mirror of
https://github.com/vegu-ai/talemate.git
synced 2025-12-16 11:47:48 +01:00
Prep 0.16.0 (#40)
* remove dbg message * more work to make clients and agents modular allow conversation and narrator to attempt to auto break AI repetition * application settings refactor setup third party api keys through application settings * runpod docs * fix wording * docs * improvements to auto-break-repetition functionality * more auto-break-repetition improvements * some cleanup to narrate on dialogue chance calculations * changing api keys via ux should now reflect to ux instantly. * memory agent / chromadb agent - wrap blocking functions calls in asyncio * clean up narrate progression prompt and function * turn off dedupe debug message for now * encourage the AI to break repetition as well * indicate if the current model is missing a LLM prompt template add prompt template to client modal fix a bunch of bad vue code * only show llm prompt when editing client * OpenHermes-2.5-neural-chat RpBird-Yi-34B * fix bug with auto rep break when no repetition was found * allow giving extra instructions to narrator agent * emit agents as needed, not constantly * fix a bunch of vue alerts * fix request-client-status event * remove undefined reference * log client / status emit * worldstate component track scene time * Tess Noromaid * fix narrate-character prompt context length overflow issues * disable worldstate refresh button while waiting for response * history timestamp moved to tooltip off of history button * fixes #39: using openai embeddings for chromadb tends to error * adjust conversation again default instructions * poetry lock * remove debug message * chromadb - agent status error if openai embeddings are selected in api key isn't set * prep 0.16.0
This commit is contained in:
@@ -171,5 +171,5 @@ Please read the documents in the `docs` folder for more advanced configuration a
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- [Prompt template overrides](docs/templates.md)
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- [Text-to-Speech (TTS)](docs/tts.md)
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- [ChromaDB (long term memory)](docs/chromadb.md)
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- Runpod Integration
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- [Runpod Integration](docs/runpod.md)
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- Creative mode
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docs/img/runpod-docs-1.png
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docs/img/runpod-docs-1.png
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docs/runpod.md
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docs/runpod.md
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@@ -0,0 +1,52 @@
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## RunPod integration
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RunPod allows you to quickly set up and run text-generation-webui instances on powerful GPUs, remotely. If you want to run the significantly larger models (like 70B parameters) with reasonable speeds, this is probably the best way to do it.
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### Create / grab your RunPod API key and add it to the talemate config
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You can manage your RunPod api keys at [https://www.runpod.io/console/user/settings](https://www.runpod.io/console/user/settings)
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Add the key to your Talemate config file (config.yaml):
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```yaml
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runpod:
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api_key: <your api key>
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```
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Then restart Talemate.
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### Create a RunPod instance
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#### Community Cloud
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The community cloud pods are cheaper and there are generally more GPUs available. They do however not support persistent storage and you will have to download your model and data every time you deploy a pod.
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#### Secure Cloud
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The secure cloud pods are more expensive and there are generally fewer GPUs available, but they do support persistent storage.
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Peristent volumes are super convenient, but optional for our purposes and are **not** free and you will have to pay for the storage you use.
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### Deploy pod
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For us it does not matter which cloud you choose. The only thing that matters is that it deploys a text-generation-webui instance, and you ensure that by choosing the right template.
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Pick the GPU you want to use, for 70B models you want at least 48GB of VRAM and click `Deploy`, then select a template and deploy.
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When choosing the template for your pod, choose the `RunPod TheBloke LLMs` template. This template is pre-configured with all the dependencies needed to run text-generation-webui. There are other text-generation-webui templates, but they are usually out of date and this one i found to be consistently good.
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> :warning: The name of your pod is important and ensures that Talemate will be able to find it. Talemate will only be able to find pods that have the word `thebloke llms` or `textgen` in their name. (case insensitive)
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Once your pod is deployed and has finished setup and is running, the client will automatically appear in the Talemate client list, making it available for you to use like you would use a locally hosted text-generation-webui instance.
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### Connecting to the text-generation-webui UI
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To manage your text-generation-webui instance, click the `Connect` button in your RunPod pod dashboard at [https://www.runpod.io/console/pods](https://www.runpod.io/console/pods) and in the popup click on `Connect to HTTP Service [Port 7860]` to open the text-generation-webui UI. Then just download and load your model as you normally would.
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## :warning: Always check your pod status on the RunPod dashboard
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Talemate is not a suitable or reliable way for you to determine whether your pod is currently running or not. **Always** check the runpod dashboard to see if your pod is running or not.
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While your pod us running it will be eating up your credits, so make sure to stop it when you're not using it.
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744
poetry.lock
generated
744
poetry.lock
generated
@@ -345,17 +345,17 @@ files = [
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[[package]]
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name = "boto3"
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version = "1.33.4"
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version = "1.33.10"
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description = "The AWS SDK for Python"
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optional = false
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python-versions = ">= 3.7"
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files = [
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{file = "boto3-1.33.4-py3-none-any.whl", hash = "sha256:7668aef805fe53e487e3742cad0ffffe5fb62c3752514dd28ef81bad5ff0cb38"},
|
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{file = "boto3-1.33.4.tar.gz", hash = "sha256:f26391fce6fec478fa1ab92f1ea80e4d057dd6240cc6fbe4e38fb3fce4cc8026"},
|
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{file = "boto3-1.33.10-py3-none-any.whl", hash = "sha256:8011445d95499ca9622d8d368ce469d51bc9e82a23b6edd6498f9ae57d69ce9e"},
|
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{file = "boto3-1.33.10.tar.gz", hash = "sha256:407b26197aced386e7ce2327ca9199b506dcf248220cfb8fc89d06497dd4522b"},
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]
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[package.dependencies]
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botocore = ">=1.33.4,<1.34.0"
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botocore = ">=1.33.10,<1.34.0"
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jmespath = ">=0.7.1,<2.0.0"
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s3transfer = ">=0.8.2,<0.9.0"
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@@ -364,13 +364,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
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[[package]]
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name = "botocore"
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version = "1.33.4"
|
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version = "1.33.10"
|
||||
description = "Low-level, data-driven core of boto 3."
