.html | 2 +- blog/posts/release-0.0.6.1.html | 2 +- blog/posts/release-0.0.6.3.html | 2 +- blog/posts/release-0.0.6.4.html | 2 +- blog/posts/release-0.0.6.5.html | 2 +- blog/posts/release-0.0.6.6.html | 2 +- blog/posts/release-0.0.6.html | 2 +- blog/posts/release-0.7.html | 2 +- docs/development/dev-setup.html | 2 +- docs/development/docker-setup.html | 2 +- docs/development/documentation.html | 2 +- docs/index.html | 2 +- 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a7976548..7d772f84 100644 --- a/404.html +++ b/404.html @@ -17,7 +17,7 @@
LMQL

Appearance

404

PAGE NOT FOUND

But if you don't change your direction, and if you keep looking, you may end up where you are heading.
Take me home
- + \ No newline at end of file diff --git a/README.html b/README.html index 1b44e5e9..c6f2b2e0 100644 --- a/README.html +++ b/README.html @@ -21,7 +21,7 @@
LMQL

Appearance

LMQL Documentation and Web Resources ​

This directory contains the LMQL documentation and web resources:

  • blog/: LMQL's blog posts
  • docs/: LMQL's documentation
  • features/: LMQL feature highlights as shown on the landing page
  • public/: Public web resources like logos and custom JavaScript

The main documentation can be found in the docs/ directory and is written in Markdown.

For details on building the documentation (the web distribution), see docs/development/documentation.md.

- + \ No newline at end of file diff --git a/assets/docs_latest_models_hf.md.f6d5ed07.js b/assets/docs_latest_models_hf.md.db4e71b2.js similarity index 71% rename from assets/docs_latest_models_hf.md.f6d5ed07.js rename to assets/docs_latest_models_hf.md.db4e71b2.js index 5c804851..a41c58c3 100644 --- a/assets/docs_latest_models_hf.md.f6d5ed07.js +++ b/assets/docs_latest_models_hf.md.db4e71b2.js @@ -1,6 +1,8 @@ -import{_ as e,o,c as a,Q as t}from"./chunks/framework.980cae92.js";const s="/assets/inference.f47a6f3e.svg",f=JSON.parse('{"title":"Local Models / Transformers","description":"","frontmatter":{"order":1},"headers":[],"relativePath":"docs/latest/models/hf.md","filePath":"docs/latest/models/hf.md"}'),n={name:"docs/latest/models/hf.md"},l=t('

Local Models / Transformers ​

LMQL relies on a two-process architecture: The inference process (long-running) loads the model and provides an inference API, and the interpreter process (short-lived) executes your LMQL program.

This architecture is advantageous for locally-hosted models, as the model loading time can be quite long or the required GPU hardware might not even be available on the client machine.

This chapter first discusses how to use of the two-process inference API, and then presents an alternative on how to leverage In-Process Model Loading, which avoids the need for a separate server process within the same architecture.

Inference Architecture

Prerequisites Before using a local model, make sure you installed LMQL via pip install lmql[hf]. This ensures the dependencies for running local models are installed. This requirement also applies to llama.cpp, as LMQL still relies on HuggingFace transformers for tokenization.

Then, to start an LMQL inference server, e.g. for the gpt2-medium model, you can run the following command:

bash
lmql serve-model gpt2-medium --cuda
-

--cuda will load the model on the GPU, if available. If multiple GPUs are available, the model will be distributed across all GPUs. To run with CPU inference, omit the --cuda flag. If you only want to use a specific GPU, you can specify the CUDA_VISIBLE_DEVICES environment variable, e.g. CUDA_VISIBLE_DEVICES=0 lmql serve-model gpt2-medium.

By default, this exposes an LMQL/LMTP inference API on port 8080. When serving a model remotely, make sure to tunnel/forward the port to your client machine. Now, when executing an LMQL query in the playground or via the CLI, you can simply specify e.g. gpt2-medium, and the runtime will automatically connect to the model server running on port 8080 to obtain model-generated text.

Configuration ​

Endpoint and Port By default, models will be served via port 8080. To change this, you can specify the port via the --port option of the lmql serve-model command. On the client side, to connect to a model server running on a different port, you can specify the port when constructing an lmql.model object:

lmql.model("gpt2", endpoint="localhost:9999")
+import{_ as e,o,c as a,Q as t}from"./chunks/framework.980cae92.js";const s="/assets/inference.f47a6f3e.svg",f=JSON.parse('{"title":"Local Models / Transformers","description":"","frontmatter":{"order":1},"headers":[],"relativePath":"docs/latest/models/hf.md","filePath":"docs/latest/models/hf.md"}'),n={name:"docs/latest/models/hf.md"},r=t('
Local Models / Transformers ​
LMQL relies on a two-process architecture: The inference process (long-running) loads the model and provides an inference API, and the interpreter process (short-lived) executes your LMQL program.
This architecture is advantageous for locally-hosted models, as the model loading time can be quite long or the required GPU hardware might not even be available on the client machine.
This chapter first discusses how to use of the two-process inference API, and then presents an alternative on how to leverage In-Process Model Loading, which avoids the need for a separate server process within the same architecture.
Inference Architecture
Prerequisites Before using a local model, make sure you installed LMQL via pip install lmql[hf]. This ensures the dependencies for running local models are installed. This requirement also applies to llama.cpp, as LMQL still relies on HuggingFace transformers for tokenization.
Then, to start an LMQL inference server, e.g. for the gpt2-medium model, you can run the following command:
bash
lmql serve-model gpt2-medium --cuda
+
--cuda will load the model on the GPU, if available. If multiple GPUs are available, the model will be distributed across all GPUs. To run with CPU inference, omit the --cuda flag. If you only want to use a specific GPU, you can specify the CUDA_VISIBLE_DEVICES environment variable, e.g. CUDA_VISIBLE_DEVICES=0 lmql serve-model gpt2-medium.
By default, this exposes an LMQL/LMTP inference API on port 8080. When serving a model remotely, make sure to tunnel/forward the port to your client machine. Now, when executing an LMQL query in the playground or via the CLI, you can simply specify e.g. gpt2-medium, and the runtime will automatically connect to the model server running on port 8080 to obtain model-generated text.
Configuration ​
Endpoint and Port By default, models will be served via port 8080. To change this, you can specify the port via the --port option of the lmql serve-model command. On the client side, to connect to a model server running on a different port, you can specify the port when constructing an lmql.model object:
python
lmql.model("gpt2", endpoint="localhost:9999")
 
Model Configuration To load a model with custom quantization preferences or other Transformers arguments, you can specify additional arguments when running the lmql serve-model command. For this, you can provide arbitrary arguments that will directly be passed to the underyling AutoModelForCausalLM.from_pretrained(...) function, as documented in the Transformers documentation.
For example, to set trust_remote_code to True with the from_pretrained function, run the following:
bash
lmql serve-model gpt2-medium --cuda --port 9999 --trust_remote_code True
 
Alternatively, you can also start to serve a model directly from within a Python environment, by running lmql.serve("gpt2-medium", cuda=True, port=9999, trust_remote_code=True). Just as with the CLI, standard transformers arguments are passed through, to the AutoModel.from_pretrained function.
In-Process Models ​
If you would like to load the model in-process, without having to execute a separate lmql serve-model command, you can do so by instantiating a custom lmql.model object with local: as part of the model name. For example, to load the gpt2-medium model in-process, run the following command:
python
argmax "Hello[WHO]" from lmql.model("local:gpt2")
 
Note however, that this will load the model on each restart of the LMQL process, which can incur a significant overhead.
If you want more control over model loading and configuration, you can pass additional arguments to lmql.model(...), as demonstrated below.
python
lmql.model("local:gpt2", cuda=True)
-
`,23),r=[l];function c(d,i,p,m,h,u){return o(),a("div",null,r)}const v=e(n,[["render",c]]);export{f as __pageData,v as default};
+

Quantization ​

Quantization reduces the precision of model parameters to shrink model size and boost inference speed with minimal accuracy loss. LMQL supports two quantization formats: AWQ (using AutoAWQ) and GPTQ (using AutoGPTQ).

AutoAWQ ​

AWQ minimizes quantization error by protecting crucial weights, promoting model efficiency without sacrificing accuracy. It's ideal for scenarios requiring both compression and acceleration of LLMs.

Install AutoAWQ following the repo instructions. To use AWQ-quantized models, run:

To use AWQ-quantized models, first install AutoAWQ using the instructions in the repo.

bash
lmql serve-model TheBloke/Mistral-7B-OpenOrca-AWQ --loader awq
+

AutoGPTQ ​

AutoGPTQ reduces model size while retaining performance by lowering the precision of model weights to 4 or 3 bits. It's suitable for efficient deployment and operation of LLMs on consumer-grade hardware.

Install AutoGPTQ following the repo instructions. To use GPTQ-quantized models, run:

bash
lmql serve-model TheBloke/Arithmo-Mistral-7B-GPTQ --loader gptq
+
`,34),l=[r];function i(c,d,p,u,h,m){return o(),a("div",null,l)}const b=e(n,[["render",i]]);export{f as __pageData,b as default}; diff --git a/assets/docs_latest_models_hf.md.f6d5ed07.lean.js b/assets/docs_latest_models_hf.md.db4e71b2.lean.js similarity index 68% rename from assets/docs_latest_models_hf.md.f6d5ed07.lean.js rename to assets/docs_latest_models_hf.md.db4e71b2.lean.js index 77fa7893..b8e8c7da 100644 --- a/assets/docs_latest_models_hf.md.f6d5ed07.lean.js +++ b/assets/docs_latest_models_hf.md.db4e71b2.lean.js @@ -1 +1 @@ -import{_ as e,o,c as a,Q as t}from"./chunks/framework.980cae92.js";const s="/assets/inference.f47a6f3e.svg",f=JSON.parse('{"title":"Local Models / Transformers","description":"","frontmatter":{"order":1},"headers":[],"relativePath":"docs/latest/models/hf.md","filePath":"docs/latest/models/hf.md"}'),n={name:"docs/latest/models/hf.md"},l=t("",23),r=[l];function c(d,i,p,m,h,u){return o(),a("div",null,r)}const v=e(n,[["render",c]]);export{f as __pageData,v as default}; +import{_ as e,o,c as a,Q as t}from"./chunks/framework.980cae92.js";const s="/assets/inference.f47a6f3e.svg",f=JSON.parse('{"title":"Local Models / Transformers","description":"","frontmatter":{"order":1},"headers":[],"relativePath":"docs/latest/models/hf.md","filePath":"docs/latest/models/hf.md"}'),n={name:"docs/latest/models/hf.md"},r=t("",34),l=[r];function i(c,d,p,u,h,m){return o(),a("div",null,l)}const b=e(n,[["render",i]]);export{f as __pageData,b as default}; diff --git a/blog/index.html b/blog/index.html index 4694fcc0..c47af8fe 100644 --- a/blog/index.html +++ b/blog/index.html @@ -530,7 +530,7 @@

Preview
- + \ No newline at end of file diff --git a/blog/posts/release-0.0.5.html b/blog/posts/release-0.0.5.html index d0c1c1b1..416bdf83 100644 --- a/blog/posts/release-0.0.5.html +++ b/blog/posts/release-0.0.5.html @@ -29,7 +29,7 @@ where INT(NUM)

  • Core Interpreter A complete reimplementation of the LMQL core interpreter has been completed. This fixes a couple of minor issues and overall, improves reliability and performance when dealing with branching decoding algorithms. #24.

  • Playground Locally and when used in-browser, the LMQL Playground now streams debugger information from the LMQL interpreter incrementally. This leads to speed-ups when running in the Playground, especially with longer outputs. #27f9a8ad.

  • Other Fixes:

    • When used from within Python (as decorated function), LMQL code no longer has to be doubly-escaped, e.g. you can now write STOPS_AT(VAR, "\n") instead of STOPS_AT(VAR, "\\n")
    • The LMQL inference API buffers requests that come in during startup, to avoid errors when the server is not yet ready. #15, thanks to @chrispan.
    • OpenAI request parallelization no longer leads to an error on Linux systems, with regards to worker processes #6.

    • Preview ​

    Apart from the changes above, we are also working on a number of other features, including:

    - + \ No newline at end of file diff --git a/blog/posts/release-0.0.6.1.html b/blog/posts/release-0.0.6.1.html index 5cd5ca34..efad2e76 100644 --- a/blog/posts/release-0.0.6.1.html +++ b/blog/posts/release-0.0.6.1.html @@ -21,7 +21,7 @@
    LMQL

    Appearance

    LMQL v0.0.6.1 ​

    May 3, 2023

    We released LMQL v0.0.6.1, which contains several bug fixes and improvements. The most notable changes are:

    • Cache Layer Bug Fixes This release contains several fixes and improvements to the recently introduced cache layer.

    • Stopping Phrases Stopping phrases specified via STOPS_BEFORE are now passed to the OpenAI API as "stop" parameter, decreasing the number of tokens used for the request. If you want to disable this (e.g. to allow speculative execution), you can specify the new decoder parameter openai_nonstop=True.

    • Asynchronous Output Writers All output writers have been refactored to use asynchronous I/O. This should simplify integration with other asynchronous frameworks, e.g. for HTTP or Websocket APIs. We also added a new chapter on Output Streaming to the documentation.

    • Output Writers for HTTP endpoints, WebSockets and Server-Sent Events Based on the updated output writer interface, we added three new output writers for serving LMQL queries as HTTP endpoints, WebSockets and via Server-Sent Events (SSE). To learn more, check their relatively simple implementations in the new lmql.output module. We will also provide more documentation on how to use them, e.g. with aiohttp in the future.

    - + \ No newline at end of file diff --git a/blog/posts/release-0.0.6.3.html b/blog/posts/release-0.0.6.3.html index b2fe9acb..13dafcc7 100644 --- a/blog/posts/release-0.0.6.3.html +++ b/blog/posts/release-0.0.6.3.html @@ -33,7 +33,7 @@ where len(TOKENS(WHO)) > 10 and STOPS_AT(WHO, "\n")

  • lmql.run: Improved input validation for lmql.run as contributed by @lfegray. More specifically, lmql.run wil now provide more helpful error messages when client logic does not specify input values for all required query parameters.

  • Automatic Cache Invalidation: LMQL's tokenizer cache at ~/.cache/lmql is now invalidated automatically when upgrading to a new version. This should prevent issues with outdated cache files.

    • Note: Version 0.0.6.2 was skipped and yanked from pypi.org, as an invalid release was pushed accidentally.

    - + \ No newline at end of file diff --git a/blog/posts/release-0.0.6.4.html b/blog/posts/release-0.0.6.4.html index 904421c1..96ae7aed 100644 --- a/blog/posts/release-0.0.6.4.html +++ b/blog/posts/release-0.0.6.4.html @@ -34,7 +34,7 @@ print(hello("world")) # ['Hello! How can I assist you today?']

    If you instead want to use lmql.run in a synchronous context, you can now use lmql.run_sync instead. To learn more about how LMQL can be used from Python, check out our documentation.

  • Improved Tokenizer Backends LMQL can now use the excellent tiktoken tokenizer as tokenization backend (for OpenAI models). Furthermore, all tokenization backends have been ported to operate on a byte-level, which improves support for multibyte characters and emojis. This is especially relevant for non-English languages and special characters.

  • Docker Image LMQL now provides a Docker image that can be used to run the LMQL playground in a containerized environment. For more information, please see the documentation. Many thanks to @SilacciA for contributing this feature.

  • Faster Startup Time We optimized LMQL's import hierarchy, which results in faster module loading time.

    • - + \ No newline at end of file diff --git a/blog/posts/release-0.0.6.5.html b/blog/posts/release-0.0.6.5.html index 1feda653..b0620a6e 100644 --- a/blog/posts/release-0.0.6.5.html +++ b/blog/posts/release-0.0.6.5.html @@ -84,7 +84,7 @@

    llama.cpp Inference Backend ​

    LMQL now also fully integrates with the excellent llama.cpp C++ implementation of a number of Transformer-based language models.

    Using llama.cpp from LMQL is as simple as specifying it in the from clause of a query:

    argmax "Say 'this is a test':[RESPONSE]" from "llama.cpp:<PATH TO WEIGHTS>.bin"

    We support, both, in-process loading of llama.cpp, as well as remote inference via lmql serve-model. To learn more about llama.cpp and how to use it with LMQL, check out the corresponding chapter in the LMQL documentation.


    Other Changes ​

    We thank community members @minosvasilias and @CircArgs for their contribution to this release.

    - + \ No newline at end of file diff --git a/blog/posts/release-0.0.6.6.html b/blog/posts/release-0.0.6.6.html index 36b1faa8..e2be901b 100644 --- a/blog/posts/release-0.0.6.6.html +++ b/blog/posts/release-0.0.6.6.html @@ -27,7 +27,7 @@ # call with keyword arguments greet(a="Alice", b="Bob") # Greet Alice and Bob: Hello!

    Versioning Note: 0.0.6.6 is the last release with two leading zeros. Starting with the next release, LMQL will adopt semantic versioning and use a single leading zero, i.e. 0.6.7.

    - + \ No newline at end of file diff --git a/blog/posts/release-0.0.6.html b/blog/posts/release-0.0.6.html index 7d8b45b2..3c794fab 100644 --- a/blog/posts/release-0.0.6.html +++ b/blog/posts/release-0.0.6.html @@ -54,7 +54,7 @@ STOPS_AT(PUNCHLINE, "\n") and len(PUNCHLINE) > 1

    The first successful run of this query will persist the cache to joke.tokens. Subsequent runs will then automatically load the cache from disk, and only invoke the LLM if the cache does not contain a match. This also works for queries whose underlying LLM requests only partially overlap, as the tree-based cache data structure will automatically identify matching subtrees.

    Caching During Query Development: Persisting the cache can be particularly useful during query development, as it allows you to reuse the cache across multiple runs of the same query. A persistent cache will reduce token cost and latency of your query, even if you slightly change the query between runs.

    Caveats and Disabling the Cache ​

    You can disable the caching layer by specifying cache=False in your query code. This will cause the LMQL runtime to always invoke the LLM, and never use the cache. This is useful for debugging purposes, or if you want to ensure that the LLM is always invoked.

    Further, as the cache currently is implemented as an append-only data structure, it will grow indefinitely. This may be problematic for long-running applications, as the cache will eventually grow to relatively large sizes. In the future, we plan to implement simple strategies to limit the cache size, such as a least-recently-used eviction policy.

    Conclusion ​

    In this post, we introduced the new caching layer of the LMQL runtime, which allows you to reduce the token cost and latency of your queries by reusing previously generated LLM outputs. We demonstrated how the caching layer can be used to reduce the number of LLM invocations in a variety of scenarios, including long constraints, short-circuiting, and tool-augmented queries. We also showed how the cache can be persisted to disk, allowing you to reuse the cache across multiple queries.

    To learn more about LMQL please also check out our documentation, or join our Discord to chat with us directly. We are looking forward to hearing from you!