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optional = false
|
||||
python-versions = ">= 3.7"
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files = [
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||||
{file = "botocore-1.33.4-py3-none-any.whl", hash = "sha256:bbd96c211b670d17191d617423f3c4c277964eeb586a33b3759691c33904629d"},
|
||||
{file = "botocore-1.33.4.tar.gz", hash = "sha256:872decbc760c3b2942477cda905d4443bd8a97511dcee3e9ca09eeb9299ad5e2"},
|
||||
{file = "botocore-1.33.10-py3-none-any.whl", hash = "sha256:9619609692a7f99f33093a305a0fb88ca5c83104f97ead3a4405b1e4ba6058f9"},
|
||||
{file = "botocore-1.33.10.tar.gz", hash = "sha256:82be3da9ceac9d847d115a80f0a0dae020c3534ef88839ef907eb3205309fd4a"},
|
||||
]
|
||||
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[package.dependencies]
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||||
@@ -403,6 +403,70 @@ files = [
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||||
{file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
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]
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[[package]]
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name = "cffi"
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version = "1.16.0"
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description = "Foreign Function Interface for Python calling C code."
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optional = false
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python-versions = ">=3.8"
|
||||
files = [
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||||
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||||
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||||
{file = "cffi-1.16.0.tar.gz", hash = "sha256:bcb3ef43e58665bbda2fb198698fcae6776483e0c4a631aa5647806c25e02cc0"},
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pycparser = "*"
|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.3.2"
|
||||
@@ -620,6 +684,51 @@ humanfriendly = ">=9.1"
|
||||
[package.extras]
|
||||
cron = ["capturer (>=2.4)"]
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||||
|
||||
[[package]]
|
||||
name = "cryptography"
|
||||
version = "41.0.7"
|
||||
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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|
||||
[package.dependencies]
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||||
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||||
|
||||
[package.extras]
|
||||
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||||
docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
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||||
nox = ["nox"]
|
||||
pep8test = ["black", "check-sdist", "mypy", "ruff"]
|
||||
sdist = ["build"]
|
||||
ssh = ["bcrypt (>=3.1.5)"]
|
||||
test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
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||||
test-randomorder = ["pytest-randomly"]
|
||||
|
||||
[[package]]
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||||
name = "deprecated"
|
||||
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||||
@@ -826,13 +935,13 @@ files = [
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[[package]]
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||||
name = "fsspec"
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version = "2023.10.0"
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||||
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||||
description = "File-system specification"
|
||||
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||||
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||||
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||||
[package.extras]
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||||
@@ -861,13 +970,13 @@ tqdm = ["tqdm"]
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||||
|
||||
[[package]]
|
||||
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|
||||
version = "2.24.0"
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
[package.dependencies]
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||||
@@ -884,13 +993,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
|
||||
|
||||
[[package]]
|
||||
name = "googleapis-common-protos"
|
||||
version = "1.61.0"
|
||||
version = "1.62.0"
|
||||
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|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
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||||
|
||||
[package.dependencies]
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||||
@@ -901,69 +1010,69 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
[package.extras]
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||||
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||||
protobuf = ["grpcio-tools (>=1.60.0)"]
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||||
|
||||
[[package]]
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||||
name = "h11"
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||||
@@ -1129,20 +1238,20 @@ files = [
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[[package]]
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||||
name = "importlib-metadata"
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[package.dependencies]
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||||
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||||
[package.extras]
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||||
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||||
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||||
perf = ["ipython"]
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||||
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
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@@ -1172,6 +1281,24 @@ files = [
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[[package]]
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||||
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[package.dependencies]
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prompt-toolkit = ">=3.0.1,<4.0.0"
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[package.extras]
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||||
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||||
[[package]]
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||||
name = "instructorembedding"
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||||
version = "1.0.1"
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||||
@@ -1950,13 +2077,13 @@ sympy = "*"
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||||
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||||
[[package]]
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||||
name = "openai"
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description = "The official Python library for the openai API"
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[package.dependencies]
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@@ -2218,6 +2345,27 @@ files = [
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||||
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||||
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||||
description = "SSH2 protocol library"
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||||
optional = false
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python-versions = ">=3.6"
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||||
{file = "paramiko-3.3.1.tar.gz", hash = "sha256:6a3777a961ac86dbef375c5f5b8d50014a1a96d0fd7f054a43bc880134b0ff77"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
bcrypt = ">=3.2"
|
||||
cryptography = ">=3.3"
|
||||
pynacl = ">=1.5"
|
||||
|
||||
[package.extras]
|
||||
all = ["gssapi (>=1.4.1)", "invoke (>=2.0)", "pyasn1 (>=0.1.7)", "pywin32 (>=2.1.8)"]
|
||||
gssapi = ["gssapi (>=1.4.1)", "pyasn1 (>=0.1.7)", "pywin32 (>=2.1.8)"]
|
||||
invoke = ["invoke (>=2.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "parso"
|
||||
version = "0.8.3"
|
||||
@@ -2244,6 +2392,20 @@ files = [
|
||||
{file = "pathspec-0.11.2.tar.gz", hash = "sha256:e0d8d0ac2f12da61956eb2306b69f9469b42f4deb0f3cb6ed47b9cce9996ced3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pfzy"
|
||||
version = "0.3.4"
|
||||
description = "Python port of the fzy fuzzy string matching algorithm"
|
||||
optional = false
|
||||
python-versions = ">=3.7,<4.0"
|
||||
files = [
|
||||
{file = "pfzy-0.3.4-py3-none-any.whl", hash = "sha256:5f50d5b2b3207fa72e7ec0ef08372ef652685470974a107d0d4999fc5a903a96"},
|
||||
{file = "pfzy-0.3.4.tar.gz", hash = "sha256:717ea765dd10b63618e7298b2d98efd819e0b30cd5905c9707223dceeb94b3f1"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["Sphinx (>=4.1.2,<5.0.0)", "furo (>=2021.8.17-beta.43,<2022.0.0)", "myst-parser (>=0.15.1,<0.16.0)", "sphinx-autobuild (>=2021.3.14,<2022.0.0)", "sphinx-copybutton (>=0.4.0,<0.5.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "piexif"
|
||||
version = "1.1.3"
|
||||
@@ -2336,13 +2498,13 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
|
||||
|
||||
[[package]]
|
||||
name = "platformdirs"
|
||||
version = "4.0.0"
|
||||
version = "4.1.0"
|
||||
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "platformdirs-4.0.0-py3-none-any.whl", hash = "sha256:118c954d7e949b35437270383a3f2531e99dd93cf7ce4dc8340d3356d30f173b"},
|
||||
{file = "platformdirs-4.0.0.tar.gz", hash = "sha256:cb633b2bcf10c51af60beb0ab06d2f1d69064b43abf4c185ca6b28865f3f9731"},
|
||||
{file = "platformdirs-4.1.0-py3-none-any.whl", hash = "sha256:11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380"},
|
||||
{file = "platformdirs-4.1.0.tar.gz", hash = "sha256:906d548203468492d432bcb294d4bc2fff751bf84971fbb2c10918cc206ee420"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@@ -2366,13 +2528,13 @@ testing = ["pytest", "pytest-benchmark"]
|
||||
|
||||
[[package]]
|
||||
name = "posthog"
|
||||
version = "3.0.2"
|
||||
version = "3.1.0"
|
||||
description = "Integrate PostHog into any python application."
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "posthog-3.0.2-py2.py3-none-any.whl", hash = "sha256:a8c0af6f2401fbe50f90e68c4143d0824b54e872de036b1c2f23b5abb39d88ce"},
|
||||
{file = "posthog-3.0.2.tar.gz", hash = "sha256:701fba6e446a4de687c6e861b587e7b7741955ad624bf34fe013c06a0fec6fb3"},
|
||||
{file = "posthog-3.1.0-py2.py3-none-any.whl", hash = "sha256:acd033530bdfc275dce5587f205f62378991ecb9b7cd5479e79c7f4ac575d319"},
|
||||
{file = "posthog-3.1.0.tar.gz", hash = "sha256:db17a2c511e18757aec12b6632ddcc1fa318743dad88a4666010467a3d9468da"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -2385,7 +2547,38 @@ six = ">=1.5"
|
||||
[package.extras]
|
||||
dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
|
||||
sentry = ["django", "sentry-sdk"]
|
||||
test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest"]
|
||||
test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest", "pytest-timeout"]