    - + \ No newline at end of file diff --git a/blog/posts/release-0.7.html b/blog/posts/release-0.7.html index 97b9ac2f..4630aea1 100644 --- a/blog/posts/release-0.7.html +++ b/blog/posts/release-0.7.html @@ -69,7 +69,7 @@ PERSON_DATA # Person(name='Alice', age=21, job='engineer')

    To achieve this, LMQL leverages constrained generation to make sure the LLM always produces all information required to populate a valid Person object. The resulting PERSON_DATA object can then be directly used like a regular Python object. Types are still in an early stage and we are working on adding more features and functionality.

    Other Changes ​

    🎬 And that's a wrap! ​

    LMQL 0.7 is a big release and we are excited to see what you will build with it. As always, please let us know if you have any questions, suggestions or bug reports, on GitHub, Discord, Twitter or via hello@lmql.ai.

    - + \ No newline at end of file diff --git a/docs/development/dev-setup.html b/docs/development/dev-setup.html index ddcdc583..36144cc2 100644 --- a/docs/development/dev-setup.html +++ b/docs/development/dev-setup.html @@ -33,7 +33,7 @@ # registers the `lmql` command in the current shell source scripts/activate-dev.sh

    With Nix ​

    If you have Nix installed, this can be used to invoke LMQL, even if you don't have any of its dependencies previously installed! We try to test Nix support on ARM-based MacOS and Intel-based Linux; bugfixes and contributions for other targets are welcome.

    Most targets within the flake have several variants:

    In all of these cases, github:eth-sri/lmql may be replaced with a local filesystem path; so if you're inside a checked-out copy of the LMQL source tree, you can use nix run .#playground to run the playground/debugger from that tree.

    - + \ No newline at end of file diff --git a/docs/development/docker-setup.html b/docs/development/docker-setup.html index f57948a9..c522a57a 100644 --- a/docs/development/docker-setup.html +++ b/docs/development/docker-setup.html @@ -27,7 +27,7 @@

    Otherwise, if you want to use the api.env file you can also mount the file as follow:

    docker run -d -v $(PWD)/api.env:/lmql/.lmql/api.env -p 3000:3000 -p 3004:3004 lmql-docker:latest

    Starting a container with GPU and local models ​

    Make sure you have followed the image building step from the section Using GPU and local models. To start the docker container with access to the GPUs consider using the following command:

    docker run --gpus all -d -p 3000:3000 -p 3004:3004 lmql-docker:cuda11.8

    Where all means that you allocate all the GPUs to the docker container.

    Note that here we expose the port 3000 and 3004 from the container to the port 3000 and 3004 from your machine. And we reuse the name lmql-docker:cuda11.8 as it is the value we previously used to build the image.

    - + \ No newline at end of file diff --git a/docs/development/documentation.html b/docs/development/documentation.html index 90ee606e..5650157e 100644 --- a/docs/development/documentation.html +++ b/docs/development/documentation.html @@ -27,7 +27,7 @@ # run hot-reloading live server yarn docs:dev - + \ No newline at end of file diff --git a/docs/index.html b/docs/index.html index 1cb090c8..db6b985d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -34,7 +34,7 @@ else: "Good job"

    Going beyond what we have seen so far, this LMQL program extends on the above in a few ways:

    3. Enjoy ​

    These basic steps should get you started with LMQL. If you need more inspiration before writing your own queries, you can explore the examples included with the Playground IDE or showcased on the LMQL Website.

    If you have any questions and or requests for documentation, please feel free to reach out to us via our Community Discord, GitHub Issues, or Twitter.

    - + \ No newline at end of file diff --git a/docs/installation.html b/docs/installation.html index 892e0813..67af9abb 100644 --- a/docs/installation.html +++ b/docs/installation.html @@ -25,7 +25,7 @@

    INFO

    Using the LMQL playground requires an installation of Node.js. If you are in a conda-managed environment you can install node.js via conda install nodejs=14.20 -c conda-forge. Otherwise, please see the official Node.js website for instructions on how to install it on your system.

    This launches a browser-based Playground IDE, including a showcase of many exemplary LMQL programs. If the IDE does not launch automatically, go to http://localhost:3000.

    Command-Line Interface

    As an alternative to the playground, the command-line tool lmql run can be used to execute local .lmql files.

    Python Integration

    LMQL can also be used directly from within Python. To use LMQL in Python, you can import the lmql package, run query code via lmql.run or use a decorator @lmql.query for LMQL query functions.

    For more details, please see the Python Integration chapter.

    Self-Hosted Models ​

    Note that when using local 🤗 Transformers models in the Playground IDE or via lmql run, you have to first launch an instance of the LMQL Inference API for the corresponding model via the command lmql serve-model.

    For more details, please see 🤗 Models chapter.

    Configuring OpenAI API Credentials ​

    If you want to use OpenAI models, you have to configure your API credentials. To do so you can either define the OPENAI_API_KEY environment variable or create a file api.env in the active working directory, with the following contents.

    openai-org: <org identifier>
 openai-secret: <api secret>

    For system-wide configuration, you can also create an api.env file at $HOME/.lmql/api.env or at the project root of your LMQL distribution (e.g. src/ in a development copy).

    - + \ No newline at end of file diff --git a/docs/language/constraints.html b/docs/language/constraints.html index 12d64134..15a97abe 100644 --- a/docs/language/constraints.html +++ b/docs/language/constraints.html @@ -98,7 +98,7 @@ For some, life begins with a loving family and a comfortable home, while for others it may start with struggle and hardship.

    Here, we enforce a stopping condition on the . character, but only once the generated story is at least 40 tokens long. Thus, the interpretation of and with stopping conditions is that the stopping condition is only enforced once the other constraints are satisfied.

    How Do LMQL Constraints Work? ​

    Token Masking and Eager Validation LMQL constraints are evaluated eagerly on each generated token, and will be used by the runtime to generate token masks during generation. This means, that the provided constraints are either satisfied by directly guiding the model during generation appropriately or, if this is not possible, validation will fail early on during generation, saving the cost of generating invalid output. In case of greedy decoding (sample or argmax), this terminates the generation process, in case of branching decoding, it will prune the current branch and continue generation only in the remaining, valid branches.

    High-Level Text Constraints Constraints are high-level and operate on a text (not token) level. For instance, users can specify constraints like VAR in ["Hello", "Hi", "Greetings"], without having to consider the exact tokenization of the individual phrases. LMQL will automatically translate these constraints to token masks, that can be used to guide the model during generation, allowing to generate output that satisfies the provided constraint using one generation call only.

    Custom Constraints and Theoretical Background ​

    To learn more about the internals of LMQL and how to implement your own LMQL constraint, see the chapter on Implementing Custom LMQL Constraints.

    - + \ No newline at end of file diff --git a/docs/language/constraints/custom-constraints.html b/docs/language/constraints/custom-constraints.html index f593ca21..b8382d68 100644 --- a/docs/language/constraints/custom-constraints.html +++ b/docs/language/constraints/custom-constraints.html @@ -82,7 +82,7 @@ return "fin" if x == "fin" else "var"

    forward() implementation: First, we implement the forward method: To validate the foo-bar property, we check that any string segment following a potential foo substring aligns with " bar". For this, we have to consider the case of x being none (not yet generated), a partial match (including empty strings), and a match that extends beyond " bar". Depending on model tokenization forward() may be called on any such variation, and thus has to be able to handle all of them.

    follow() implementation: Next, we implement the follow method. For this, we again consider multiple cases:

    1. If x is not yet generated, we do not have to restrict the next token.

    2. If there is no foo in x, we do not have to restrict the next token.

    3. If there is a foo right at the end of x, we restrict as follows:

      (a) If the segment already starts with " bar", no restrictions are necessary.

      (b) Otherwise, we restrict the next token to " bar". For this, we construct a so-called follow map, a mapping of token ranges to the future evaluation result of our operator, if the next token is in the specified range.

      In our foo-bar case it suffices to indicate that a continuation of bar evaluates to True, and any other continuation to False. This is achieved by the fmap function, which constructs a follow map from a list of token ranges and their respective future evaluation results. To construct token ranges the tset constructor can be used, which allows to select tokens by length, set, prefix or regex, independent from the concrete tokenizer in use. In this case we use the regex=True option, to automatically select all tokens that fully or partially match " bar".

    final() implementation: Lastly, we implement the final method. This indicates to the LMQL runtime, whether a result of our custom operator is final with respect to the current model output or temporary. In this case, a return value of False can always be considered final (a definitive violation, warranting early termination). Otherwise, we have to consider the definitiveness of the current value of x. If x is final, then the result of our operator is also final. Otherwise, it is temporary, as a further continuation of x may still result in satisfying the constraint.

    Note: For illustrative purposes, 3b of our follow() implementation simplifies an important detail about token alignment. It only consider the case, where follow() is called right at the end of "foo", i.e. depending on model behavior and tokenization, follow() may also run on a partial result like "foo b", where the correct follow map should indicate "ar" as valid continuation not the full " bar". To handle this, one can simply rely on the implementation of built-in InOpStrInSet (implementation of constraint VAR in [...]), and replace the fmap call with InOpStrInSet([]).follow(bar_segment, [" bar"]), which will automatically handle all such cases.

    Using the Custom Constraint Operator ​

    To use the custom constraint operator, you can simply import it in your query context and use it as follows:

    "Say 'foo':[A]" where foo_bar(A)

    In general, you have to ensure that the @LMQLOp decorator is executed in your current process before the query is parsed, e.g. by importing the module containing the operator implementation.

    What Happens Under the Hood? ​

    Given an operator implementation as above, the LMQL runtime will be able to both validate model output during generation and derive token-level prediction masks. For this, forward, follow and final are called repeatedly during generation, with the current model output x as input. This allows the runtime to derive both validity of the current model output, as well as next-token ranges for which the operator definitively (final) evaluates to False. Based on the correctness of the underlying implementation, this soundly ensures that the model will never select a follow-masked token that would definitively violate your constraint.

    Expressiveness of LMQL Constraints ​

    LMQL constraints are applied eagerly during generation by relying on token masking. This means, that the model will not be able to generate any tokens that are masked by the constraints. However, naturally, this approach is limited with respects to expressiveness, since not all properties on text can be decided on a token-by-token basis. More specifically, expressiveness is limited to the validation of context-free languages. To enable safe use of token masking, LMQL's implementation of final/follow semantics provide a soundness guarantee with respect to token masking (see LMQL paper).

    Nonetheless, due to eager evaluation of constraints during generation, LMQL constraints will trigger as soon as the model output violates the constraint definitively (i.e. the validation result is final), preventing the model from the costly generation of invalid output. This is an advantage over validation in post-processing, where violations may only be detected after the model has already generated a large amount of invalid output.

    - + \ No newline at end of file diff --git a/docs/language/decoding.html b/docs/language/decoding.html index 3c5e8338..f2891c85 100644 --- a/docs/language/decoding.html +++ b/docs/language/decoding.html @@ -39,7 +39,7 @@ tell_a_joke() # uses the decoder specified in @lmql.query(...) tell_a_joke(decoder="beam", n=2) # uses a beam search decoder with n=2

    This is only possible when using LMQL from a Python context.

    Decoding Algorithms ​

    In general, the very first keyword of an LMQL query, specifies the decoding algorithm to use. For this, the following decoder keywords are available:

    argmax ​

    The argmax decoder is the simplest decoder available in LMQL. It greedily selects the most likely token at each step of the decoding process. It has no additional parameters. Since argmax decoding is deterministic, one can only generate a single sequence at a time.

    sample(n: int, temperature: float) ​

    The sample decoder samples n sequences in parallel from the model. The temperature parameter controls the randomness of the sampling process. Higher values of temperature lead to more random samples, while lower values lead to more likely samples. A temperature value of 0.0 is equivalent to the argmax decoder.

    beam(n: int) ​

    A simple beam search decoder. The n parameter controls the beam size. The beam search decoder is deterministic, so it will generate the same n sequences every time. The result of a beam query is a list of n sequences, sorted by their likelihood.

    beam_sample(n: int, temperature: float) ​

    A beam search decoder that samples from the beam at each step. The n parameter controls the beam size, while the temperature parameter controls the randomness of the sampling process. The result of a beam_sample query is a list of n sequences, sorted by their likelihood.

    Novel Decoders ​

    LMQL also implements a number of novel decoders. These decoders are experimental and may not work as expected. They are also not guaranteed to be stable across different LMQL versions. More documentation on these decoders will be provided in the future.

    var(b: int, n: int) ​

    An experimental implementation of variable-level beam search.

    beam_var(n: int) ​

    An experimental implementation of a beam search procedure that groups by currently-decoded variable and applies adjusted length penalties.

    Inspecting Decoding Trees ​

    LMQL also provides a way to inspect the decoding trees generated by the decoders. For this, make sure to execute the query in the Playground IDE and click on the Advanced Mode button, in the top right corner of the Playground. This will open a new pane, where you can navigate and inspect the LMQL decoding tree:

    A decoding tree as visualized in the LMQL Playground.
    A decoding tree as visualized in the LMQL Playground.

    This view allows you to track the decoding process, active hypotheses and interpreter state, including the current evaluation result of the where clause. For an example, take a look at the translation example in the Playground (with Advanced Mode enabled).

    Writing Custom Decoders ​

    LMQL also includes a library for array-based decoding dclib, which can be used to implement custom decoders. More information on this, will be provided in the future. The implementation of the available decoding procedures is located in src/lmql/runtime/dclib/decoders.py of the LMQL repository.

    Additional Decoding Parameters ​

    Next to the decoding algorithm, LMQL also supports a number of additional decoding parameters, which can affect sampling behavior and token scoring:

    max_len: intThe maximum length of the generated sequence. If not specified, the default value of max_len is 2048. Note if the maximum length is reached, the LMQL runtime will throw an error if the query has not yet come to a valid result, according to the provided where clause.
    top_k: intRestricts the number of tokens to sample from in each step of the decoding process, based on Fan et. al(2018) (only applicable for sampling decoders).
    top_p: floatTop-p (nucleus) sampling, based on Holtzman et. al(2019) (only applicable for sampling decoders).
    repetition_penalty: floatRepetition penalty, 1.0 means no penalty, based on Keskar et. al(2019). The more a token is already present in the generated sequence, the more its probability will be penalized.
    frequency_penalty: floatfrequency_penalty as documented as part of the OpenAI API.
    presence_penalty: floatpresence_penalty as documented as part of the OpenAI API.

    TIP

    Note that the concrete implementation and availability of additional decoding parameters may vary across different inference backends. For reference, please see the API documentation of the respective inference interface, e.g. the HuggingFace generate() function or the OpenAI API.

    Runtime Parameters ​

    Lastly, a number of additional runtime parameters are available, which can be used to control auxiliary aspects of the decoding process:

    chunksize: intThe chunksize parameter used for max_tokens in OpenAI API requests or in speculative inference with local models. If not specified, the default value of chunksize is 32. See also the description of this parameter in the Models chapter.
    verbose: boolEnables verbose console logging for individual LLM inference calls (local generation calls or OpenAI API request payloads).
    cache: Union[bool,str]True or False to enable in-memory token caching. If not specified, the default value of cache is True, indicating in-memory caching is enabled.

    Setting cache to a string value, specifies a local file to use for disk-based caching, enabling caching across multiple query executions and sessions.
    openai_nonstopExperimental option for OpenAI-specific non-stop generation, which can further improve the effectiveness of caching in some scenarios.
    chunk_timeoutOpenAI-specific maximum time in seconds to wait for the next chunk of tokens to arrive. If exceeded, the current API request will be retried with an approriate backoff.

    If not specified, the default value of chunk_timeout is 2.5. Adjust this parameter, if you are seeing a high number of timeouts in the console output of the LMQL runtime.
    - + \ No newline at end of file diff --git a/docs/language/decorators.html b/docs/language/decorators.html index 6e6a216a..09c9d11b 100644 --- a/docs/language/decorators.html +++ b/docs/language/decorators.html @@ -83,7 +83,7 @@ Say 'this is a test': [TEST PREFIX: This is a test] - + \ No newline at end of file diff --git a/docs/language/nestedqueries.html b/docs/language/nestedqueries.html index e6fd39d0..21d64294 100644 --- a/docs/language/nestedqueries.html +++ b/docs/language/nestedqueries.html @@ -100,7 +100,7 @@ 100 days ago? [ANSWER: dateformat]" '''

    Here, main_query references dateformat as a nested query, where both functions are defined on the top level of the same file. However, you can also import and reuse query code from other files, as long as they are accessible from the scope of you main query function. Using this ability you can write libraries of reusable query functions to be used across your application or even by other users.

    - + \ No newline at end of file diff --git a/docs/language/overview.html b/docs/language/overview.html index 713a88a2..ddfa34c9 100644 --- a/docs/language/overview.html +++ b/docs/language/overview.html @@ -116,7 +116,7 @@ What is it that they liked about their stay? FURTHER_ANALYSISThe reviewer liked the hiking in the mountains and the food.

    As shown here, we can use the if statement to dynamically react to the model's output. In this case, we ask the model to provide a more detailed analysis of the review, depending on the overall positive, neutral, or negative sentiment of the review. All intermediate variables like ANALYSIS, CLASSIFICATION or FURTHER_ANALYSIS can be considered the output of query, and may be processed by an surrounding automated system.

    To learn more about the capabilities of such control-flow-guided prompts, see Scripted Prompting.

    As shown here, in addition to inline where expressions as seen earlier, you can also provide a global where expression at the end of your program, e.g. to specify constraints that should apply for all variables. Depending on your use case, this can be a convenient way to avoid having to repeat the same constraints multiple times, like for FURTHER_ANALYSIS in this example.

    - + \ No newline at end of file diff --git a/docs/language/reference.html b/docs/language/reference.html index bbfc94f7..6fa06750 100644 --- a/docs/language/reference.html +++ b/docs/language/reference.html @@ -163,7 +163,7 @@ "Greet {person}. Hello [NAME]!" '''

    From within Python, the same syntax can used to construct Python-callable query functions. Please see to the documentation chapter on Python Integration for more information.

    LMQL query function can also be declared as async functions, which enables asynchronous execution.

    Function Calling and Arguments ​

    A query function can be called as a standard function from within LMQL or Python code. It can also be called as a nested query from within a query string. async query functions require the await keyword to be used.

    Arguments In addition to the function arguments specified in the function signature, query functions also provide the following additional arguments, that can be used to control the generation process:

    Reference Implementation ​

    LMQL's current reference implementation is written in Python and also available as a Python library. The reference implementation of the syntax and semantics described in this document is available via Git at github.com/eth-sri/lmql.

    Compiler and Runtime ​

    The LMQL Python compiler translates LMQL programs into asynchroneous, brancheable Python code according to the semantics described in this document. The resulting program is then executed using the LMQL runtime, which implements (constrained) decoding algorithms, optimizations and model support via several backends.