|
||||
|
||||
[[package]]
|
||||
name = "prettytable"
|
||||
version = "3.9.0"
|
||||
description = "A simple Python library for easily displaying tabular data in a visually appealing ASCII table format"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "prettytable-3.9.0-py3-none-any.whl", hash = "sha256:a71292ab7769a5de274b146b276ce938786f56c31cf7cea88b6f3775d82fe8c8"},
|
||||
{file = "prettytable-3.9.0.tar.gz", hash = "sha256:f4ed94803c23073a90620b201965e5dc0bccf1760b7a7eaf3158cab8aaffdf34"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
wcwidth = "*"
|
||||
|
||||
[package.extras]
|
||||
tests = ["pytest", "pytest-cov", "pytest-lazy-fixture"]
|
||||
|
||||
[[package]]
|
||||
name = "prompt-toolkit"
|
||||
version = "3.0.41"
|
||||
description = "Library for building powerful interactive command lines in Python"
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "prompt_toolkit-3.0.41-py3-none-any.whl", hash = "sha256:f36fe301fafb7470e86aaf90f036eef600a3210be4decf461a5b1ca8403d3cb2"},
|
||||
{file = "prompt_toolkit-3.0.41.tar.gz", hash = "sha256:941367d97fc815548822aa26c2a269fdc4eb21e9ec05fc5d447cf09bad5d75f0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
wcwidth = "*"
|
||||
|
||||
[[package]]
|
||||
name = "protobuf"
|
||||
@@ -2501,6 +2694,17 @@ files = [
|
||||
[package.dependencies]
|
||||
pyasn1 = ">=0.4.6,<0.6.0"
|
||||
|
||||
[[package]]
|
||||
name = "pycparser"
|
||||
version = "2.21"
|
||||
description = "C parser in Python"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
|
||||
files = [
|
||||
{file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
|
||||
{file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.5.2"
|
||||
@@ -2669,6 +2873,32 @@ files = [
|
||||
pydantic = ">=2.3.0"
|
||||
python-dotenv = ">=0.21.0"
|
||||
|
||||
[[package]]
|
||||
name = "pynacl"
|
||||
version = "1.5.0"
|
||||
description = "Python binding to the Networking and Cryptography (NaCl) library"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-macosx_10_10_universal2.whl", hash = "sha256:401002a4aaa07c9414132aaed7f6836ff98f59277a234704ff66878c2ee4a0d1"},
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_24_aarch64.whl", hash = "sha256:52cb72a79269189d4e0dc537556f4740f7f0a9ec41c1322598799b0bdad4ef92"},
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a36d4a9dda1f19ce6e03c9a784a2921a4b726b02e1c736600ca9c22029474394"},
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl", hash = "sha256:0c84947a22519e013607c9be43706dd42513f9e6ae5d39d3613ca1e142fba44d"},
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:06b8f6fa7f5de8d5d2f7573fe8c863c051225a27b61e6860fd047b1775807858"},
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:a422368fc821589c228f4c49438a368831cb5bbc0eab5ebe1d7fac9dded6567b"},
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:61f642bf2378713e2c2e1de73444a3778e5f0a38be6fee0fe532fe30060282ff"},
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-win32.whl", hash = "sha256:e46dae94e34b085175f8abb3b0aaa7da40767865ac82c928eeb9e57e1ea8a543"},
|
||||
{file = "PyNaCl-1.5.0-cp36-abi3-win_amd64.whl", hash = "sha256:20f42270d27e1b6a29f54032090b972d97f0a1b0948cc52392041ef7831fee93"},
|
||||
{file = "PyNaCl-1.5.0.tar.gz", hash = "sha256:8ac7448f09ab85811607bdd21ec2464495ac8b7c66d146bf545b0f08fb9220ba"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cffi = ">=1.4.1"
|
||||
|
||||
[package.extras]
|
||||
docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"]
|
||||
tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pypika"
|
||||
version = "0.48.9"
|
||||
@@ -3083,32 +3313,36 @@ pyasn1 = ">=0.1.3"
|
||||
|
||||
[[package]]
|
||||
name = "runpod"
|
||||
version = "1.2.0"
|
||||
version = "1.3.7"
|
||||
description = "🐍 | Python library for RunPod API and serverless worker SDK."
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "runpod-1.2.0-py3-none-any.whl", hash = "sha256:2faf3742ed4e9dc80f4b737bcdebd4527efed621be99a8934d5c81f89de4d3ed"},
|
||||
{file = "runpod-1.2.0.tar.gz", hash = "sha256:505d4386a6e9018f28ac51b9dbbe37020d0f72b291da231ce2bae7348259d22f"},
|
||||
{file = "runpod-1.3.7-py3-none-any.whl", hash = "sha256:b401abd6613961d9b46dc40d60f1549349ebf15aaab27d116230d885fbf3dca8"},
|
||||
{file = "runpod-1.3.7.tar.gz", hash = "sha256:87c70b00f299d8ddfd66466204cbc9a16aa465dbfdc245106ed7006c37b329c3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = ">=3.8.4"
|
||||
aiohttp = "3.9.1"
|
||||
aiohttp-retry = ">=2.8.3"
|
||||
backoff = ">=2.2.1"
|
||||
boto3 = ">=1.26.165"
|
||||
click = ">=8.1.7"
|
||||
fastapi = {version = ">=0.99.0", extras = ["all"]}
|
||||
pillow = ">=9.5.0"
|
||||
colorama = ">=0.4.6"
|
||||
fastapi = {version = ">=0.94.0", extras = ["all"]}
|
||||
inquirerpy = "0.3.4"
|
||||
paramiko = ">=3.3.1"
|
||||
prettytable = ">=3.9.0"
|
||||
py-cpuinfo = ">=9.0.0"
|
||||
python-dotenv = ">=1.0.0"
|
||||
requests = ">=2.31.0"
|
||||
tomli = ">=2.0.1"
|
||||
tomlkit = ">=0.12.2"
|
||||
tqdm-loggable = ">=0.1.4"
|
||||
urllib3 = ">=1.26.6"
|
||||
watchdog = ">=3.0.0"
|
||||
|
||||
[package.extras]
|
||||
test = ["asynctest", "nest-asyncio", "pytest", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
|
||||
test = ["asynctest", "nest-asyncio", "pylint", "pytest", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
|
||||
|
||||
[[package]]
|
||||
name = "s3transfer"
|
||||
@@ -3549,40 +3783,47 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "tiktoken"
|
||||
version = "0.5.1"
|
||||
version = "0.5.2"
|
||||
description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "tiktoken-0.5.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2b0bae3fd56de1c0a5874fb6577667a3c75bf231a6cef599338820210c16e40a"},
|
||||
{file = "tiktoken-0.5.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e529578d017045e2f0ed12d2e00e7e99f780f477234da4aae799ec4afca89f37"},
|
||||
{file = "tiktoken-0.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:edd2ffbb789712d83fee19ab009949f998a35c51ad9f9beb39109357416344ff"},
|
||||
{file = "tiktoken-0.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4c73d47bdc1a3f1f66ffa019af0386c48effdc6e8797e5e76875f6388ff72e9"},
|
||||
{file = "tiktoken-0.5.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:46b8554b9f351561b1989157c6bb54462056f3d44e43aa4e671367c5d62535fc"},
|
||||
{file = "tiktoken-0.5.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:92ed3bbf71a175a6a4e5fbfcdb2c422bdd72d9b20407e00f435cf22a68b4ea9b"},
|
||||
{file = "tiktoken-0.5.1-cp310-cp310-win_amd64.whl", hash = "sha256:714efb2f4a082635d9f5afe0bf7e62989b72b65ac52f004eb7ac939f506c03a4"},
|
||||
{file = "tiktoken-0.5.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a10488d1d1a5f9c9d2b2052fdb4cf807bba545818cb1ef724a7f5d44d9f7c3d4"},
|
||||
{file = "tiktoken-0.5.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8079ac065572fe0e7c696dbd63e1fdc12ce4cdca9933935d038689d4732451df"},
|
||||
{file = "tiktoken-0.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ef730db4097f5b13df8d960f7fdda2744fe21d203ea2bb80c120bb58661b155"},
|
||||
{file = "tiktoken-0.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:426e7def5f3f23645dada816be119fa61e587dfb4755de250e136b47a045c365"},
|
||||
{file = "tiktoken-0.5.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:323cec0031358bc09aa965c2c5c1f9f59baf76e5b17e62dcc06d1bb9bc3a3c7c"},
|
||||
{file = "tiktoken-0.5.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5abd9436f02e2c8eda5cce2ff8015ce91f33e782a7423de2a1859f772928f714"},
|
||||
{file = "tiktoken-0.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:1fe99953b63aabc0c9536fbc91c3c9000d78e4755edc28cc2e10825372046a2d"},
|
||||
{file = "tiktoken-0.5.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:dcdc630461927718b317e6f8be7707bd0fc768cee1fdc78ddaa1e93f4dc6b2b1"},
|
||||
{file = "tiktoken-0.5.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:1f2b3b253e22322b7f53a111e1f6d7ecfa199b4f08f3efdeb0480f4033b5cdc6"},
|
||||
{file = "tiktoken-0.5.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:43ce0199f315776dec3ea7bf86f35df86d24b6fcde1babd3e53c38f17352442f"},
|
||||
{file = "tiktoken-0.5.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a84657c083d458593c0235926b5c993eec0b586a2508d6a2020556e5347c2f0d"},
|
||||
{file = "tiktoken-0.5.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:c008375c0f3d97c36e81725308699116cd5804fdac0f9b7afc732056329d2790"},
|
||||
{file = "tiktoken-0.5.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:779c4dea5edd1d3178734d144d32231e0b814976bec1ec09636d1003ffe4725f"},
|
||||
{file = "tiktoken-0.5.1-cp38-cp38-win_amd64.whl", hash = "sha256:b5dcfcf9bfb798e86fbce76d40a1d5d9e3f92131aecfa3d1e5c9ea1a20f1ef1a"},
|
||||
{file = "tiktoken-0.5.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9b180a22db0bbcc447f691ffc3cf7a580e9e0587d87379e35e58b826ebf5bc7b"},
|
||||
{file = "tiktoken-0.5.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2b756a65d98b7cf760617a6b68762a23ab8b6ef79922be5afdb00f5e8a9f4e76"},
|
||||
{file = "tiktoken-0.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ba9873c253ca1f670e662192a0afcb72b41e0ba3e730f16c665099e12f4dac2d"},
|
||||
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||||
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||||
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||||
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||||
{file = "tiktoken-0.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42adf7d4fb1ed8de6e0ff2e794a6a15005f056a0d83d22d1d6755a39bffd9e7f"},
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||||
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||||
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||||
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||||
{file = "tiktoken-0.5.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2ddee082dcf1231ccf3a591d234935e6acf3e82ee28521fe99af9630bc8d2a60"},
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||||
{file = "tiktoken-0.5.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:35c057a6a4e777b5966a7540481a75a31429fc1cb4c9da87b71c8b75b5143037"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c4a049b87e28f1dc60509f8eb7790bc8d11f9a70d99b9dd18dfdd81a084ffe6"},
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||||
{file = "tiktoken-0.5.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5bf5ce759089f4f6521ea6ed89d8f988f7b396e9f4afb503b945f5c949c6bec2"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:0c964f554af1a96884e01188f480dad3fc224c4bbcf7af75d4b74c4b74ae0125"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-win_amd64.whl", hash = "sha256:368dd5726d2e8788e47ea04f32e20f72a2012a8a67af5b0b003d1e059f1d30a3"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a2deef9115b8cd55536c0a02c0203512f8deb2447f41585e6d929a0b878a0dd2"},
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||||