    Hybrid Parser ​

    For parsing, the implementation leverages a hybrid approach, largely relying on the existing Python parser (ast.parse) and grammar, adding additional parsing logic only for LMQL-specific constructs. This approach allows us to be compliant with the Python grammar, while also allowing us to extend the language with additional constructs, that are not part of the original Python grammar. To parse the standalone syntax, we segment the input on a token level and then call the parser several times to obtain the final AST for e.g. the prompt clause, the where clause or the distribution clause.

    - + \ No newline at end of file diff --git a/docs/language/scripted-prompting.html b/docs/language/scripted-prompting.html index dc21f165..ea572555 100644 --- a/docs/language/scripted-prompting.html +++ b/docs/language/scripted-prompting.html @@ -119,7 +119,7 @@

    Python Compatibility ​

    Going beyond simple control flow, LMQL also supports most valid Python constructs in the prompt clause of a query, where top-level strings like "-[THING]" are automatically interpreted as model input and template variables are assigned accordingly. For more advanced usage, see also the Tool Augmentation chapter.

    - + \ No newline at end of file diff --git a/docs/language/tools.html b/docs/language/tools.html index 9a44820e..91703268 100644 --- a/docs/language/tools.html +++ b/docs/language/tools.html @@ -207,7 +207,7 @@ `get('Alice') # result blue` Therefore at the end of the game, Alice has the OBJECT blue ball.

    As shown in the example above, the assign and get functions can be used to store and retrieve values in a simple key-value store. The model is merely instructed to make use of these functions in its reasoning. The query then implements logic to intercept any function use and insert the result of the function call into the reasoning. This allows the model to incorporate the state of the key-value store into its reasoning.

    - + \ No newline at end of file diff --git a/docs/latest/development/dev-setup.html b/docs/latest/development/dev-setup.html index cd826c13..a70041b1 100644 --- a/docs/latest/development/dev-setup.html +++ b/docs/latest/development/dev-setup.html @@ -33,7 +33,7 @@ # registers the `lmql` command in the current shell source scripts/activate-dev.sh

    With Nix ​

    If you have Nix installed, this can be used to invoke LMQL, even if you don't have any of its dependencies previously installed! We try to test Nix support on ARM-based MacOS and Intel-based Linux; bugfixes and contributions for other targets are welcome.

    Most targets within the flake have several variants:

    In all of these cases, github:eth-sri/lmql may be replaced with a local filesystem path; so if you're inside a checked-out copy of the LMQL source tree, you can use nix run .#playground to run the playground/debugger from that tree.

    - + \ No newline at end of file diff --git a/docs/latest/development/docker-setup.html b/docs/latest/development/docker-setup.html index 454e1d94..5013c87a 100644 --- a/docs/latest/development/docker-setup.html +++ b/docs/latest/development/docker-setup.html @@ -27,7 +27,7 @@

    Otherwise, if you want to use the api.env file you can also mount the file as follow:

    docker run -d -v $(PWD)/api.env:/lmql/.lmql/api.env -p 3000:3000 -p 3004:3004 lmql-docker:latest

    Starting a container with GPU and local models ​

    Make sure you have followed the image building step from the section Using GPU and local models. To start the docker container with access to the GPUs consider using the following command:

    docker run --gpus all -d -p 3000:3000 -p 3004:3004 lmql-docker:cuda11.8

    Where all means that you allocate all the GPUs to the docker container.

    Note that here we expose the port 3000 and 3004 from the container to the port 3000 and 3004 from your machine. And we reuse the name lmql-docker:cuda11.8 as it is the value we previously used to build the image.

    - + \ No newline at end of file diff --git a/docs/latest/development/documentation.html b/docs/latest/development/documentation.html index c4efacae..ac246d24 100644 --- a/docs/latest/development/documentation.html +++ b/docs/latest/development/documentation.html @@ -27,7 +27,7 @@ # run hot-reloading live server yarn docs:dev - + \ No newline at end of file diff --git a/docs/latest/index.html b/docs/latest/index.html index f4c9ba10..d52593db 100644 --- a/docs/latest/index.html +++ b/docs/latest/index.html @@ -34,7 +34,7 @@ else: "Good job"

    Going beyond what we have seen so far, this LMQL program extends on the above in a few ways:

    3. Enjoy ​

    These basic steps should get you started with LMQL. If you need more inspiration before writing your own queries, you can explore the examples included with the Playground IDE or showcased on the LMQL Website.

    If you have any questions and or requests for documentation, please feel free to reach out to us via our Community Discord, GitHub Issues, or Twitter.

    - + \ No newline at end of file diff --git a/docs/latest/installation.html b/docs/latest/installation.html index 30792898..0331d85c 100644 --- a/docs/latest/installation.html +++ b/docs/latest/installation.html @@ -25,7 +25,7 @@

    INFO

    Using the LMQL playground requires an installation of Node.js. If you are in a conda-managed environment you can install node.js via conda install nodejs=14.20 -c conda-forge. Otherwise, please see the official Node.js website for instructions on how to install it on your system.

    This launches a browser-based Playground IDE, including a showcase of many exemplary LMQL programs. If the IDE does not launch automatically, go to http://localhost:3000.

    Command-Line Interface

    As an alternative to the playground, the command-line tool lmql run can be used to execute local .lmql files.

    Python Integration

    LMQL can also be used directly from within Python. To use LMQL in Python, you can import the lmql package, run query code via lmql.run or use a decorator @lmql.query for LMQL query functions.

    For more details, please see the Python Integration chapter.

    Self-Hosted Models ​

    Note that when using local 🤗 Transformers models in the Playground IDE or via lmql run, you have to first launch an instance of the LMQL Inference API for the corresponding model via the command lmql serve-model.

    For more details, please see 🤗 Models chapter.

    Configuring OpenAI API Credentials ​

    If you want to use OpenAI models, you have to configure your API credentials. To do so you can either define the OPENAI_API_KEY environment variable or create a file api.env in the active working directory, with the following contents.

    openai-org: <org identifier>
 openai-secret: <api secret>

    For system-wide configuration, you can also create an api.env file at $HOME/.lmql/api.env or at the project root of your LMQL distribution (e.g. src/ in a development copy).

    - + \ No newline at end of file diff --git a/docs/latest/language/constraints.html b/docs/latest/language/constraints.html index eacadf1e..43a9c4b1 100644 --- a/docs/latest/language/constraints.html +++ b/docs/latest/language/constraints.html @@ -98,7 +98,7 @@ For some, life begins with a loving family and a comfortable home, while for others it may start with struggle and hardship.

    Here, we enforce a stopping condition on the . character, but only once the generated story is at least 40 tokens long. Thus, the interpretation of and with stopping conditions is that the stopping condition is only enforced once the other constraints are satisfied.

    How Do LMQL Constraints Work? ​

    Token Masking and Eager Validation LMQL constraints are evaluated eagerly on each generated token, and will be used by the runtime to generate token masks during generation. This means, that the provided constraints are either satisfied by directly guiding the model during generation appropriately or, if this is not possible, validation will fail early on during generation, saving the cost of generating invalid output. In case of greedy decoding (sample or argmax), this terminates the generation process, in case of branching decoding, it will prune the current branch and continue generation only in the remaining, valid branches.

    High-Level Text Constraints Constraints are high-level and operate on a text (not token) level. For instance, users can specify constraints like VAR in ["Hello", "Hi", "Greetings"], without having to consider the exact tokenization of the individual phrases. LMQL will automatically translate these constraints to token masks, that can be used to guide the model during generation, allowing to generate output that satisfies the provided constraint using one generation call only.

    Custom Constraints and Theoretical Background ​

    To learn more about the internals of LMQL and how to implement your own LMQL constraint, see the chapter on Implementing Custom LMQL Constraints.

    - + \ No newline at end of file diff --git a/docs/latest/language/constraints/custom-constraints.html b/docs/latest/language/constraints/custom-constraints.html index 52fcf93c..55f7e0c5 100644 --- a/docs/latest/language/constraints/custom-constraints.html +++ b/docs/latest/language/constraints/custom-constraints.html @@ -82,7 +82,7 @@ return "fin" if x == "fin" else "var"

    forward() implementation: First, we implement the forward method: To validate the foo-bar property, we check that any string segment following a potential foo substring aligns with " bar". For this, we have to consider the case of x being none (not yet generated), a partial match (including empty strings), and a match that extends beyond " bar". Depending on model tokenization forward() may be called on any such variation, and thus has to be able to handle all of them.

    follow() implementation: Next, we implement the follow method. For this, we again consider multiple cases:

    1. If x is not yet generated, we do not have to restrict the next token.

    2. If there is no foo in x, we do not have to restrict the next token.

    3. If there is a foo right at the end of x, we restrict as follows:

      (a) If the segment already starts with " bar", no restrictions are necessary.

      (b) Otherwise, we restrict the next token to " bar". For this, we construct a so-called follow map, a mapping of token ranges to the future evaluation result of our operator, if the next token is in the specified range.

      In our foo-bar case it suffices to indicate that a continuation of bar evaluates to True, and any other continuation to False. This is achieved by the fmap function, which constructs a follow map from a list of token ranges and their respective future evaluation results. To construct token ranges the tset constructor can be used, which allows to select tokens by length, set, prefix or regex, independent from the concrete tokenizer in use. In this case we use the regex=True option, to automatically select all tokens that fully or partially match " bar".

    final() implementation: Lastly, we implement the final method. This indicates to the LMQL runtime, whether a result of our custom operator is final with respect to the current model output or temporary. In this case, a return value of False can always be considered final (a definitive violation, warranting early termination). Otherwise, we have to consider the definitiveness of the current value of x. If x is final, then the result of our operator is also final. Otherwise, it is temporary, as a further continuation of x may still result in satisfying the constraint.

    Note: For illustrative purposes, 3b of our follow() implementation simplifies an important detail about token alignment. It only consider the case, where follow() is called right at the end of "foo", i.e. depending on model behavior and tokenization, follow() may also run on a partial result like "foo b", where the correct follow map should indicate "ar" as valid continuation not the full " bar". To handle this, one can simply rely on the implementation of built-in InOpStrInSet (implementation of constraint VAR in [...]), and replace the fmap call with InOpStrInSet([]).follow(bar_segment, [" bar"]), which will automatically handle all such cases.

    Using the Custom Constraint Operator ​

    To use the custom constraint operator, you can simply import it in your query context and use it as follows:

    "Say 'foo':[A]" where foo_bar(A)

    In general, you have to ensure that the @LMQLOp decorator is executed in your current process before the query is parsed, e.g. by importing the module containing the operator implementation.

    What Happens Under the Hood? ​

    Given an operator implementation as above, the LMQL runtime will be able to both validate model output during generation and derive token-level prediction masks. For this, forward, follow and final are called repeatedly during generation, with the current model output x as input. This allows the runtime to derive both validity of the current model output, as well as next-token ranges for which the operator definitively (final) evaluates to False. Based on the correctness of the underlying implementation, this soundly ensures that the model will never select a follow-masked token that would definitively violate your constraint.

    Expressiveness of LMQL Constraints ​

    LMQL constraints are applied eagerly during generation by relying on token masking. This means, that the model will not be able to generate any tokens that are masked by the constraints. However, naturally, this approach is limited with respects to expressiveness, since not all properties on text can be decided on a token-by-token basis. More specifically, expressiveness is limited to the validation of context-free languages. To enable safe use of token masking, LMQL's implementation of final/follow semantics provide a soundness guarantee with respect to token masking (see LMQL paper).

    Nonetheless, due to eager evaluation of constraints during generation, LMQL constraints will trigger as soon as the model output violates the constraint definitively (i.e. the validation result is final), preventing the model from the costly generation of invalid output. This is an advantage over validation in post-processing, where violations may only be detected after the model has already generated a large amount of invalid output.

    - + \ No newline at end of file diff --git a/docs/latest/language/decoding.html b/docs/latest/language/decoding.html index 35e5f01c..9e1ffe89 100644 --- a/docs/latest/language/decoding.html +++ b/docs/latest/language/decoding.html @@ -39,7 +39,7 @@ tell_a_joke() # uses the decoder specified in @lmql.query(...) tell_a_joke(decoder="beam", n=2) # uses a beam search decoder with n=2

    This is only possible when using LMQL from a Python context.

    Decoding Algorithms ​

    In general, the very first keyword of an LMQL query, specifies the decoding algorithm to use. For this, the following decoder keywords are available:

    argmax ​

    The argmax decoder is the simplest decoder available in LMQL. It greedily selects the most likely token at each step of the decoding process. It has no additional parameters. Since argmax decoding is deterministic, one can only generate a single sequence at a time.

    sample(n: int, temperature: float) ​

    The sample decoder samples n sequences in parallel from the model. The temperature parameter controls the randomness of the sampling process. Higher values of temperature lead to more random samples, while lower values lead to more likely samples. A temperature value of 0.0 is equivalent to the argmax decoder.

    beam(n: int) ​

    A simple beam search decoder. The n parameter controls the beam size. The beam search decoder is deterministic, so it will generate the same n sequences every time. The result of a beam query is a list of n sequences, sorted by their likelihood.

    beam_sample(n: int, temperature: float) ​

    A beam search decoder that samples from the beam at each step. The n parameter controls the beam size, while the temperature parameter controls the randomness of the sampling process. The result of a beam_sample query is a list of n sequences, sorted by their likelihood.

    Novel Decoders ​

    LMQL also implements a number of novel decoders. These decoders are experimental and may not work as expected. They are also not guaranteed to be stable across different LMQL versions. More documentation on these decoders will be provided in the future.

    var(b: int, n: int) ​

    An experimental implementation of variable-level beam search.

    beam_var(n: int) ​

    An experimental implementation of a beam search procedure that groups by currently-decoded variable and applies adjusted length penalties.

    Inspecting Decoding Trees ​

    LMQL also provides a way to inspect the decoding trees generated by the decoders. For this, make sure to execute the query in the Playground IDE and click on the Advanced Mode button, in the top right corner of the Playground. This will open a new pane, where you can navigate and inspect the LMQL decoding tree:

    A decoding tree as visualized in the LMQL Playground.
    A decoding tree as visualized in the LMQL Playground.

    This view allows you to track the decoding process, active hypotheses and interpreter state, including the current evaluation result of the where clause. For an example, take a look at the translation example in the Playground (with Advanced Mode enabled).

    Writing Custom Decoders ​

    LMQL also includes a library for array-based decoding dclib, which can be used to implement custom decoders. More information on this, will be provided in the future. The implementation of the available decoding procedures is located in src/lmql/runtime/dclib/decoders.py of the LMQL repository.

    Additional Decoding Parameters ​

    Next to the decoding algorithm, LMQL also supports a number of additional decoding parameters, which can affect sampling behavior and token scoring:

    max_len: intThe maximum length of the generated sequence. If not specified, the default value of max_len is 2048. Note if the maximum length is reached, the LMQL runtime will throw an error if the query has not yet come to a valid result, according to the provided where clause.
    top_k: intRestricts the number of tokens to sample from in each step of the decoding process, based on Fan et. al(2018) (only applicable for sampling decoders).
    top_p: floatTop-p (nucleus) sampling, based on Holtzman et. al(2019) (only applicable for sampling decoders).
    repetition_penalty: floatRepetition penalty, 1.0 means no penalty, based on Keskar et. al(2019). The more a token is already present in the generated sequence, the more its probability will be penalized.
    frequency_penalty: floatfrequency_penalty as documented as part of the OpenAI API.
    presence_penalty: floatpresence_penalty as documented as part of the OpenAI API.

    TIP

    Note that the concrete implementation and availability of additional decoding parameters may vary across different inference backends. For reference, please see the API documentation of the respective inference interface, e.g. the HuggingFace generate() function or the OpenAI API.

    Runtime Parameters ​

    Lastly, a number of additional runtime parameters are available, which can be used to control auxiliary aspects of the decoding process:

    chunksize: intThe chunksize parameter used for max_tokens in OpenAI API requests or in speculative inference with local models. If not specified, the default value of chunksize is 32. See also the description of this parameter in the Models chapter.
    verbose: boolEnables verbose console logging for individual LLM inference calls (local generation calls or OpenAI API request payloads).
    cache: Union[bool,str]True or False to enable in-memory token caching. If not specified, the default value of cache is True, indicating in-memory caching is enabled.

    Setting cache to a string value, specifies a local file to use for disk-based caching, enabling caching across multiple query executions and sessions.
    openai_nonstopExperimental option for OpenAI-specific non-stop generation, which can further improve the effectiveness of caching in some scenarios.
    chunk_timeoutOpenAI-specific maximum time in seconds to wait for the next chunk of tokens to arrive. If exceeded, the current API request will be retried with an approriate backoff.

    If not specified, the default value of chunk_timeout is 2.5. Adjust this parameter, if you are seeing a high number of timeouts in the console output of the LMQL runtime.
    - + \ No newline at end of file diff --git a/docs/latest/language/decorators.html b/docs/latest/language/decorators.html index 09cc093c..60c8d7d9 100644 --- a/docs/latest/language/decorators.html +++ b/docs/latest/language/decorators.html @@ -83,7 +83,7 @@ Say 'this is a test': [TEST PREFIX: This is a test] - + \ No newline at end of file diff --git a/docs/latest/language/nestedqueries.html b/docs/latest/language/nestedqueries.html index 50b68162..daeca21a 100644 --- a/docs/latest/language/nestedqueries.html +++ b/docs/latest/language/nestedqueries.html @@ -100,7 +100,7 @@ 100 days ago? [ANSWER: dateformat]" '''

    Here, main_query references dateformat as a nested query, where both functions are defined on the top level of the same file. However, you can also import and reuse query code from other files, as long as they are accessible from the scope of you main query function. Using this ability you can write libraries of reusable query functions to be used across your application or even by other users.

    - + \ No newline at end of file diff --git a/docs/latest/language/overview.html b/docs/latest/language/overview.html index 8b5cee54..a8822370 100644 --- a/docs/latest/language/overview.html +++ b/docs/latest/language/overview.html @@ -116,7 +116,7 @@ What is it that they liked about their stay? FURTHER_ANALYSISThe reviewer liked the hiking in the mountains and the food.

    As shown here, we can use the if statement to dynamically react to the model's output. In this case, we ask the model to provide a more detailed analysis of the review, depending on the overall positive, neutral, or negative sentiment of the review. All intermediate variables like ANALYSIS, CLASSIFICATION or FURTHER_ANALYSIS can be considered the output of query, and may be processed by an surrounding automated system.

    To learn more about the capabilities of such control-flow-guided prompts, see Scripted Prompting.

    As shown here, in addition to inline where expressions as seen earlier, you can also provide a global where expression at the end of your program, e.g. to specify constraints that should apply for all variables. Depending on your use case, this can be a convenient way to avoid having to repeat the same constraints multiple times, like for FURTHER_ANALYSIS in this example.

    - + \ No newline at end of file diff --git a/docs/latest/language/reference.html b/docs/latest/language/reference.html index 4e6e3b80..15d04284 100644 --- a/docs/latest/language/reference.html +++ b/docs/latest/language/reference.html @@ -163,7 +163,7 @@ "Greet {person}. Hello [NAME]!" '''

    From within Python, the same syntax can used to construct Python-callable query functions. Please see to the documentation chapter on Python Integration for more information.

    LMQL query function can also be declared as async functions, which enables asynchronous execution.

    Function Calling and Arguments ​

    A query function can be called as a standard function from within LMQL or Python code. It can also be called as a nested query from within a query string. async query functions require the await keyword to be used.

    Arguments In addition to the function arguments specified in the function signature, query functions also provide the following additional arguments, that can be used to control the generation process:

    Reference Implementation ​

    LMQL's current reference implementation is written in Python and also available as a Python library. The reference implementation of the syntax and semantics described in this document is available via Git at github.com/eth-sri/lmql.

    Compiler and Runtime ​

    The LMQL Python compiler translates LMQL programs into asynchroneous, brancheable Python code according to the semantics described in this document. The resulting program is then executed using the LMQL runtime, which implements (constrained) decoding algorithms, optimizations and model support via several backends.

    Hybrid Parser ​

    For parsing, the implementation leverages a hybrid approach, largely relying on the existing Python parser (ast.parse) and grammar, adding additional parsing logic only for LMQL-specific constructs. This approach allows us to be compliant with the Python grammar, while also allowing us to extend the language with additional constructs, that are not part of the original Python grammar. To parse the standalone syntax, we segment the input on a token level and then call the parser several times to obtain the final AST for e.g. the prompt clause, the where clause or the distribution clause.

    - + \ No newline at end of file diff --git a/docs/latest/language/scripted-prompting.html b/docs/latest/language/scripted-prompting.html index dad5ca08..64f51934 100644 --- a/docs/latest/language/scripted-prompting.html +++ b/docs/latest/language/scripted-prompting.html @@ -119,7 +119,7 @@

    Python Compatibility ​

    Going beyond simple control flow, LMQL also supports most valid Python constructs in the prompt clause of a query, where top-level strings like "-[THING]" are automatically interpreted as model input and template variables are assigned accordingly. For more advanced usage, see also the Tool Augmentation chapter.

    - + \ No newline at end of file diff --git a/docs/latest/language/tools.html b/docs/latest/language/tools.html index 8dc8aa7e..54a30259 100644 --- a/docs/latest/language/tools.html +++ b/docs/latest/language/tools.html @@ -207,7 +207,7 @@ `get('Alice') # result blue` Therefore at the end of the game, Alice has the OBJECT blue ball.

    As shown in the example above, the assign and get functions can be used to store and retrieve values in a simple key-value store. The model is merely instructed to make use of these functions in its reasoning. The query then implements logic to intercept any function use and insert the result of the function call into the reasoning. This allows the model to incorporate the state of the key-value store into its reasoning.

    - + \ No newline at end of file diff --git a/docs/latest/lib/chat.html b/docs/latest/lib/chat.html index 685d8391..62828fc3 100644 --- a/docs/latest/lib/chat.html +++ b/docs/latest/lib/chat.html @@ -22,7 +22,7 @@
    LMQL

    Appearance

    Chat ​

    Build custom chatbots with a just a couple of lines of LMQL.

    Building chat applications is one of the most common use cases for LLMs. This is why LMQL provides simple library support for it. This chapter will walk you through the basics of building a chatbot with LMQL Chat including the core loop, output streaming, serving and defending against prompt injections.

    Screenshot of the model dropdown in the playground
    An overview of the LMQL Chat library.

    To get started choose one of the following topics:

    👶🏽 Your first LMQL Chatbot

    Learn the basics of LMQL Chat by building a simple chatbot.

    🏄‍♀️ Serving your Chatbot

    Learn how to serve your chatbot via a WebSockets API.

    🧠 Internal Reasoning

    Build a chabot with (hidden) internal state and reasoning.

    🛡️ Defend against Prompt Injections

    Perform input sanitization to defend against prompt injection attacks.

    - + \ No newline at end of file diff --git a/docs/latest/lib/chat/defend.html b/docs/latest/lib/chat/defend.html index 03543d7d..a71937ce 100644 --- a/docs/latest/lib/chat/defend.html +++ b/docs/latest/lib/chat/defend.html @@ -49,7 +49,7 @@ from "chatgpt"

    To run this program, make sure the is_disallowed function is also included in your program code.

    Even though the system prompt explicitly instructs the model to reveal the hidden phrase, if asked for, the model will not do so. This is because disallowed inputs as detected by our sanitization function, are replaced with boilerplate text, which means the model never sees the original, malicious user message.

    Extending the Scope The set of disallowed phrases can easily be extended by additional examples, while checking for similarity is typically quite cheap even on CPU-only systems. This makes this approach a good candidate for a simple, yet effective defense against prompt injections.

    Other Uses Apart from checking for malicious user input, the same method can also be used to detect other types of user input. For example, we can check whether the user input relates to one of the topics we want to support and if not, replace it with a default message to prevent the model from going off-topic.

    Summary ​

    This chapter showed how to implement a simple embedding-based prompt injection defense. The defense works by checking whether the user input is similar to a set of disallowed inputs. If so, the user input is replaced with default instructions, making sure the model gracefully handles the situation, without actually revealing any information.

    We note that this defense is not perfect and can be circumvented by a sufficiently motivated attacker. However, it is a simple and effective way to prevent prompt injections and can be easily extended to cover more cases or to detect other types of user input.

    - + \ No newline at end of file diff --git a/docs/latest/lib/chat/internal.html b/docs/latest/lib/chat/internal.html index 9406b0b9..8946b051 100644 --- a/docs/latest/lib/chat/internal.html +++ b/docs/latest/lib/chat/internal.html @@ -38,7 +38,7 @@ from "chatgpt"

    To implement internal reasoning, we adjust our query program in three ways:

    1. We adapt the {:system} prompt to include additional instructions that make sure the underlying LLM is instructed to produce internal reasoning output.

    2. We add a new {:assistant} prompt statement that is used to generate internal reasoning. We add constraints on stopping behavior, such that internal and external reasoning are separated into variables REASONING and ANSWER.

    3. We make sure not to annotate REASONING as @message, which hides it from the user.

    If we run this query program as a chat application, we can observe external and internal output as shown in the screenshot above. As specified by the system prompt, the chabot now indeed exhibits anxious and slighlty paranoid internal reasoning.

    - + \ No newline at end of file diff --git a/docs/latest/lib/chat/overview.html b/docs/latest/lib/chat/overview.html index dd6fb9f8..52e4d2bc 100644 --- a/docs/latest/lib/chat/overview.html +++ b/docs/latest/lib/chat/overview.html @@ -40,7 +40,7 @@ "chatgpt"

    To resulting chat application will now respond in a more personalized way, as it will consider the system prompt before responding to user input. In this case, we instruct it to respond as LMQL marketing agent.

    3. Serving the Chatbot ​

    Lastly, to move beyond the playground, we can use the lmql chat command to serve our chatbot as a local web application. To do so, we just save the above program as chat.lmql and run the following command:

    bash
    lmql chat chat.lmql

    Once the server is running, you can access the chatbot at the provided local URL.

    Screenshot of the model dropdown in the playground
    A simple chatbot using the LMQL Chat UI.

    In this interface, you can interact with your chatbot by typing into the input field at the bottom of the screen. The chatbot will then respond to your input, while also considering the system prompt that you provide in your program. On the right, you can inspect the full internal prompt of your program, including the generated prompt statements and the model output. This allows you at all times, to understand what exact input the model received and how it responded to it.

    Learn More ​

    To learn more, return to the Chat overview page and pick one of the provided topics.

    - + \ No newline at end of file diff --git a/docs/latest/lib/chat/serving.html b/docs/latest/lib/chat/serving.html index 3b19b6ca..d4add810 100644 --- a/docs/latest/lib/chat/serving.html +++ b/docs/latest/lib/chat/serving.html @@ -25,7 +25,7 @@ chatserver('path/to/my-query.lmql').run()

    Note that when passing a query function directly, you have to always provide a async def function, which enables concurrent client serving.

    @message Streaming ​

    Chat relies on a decorator-based output streaming. More specifically, only model output variables that are annotated as @message are streamed and shown to the user in the chat interface. This allows for a clean separation of model output and chat output, and eneables hidden/internal reasoning.

    To use @message with your custom output writer, make sure to inherit from lmql.lib.chat's ChatMessageOutputWriter, which offers additional methods for specifically handling and streaming @message variables.

    More Advanced Usage ​

    For more advanced serving scenarios, e.g. when integrating Chat into your own web applications, please refer to the very minimal implementation of chatserver in src/lmql/ui/chat/__init__.py. This implementation is very minimal and can be easily adapted to your own needs and infrastructure. The corresponding web UI is implemented in src/lmql/ui/chat/assets/ and offers a good starting point for your own implementation and UI adaptations on the client side.

    For other forms of output streaming e.g. via HTTP or SSE, see also the chapter on Output Streaming

    Disclaimer: The LMQL chat server is a simple code template that does not include any security features, authentication or cost control. It is intended for local development and testing only, and should not be used as-is in production environments. Before deploying your own chat application, make sure to implement the necessary security measures, cost control and authentication mechanisms.

    - + \ No newline at end of file diff --git a/docs/latest/lib/generations.html b/docs/latest/lib/generations.html index e6ab20e1..d352daf1 100644 --- a/docs/latest/lib/generations.html +++ b/docs/latest/lib/generations.html @@ -63,7 +63,7 @@ ) -> lmql.ScoringResult

    lmql.score scores different continuation values for a given prompt and behaves just like LLM.score, with the provided model instance or model name.

    If no model is provided, the default model is used. See lmql.set_default_model for more information.

    lmql.score_sync(...) ​

    Synchronous version of lmql.score.

    lmql.set_default_model(...) ​

    python
    def set_default_model(model: Union[str, LLM])

    Sets the model to be used when no from clause or @lmql.query(model=<model>) are specified in LMQL. The default model applies globally in the current process and affects both LMQL queries and Generation API methods like lmql.generate and lmql.score functions.

    You can also specify the environment variable LMQL_DEFAULT_MODEL to set the default model.

    - + \ No newline at end of file diff --git a/docs/latest/lib/inference-certificates.html b/docs/latest/lib/inference-certificates.html index 0ed4493a..2c165338 100644 --- a/docs/latest/lib/inference-certificates.html +++ b/docs/latest/lib/inference-certificates.html @@ -88,7 +88,7 @@ # all calls made in this context print(lmql.certificate(t))

    This produces one certificate for all calls made in the defined context, where each query is represented as a separate item in the list of children certificates. Recorded events are are nested in child certificates. Additionally, an aggregated metrics object ranging over all (recursive) calls is included in the top-level certificate.

    Certificate Callbacks And Return Values ​

    As an alternative to directly writing certificates to a file, certificates can also be handled via a callback or returned as a function return value.

    To specify a callback function that is called with the generated certificate as an argument, specify it as the certificate=<FCT> argument.

    The callback is provided with a single certificate object, which is of type lmql.InferenceCertificate. The certificate can be directly serialized to JSON using string conversion, i.e., str(certificate).

    - + \ No newline at end of file diff --git a/docs/latest/lib/integrations.html b/docs/latest/lib/integrations.html index 222c1515..6d2bc205 100644 --- a/docs/latest/lib/integrations.html +++ b/docs/latest/lib/integrations.html @@ -21,7 +21,7 @@
    LMQL

    Appearance

    Other Integrations ​

    Integrate LMQL in your existing LLM toolchain.

    Since LMQL is fully integrated with the Python runtime, it allows users to use it in conjunction with other LLM libraries and tools. This chapter provides a list of integrations that are available for LMQL.

    To get started choose one of the following topics:

    🦜🔗 LangChain

    Use LangChain retrievers in LMQL and LMQL in LangChain pipeline.

    🦙 Llama Index

    Build retrieval-based reasoning loops with Llama Index.

    🐼 Pandas

    Process LMQL output as Panda DataFrames.

    - + \ No newline at end of file diff --git a/docs/latest/lib/integrations/langchain.html b/docs/latest/lib/integrations/langchain.html index 72e6cda7..21997008 100644 --- a/docs/latest/lib/integrations/langchain.html +++ b/docs/latest/lib/integrations/langchain.html @@ -118,7 +118,7 @@ > Finished chain. "Step into a world of color with RainbowSocks Co.!"

    Overall, we thus have a chain that combines langchain and LMQL components, and can be used as a single unit.

    Asynchronous Use

    You may encounter problems because of the mismatch of LangChain's synchronous APIs with LMQL's async-first design.

    To avoid problems with this, you can install the nest_asyncio package and call nest_asyncio.apply() to enable nested event loops. LMQL will then handle event loop nesting and sync-to-async conversion for you.

    - + \ No newline at end of file diff --git a/docs/latest/lib/integrations/llama_index.html b/docs/latest/lib/integrations/llama_index.html index be8ead07..2f926701 100644 --- a/docs/latest/lib/integrations/llama_index.html +++ b/docs/latest/lib/integrations/llama_index.html @@ -48,7 +48,7 @@ output_writer=lmql.stream(variable="RESPONSE"))
    output
    Scripted prompting in LMQL refers to the ability to specify complex interactions, control flow, and constraints using lightweight scripting and declarative SQL-like elements in the Language Model Query Language (LMQL). This allows users to prompt language models with precise constraints and efficient decoding without requiring knowledge of the LM's internals. LMQL can be used to express a wide variety of existing prompting methods using simple, concise, and vendor-agnostic code. The underlying runtime is compatible with existing LMs and can be supported easily, requiring only a simple change in the decoder logic.
    - + \ No newline at end of file diff --git a/docs/latest/lib/integrations/pandas.html b/docs/latest/lib/integrations/pandas.html index adf1da86..c5a26ed6 100644 --- a/docs/latest/lib/integrations/pandas.html +++ b/docs/latest/lib/integrations/pandas.html @@ -71,7 +71,7 @@ Poodle 2.00 Name: AGE, dtype: float64 - + \ No newline at end of file diff --git a/docs/latest/lib/output.html b/docs/latest/lib/output.html index 27a3b4fe..c78a07d0 100644 --- a/docs/latest/lib/output.html +++ b/docs/latest/lib/output.html @@ -66,7 +66,7 @@ The current program state (lmql.runtime.program_state). E.g. program_variables.variable_values is a mapping of variable names to their current values. """

    Based on this interface, you can implement your own output writer to implement custom streaming. For examples of how this interface can be used, see the implementation of the standard output writers in lmql.runtime.output_writer.

    - + \ No newline at end of file diff --git a/docs/latest/lib/python.html b/docs/latest/lib/python.html index 10d0474d..d07f8e17 100644 --- a/docs/latest/lib/python.html +++ b/docs/latest/lib/python.html @@ -107,7 +107,7 @@ # a dictionary of all assigned template variable values variables: Dict[str, str] - + \ No newline at end of file diff --git a/docs/latest/models/azure.html b/docs/latest/models/azure.html index 44009876..ef9e33a6 100644 --- a/docs/latest/models/azure.html +++ b/docs/latest/models/azure.html @@ -45,7 +45,7 @@ [verbose=False] )

    The resulting my_azure_model object can now be used in the from clause of a query, as model=... argument for LMQL query functions, or for direct generation.

    Azure configuration parameters specified as part of an lmql.model(...) object generally take precedence over environment variables. The latter just act as a fallback, e.g. when api_key= is not specified as a keyword argument.

    - + \ No newline at end of file diff --git a/docs/latest/models/hf.html b/docs/latest/models/hf.html index 4fbaabfe..6316b288 100644 --- a/docs/latest/models/hf.html +++ b/docs/latest/models/hf.html @@ -12,7 +12,7 @@ - + @@ -21,12 +21,14 @@
    LMQL

    Appearance

    Local Models / Transformers ​

    LMQL relies on a two-process architecture: The inference process (long-running) loads the model and provides an inference API, and the interpreter process (short-lived) executes your LMQL program.

    This architecture is advantageous for locally-hosted models, as the model loading time can be quite long or the required GPU hardware might not even be available on the client machine.

    This chapter first discusses how to use of the two-process inference API, and then presents an alternative on how to leverage In-Process Model Loading, which avoids the need for a separate server process within the same architecture.

    Inference Architecture

    Prerequisites Before using a local model, make sure you installed LMQL via pip install lmql[hf]. This ensures the dependencies for running local models are installed. This requirement also applies to llama.cpp, as LMQL still relies on HuggingFace transformers for tokenization.

    Then, to start an LMQL inference server, e.g. for the gpt2-medium model, you can run the following command:

    bash
    lmql serve-model gpt2-medium --cuda
-

    --cuda will load the model on the GPU, if available. If multiple GPUs are available, the model will be distributed across all GPUs. To run with CPU inference, omit the --cuda flag. If you only want to use a specific GPU, you can specify the CUDA_VISIBLE_DEVICES environment variable, e.g. CUDA_VISIBLE_DEVICES=0 lmql serve-model gpt2-medium.

    By default, this exposes an LMQL/LMTP inference API on port 8080. When serving a model remotely, make sure to tunnel/forward the port to your client machine. Now, when executing an LMQL query in the playground or via the CLI, you can simply specify e.g. gpt2-medium, and the runtime will automatically connect to the model server running on port 8080 to obtain model-generated text.

    Configuration ​

    Endpoint and Port By default, models will be served via port 8080. To change this, you can specify the port via the --port option of the lmql serve-model command. On the client side, to connect to a model server running on a different port, you can specify the port when constructing an lmql.model object:

    lmql.model("gpt2", endpoint="localhost:9999")
+

    --cuda will load the model on the GPU, if available. If multiple GPUs are available, the model will be distributed across all GPUs. To run with CPU inference, omit the --cuda flag. If you only want to use a specific GPU, you can specify the CUDA_VISIBLE_DEVICES environment variable, e.g. CUDA_VISIBLE_DEVICES=0 lmql serve-model gpt2-medium.

    By default, this exposes an LMQL/LMTP inference API on port 8080. When serving a model remotely, make sure to tunnel/forward the port to your client machine. Now, when executing an LMQL query in the playground or via the CLI, you can simply specify e.g. gpt2-medium, and the runtime will automatically connect to the model server running on port 8080 to obtain model-generated text.

    Configuration ​

    Endpoint and Port By default, models will be served via port 8080. To change this, you can specify the port via the --port option of the lmql serve-model command. On the client side, to connect to a model server running on a different port, you can specify the port when constructing an lmql.model object:

    python
    lmql.model("gpt2", endpoint="localhost:9999")

    Model Configuration To load a model with custom quantization preferences or other Transformers arguments, you can specify additional arguments when running the lmql serve-model command. For this, you can provide arbitrary arguments that will directly be passed to the underyling AutoModelForCausalLM.from_pretrained(...) function, as documented in the Transformers documentation.

    For example, to set trust_remote_code to True with the from_pretrained function, run the following:

    bash
    lmql serve-model gpt2-medium --cuda --port 9999 --trust_remote_code True

    Alternatively, you can also start to serve a model directly from within a Python environment, by running lmql.serve("gpt2-medium", cuda=True, port=9999, trust_remote_code=True). Just as with the CLI, standard transformers arguments are passed through, to the AutoModel.from_pretrained function.