{file = "tiktoken-0.5.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2ed7d380195affbf886e2f8b92b14edfe13f4768ff5fc8de315adba5b773815e"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c76fce01309c8140ffe15eb34ded2bb94789614b7d1d09e206838fc173776a18"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:60a5654d6a2e2d152637dd9a880b4482267dfc8a86ccf3ab1cec31a8c76bfae8"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:41d4d3228e051b779245a8ddd21d4336f8975563e92375662f42d05a19bdff41"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a5c1cdec2c92fcde8c17a50814b525ae6a88e8e5b02030dc120b76e11db93f13"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-win_amd64.whl", hash = "sha256:84ddb36faedb448a50b246e13d1b6ee3437f60b7169b723a4b2abad75e914f3e"},
|
||||
{file = "tiktoken-0.5.2.tar.gz", hash = "sha256:f54c581f134a8ea96ce2023ab221d4d4d81ab614efa0b2fbce926387deb56c80"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3729,6 +3970,17 @@ files = [
|
||||
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tomlkit"
|
||||
version = "0.12.3"
|
||||
description = "Style preserving TOML library"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
{file = "tomlkit-0.12.3-py3-none-any.whl", hash = "sha256:b0a645a9156dc7cb5d3a1f0d4bab66db287fcb8e0430bdd4664a095ea16414ba"},
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||||
{file = "tomlkit-0.12.3.tar.gz", hash = "sha256:75baf5012d06501f07bee5bf8e801b9f343e7aac5a92581f20f80ce632e6b5a4"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.1.1"
|
||||
@@ -4150,6 +4402,45 @@ files = [
|
||||
docs = ["Sphinx (>=4.1.2,<4.2.0)", "sphinx-rtd-theme (>=0.5.2,<0.6.0)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
|
||||
test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)", "flake8 (>=5.0,<6.0)", "mypy (>=0.800)", "psutil", "pyOpenSSL (>=23.0.0,<23.1.0)", "pycodestyle (>=2.9.0,<2.10.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "watchdog"
|
||||
version = "3.0.0"
|
||||
description = "Filesystem events monitoring"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:336adfc6f5cc4e037d52db31194f7581ff744b67382eb6021c868322e32eef41"},
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
{file = "watchdog-3.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3aa7f6a12e831ddfe78cdd4f8996af9cf334fd6346531b16cec61c3b3c0d8da0"},
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||||
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||||
{file = "watchdog-3.0.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:13bbbb462ee42ec3c5723e1205be8ced776f05b100e4737518c67c8325cf6100"},
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||||
{file = "watchdog-3.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8f3ceecd20d71067c7fd4c9e832d4e22584318983cabc013dbf3f70ea95de346"},
|
||||
{file = "watchdog-3.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:c9d8c8ec7efb887333cf71e328e39cffbf771d8f8f95d308ea4125bf5f90ba64"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0e06ab8858a76e1219e68c7573dfeba9dd1c0219476c5a44d5333b01d7e1743a"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:d00e6be486affb5781468457b21a6cbe848c33ef43f9ea4a73b4882e5f188a44"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:c07253088265c363d1ddf4b3cdb808d59a0468ecd017770ed716991620b8f77a"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:5113334cf8cf0ac8cd45e1f8309a603291b614191c9add34d33075727a967709"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:51f90f73b4697bac9c9a78394c3acbbd331ccd3655c11be1a15ae6fe289a8c83"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:ba07e92756c97e3aca0912b5cbc4e5ad802f4557212788e72a72a47ff376950d"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:d429c2430c93b7903914e4db9a966c7f2b068dd2ebdd2fa9b9ce094c7d459f33"},
|
||||
{file = "watchdog-3.0.0-py3-none-win32.whl", hash = "sha256:3ed7c71a9dccfe838c2f0b6314ed0d9b22e77d268c67e015450a29036a81f60f"},
|
||||
{file = "watchdog-3.0.0-py3-none-win_amd64.whl", hash = "sha256:4c9956d27be0bb08fc5f30d9d0179a855436e655f046d288e2bcc11adfae893c"},
|
||||
{file = "watchdog-3.0.0-py3-none-win_ia64.whl", hash = "sha256:5d9f3a10e02d7371cd929b5d8f11e87d4bad890212ed3901f9b4d68767bee759"},
|
||||
{file = "watchdog-3.0.0.tar.gz", hash = "sha256:4d98a320595da7a7c5a18fc48cb633c2e73cda78f93cac2ef42d42bf609a33f9"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
watchmedo = ["PyYAML (>=3.10)"]
|
||||
|
||||
[[package]]
|
||||
name = "watchfiles"
|
||||
version = "0.21.0"
|
||||
@@ -4237,15 +4528,26 @@ files = [
|
||||
[package.dependencies]
|
||||
anyio = ">=3.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "wcwidth"
|
||||
version = "0.2.12"
|
||||
description = "Measures the displayed width of unicode strings in a terminal"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "wcwidth-0.2.12-py2.py3-none-any.whl", hash = "sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c"},
|
||||
{file = "wcwidth-0.2.12.tar.gz", hash = "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "websocket-client"
|
||||
version = "1.6.4"
|
||||
version = "1.7.0"
|
||||
description = "WebSocket client for Python with low level API options"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"},
|
||||
{file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"},
|
||||
{file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
|
||||
{file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@@ -4413,101 +4715,101 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "yarl"
|
||||
version = "1.9.3"
|
||||
version = "1.9.4"
|