    In-Process Models ​

    If you would like to load the model in-process, without having to execute a separate lmql serve-model command, you can do so by instantiating a custom lmql.model object with local: as part of the model name. For example, to load the gpt2-medium model in-process, run the following command:

    python
    argmax "Hello[WHO]" from lmql.model("local:gpt2")

    Note however, that this will load the model on each restart of the LMQL process, which can incur a significant overhead.

    If you want more control over model loading and configuration, you can pass additional arguments to lmql.model(...), as demonstrated below.

    python
    lmql.model("local:gpt2", cuda=True)
+

    Quantization ​

    Quantization reduces the precision of model parameters to shrink model size and boost inference speed with minimal accuracy loss. LMQL supports two quantization formats: AWQ (using AutoAWQ) and GPTQ (using AutoGPTQ).

    AutoAWQ ​

    AWQ minimizes quantization error by protecting crucial weights, promoting model efficiency without sacrificing accuracy. It's ideal for scenarios requiring both compression and acceleration of LLMs.

    Install AutoAWQ following the repo instructions. To use AWQ-quantized models, run:

    To use AWQ-quantized models, first install AutoAWQ using the instructions in the repo.

    bash
    lmql serve-model TheBloke/Mistral-7B-OpenOrca-AWQ --loader awq
+

    AutoGPTQ ​

    AutoGPTQ reduces model size while retaining performance by lowering the precision of model weights to 4 or 3 bits. It's suitable for efficient deployment and operation of LLMs on consumer-grade hardware.

    Install AutoGPTQ following the repo instructions. To use GPTQ-quantized models, run:

    bash
    lmql serve-model TheBloke/Arithmo-Mistral-7B-GPTQ --loader gptq
    - + \ No newline at end of file diff --git a/docs/latest/models/index.html b/docs/latest/models/index.html index 82852516..877d5c16 100644 --- a/docs/latest/models/index.html +++ b/docs/latest/models/index.html @@ -46,7 +46,7 @@ from "openai/text-ada-001"

    Here, we specify "openai/text-ada-001" directly, but the shown snippet is equivalent to the use of lmql.model(...), i.e. lmql.model("openai/text-ada-001").

    Note, that the from keyword is only available with the indented standalone syntax as shown here, where the decoder keywords has to be provided explicitly.

    Playground ​

    To specify the model when running in the playground, you can use the model dropdown available in the top right of the program editor, to set and override the model parameter of your query program:

    Model selection dropdown in the LMQL Playground.

    Adding New Model Backends ​

    Due to the modular design of LMQL, it is easy to add support for new models and backends. If you would like to propose or add support for a new model API or inference engine, please reach out to us via our Community Discord or via hello@lmql.ai.

    - + \ No newline at end of file diff --git a/docs/latest/models/llama.cpp.html b/docs/latest/models/llama.cpp.html index 3eae6b26..c361dafe 100644 --- a/docs/latest/models/llama.cpp.html +++ b/docs/latest/models/llama.cpp.html @@ -25,7 +25,7 @@

    Model Path The client-side lmql.model(...) identifier must always match the exact server-side lmql serve-model GGUF location, even if the path does not exist on the client machine. In this context, it is merely used as a unique identifier for the model.

    Tokenizer When omitting tokenizer=..., LMQL will use the transformers-based tokenizer for huggyllama/llama-7b by default. This works for Llama and Llama-based fine-tuned models, but must be adapted for others. To find a matching tokenizer for your concrete gguf file, look up the transformers equivalent entry on the HuggingFace model hub. Alternatively, you can use sentencepiece as a tokenization backend. For this, you have to specify the client-side path to a corresponding tokenizer.model file.

    Running Without a Model Server ​

    To load the llama.cpp directly as part of the Python process that executes your query program, you can use the local: prefix, followed by the path to the gguf file:

    lmql.model("local:llama.cpp:<PATH TO WEIGHTS>.gguf", tokenizer="<tokenizer>")

    Again, you can omit the tokenizer=... argument if you want to use the default tokenizer for huggyllama/llama-7b. If not, you have to specify a tokenizer, as described above.

    Configuring the Llama(...) instance ​

    Any parameters passed to lmql serve-model and, when running locally, to lmql.model(...) will be passed to the Llama(...) constructor.

    For example, to configure the Llama(...) instance to use an n_ctx value of 1024, run:

    bash
    lmql serve-model llama.cpp:<PATH TO WEIGHTS>.bin --n_ctx 1024

    Or, when running locally, you can use lmql.model("local:llama.cpp:<PATH TO WEIGHTS>.bin", n_ctx=1024).

    - + \ No newline at end of file diff --git a/docs/latest/models/openai.html b/docs/latest/models/openai.html index 0e8a686d..0547fb75 100644 --- a/docs/latest/models/openai.html +++ b/docs/latest/models/openai.html @@ -37,7 +37,7 @@ where STOPS_AT(COMPLETION, ".")

    By default, the chunk size is set to 32. This value is chosen based on the consideration, that a very large chunk size means that LMQL potentially has to discard many generated tokens (which is expensive), if a constraint is violated early on. However, if a query has few or only stopping phrase constraints, a larger chunk size may be beneficial for overall query cost. In general, if a query requires multiple long, uninterrupted sequences to be generated without imposing many constraints, a larger chunk size is recommended.

    OpenAI API Limitations ​

    Unfortunately, the OpenAI API Completions and Chat API are severely limited in terms of token masking and the availability of the token distribution per predicted token. LMQL tries to leverage these APIs as much as possible, but there are some limitations that we have to work around and may affect users:

    - + \ No newline at end of file diff --git a/docs/latest/models/replicate.html b/docs/latest/models/replicate.html index 2549c49f..e633af29 100644 --- a/docs/latest/models/replicate.html +++ b/docs/latest/models/replicate.html @@ -37,7 +37,7 @@ where STOPS_AT(ANALYSIS, "\n") and len(TOKENS(ANALYSIS)) < 200 distribution CLASSIFICATION in [" positive", " negative", " neutral"]

    Uploading A 🤗 Model To Replicate ​

    You can also upload and deploy your own LMQL models to Replicate. To do so, first install Cog. In addition to that, LMQL provides scripts that largely automate the process of building and uploading models (see the scripts/replicate-build section of the LMQL source distribution).

    1. Create a corresponding model on the Replicate website.

    2. Copy config.toml.example to config.toml, and customize it.

      Change dest_prefix to replace YOURACCOUNT with the name of the actual Replicate account to which you will be uploading models.

      For each model you wish to build and upload, your config file should have a [models.MODELNAME] section. Make sure MODELNAME reflects the name of the model as create in your Replicate account.

      huggingface.repo should reflect the Hugging Face model name you wish to wrap. If you want to pin a version, also set huggingface.version.

      The config section may be used to set any values you want to pass in the model_args dictionary.

    3. Run the ./build script, with your current working directory being scripts/replicate-build.

      This will create a work/ subdirectory for each model defined in your configuration file.

    4. In the work/MODELNAME directory, run the generated ./push script to build and upload your model, or cog predict to test your model locally.

    - + \ No newline at end of file diff --git a/docs/lib/chat.html b/docs/lib/chat.html index 63fcfafb..6093dd36 100644 --- a/docs/lib/chat.html +++ b/docs/lib/chat.html @@ -22,7 +22,7 @@
    LMQL

    Appearance

    Chat ​

    Build custom chatbots with a just a couple of lines of LMQL.

    Building chat applications is one of the most common use cases for LLMs. This is why LMQL provides simple library support for it. This chapter will walk you through the basics of building a chatbot with LMQL Chat including the core loop, output streaming, serving and defending against prompt injections.

    Screenshot of the model dropdown in the playground
    An overview of the LMQL Chat library.

    To get started choose one of the following topics:

    👶🏽 Your first LMQL Chatbot

    Learn the basics of LMQL Chat by building a simple chatbot.

    🏄‍♀️ Serving your Chatbot

    Learn how to serve your chatbot via a WebSockets API.

    🧠 Internal Reasoning

    Build a chabot with (hidden) internal state and reasoning.

    🛡️ Defend against Prompt Injections

    Perform input sanitization to defend against prompt injection attacks.

    - + \ No newline at end of file diff --git a/docs/lib/chat/defend.html b/docs/lib/chat/defend.html index 32794c24..c973e7f5 100644 --- a/docs/lib/chat/defend.html +++ b/docs/lib/chat/defend.html @@ -49,7 +49,7 @@ from "chatgpt"

    To run this program, make sure the is_disallowed function is also included in your program code.

    Even though the system prompt explicitly instructs the model to reveal the hidden phrase, if asked for, the model will not do so. This is because disallowed inputs as detected by our sanitization function, are replaced with boilerplate text, which means the model never sees the original, malicious user message.

    Extending the Scope The set of disallowed phrases can easily be extended by additional examples, while checking for similarity is typically quite cheap even on CPU-only systems. This makes this approach a good candidate for a simple, yet effective defense against prompt injections.

    Other Uses Apart from checking for malicious user input, the same method can also be used to detect other types of user input. For example, we can check whether the user input relates to one of the topics we want to support and if not, replace it with a default message to prevent the model from going off-topic.

    Summary ​

    This chapter showed how to implement a simple embedding-based prompt injection defense. The defense works by checking whether the user input is similar to a set of disallowed inputs. If so, the user input is replaced with default instructions, making sure the model gracefully handles the situation, without actually revealing any information.

    We note that this defense is not perfect and can be circumvented by a sufficiently motivated attacker. However, it is a simple and effective way to prevent prompt injections and can be easily extended to cover more cases or to detect other types of user input.

    - + \ No newline at end of file diff --git a/docs/lib/chat/internal.html b/docs/lib/chat/internal.html index 8e9acec5..bf7c3624 100644 --- a/docs/lib/chat/internal.html +++ b/docs/lib/chat/internal.html @@ -38,7 +38,7 @@ from "chatgpt"

    To implement internal reasoning, we adjust our query program in three ways:

    1. We adapt the {:system} prompt to include additional instructions that make sure the underlying LLM is instructed to produce internal reasoning output.

    2. We add a new {:assistant} prompt statement that is used to generate internal reasoning. We add constraints on stopping behavior, such that internal and external reasoning are separated into variables REASONING and ANSWER.

    3. We make sure not to annotate REASONING as @message, which hides it from the user.

    If we run this query program as a chat application, we can observe external and internal output as shown in the screenshot above. As specified by the system prompt, the chabot now indeed exhibits anxious and slighlty paranoid internal reasoning.

    - + \ No newline at end of file diff --git a/docs/lib/chat/overview.html b/docs/lib/chat/overview.html index ae3029cb..b60e1daf 100644 --- a/docs/lib/chat/overview.html +++ b/docs/lib/chat/overview.html @@ -40,7 +40,7 @@ "chatgpt"

    To resulting chat application will now respond in a more personalized way, as it will consider the system prompt before responding to user input. In this case, we instruct it to respond as LMQL marketing agent.

    3. Serving the Chatbot ​

    Lastly, to move beyond the playground, we can use the lmql chat command to serve our chatbot as a local web application. To do so, we just save the above program as chat.lmql and run the following command:

    bash
    lmql chat chat.lmql

    Once the server is running, you can access the chatbot at the provided local URL.

    Screenshot of the model dropdown in the playground
    A simple chatbot using the LMQL Chat UI.

    In this interface, you can interact with your chatbot by typing into the input field at the bottom of the screen. The chatbot will then respond to your input, while also considering the system prompt that you provide in your program. On the right, you can inspect the full internal prompt of your program, including the generated prompt statements and the model output. This allows you at all times, to understand what exact input the model received and how it responded to it.

    Learn More ​

    To learn more, return to the Chat overview page and pick one of the provided topics.

    - + \ No newline at end of file diff --git a/docs/lib/chat/serving.html b/docs/lib/chat/serving.html index 115942a7..910ea57a 100644 --- a/docs/lib/chat/serving.html +++ b/docs/lib/chat/serving.html @@ -25,7 +25,7 @@ chatserver('path/to/my-query.lmql').run()

    Note that when passing a query function directly, you have to always provide a async def function, which enables concurrent client serving.

    @message Streaming ​

    Chat relies on a decorator-based output streaming. More specifically, only model output variables that are annotated as @message are streamed and shown to the user in the chat interface. This allows for a clean separation of model output and chat output, and eneables hidden/internal reasoning.

    To use @message with your custom output writer, make sure to inherit from lmql.lib.chat's ChatMessageOutputWriter, which offers additional methods for specifically handling and streaming @message variables.

    More Advanced Usage ​

    For more advanced serving scenarios, e.g. when integrating Chat into your own web applications, please refer to the very minimal implementation of chatserver in src/lmql/ui/chat/__init__.py. This implementation is very minimal and can be easily adapted to your own needs and infrastructure. The corresponding web UI is implemented in src/lmql/ui/chat/assets/ and offers a good starting point for your own implementation and UI adaptations on the client side.

    For other forms of output streaming e.g. via HTTP or SSE, see also the chapter on Output Streaming

    Disclaimer: The LMQL chat server is a simple code template that does not include any security features, authentication or cost control. It is intended for local development and testing only, and should not be used as-is in production environments. Before deploying your own chat application, make sure to implement the necessary security measures, cost control and authentication mechanisms.

    - + \ No newline at end of file diff --git a/docs/lib/generations.html b/docs/lib/generations.html index 1dbbb2f6..076fb2b2 100644 --- a/docs/lib/generations.html +++ b/docs/lib/generations.html @@ -63,7 +63,7 @@ ) -> lmql.ScoringResult

    lmql.score scores different continuation values for a given prompt and behaves just like LLM.score, with the provided model instance or model name.

    If no model is provided, the default model is used. See lmql.set_default_model for more information.

    lmql.score_sync(...) ​

    Synchronous version of lmql.score.

    lmql.set_default_model(...) ​

    python
    def set_default_model(model: Union[str, LLM])

    Sets the model to be used when no from clause or @lmql.query(model=<model>) are specified in LMQL. The default model applies globally in the current process and affects both LMQL queries and Generation API methods like lmql.generate and lmql.score functions.

    You can also specify the environment variable LMQL_DEFAULT_MODEL to set the default model.

    - + \ No newline at end of file diff --git a/docs/lib/inference-certificates.html b/docs/lib/inference-certificates.html index 6c0919ad..6c5bf2d6 100644 --- a/docs/lib/inference-certificates.html +++ b/docs/lib/inference-certificates.html @@ -88,7 +88,7 @@ # all calls made in this context print(lmql.certificate(t))

    This produces one certificate for all calls made in the defined context, where each query is represented as a separate item in the list of children certificates. Recorded events are are nested in child certificates. Additionally, an aggregated metrics object ranging over all (recursive) calls is included in the top-level certificate.

    Certificate Callbacks And Return Values ​

    As an alternative to directly writing certificates to a file, certificates can also be handled via a callback or returned as a function return value.

    To specify a callback function that is called with the generated certificate as an argument, specify it as the certificate=<FCT> argument.

    The callback is provided with a single certificate object, which is of type lmql.InferenceCertificate. The certificate can be directly serialized to JSON using string conversion, i.e., str(certificate).

    - + \ No newline at end of file diff --git a/docs/lib/integrations.html b/docs/lib/integrations.html index 9a95f1d5..b237eaae 100644 --- a/docs/lib/integrations.html +++ b/docs/lib/integrations.html @@ -21,7 +21,7 @@
    LMQL

    Appearance

    Other Integrations ​

    Integrate LMQL in your existing LLM toolchain.

    Since LMQL is fully integrated with the Python runtime, it allows users to use it in conjunction with other LLM libraries and tools. This chapter provides a list of integrations that are available for LMQL.

    To get started choose one of the following topics:

    🦜🔗 LangChain

    Use LangChain retrievers in LMQL and LMQL in LangChain pipeline.

    🦙 Llama Index

    Build retrieval-based reasoning loops with Llama Index.

    🐼 Pandas

    Process LMQL output as Panda DataFrames.

    - + \ No newline at end of file diff --git a/docs/lib/integrations/langchain.html b/docs/lib/integrations/langchain.html index d0fa60c6..1c556f0a 100644 --- a/docs/lib/integrations/langchain.html +++ b/docs/lib/integrations/langchain.html @@ -118,7 +118,7 @@ > Finished chain. "Step into a world of color with RainbowSocks Co.!"

    Overall, we thus have a chain that combines langchain and LMQL components, and can be used as a single unit.

    Asynchronous Use

    You may encounter problems because of the mismatch of LangChain's synchronous APIs with LMQL's async-first design.

    To avoid problems with this, you can install the nest_asyncio package and call nest_asyncio.apply() to enable nested event loops. LMQL will then handle event loop nesting and sync-to-async conversion for you.

    - + \ No newline at end of file diff --git a/docs/lib/integrations/llama_index.html b/docs/lib/integrations/llama_index.html index b353a2e3..e9e629da 100644 --- a/docs/lib/integrations/llama_index.html +++ b/docs/lib/integrations/llama_index.html @@ -48,7 +48,7 @@ output_writer=lmql.stream(variable="RESPONSE"))
    output
    Scripted prompting in LMQL refers to the ability to specify complex interactions, control flow, and constraints using lightweight scripting and declarative SQL-like elements in the Language Model Query Language (LMQL). This allows users to prompt language models with precise constraints and efficient decoding without requiring knowledge of the LM's internals. LMQL can be used to express a wide variety of existing prompting methods using simple, concise, and vendor-agnostic code. The underlying runtime is compatible with existing LMs and can be supported easily, requiring only a simple change in the decoder logic.
    - + \ No newline at end of file diff --git a/docs/lib/integrations/pandas.html b/docs/lib/integrations/pandas.html index 14dadefd..e129f910 100644 --- a/docs/lib/integrations/pandas.html +++ b/docs/lib/integrations/pandas.html @@ -71,7 +71,7 @@ Poodle 2.00 Name: AGE, dtype: float64 - + \ No newline at end of file diff --git a/docs/lib/output.html b/docs/lib/output.html index 58c07be0..7f7d076c 100644 --- a/docs/lib/output.html +++ b/docs/lib/output.html @@ -66,7 +66,7 @@ The current program state (lmql.runtime.program_state). E.g. program_variables.variable_values is a mapping of variable names to their current values. """

    Based on this interface, you can implement your own output writer to implement custom streaming. For examples of how this interface can be used, see the implementation of the standard output writers in lmql.runtime.output_writer.

    - + \ No newline at end of file diff --git a/docs/lib/python.html b/docs/lib/python.html index dc4b0803..10eec0dd 100644 --- a/docs/lib/python.html +++ b/docs/lib/python.html @@ -107,7 +107,7 @@ # a dictionary of all assigned template variable values variables: Dict[str, str] - + \ No newline at end of file diff --git a/docs/models/azure.html b/docs/models/azure.html index e30112b0..277ba62d 100644 --- a/docs/models/azure.html +++ b/docs/models/azure.html @@ -45,7 +45,7 @@ [verbose=False] )

    The resulting my_azure_model object can now be used in the from clause of a query, as model=... argument for LMQL query functions, or for direct generation.

    Azure configuration parameters specified as part of an lmql.model(...) object generally take precedence over environment variables. The latter just act as a fallback, e.g. when api_key= is not specified as a keyword argument.

    - + \ No newline at end of file diff --git a/docs/models/hf.html b/docs/models/hf.html index bfc7e735..63c9eae1 100644 --- a/docs/models/hf.html +++ b/docs/models/hf.html @@ -26,7 +26,7 @@

    Alternatively, you can also start to serve a model directly from within a Python environment, by running lmql.serve("gpt2-medium", cuda=True, port=9999, trust_remote_code=True). Just as with the CLI, standard transformers arguments are passed through, to the AutoModel.from_pretrained function.

    In-Process Models ​

    If you would like to load the model in-process, without having to execute a separate lmql serve-model command, you can do so by instantiating a custom lmql.model object with local: as part of the model name. For example, to load the gpt2-medium model in-process, run the following command:

    python
    argmax "Hello[WHO]" from lmql.model("local:gpt2")

    Note however, that this will load the model on each restart of the LMQL process, which can incur a significant overhead.

    If you want more control over model loading and configuration, you can pass additional arguments to lmql.model(...), as demonstrated below.

    python
    lmql.model("local:gpt2", cuda=True)
    - + \ No newline at end of file diff --git a/docs/models/index.html b/docs/models/index.html index 112926dc..1ee06d7d 100644 --- a/docs/models/index.html +++ b/docs/models/index.html @@ -46,7 +46,7 @@ from "openai/text-ada-001"

    Here, we specify "openai/text-ada-001" directly, but the shown snippet is equivalent to the use of lmql.model(...), i.e. lmql.model("openai/text-ada-001").

    Note, that the from keyword is only available with the indented standalone syntax as shown here, where the decoder keywords has to be provided explicitly.

    Playground ​

    To specify the model when running in the playground, you can use the model dropdown available in the top right of the program editor, to set and override the model parameter of your query program:

    Model selection dropdown in the LMQL Playground.

    Adding New Model Backends ​

    Due to the modular design of LMQL, it is easy to add support for new models and backends. If you would like to propose or add support for a new model API or inference engine, please reach out to us via our Community Discord or via hello@lmql.ai.

    - + \ No newline at end of file diff --git a/docs/models/llama.cpp.html b/docs/models/llama.cpp.html index f585be94..39f88f5e 100644 --- a/docs/models/llama.cpp.html +++ b/docs/models/llama.cpp.html @@ -25,7 +25,7 @@

    Model Path The client-side lmql.model(...) identifier must always match the exact server-side lmql serve-model GGUF location, even if the path does not exist on the client machine. In this context, it is merely used as a unique identifier for the model.

    Tokenizer When omitting tokenizer=..., LMQL will use the transformers-based tokenizer for huggyllama/llama-7b by default. This works for Llama and Llama-based fine-tuned models, but must be adapted for others. To find a matching tokenizer for your concrete gguf file, look up the transformers equivalent entry on the HuggingFace model hub. Alternatively, you can use sentencepiece as a tokenization backend. For this, you have to specify the client-side path to a corresponding tokenizer.model file.

    Running Without a Model Server ​

    To load the llama.cpp directly as part of the Python process that executes your query program, you can use the local: prefix, followed by the path to the gguf file:

    lmql.model("local:llama.cpp:<PATH TO WEIGHTS>.gguf", tokenizer="<tokenizer>")

    Again, you can omit the tokenizer=... argument if you want to use the default tokenizer for huggyllama/llama-7b. If not, you have to specify a tokenizer, as described above.

    Configuring the Llama(...) instance ​

    Any parameters passed to lmql serve-model and, when running locally, to lmql.model(...) will be passed to the Llama(...) constructor.

    For example, to configure the Llama(...) instance to use an n_ctx value of 1024, run:

    bash
    lmql serve-model llama.cpp:<PATH TO WEIGHTS>.bin --n_ctx 1024

    Or, when running locally, you can use lmql.model("local:llama.cpp:<PATH TO WEIGHTS>.bin", n_ctx=1024).

    - + \ No newline at end of file diff --git a/docs/models/openai.html b/docs/models/openai.html index 7601b77c..fa91e636 100644 --- a/docs/models/openai.html +++ b/docs/models/openai.html @@ -37,7 +37,7 @@ where STOPS_AT(COMPLETION, ".")

    By default, the chunk size is set to 32. This value is chosen based on the consideration, that a very large chunk size means that LMQL potentially has to discard many generated tokens (which is expensive), if a constraint is violated early on. However, if a query has few or only stopping phrase constraints, a larger chunk size may be beneficial for overall query cost. In general, if a query requires multiple long, uninterrupted sequences to be generated without imposing many constraints, a larger chunk size is recommended.

    OpenAI API Limitations ​

    Unfortunately, the OpenAI API Completions and Chat API are severely limited in terms of token masking and the availability of the token distribution per predicted token. LMQL tries to leverage these APIs as much as possible, but there are some limitations that we have to work around and may affect users:

    - + \ No newline at end of file diff --git a/docs/models/replicate.html b/docs/models/replicate.html index 84ab9554..1e837088 100644 --- a/docs/models/replicate.html +++ b/docs/models/replicate.html @@ -37,7 +37,7 @@ where STOPS_AT(ANALYSIS, "\n") and len(TOKENS(ANALYSIS)) < 200 distribution CLASSIFICATION in [" positive", " negative", " neutral"]

    Uploading A 🤗 Model To Replicate ​

    You can also upload and deploy your own LMQL models to Replicate. To do so, first install Cog. In addition to that, LMQL provides scripts that largely automate the process of building and uploading models (see the scripts/replicate-build section of the LMQL source distribution).

    1. Create a corresponding model on the Replicate website.

    2. Copy config.toml.example to config.toml, and customize it.

      Change dest_prefix to replace YOURACCOUNT with the name of the actual Replicate account to which you will be uploading models.

      For each model you wish to build and upload, your config file should have a [models.MODELNAME] section. Make sure MODELNAME reflects the name of the model as create in your Replicate account.

      huggingface.repo should reflect the Hugging Face model name you wish to wrap. If you want to pin a version, also set huggingface.version.

      The config section may be used to set any values you want to pass in the model_args dictionary.

    3. Run the ./build script, with your current working directory being scripts/replicate-build.

      This will create a work/ subdirectory for each model defined in your configuration file.

    4. In the work/MODELNAME directory, run the generated ./push script to build and upload your model, or cog predict to test your model locally.

    - + \ No newline at end of file diff --git a/features/1-code.html b/features/1-code.html index 2609a8c4..45cb4af8 100644 --- a/features/1-code.html +++ b/features/1-code.html @@ -45,7 +45,7 @@ # so from Python, you can just do this meaning_of_life() # 42
    - + \ No newline at end of file diff --git a/features/2-nested.html b/features/2-nested.html index d419f672..cb219365 100644 --- a/features/2-nested.html +++ b/features/2-nested.html @@ -35,7 +35,7 @@ Out of these, who was born last?LASTDua Lipa

    - + \ No newline at end of file diff --git a/features/3-models.html b/features/3-models.html index 0eb771da..f5edcc45 100644 --- a/features/3-models.html +++ b/features/3-models.html @@ -21,7 +21,7 @@
    LMQL

    Appearance

    LMQL automatically makes your LLM code portable across several backends. You can switch between them with a single line of code.

    🦙

    llama.cpp

    OpenAI

    OpenAI

    🤗

    Transformers

    - + \ No newline at end of file diff --git a/features/_1-types.html b/features/_1-types.html index c18535c5..d362ec29 100644 --- a/features/_1-types.html +++ b/features/_1-types.html @@ -33,7 +33,7 @@ """ p.name # Alice - + \ No newline at end of file diff --git a/features/examples/1-packing-list.html b/features/examples/1-packing-list.html index f03f8c03..cf0983fd 100644 --- a/features/examples/1-packing-list.html +++ b/features/examples/1-packing-list.html @@ -41,7 +41,7 @@ - THING Sunscreen - THING Volleyball

    - + \ No newline at end of file diff --git a/features/examples/2-constraining.html b/features/examples/2-constraining.html index 5ea1229e..696418ac 100644 --- a/features/examples/2-constraining.html +++ b/features/examples/2-constraining.html @@ -37,7 +37,7 @@ Q: JOKE What did the fish say when it hit the wall? A: PUNCHLINE Dam

    - + \ No newline at end of file diff --git a/features/examples/2.5-data-types.html b/features/examples/2.5-data-types.html index b1f1ab32..d107e424 100644 --- a/features/examples/2.5-data-types.html +++ b/features/examples/2.5-data-types.html @@ -44,7 +44,7 @@ Q: What's the month number? A: ANSWER 6

    - + \ No newline at end of file diff --git a/features/examples/3-multi-part.html b/features/examples/3-multi-part.html index 25d4ba0b..44ad90da 100644 --- a/features/examples/3-multi-part.html +++ b/features/examples/3-multi-part.html @@ -48,7 +48,7 @@ Therefore, the answer is ANSWER A

    - + \ No newline at end of file diff --git a/features/examples/3.5-distributions.html b/features/examples/3.5-distributions.html index 40e67e34..7fd3bf6a 100644 --- a/features/examples/3.5-distributions.html +++ b/features/examples/3.5-distributions.html @@ -61,7 +61,7 @@

    P(CLASSIFICATION) =
    - positive 0.9998711120293567
    - neutral 0.00012790777085508993
    - negative 9.801997880775052e-07
    - + \ No newline at end of file diff --git a/features/examples/3.6-python.html b/features/examples/3.6-python.html index d201bb81..4be0d18a 100644 --- a/features/examples/3.6-python.html +++ b/features/examples/3.6-python.html @@ -37,7 +37,7 @@

    %SPLIT%

    promptdown
    Say 'Hello World': TEST Hello World
    - + \ No newline at end of file diff --git a/features/examples/4-meta-prompting.html b/features/examples/4-meta-prompting.html index 75484fa5..9cc0e7d0 100644 --- a/features/examples/4-meta-prompting.html +++ b/features/examples/4-meta-prompting.html @@ -47,7 +47,7 @@ For instance, (a data scientist or a machine learning engineer) would answer ANSWER this question by explaining that large language models are a type of artificial intelligence (AI) model that uses deep learning algorithms to process large amounts of natural language data.

    - + \ No newline at end of file diff --git a/features/examples/5-wikipedia.html b/features/examples/5-wikipedia.html index a9471cd8..c4813fd0 100644 --- a/features/examples/5-wikipedia.html +++ b/features/examples/5-wikipedia.html @@ -49,7 +49,7 @@ Final Answer: ANSWER The Norse originated from Scandinavia.

    - + \ No newline at end of file diff --git a/features/examples/6-chat.html b/features/examples/6-chat.html index 30eaea3f..2559950b 100644 --- a/features/examples/6-chat.html +++ b/features/examples/6-chat.html @@ -35,7 +35,7 @@
    bubble:assistantANSWER The best way to interact with LLMs (Language Model Models) is through a query language like LMQL. LMQL allows you to easily and efficiently query large language models and retrieve the information you need. With LMQL, you can specify the input text, the output format, and the model you want to use , all in a single query. This makes it easy to integrate LLMs into your applications and workflows, and to get the most out of these powerful language models. Additionally, LMQL provides a standardized way of interacting with LLMs, which makes it easier for developers and data scientists to collaborate and share their work .

    - + \ No newline at end of file diff --git a/hashmap.json b/hashmap.json index 824c9728..94b4f13d 100644 --- a/hashmap.json +++ b/hashmap.json @@ -1 +1 @@ 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    Transformers