||||
description = "Yet another URL library"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "yarl-1.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:32435d134414e01d937cd9d6cc56e8413a8d4741dea36af5840c7750f04d16ab"},
|
||||
{file = "yarl-1.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9a5211de242754b5e612557bca701f39f8b1a9408dff73c6db623f22d20f470e"},
|
||||
{file = "yarl-1.9.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:525cd69eff44833b01f8ef39aa33a9cc53a99ff7f9d76a6ef6a9fb758f54d0ff"},
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||||
|
||||
[package.dependencies]
|
||||
@@ -4532,4 +4834,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<4.0"
|
||||
content-hash = "5d4e6e5b3a1418ff9ac1a40d3401fd5a0d950473a7f068e349561ec6c1f3e1da"
|
||||
content-hash = "7e6598cf96a071893e7f3245bc0c198891352e80d8c06630051c3ad3c9e5591b"
|
||||
|
||||
@@ -4,7 +4,7 @@ build-backend = "poetry.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "talemate"
|
||||
version = "0.15.0"
|
||||
version = "0.16.0"
|
||||
description = "AI-backed roleplay and narrative tools"
|
||||
authors = ["FinalWombat"]
|
||||
license = "GNU Affero General Public License v3.0"
|
||||
@@ -32,7 +32,7 @@ beautifulsoup4 = "^4.12.2"
|
||||
python-dotenv = "^1.0.0"
|
||||
websockets = "^11.0.3"
|
||||
structlog = "^23.1.0"
|
||||
runpod = "==1.2.0"
|
||||
runpod = "^1.2.0"
|
||||
nest_asyncio = "^1.5.7"
|
||||
isodate = ">=0.6.1"
|
||||
thefuzz = ">=0.20.0"
|
||||
|
||||
@@ -2,4 +2,4 @@ from .agents import Agent
|
||||
from .client import TextGeneratorWebuiClient
|
||||
from .tale_mate import *
|
||||
|
||||
VERSION = "0.15.0"
|
||||
VERSION = "0.16.0"
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
from .base import Agent
|
||||
from .creator import CreatorAgent
|
||||
from .context import ContextAgent
|
||||
from .conversation import ConversationAgent
|
||||
from .director import DirectorAgent
|
||||
from .memory import ChromaDBMemoryAgent, MemoryAgent
|
||||
|
||||
@@ -9,6 +9,7 @@ from blinker import signal
|
||||
|
||||
import talemate.instance as instance
|
||||
import talemate.util as util
|
||||
from talemate.agents.context import ActiveAgent
|
||||
from talemate.emit import emit
|
||||
from talemate.events import GameLoopStartEvent
|
||||
import talemate.emit.async_signals
|
||||
@@ -23,15 +24,6 @@ __all__ = [
|
||||
|
||||
log = structlog.get_logger("talemate.agents.base")
|
||||
|
||||
class CallableConfigValue:
|
||||
def __init__(self, fn):
|
||||
self.fn = fn
|
||||
|
||||
def __str__(self):
|
||||
return "CallableConfigValue"
|
||||
|
||||
def __repr__(self):
|
||||
return "CallableConfigValue"
|
||||
|
||||
class AgentActionConfig(pydantic.BaseModel):
|
||||
type: str
|
||||
@@ -65,6 +57,7 @@ def set_processing(fn):
|
||||
"""
|
||||
|
||||
async def wrapper(self, *args, **kwargs):
|
||||
with ActiveAgent(self, fn):
|
||||
try:
|
||||
await self.emit_status(processing=True)
|
||||
return await fn(self, *args, **kwargs)
|
||||
@@ -85,6 +78,7 @@ class Agent(ABC):
|
||||
verbose_name = None
|
||||
set_processing = set_processing
|
||||
requires_llm_client = True
|
||||
auto_break_repetition = False
|
||||
|
||||
@property
|
||||
def agent_details(self):
|
||||
@@ -292,6 +286,22 @@ class Agent(ABC):
|
||||
current_memory_context.append(memory)
|
||||
return current_memory_context
|
||||
|
||||
# LLM client related methods. These are called during or after the client
|
||||
# sends the prompt to the API.
|
||||
|
||||
def inject_prompt_paramters(self, prompt_param:dict, kind:str, agent_function_name:str):
|
||||
"""
|
||||
Injects prompt parameters before the client sends off the prompt
|
||||
Override as needed.
|
||||
"""
|
||||
pass
|
||||
|
||||
def allow_repetition_break(self, kind:str, agent_function_name:str, auto:bool=False):
|
||||
"""
|
||||
Returns True if repetition breaking is allowed, False otherwise.
|
||||
"""
|
||||
return False
|
||||
|
||||
@dataclasses.dataclass
|
||||
class AgentEmission:
|
||||
agent: Agent
|
||||
@@ -1,54 +1,33 @@
|
||||
from .base import Agent
|
||||
from .registry import register
|
||||
|
||||
from typing import Callable, TYPE_CHECKING
|
||||
import contextvars
|
||||
import pydantic
|
||||
|
||||
@register
|
||||
class ContextAgent(Agent):
|
||||
"""
|
||||
Agent that helps retrieve context for the continuation
|
||||
of dialogue.
|
||||
"""
|
||||
|
||||
agent_type = "context"
|
||||
|
||||
def __init__(self, client, **kwargs):
|
||||
self.client = client
|
||||
|
||||
def determine_questions(self, scene_text):
|
||||
prompt = [
|
||||
"You are tasked to continue the following dialogue in a roleplaying session, but before you can do so you can ask three questions for extra context."
|
||||
"",
|
||||
"What are the questions you would ask?",
|
||||
"",
|
||||
"Known context and dialogue:" "",
|
||||
scene_text,
|
||||
"",
|
||||
"Questions:",
|
||||
"",
|
||||
__all__ = [
|
||||
"active_agent",
|
||||
]
|
||||
|
||||
prompt = "\n".join(prompt)
|
||||
active_agent = contextvars.ContextVar("active_agent", default=None)
|
||||
|
||||
questions = self.client.send_prompt(prompt, kind="question")
|
||||
class ActiveAgentContext(pydantic.BaseModel):
|
||||
agent: object
|
||||
fn: Callable
|
||||
|
||||
questions = self.clean_result(questions)
|
||||
class Config:
|
||||
arbitrary_types_allowed=True
|
||||
|
||||
return questions.split("\n")
|
||||
@property
|
||||
def action(self):
|
||||
return self.fn.__name__
|
||||
|
||||