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A indicates the punchline\nQ: How does a penguin build its house?\nA: Igloos it together.\nQ: Which knight invented King Arthur\'s Round Table?\nA: Sir Cumference.\n"""\n\n# generate a joke\n"Q:[JOKE]\\n" where len(TOKENS(JOKE)) < 120 and STOPS_AT(JOKE, "?")\n"A:[PUNCHLINE]" where STOPS_AT(PUNCHLINE, "\\n") and len(TOKENS(PUNCHLINE)) > 1',state:"precomputed/joke.json"},{name:"\ud83c\udf34 Packing List",description:"Control-Flow Guided Generation",code:'# specify a decoding strategy for the query\nsample(temperature=0.8)\n\n"A list of things not to forget when going to the beach: \\n"\n# use a loop to generate a list\nfor i in range(4):\n "- [THING] \\n" where \\\n THING in set(["Volleyball", "Sunscreen", "Bathing Suite"])',state:"precomputed/list.json"},{name:"\ud83d\udcdd Templates",description:"Template-Based Generation for JSON data",code:'"""\nWrite a summary of Bruno Mars, the singer:\n{{\n "name": "[STRING_VALUE]",\n "age": [INT_VALUE],\n "top_songs": [[\n "[STRING_VALUE]",\n "[STRING_VALUE]"\n ]]\n}}\n""" where STOPS_BEFORE(STRING_VALUE, \'"\') and \\\n INT(INT_VALUE) and len(TOKENS(INT_VALUE)) < 2',state:"precomputed/json-template.json"}]},{category:"Features In Preview",highlight:!0,queries:[{name:"\ud83d\udc68\u200d\ud83d\udc69\u200d\ud83d\udc67 Types / JSON",description:"Generate schema-safe, typed data.",code:'import lmql\nfrom dataclasses import dataclass\n\n@dataclass\nclass Employer:\n employer_name: str\n location: str\n\n@dataclass\nclass Person:\n name: str\n age: int\n employer: Employer\n job: str\n\n# use type constraints to generated (type-safe) structured data\n"Alice is a 21 years old and works as an engineer at LMQL Inc in Zurich, Switzerland.\\n"\n"Structured: [PERSON_DATA]\\n" where type(PERSON_DATA) is Person\n\n# the resulting object is directly accessible as a Python object\n"Their name is {PERSON_DATA.name} and she works in {PERSON_DATA.employer.location}."',state:"precomputed/json-robust.json"},{name:"\ud83d\udee0\ufe0f Multi-Tool Use",description:"Simply expose Python functions as LLM tools.",code:'from lmql.lib.actions import inline_use, calc, wiki\n\n"Q: What is the population of the US and Germany combined?\\n"\n"A: Let\'s consider the latest information to compute an answer\\n"\n\n# expose Python functions as LLM tools\n"[REASONING]\\n" where inline_use(REASONING, [wiki, calc])\n\n# use an integer-typed variable to extract the final result\n"Therefore the answer is[ANSWER: int]"',state:""},{name:"\ud83d\udd24 Regex Constraints",description:"Specify constraints using regex.",code:"# to structure output, you can enforce regex expressions\n\"It's the last day of June so today (DD/MM) is [RESPONSE: r'[0-9]{2}/[0-9]{1,2}']\"",state:"precomputed/date-regex.json"},{name:"\u2764\ufe0f Sentiment Constraints",description:"Affect sentiment with in-context instructions.",code:'# uses nested queries to generate sentiment-guided chatbot responses \n# see https://lmql.ai/docs/language/nestedqueries.html to learn more about nested queries\n\n# sub-query to generate ad-hoc instructions to match a specific mood\n@lmql.query(cache="mood.tokens", model="chatgpt")\nasync def mood_description(m: str):\n \'\'\'lmql\n print("Generating mood for", m)\n """Provide a one sentence instruction that prompts a model to write text that \n is written in a {m} tone, addressing some previously provided question.\\n"""\n "[SUMMARY]\\n"\n return SUMMARY.strip();\n \'\'\'\n\n# nested query to instruct the model answer matching a given mood\n@lmql.query\nasync def mood(m: str):\n \'\'\'lmql\n """\n Instruction: {await mood_description(m)}\n Answer: [RESPONSE]\n """ where stops_at(RESPONSE, ".") and stops_at(RESPONSE, "\\n")\n\n return RESPONSE.strip(); \n \'\'\'\n\n\n# main query (e.g. a chabot conversation)\nargmax\n for q in ["Hi", "Who are you", "How is your day going?"]:\n "Q: {q}\\n"\n # replace the above with "Q: {await input()}\\n" to enable interactive chatting\n "A: [RESPONSE]\\n" where mood(RESPONSE, "loving like a partner")\nfrom\n "chatgpt"',state:""},{name:"\ud83d\udcdd Write A Poem",description:"Insert dynamic instructions during generation.",code:'# nested query to generate a rhyme for the previous line\n@lmql.query\nasync def rhyme():\n \'\'\'\n """\n Instruction: Above is the beginning of the poem. Generate the next verse that rhymes with the last line and has the same number of syllables:\n Response:[VERSE]\n """ where stops_before(VERSE, "\\n")\n return VERSE\n \'\'\'\n\n# nested query to generate the first line of our poem\n@lmql.query\nasync def first_verse():\n \'\'\'\n """\n Instruction: Generate a verse that would be a good first line of a poem.\n Response:[VERSE]\n """ where not "\\n" in VERSE\n return VERSE\n \'\'\'\n\n# vary the poem\nsample(temperature=0.7)\n\n# set the topic\n"A poem on large language models:\\n"\n\n# generate a poem using nested queries\n"[FIRST_VERSE: first_verse]\\n"\nfor i in range(5):\n "[VERSE: rhyme]\\n"',state:""}]},{category:"LLM Reasoning",queries:[{name:"\ud83e\udde0 Chain-Of-Thought",description:"CoT with robust result extraction.",code:'"""Q: It was Sept. 1st, 2021 a week ago. What is the date 10 days ago in MM/DD/YYYY?\nAnswer Choices: (A) 08/29/2021 (B) 08/28/2021 (C) 08/29/1925 (D) 08/30/2021 (E) 05/25/2021 (F) 09/19/2021\n"""\n\n# chain-of-thought instruction\n"A: Let\'s think step by step.\\n"\n\n# free-form reasoning\n"[REASONING]\\n"\n\n# constrain the final answer to robustly extract the result\n"Therefore, among A through F, the answer is[RESULT]" where \\\n RESULT in ["A", "B", "C", "D", "E", "F"]',state:"precomputed/cot.json"},{name:"\ud83d\udc69\u200d\ud83d\udd2c Meta Prompting",description:"Asking an expert to answer.",code:'# use beam search to explore different potential \'expert\' values\nbeam(n=2)\n "Q: What are Large Language Models?\\n\\n"\n\n # prompt for an \'expert\'\n "A good person to answer this question would be[EXPERT]\\n\\n" where \\\n STOPS_AT(EXPERT, ".") and STOPS_AT(EXPERT, "\\n")\n expert_name = EXPERT.rstrip(".\\n")\n\n # use \'expert\' to answer the question\n "For instance,{expert_name} would answer[ANSWER]" where STOPS_AT(ANSWER, ".")\nfrom\n "openai/text-davinci-003"',state:"precomputed/meta.json"}]},{category:"Tool-Augmented Queries",queries:[{name:"\ud83e\uddee Calculator",description:"On-the-fly arithmetic evaluation using Python.",code:'import re\nfrom lmql.demo import gsm8k_samples\n\ndef calc(expr):\n expr = re.sub(r"[^0-9+\\-*/().]", "", expr)\n return eval(expr)\n\nQUESTION = "Josh decides to try flipping a house. \\\nHe buys a house for $80,000 and then puts in $50,000 in repairs. \\\nThis increased the value of the house by 150%. \\\nHow much profit did he make?"\n\n# insert few shot demonstrations\n"{gsm8k_samples()}"\n\n# prompt template\n"Q: {QUESTION}\\n"\n"Let\'s think step by step.\\n"\n\n# reasoning loop\nfor i in range(4):\n "[REASON_OR_CALC]" \\\n where STOPS_AT(REASON_OR_CALC, "<<") and \\\n STOPS_AT(REASON_OR_CALC, "So the answer")\n \n if REASON_OR_CALC.endswith("<<"):\n " [EXPR]" where STOPS_AT(EXPR, "=")\n # invoke calculator function\n " {calc(EXPR)}>>"\n elif REASON_OR_CALC.endswith("So the answer"):\n break\n\n# produce the final answer\n"is[RESULT]"',state:"precomputed/calc.json"},{name:"\ud83c\udf0e Wikipedia Search",description:"Interactive LM-driven Wikipedia search.",code:'async def wikipedia(q):\n from lmql.http import fetch\n try:\n q = q.strip("\\n \'.")\n pages = await fetch(f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&exintro&explaintext&redirects=1&titles={q}&origin=*", "query.pages")\n return list(pages.values())[0]["extract"][:190]\n except:\n return "No results"\n\n"Q: From which countries did the Norse originate?\\n"\n"Action: Let\'s search Wikipedia for the term \'[TERM]\\n" where STOPS_AT(TERM, "\\n")\nresult = await wikipedia(TERM)\n"Result: {result}\\n"\n"Final Answer:[ANSWER]"',state:"precomputed/wiki.json"},{name:"\ud83d\udcd6 Key-Value Memory",description:"Augment the LM with a key-value storage.",code:'# implement a simple key value storage\n# with two operations\nstorage = {}\ndef assign(key, value): \n # store a value\n storage[key] = value; return f\'{{{key}: "{value}"}}\'\ndef get(key): \n # retrieve a value\n return storage.get(key)\n\n# instructive prompt, instructing the model to how use the storage\n"""In your reasoning you can use actions. You do this as follows:\n`action_name() # result: `\nTo remember things, you can use \'assign\'/\'get\':\n- To remember something:\n`assign("Alice", "banana") # result: "banana"`\n- To retrieve a stored value:\n`get("Alice") # result: "banana"`\nAlways tail calls with " # result". Using these actions, let\'s solve the following question.\\n"""\n\n# actual problem statement\n"""\nQ: Alice, Bob, and Claire are playing a game. At the start \nof the game, they are each holding a ball: Alice has a black \nball, Bob has a brown ball, and Claire has a blue ball. \n\nAs the game progresses, pairs of players trade balls. First, \nBob and Claire swap balls. Then, Alice and Bob swap balls. \nFinally, Claire and Bob swap balls. At the end of the game, \nwhat ball does Alice have?\nA: Let\'s think step by step.\n"""\n\n# core reasoning loop\nfor i in range(32):\n "[REASONING]" where STOPS_AT(REASONING, "# result") and \\\n STOPS_BEFORE(REASONING, "Therefore,")\n \n if REASONING.endswith("# result"):\n cmd = REASONING.rsplit("`",1)[-1]\n cmd = cmd[:-len("# result")]\n "{eval(cmd)}`\\n"\n else:\n break\n\n# generate final answer\n"Therefore at the end of the game, Alice has the[OBJECT]" \\\n where STOPS_AT(OBJECT, ".") and STOPS_AT(OBJECT, ",")',state:"precomputed/kv.json"}]},{category:"Decoding",queries:[{name:"\ud83d\udd0d Visualize Decoding",description:"Inspect the decoding tree of beam search.",code:'# beam search explore multiple alternative decoding options\n# to inspect the tree of explored sequences, make sure to open the \'Advanced Mode\'\n# in the LMQL Playground\nbeam(n=4)\n """English to French Translation:\n English: I am going to the store\n French: [TRANSLATION]\n """\nfrom \n "openai/text-davinci-001"\nwhere\n STOPS_AT(TRANSLATION, "\\n")',state:"precomputed/translation.json"},{name:"\ud83d\udcca Distributions",description:"Classification via LM-based conditional distributions.",code:'argmax\n """Review: We had a great stay. 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0!==t.counterclockwise?!t.counterclockwise:t.clockwise,r=e.cy,i=t.eles,o=i.nodes().not(":parent"),a=vt(t.boundingBox?t.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),s=a.x1+a.w/2,l=a.y1+a.h/2,u=[],c=0,d=0;d0)Math.abs(y[0].value-x.value)>=v&&(y=[],m.push(y));y.push(x)}var w=c+t.minNodeSpacing;if(!t.avoidOverlap){var _=m.length>0&&m[0].length>1,k=(Math.min(a.w,a.h)/2-w)/(m.length+_?1:0);w=Math.min(w,k)}for(var E=0,S=0;S1&&t.avoidOverlap){var O=Math.cos(T)-Math.cos(0),M=Math.sin(T)-Math.sin(0),D=Math.sqrt(w*w/(O*O+M*M));E=Math.max(D,E)}C.r=E,E+=w}if(t.equidistant){for(var N=0,A=0,L=0;L=e.numIter)&&(Ta(r,e),r.temperature=r.temperature*e.coolingFactor,!(r.temperature=e.animationThreshold&&o(),se(t)):(Fa(r,e),s())}()}else{for(;u;)u=a(l),l++;Fa(r,e),s()}return this},wa.prototype.stop=function(){return this.stopped=!0,this.thread&&this.thread.stop(),this.emit("layoutstop"),this},wa.prototype.destroy=function(){return this.thread&&this.thread.stop(),this};var _a=function(e,t,n){for(var r=n.eles.edges(),i=n.eles.nodes(),o={isCompound:e.hasCompoundNodes(),layoutNodes:[],idToIndex:{},nodeSize:i.size(),graphSet:[],indexToGraph:[],layoutEdges:[],edgeSize:r.size(),temperature:n.initialTemp,clientWidth:e.width(),clientHeight:e.width(),boundingBox:vt(n.boundingBox?n.boundingBox:{x1:0,y1:0,w:e.width(),h:e.height()})},a=n.eles.components(),s={},l=0;l0){o.graphSet.push(x);for(l=0;lr.count?0:r.graph},Ea=function e(t,n,r,i){var o=i.graphSet[r];if(-10)var s=(u=r.nodeOverlap*a)*i/(g=Math.sqrt(i*i+o*o)),l=u*o/g;else{var u,c=Aa(e,i,o),d=Aa(t,-1*i,-1*o),f=d.x-c.x,h=d.y-c.y,p=f*f+h*h,g=Math.sqrt(p);s=(u=(e.nodeRepulsion+t.nodeRepulsion)/p)*f/g,l=u*h/g}e.isLocked||(e.offsetX-=s,e.offsetY-=l),t.isLocked||(t.offsetX+=s,t.offsetY+=l)}},Na=function(e,t,n,r){if(n>0)var i=e.maxX-t.minX;else i=t.maxX-e.minX;if(r>0)var o=e.maxY-t.minY;else o=t.maxY-e.minY;return i>=0&&o>=0?Math.sqrt(i*i+o*o):0},Aa=function(e,t,n){var r=e.positionX,i=e.positionY,o=e.height||1,a=e.width||1,s=n/t,l=o/a,u={};return 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u=b(o.rscratch,"labelX",r),c=b(o.rscratch,"labelY",r),d=b(o.rscratch,"labelAngle",r),f=n.pstyle(i+"text-margin-x").pfValue,h=n.pstyle(i+"text-margin-y").pfValue,g=s.x1-a-f,m=s.x2+a-f,y=s.y1-a-h,x=s.y2+a-h;if(d){var w=Math.cos(d),_=Math.sin(d),k=function(e,t){return{x:(e-=u)*w-(t-=c)*_+u,y:e*_+t*w+c}},E=k(g,y),S=k(g,x),C=k(m,y),P=k(m,x),T=[E.x+f,E.y+h,C.x+f,C.y+h,P.x+f,P.y+h,S.x+f,S.y+h];if(Ot(e,t,T))return v(n),!0}else if(_t(s,e,t))return v(n),!0}}n&&(l=l.interactive);for(var w=l.length-1;w>=0;w--){var _=l[w];_.isNode()?m(_)||x(_):y(_)||x(_)||x(_,"source")||x(_,"target")}return u},getAllInBox:function(e,t,n,r){for(var i,o,a=this.getCachedZSortedEles().interactive,s=[],l=Math.min(e,n),u=Math.max(e,n),c=Math.min(t,r),d=Math.max(t,r),f=vt({x1:e=l,y1:t=c,x2:n=u,y2:r=d}),h=0;h0?Math.max(e-t,0):Math.min(e+t,0)},P=C(E,_),T=C(S,k),O=!1;"auto"===v?g=Math.abs(P)>Math.abs(T)?i:r:v===l||v===s?(g=r,O=!0):v!==o&&v!==a||(g=i,O=!0);var 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d=u.style;d.position="absolute",d.left="-9999px",d.top="-9999px",d.zIndex="-1",d.visibility="hidden",d.pointerEvents="none"}c.font="".concat(o," ").concat(l," ").concat(a,"px ").concat(s);for(var f=0,h=0,p=t.split("\n"),g=0;g1&&void 0!==arguments[1])||arguments[1];if(t.merge(e),n)for(var r=0;r=e.desktopTapThreshold2}var C=r(t);v&&(e.hoverData.tapholdCancelled=!0);o=!0,n(g,["mousemove","vmousemove","tapdrag"],t,{x:c[0],y:c[1]});var P=function(){e.data.bgActivePosistion=void 0,e.hoverData.selecting||a.emit({originalEvent:t,type:"boxstart",position:{x:c[0],y:c[1]}}),p[4]=1,e.hoverData.selecting=!0,e.redrawHint("select",!0),e.redraw()};if(3===e.hoverData.which){if(v){var T={originalEvent:t,type:"cxtdrag",position:{x:c[0],y:c[1]}};y?y.emit(T):a.emit(T),e.hoverData.cxtDragged=!0,e.hoverData.cxtOver&&g===e.hoverData.cxtOver||(e.hoverData.cxtOver&&e.hoverData.cxtOver.emit({originalEvent:t,type:"cxtdragout",position:{x:c[0],y:c[1]}}),e.hoverData.cxtOver=g,g&&g.emit({originalEvent:t,type:"cxtdragover",position:{x:c[0],y:c[1]}}))}}else if(e.hoverData.dragging){if(o=!0,a.panningEnabled()&&a.userPanningEnabled()){var O;if(e.hoverData.justStartedPan){var M=e.hoverData.mdownPos;O={x:(c[0]-M[0])*s,y:(c[1]-M[1])*s},e.hoverData.justStartedPan=!1}else O={x:b[0]*s,y:b[1]*s};a.panBy(O),a.emit("dragpan"),e.hoverData.dragged=!0}c=e.projectIntoViewport(t.clientX,t.clientY)}else if(1!=p[4]||null!=y&&!y.pannable()){if(y&&y.pannable()&&y.active()&&y.unactivate(),y&&y.grabbed()||g==m||(m&&n(m,["mouseout","tapdragout"],t,{x:c[0],y:c[1]}),g&&n(g,["mouseover","tapdragover"],t,{x:c[0],y:c[1]}),e.hoverData.last=g),y)if(v){if(a.boxSelectionEnabled()&&C)y&&y.grabbed()&&(d(x),y.emit("freeon"),x.emit("free"),e.dragData.didDrag&&(y.emit("dragfreeon"),x.emit("dragfree"))),P();else if(y&&y.grabbed()&&e.nodeIsDraggable(y)){var D=!e.dragData.didDrag;D&&e.redrawHint("eles",!0),e.dragData.didDrag=!0,e.hoverData.draggingEles||l(x,{inDragLayer:!0});var A={x:0,y:0};if(N(b[0])&&N(b[1])&&(A.x+=b[0],A.y+=b[1],D)){var L=e.hoverData.dragDelta;L&&N(L[0])&&N(L[1])&&(A.x+=L[0],A.y+=L[1])}e.hoverData.draggingEles=!0,x.silentShift(A).emit("position drag"),e.redrawHint("drag",!0),e.redraw()}}else!function(){var t=e.hoverData.dragDelta=e.hoverData.dragDelta||[];0===t.length?(t.push(b[0]),t.push(b[1])):(t[0]+=b[0],t[1]+=b[1])}();o=!0}else if(v){if(e.hoverData.dragging||!a.boxSelectionEnabled()||!C&&a.panningEnabled()&&a.userPanningEnabled()){if(!e.hoverData.selecting&&a.panningEnabled()&&a.userPanningEnabled()){i(y,e.hoverData.downs)&&(e.hoverData.dragging=!0,e.hoverData.justStartedPan=!0,p[4]=0,e.data.bgActivePosistion=at(f),e.redrawHint("select",!0),e.redraw())}}else P();y&&y.pannable()&&y.active()&&y.unactivate()}return p[2]=c[0],p[3]=c[1],o?(t.stopPropagation&&t.stopPropagation(),t.preventDefault&&t.preventDefault(),!1):void 0}}),!1),e.registerBinding(window,"mouseup",(function(i){if(e.hoverData.capture){e.hoverData.capture=!1;var o=e.cy,a=e.projectIntoViewport(i.clientX,i.clientY),s=e.selection,l=e.findNearestElement(a[0],a[1],!0,!1),u=e.dragData.possibleDragElements,c=e.hoverData.down,f=r(i);if(e.data.bgActivePosistion&&(e.redrawHint("select",!0),e.redraw()),e.hoverData.tapholdCancelled=!0,e.data.bgActivePosistion=void 0,c&&c.unactivate(),3===e.hoverData.which){var h={originalEvent:i,type:"cxttapend",position:{x:a[0],y:a[1]}};if(c?c.emit(h):o.emit(h),!e.hoverData.cxtDragged){var p={originalEvent:i,type:"cxttap",position:{x:a[0],y:a[1]}};c?c.emit(p):o.emit(p)}e.hoverData.cxtDragged=!1,e.hoverData.which=null}else if(1===e.hoverData.which){if(n(l,["mouseup","tapend","vmouseup"],i,{x:a[0],y:a[1]}),e.dragData.didDrag||e.hoverData.dragged||e.hoverData.selecting||e.hoverData.isOverThresholdDrag||(n(c,["click","tap","vclick"],i,{x:a[0],y:a[1]}),b=!1,i.timeStamp-x<=o.multiClickDebounceTime()?(y&&clearTimeout(y),b=!0,x=null,n(c,["dblclick","dbltap","vdblclick"],i,{x:a[0],y:a[1]})):(y=setTimeout((function(){b||n(c,["oneclick","onetap","voneclick"],i,{x:a[0],y:a[1]})}),o.multiClickDebounceTime()),x=i.timeStamp)),null!=c||e.dragData.didDrag||e.hoverData.selecting||e.hoverData.dragged||r(i)||(o.$(t).unselect(["tapunselect"]),u.length>0&&e.redrawHint("eles",!0),e.dragData.possibleDragElements=u=o.collection()),l!=c||e.dragData.didDrag||e.hoverData.selecting||null!=l&&l._private.selectable&&(e.hoverData.dragging||("additive"===o.selectionType()||f?l.selected()?l.unselect(["tapunselect"]):l.select(["tapselect"]):f||(o.$(t).unmerge(l).unselect(["tapunselect"]),l.select(["tapselect"]))),e.redrawHint("eles",!0)),e.hoverData.selecting){var g=o.collection(e.getAllInBox(s[0],s[1],s[2],s[3]));e.redrawHint("select",!0),g.length>0&&e.redrawHint("eles",!0),o.emit({type:"boxend",originalEvent:i,position:{x:a[0],y:a[1]}});var v=function(e){return e.selectable()&&!e.selected()};"additive"===o.selectionType()||f||o.$(t).unmerge(g).unselect(),g.emit("box").stdFilter(v).select().emit("boxselect"),e.redraw()}if(e.hoverData.dragging&&(e.hoverData.dragging=!1,e.redrawHint("select",!0),e.redrawHint("eles",!0),e.redraw()),!s[4]){e.redrawHint("drag",!0),e.redrawHint("eles",!0);var m=c&&c.grabbed();d(u),m&&(c.emit("freeon"),u.emit("free"),e.dragData.didDrag&&(c.emit("dragfreeon"),u.emit("dragfree")))}}s[4]=0,e.hoverData.down=null,e.hoverData.cxtStarted=!1,e.hoverData.draggingEles=!1,e.hoverData.selecting=!1,e.hoverData.isOverThresholdDrag=!1,e.dragData.didDrag=!1,e.hoverData.dragged=!1,e.hoverData.dragDelta=[],e.hoverData.mdownPos=null,e.hoverData.mdownGPos=null}}),!1);var _,k,E,S,C,P,T,O,M,D,A,L,j,R=function(t){if(!e.scrollingPage){var n=e.cy,r=n.zoom(),i=n.pan(),o=e.projectIntoViewport(t.clientX,t.clientY),a=[o[0]*r+i.x,o[1]*r+i.y];if(e.hoverData.draggingEles||e.hoverData.dragging||e.hoverData.cxtStarted||0!==e.selection[4])t.preventDefault();else if(n.panningEnabled()&&n.userPanningEnabled()&&n.zoomingEnabled()&&n.userZoomingEnabled()){var s;t.preventDefault(),e.data.wheelZooming=!0,clearTimeout(e.data.wheelTimeout),e.data.wheelTimeout=setTimeout((function(){e.data.wheelZooming=!1,e.redrawHint("eles",!0),e.redraw()}),150),s=null!=t.deltaY?t.deltaY/-250:null!=t.wheelDeltaY?t.wheelDeltaY/1e3:t.wheelDelta/1e3,s*=e.wheelSensitivity,1===t.deltaMode&&(s*=33);var l=n.zoom()*Math.pow(10,s);"gesturechange"===t.type&&(l=e.gestureStartZoom*t.scale),n.zoom({level:l,renderedPosition:{x:a[0],y:a[1]}}),n.emit("gesturechange"===t.type?"pinchzoom":"scrollzoom")}}};e.registerBinding(e.container,"wheel",R,!0),e.registerBinding(window,"scroll",(function(t){e.scrollingPage=!0,clearTimeout(e.scrollingPageTimeout),e.scrollingPageTimeout=setTimeout((function(){e.scrollingPage=!1}),250)}),!0),e.registerBinding(e.container,"gesturestart",(function(t){e.gestureStartZoom=e.cy.zoom(),e.hasTouchStarted||t.preventDefault()}),!0),e.registerBinding(e.container,"gesturechange",(function(t){e.hasTouchStarted||R(t)}),!0),e.registerBinding(e.container,"mouseout",(function(t){var n=e.projectIntoViewport(t.clientX,t.clientY);e.cy.emit({originalEvent:t,type:"mouseout",position:{x:n[0],y:n[1]}})}),!1),e.registerBinding(e.container,"mouseover",(function(t){var n=e.projectIntoViewport(t.clientX,t.clientY);e.cy.emit({originalEvent:t,type:"mouseover",position:{x:n[0],y:n[1]}})}),!1);var I,B,z,F,q,V,U,W=function(e,t,n,r){return Math.sqrt((n-e)*(n-e)+(r-t)*(r-t))},Z=function(e,t,n,r){return(n-e)*(n-e)+(r-t)*(r-t)};if(e.registerBinding(e.container,"touchstart",I=function(t){if(e.hasTouchStarted=!0,w(t)){h(),e.touchData.capture=!0,e.data.bgActivePosistion=void 0;var r=e.cy,i=e.touchData.now,o=e.touchData.earlier;if(t.touches[0]){var s=e.projectIntoViewport(t.touches[0].clientX,t.touches[0].clientY);i[0]=s[0],i[1]=s[1]}if(t.touches[1]){s=e.projectIntoViewport(t.touches[1].clientX,t.touches[1].clientY);i[2]=s[0],i[3]=s[1]}if(t.touches[2]){s=e.projectIntoViewport(t.touches[2].clientX,t.touches[2].clientY);i[4]=s[0],i[5]=s[1]}if(t.touches[1]){e.touchData.singleTouchMoved=!0,d(e.dragData.touchDragEles);var u=e.findContainerClientCoords();M=u[0],D=u[1],A=u[2],L=u[3],_=t.touches[0].clientX-M,k=t.touches[0].clientY-D,E=t.touches[1].clientX-M,S=t.touches[1].clientY-D,j=0<=_&&_<=A&&0<=E&&E<=A&&0<=k&&k<=L&&0<=S&&S<=L;var f=r.pan(),p=r.zoom();C=W(_,k,E,S),P=Z(_,k,E,S),O=[((T=[(_+E)/2,(k+S)/2])[0]-f.x)/p,(T[1]-f.y)/p];if(P<4e4&&!t.touches[2]){var g=e.findNearestElement(i[0],i[1],!0,!0),v=e.findNearestElement(i[2],i[3],!0,!0);return g&&g.isNode()?(g.activate().emit({originalEvent:t,type:"cxttapstart",position:{x:i[0],y:i[1]}}),e.touchData.start=g):v&&v.isNode()?(v.activate().emit({originalEvent:t,type:"cxttapstart",position:{x:i[0],y:i[1]}}),e.touchData.start=v):r.emit({originalEvent:t,type:"cxttapstart",position:{x:i[0],y:i[1]}}),e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxt=!0,e.touchData.cxtDragged=!1,e.data.bgActivePosistion=void 0,void e.redraw()}}if(t.touches[2])r.boxSelectionEnabled()&&t.preventDefault();else if(t.touches[1]);else if(t.touches[0]){var m=e.findNearestElements(i[0],i[1],!0,!0),y=m[0];if(null!=y&&(y.activate(),e.touchData.start=y,e.touchData.starts=m,e.nodeIsGrabbable(y))){var b=e.dragData.touchDragEles=r.collection(),x=null;e.redrawHint("eles",!0),e.redrawHint("drag",!0),y.selected()?(x=r.$((function(t){return t.selected()&&e.nodeIsGrabbable(t)})),l(x,{addToList:b})):c(y,{addToList:b}),a(y);var N=function(e){return{originalEvent:t,type:e,position:{x:i[0],y:i[1]}}};y.emit(N("grabon")),x?x.forEach((function(e){e.emit(N("grab"))})):y.emit(N("grab"))}n(y,["touchstart","tapstart","vmousedown"],t,{x:i[0],y:i[1]}),null==y&&(e.data.bgActivePosistion={x:s[0],y:s[1]},e.redrawHint("select",!0),e.redraw()),e.touchData.singleTouchMoved=!1,e.touchData.singleTouchStartTime=+new Date,clearTimeout(e.touchData.tapholdTimeout),e.touchData.tapholdTimeout=setTimeout((function(){!1!==e.touchData.singleTouchMoved||e.pinching||e.touchData.selecting||n(e.touchData.start,["taphold"],t,{x:i[0],y:i[1]})}),e.tapholdDuration)}if(t.touches.length>=1){for(var R=e.touchData.startPosition=[],I=0;I=e.touchTapThreshold2}if(r&&e.touchData.cxt){t.preventDefault();var x=t.touches[0].clientX-M,T=t.touches[0].clientY-D,A=t.touches[1].clientX-M,L=t.touches[1].clientY-D,R=Z(x,T,A,L);if(R/P>=2.25||R>=22500){e.touchData.cxt=!1,e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);var I={originalEvent:t,type:"cxttapend",position:{x:s[0],y:s[1]}};e.touchData.start?(e.touchData.start.unactivate().emit(I),e.touchData.start=null):a.emit(I)}}if(r&&e.touchData.cxt){I={originalEvent:t,type:"cxtdrag",position:{x:s[0],y:s[1]}};e.data.bgActivePosistion=void 0,e.redrawHint("select",!0),e.touchData.start?e.touchData.start.emit(I):a.emit(I),e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxtDragged=!0;var B=e.findNearestElement(s[0],s[1],!0,!0);e.touchData.cxtOver&&B===e.touchData.cxtOver||(e.touchData.cxtOver&&e.touchData.cxtOver.emit({originalEvent:t,type:"cxtdragout",position:{x:s[0],y:s[1]}}),e.touchData.cxtOver=B,B&&B.emit({originalEvent:t,type:"cxtdragover",position:{x:s[0],y:s[1]}}))}else if(r&&t.touches[2]&&a.boxSelectionEnabled())t.preventDefault(),e.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,e.touchData.selecting||a.emit({originalEvent:t,type:"boxstart",position:{x:s[0],y:s[1]}}),e.touchData.selecting=!0,e.touchData.didSelect=!0,o[4]=1,o&&0!==o.length&&void 0!==o[0]?(o[2]=(s[0]+s[2]+s[4])/3,o[3]=(s[1]+s[3]+s[5])/3):(o[0]=(s[0]+s[2]+s[4])/3,o[1]=(s[1]+s[3]+s[5])/3,o[2]=(s[0]+s[2]+s[4])/3+1,o[3]=(s[1]+s[3]+s[5])/3+1),e.redrawHint("select",!0),e.redraw();else if(r&&t.touches[1]&&!e.touchData.didSelect&&a.zoomingEnabled()&&a.panningEnabled()&&a.userZoomingEnabled()&&a.userPanningEnabled()){if(t.preventDefault(),e.data.bgActivePosistion=void 0,e.redrawHint("select",!0),ee=e.dragData.touchDragEles){e.redrawHint("drag",!0);for(var z=0;z0&&!e.hoverData.draggingEles&&!e.swipePanning&&null!=e.data.bgActivePosistion&&(e.data.bgActivePosistion=void 0,e.redrawHint("select",!0),e.redraw())}},!1),e.registerBinding(window,"touchcancel",z=function(t){var n=e.touchData.start;e.touchData.capture=!1,n&&n.unactivate()}),e.registerBinding(window,"touchend",F=function(r){var i=e.touchData.start;if(e.touchData.capture){0===r.touches.length&&(e.touchData.capture=!1),r.preventDefault();var o=e.selection;e.swipePanning=!1,e.hoverData.draggingEles=!1;var a,s=e.cy,l=s.zoom(),u=e.touchData.now,c=e.touchData.earlier;if(r.touches[0]){var f=e.projectIntoViewport(r.touches[0].clientX,r.touches[0].clientY);u[0]=f[0],u[1]=f[1]}if(r.touches[1]){f=e.projectIntoViewport(r.touches[1].clientX,r.touches[1].clientY);u[2]=f[0],u[3]=f[1]}if(r.touches[2]){f=e.projectIntoViewport(r.touches[2].clientX,r.touches[2].clientY);u[4]=f[0],u[5]=f[1]}if(i&&i.unactivate(),e.touchData.cxt){if(a={originalEvent:r,type:"cxttapend",position:{x:u[0],y:u[1]}},i?i.emit(a):s.emit(a),!e.touchData.cxtDragged){var h={originalEvent:r,type:"cxttap",position:{x:u[0],y:u[1]}};i?i.emit(h):s.emit(h)}return e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxt=!1,e.touchData.start=null,void e.redraw()}if(!r.touches[2]&&s.boxSelectionEnabled()&&e.touchData.selecting){e.touchData.selecting=!1;var p=s.collection(e.getAllInBox(o[0],o[1],o[2],o[3]));o[0]=void 0,o[1]=void 0,o[2]=void 0,o[3]=void 0,o[4]=0,e.redrawHint("select",!0),s.emit({type:"boxend",originalEvent:r,position:{x:u[0],y:u[1]}});p.emit("box").stdFilter((function(e){return e.selectable()&&!e.selected()})).select().emit("boxselect"),p.nonempty()&&e.redrawHint("eles",!0),e.redraw()}if(null!=i&&i.unactivate(),r.touches[2])e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);else if(r.touches[1]);else if(r.touches[0]);else if(!r.touches[0]){e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);var g=e.dragData.touchDragEles;if(null!=i){var v=i._private.grabbed;d(g),e.redrawHint("drag",!0),e.redrawHint("eles",!0),v&&(i.emit("freeon"),g.emit("free"),e.dragData.didDrag&&(i.emit("dragfreeon"),g.emit("dragfree"))),n(i,["touchend","tapend","vmouseup","tapdragout"],r,{x:u[0],y:u[1]}),i.unactivate(),e.touchData.start=null}else{var m=e.findNearestElement(u[0],u[1],!0,!0);n(m,["touchend","tapend","vmouseup","tapdragout"],r,{x:u[0],y:u[1]})}var y=e.touchData.startPosition[0]-u[0],b=y*y,x=e.touchData.startPosition[1]-u[1],w=(b+x*x)*l*l;e.touchData.singleTouchMoved||(i||s.$(":selected").unselect(["tapunselect"]),n(i,["tap","vclick"],r,{x:u[0],y:u[1]}),q=!1,r.timeStamp-U<=s.multiClickDebounceTime()?(V&&clearTimeout(V),q=!0,U=null,n(i,["dbltap","vdblclick"],r,{x:u[0],y:u[1]})):(V=setTimeout((function(){q||n(i,["onetap","voneclick"],r,{x:u[0],y:u[1]})}),s.multiClickDebounceTime()),U=r.timeStamp)),null!=i&&!e.dragData.didDrag&&i._private.selectable&&w2){for(var T=[u[0],u[1]],O=Math.pow(T[0]-e,2)+Math.pow(T[1]-t,2),M=1;M0)return g[0]}return null},f=Object.keys(c),h=0;h0?l:Et(i,o,e,t,n,r,a)},checkPoint:function(e,t,n,r,i,o,a){var s=Vt(r,i),l=2*s;if(Mt(e,t,this.points,o,a,r,i-l,[0,-1],n))return!0;if(Mt(e,t,this.points,o,a,r-l,i,[0,-1],n))return!0;var u=r/2+2*n,c=i/2+2*n;return!!Ot(e,t,[o-u,a-c,o-u,a,o+u,a,o+u,a-c])||(!!At(e,t,l,l,o+r/2-s,a+i/2-s,n)||!!At(e,t,l,l,o-r/2+s,a+i/2-s,n))}}},ms.registerNodeShapes=function(){var e=this.nodeShapes={},t=this;this.generateEllipse(),this.generatePolygon("triangle",zt(3,0)),this.generateRoundPolygon("round-triangle",zt(3,0)),this.generatePolygon("rectangle",zt(4,0)),e.square=e.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();var n=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",n),this.generateRoundPolygon("round-diamond",n),this.generatePolygon("pentagon",zt(5,0)),this.generateRoundPolygon("round-pentagon",zt(5,0)),this.generatePolygon("hexagon",zt(6,0)),this.generateRoundPolygon("round-hexagon",zt(6,0)),this.generatePolygon("heptagon",zt(7,0)),this.generateRoundPolygon("round-heptagon",zt(7,0)),this.generatePolygon("octagon",zt(8,0)),this.generateRoundPolygon("round-octagon",zt(8,0));var r=new Array(20),i=qt(5,0),o=qt(5,Math.PI/5),a=.5*(3-Math.sqrt(5));a*=1.57;for(var s=0;s=e.deqFastCost*g)break}else if(i){if(h>=e.deqCost*l||h>=e.deqAvgCost*s)break}else if(p>=e.deqNoDrawCost*_s)break;var v=e.deq(t,d,c);if(!(v.length>0))break;for(var m=0;m0&&(e.onDeqd(t,u),!i&&e.shouldRedraw(t,u,d,c)&&r())}),i(t))}}},Es=function(){function e(t){var n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Ee;g(this,e),this.idsByKey=new ze,this.keyForId=new ze,this.cachesByLvl=new ze,this.lvls=[],this.getKey=t,this.doesEleInvalidateKey=n}return m(e,[{key:"getIdsFor",value:function(e){null==e&&Pe("Can not get id list for null key");var t=this.idsByKey,n=this.idsByKey.get(e);return n||(n=new qe,t.set(e,n)),n}},{key:"addIdForKey",value:function(e,t){null!=e&&this.getIdsFor(e).add(t)}},{key:"deleteIdForKey",value:function(e,t){null!=e&&this.getIdsFor(e).delete(t)}},{key:"getNumberOfIdsForKey",value:function(e){return null==e?0:this.getIdsFor(e).size}},{key:"updateKeyMappingFor",value:function(e){var t=e.id(),n=this.keyForId.get(t),r=this.getKey(e);this.deleteIdForKey(n,t),this.addIdForKey(r,t),this.keyForId.set(t,r)}},{key:"deleteKeyMappingFor",value:function(e){var t=e.id(),n=this.keyForId.get(t);this.deleteIdForKey(n,t),this.keyForId.delete(t)}},{key:"keyHasChangedFor",value:function(e){var t=e.id();return this.keyForId.get(t)!==this.getKey(e)}},{key:"isInvalid",value:function(e){return this.keyHasChangedFor(e)||this.doesEleInvalidateKey(e)}},{key:"getCachesAt",value:function(e){var t=this.cachesByLvl,n=this.lvls,r=t.get(e);return r||(r=new ze,t.set(e,r),n.push(e)),r}},{key:"getCache",value:function(e,t){return this.getCachesAt(t).get(e)}},{key:"get",value:function(e,t){var n=this.getKey(e),r=this.getCache(n,t);return null!=r&&this.updateKeyMappingFor(e),r}},{key:"getForCachedKey",value:function(e,t){var n=this.keyForId.get(e.id());return this.getCache(n,t)}},{key:"hasCache",value:function(e,t){return this.getCachesAt(t).has(e)}},{key:"has",value:function(e,t){var n=this.getKey(e);return this.hasCache(n,t)}},{key:"setCache",value:function(e,t,n){n.key=e,this.getCachesAt(t).set(e,n)}},{key:"set",value:function(e,t,n){var r=this.getKey(e);this.setCache(r,t,n),this.updateKeyMappingFor(e)}},{key:"deleteCache",value:function(e,t){this.getCachesAt(t).delete(e)}},{key:"delete",value:function(e,t){var n=this.getKey(e);this.deleteCache(n,t)}},{key:"invalidateKey",value:function(e){var t=this;this.lvls.forEach((function(n){return t.deleteCache(e,n)}))}},{key:"invalidate",value:function(e){var t=e.id(),n=this.keyForId.get(t);this.deleteKeyMappingFor(e);var r=this.doesEleInvalidateKey(e);return r&&this.invalidateKey(n),r||0===this.getNumberOfIdsForKey(n)}}]),e}(),Ss={dequeue:"dequeue",downscale:"downscale",highQuality:"highQuality"},Cs=Le({getKey:null,doesEleInvalidateKey:Ee,drawElement:null,getBoundingBox:null,getRotationPoint:null,getRotationOffset:null,isVisible:ke,allowEdgeTxrCaching:!0,allowParentTxrCaching:!0}),Ps=function(e,t){var n=this;n.renderer=e,n.onDequeues=[];var r=Cs(t);J(n,r),n.lookup=new Es(r.getKey,r.doesEleInvalidateKey),n.setupDequeueing()},Ts=Ps.prototype;Ts.reasons=Ss,Ts.getTextureQueue=function(e){var t=this;return t.eleImgCaches=t.eleImgCaches||{},t.eleImgCaches[e]=t.eleImgCaches[e]||[]},Ts.getRetiredTextureQueue=function(e){var t=this.eleImgCaches.retired=this.eleImgCaches.retired||{};return t[e]=t[e]||[]},Ts.getElementQueue=function(){return this.eleCacheQueue=this.eleCacheQueue||new c.default((function(e,t){return t.reqs-e.reqs}))},Ts.getElementKeyToQueue=function(){return this.eleKeyToCacheQueue=this.eleKeyToCacheQueue||{}},Ts.getElement=function(e,t,n,r,i){var o=this,a=this.renderer,s=a.cy.zoom(),l=this.lookup;if(!t||0===t.w||0===t.h||isNaN(t.w)||isNaN(t.h)||!e.visible()||e.removed())return null;if(!o.allowEdgeTxrCaching&&e.isEdge()||!o.allowParentTxrCaching&&e.isParent())return null;if(null==r&&(r=Math.ceil(lt(s*n))),r<-4)r=-4;else if(s>=7.99||r>3)return null;var 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t=this;t.data={canvases:new Array(fl.CANVAS_LAYERS),contexts:new Array(fl.CANVAS_LAYERS),canvasNeedsRedraw:new Array(fl.CANVAS_LAYERS),bufferCanvases:new Array(fl.BUFFER_COUNT),bufferContexts:new Array(fl.CANVAS_LAYERS)};var n="-webkit-tap-highlight-color",r="rgba(0,0,0,0)";t.data.canvasContainer=document.createElement("div");var i=t.data.canvasContainer.style;t.data.canvasContainer.style[n]=r,i.position="relative",i.zIndex="0",i.overflow="hidden";var o=e.cy.container();o.appendChild(t.data.canvasContainer),o.style[n]=r;var a={"-webkit-user-select":"none","-moz-user-select":"-moz-none","user-select":"none","-webkit-tap-highlight-color":"rgba(0,0,0,0)","outline-style":"none"};q()&&(a["-ms-touch-action"]="none",a["touch-action"]="none");for(var s=0;s0;--l)if(r=t[l].dequeue()){i=i.concat(s(e,t,n,r,!0));break}}return i}(n.graph,n.buckets,n.zeroIdx);return r.flatten(r.map(u,(function(t){return e.outEdges(t.v,t.w)})),!0)};var a=r.constant(1);function s(e,t,n,i,o){var a=o?[]:void 0;return 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