def get_answer(self, question, context):
|
||||
prompt = [
|
||||
"Read the context and answer the question:",
|
||||
"",
|
||||
"Context:",
|
||||
"",
|
||||
context,
|
||||
"",
|
||||
f"Question: {question}",
|
||||
"Answer:",
|
||||
]
|
||||
class ActiveAgent:
|
||||
|
||||
prompt = "\n".join(prompt)
|
||||
def __init__(self, agent, fn):
|
||||
self.agent = ActiveAgentContext(agent=agent, fn=fn)
|
||||
|
||||
answer = self.client.send_prompt(prompt, kind="answer")
|
||||
answer = self.clean_result(answer)
|
||||
return answer
|
||||
def __enter__(self):
|
||||
self.token = active_agent.set(self.agent)
|
||||
|
||||
def __exit__(self, *args, **kwargs):
|
||||
active_agent.reset(self.token)
|
||||
return False
|
||||
|
||||
@@ -85,20 +85,25 @@ class ConversationAgent(Agent):
|
||||
"instructions": AgentActionConfig(
|
||||
type="text",
|
||||
label="Instructions",
|
||||
value="1-3 sentences.",
|
||||
value="Write 1-3 sentences. Never wax poetic.",
|
||||
description="Extra instructions to give the AI for dialog generatrion.",
|
||||
),
|
||||
"jiggle": AgentActionConfig(
|
||||
type="number",
|
||||
label="Jiggle",
|
||||
label="Jiggle (Increased Randomness)",
|
||||
description="If > 0.0 will cause certain generation parameters to have a slight random offset applied to them. The bigger the number, the higher the potential offset.",
|
||||
value=0.0,
|
||||
min=0.0,
|
||||
max=1.0,
|
||||
step=0.1,
|
||||
),
|
||||
)
|
||||
}
|
||||
),
|
||||
"auto_break_repetition": AgentAction(
|
||||
enabled = True,
|
||||
label = "Auto Break Repetition",
|
||||
description = "Will attempt to automatically break AI repetition.",
|
||||
),
|
||||
"natural_flow": AgentAction(
|
||||
enabled = True,
|
||||
label = "Natural Flow",
|
||||
@@ -131,7 +136,7 @@ class ConversationAgent(Agent):
|
||||
config = {
|
||||
"ai_selected": AgentActionConfig(
|
||||
type="bool",
|
||||
label="AI Selected",
|
||||
label="AI memory retrieval",
|
||||
description="If enabled, the AI will select the long term memory to use. (will increase how long it takes to generate a response)",
|
||||
value=False,
|
||||
),
|
||||
@@ -534,3 +539,11 @@ class ConversationAgent(Agent):
|
||||
actor.scene.push_history(messages)
|
||||
|
||||
return messages
|
||||
|
||||
|
||||
def allow_repetition_break(self, kind: str, agent_function_name: str, auto: bool = False):
|
||||
|
||||
if auto and not self.actions["auto_break_repetition"].enabled:
|
||||
return False
|
||||
|
||||
return agent_function_name == "converse"
|
||||
@@ -6,10 +6,14 @@ from typing import TYPE_CHECKING, Callable, List, Optional, Union
|
||||
from chromadb.config import Settings
|
||||
import talemate.events as events
|
||||
import talemate.util as util
|
||||
from talemate.emit import emit
|
||||
from talemate.emit.signals import handlers
|
||||
from talemate.context import scene_is_loading
|
||||
from talemate.config import load_config
|
||||
from talemate.agents.base import set_processing
|
||||
import structlog
|
||||
import shutil
|
||||
import functools
|
||||
|
||||
try:
|
||||
import chromadb
|
||||
@@ -58,6 +62,15 @@ class MemoryAgent(Agent):
|
||||
self.memory_tracker = {}
|
||||
self.config = load_config()
|
||||
|
||||
handlers["config_saved"].connect(self.on_config_saved)
|
||||
|
||||
def on_config_saved(self, event):
|
||||
openai_key = self.openai_api_key
|
||||
self.config = load_config()
|
||||
if openai_key != self.openai_api_key:
|
||||
loop = asyncio.get_running_loop()
|
||||
loop.run_until_complete(self.emit_status())
|
||||
|
||||
async def set_db(self):
|
||||
raise NotImplementedError()
|
||||
|
||||
@@ -67,33 +80,43 @@ class MemoryAgent(Agent):
|
||||
async def count(self):
|
||||
raise NotImplementedError()
|
||||
|
||||
@set_processing
|
||||
async def add(self, text, character=None, uid=None, ts:str=None, **kwargs):
|
||||
if not text:
|
||||
return
|
||||
if self.readonly:
|
||||
log.debug("memory agent", status="readonly")
|
||||
return
|
||||
await self._add(text, character=character, uid=uid, ts=ts, **kwargs)
|
||||
|
||||
async def _add(self, text, character=None, ts:str=None, **kwargs):
|
||||
loop = asyncio.get_running_loop()
|
||||
|
||||
await loop.run_in_executor(None, functools.partial(self._add, text, character, uid=uid, ts=ts, **kwargs))
|
||||
|
||||
def _add(self, text, character=None, ts:str=None, **kwargs):
|
||||
raise NotImplementedError()
|
||||
|
||||
@set_processing
|
||||
async def add_many(self, objects: list[dict]):
|
||||
if self.readonly:
|
||||
log.debug("memory agent", status="readonly")
|
||||
return
|
||||
await self._add_many(objects)
|
||||
|
||||
async def _add_many(self, objects: list[dict]):
|
||||
loop = asyncio.get_running_loop()
|
||||
await loop.run_in_executor(None, self._add_many, objects)
|
||||
|
||||
def _add_many(self, objects: list[dict]):
|
||||
"""
|
||||
Add multiple objects to the memory
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
@set_processing
|
||||
async def get(self, text, character=None, **query):
|
||||
return await self._get(str(text), character, **query)
|
||||
loop = asyncio.get_running_loop()
|
||||
|
||||
async def _get(self, text, character=None, **query):
|
||||
return await loop.run_in_executor(None, functools.partial(self._get, text, character, **query))
|
||||
|
||||
def _get(self, text, character=None, **query):
|
||||
raise NotImplementedError()
|
||||
|
||||
def get_document(self, id):
|
||||
@@ -140,6 +163,10 @@ class MemoryAgent(Agent):
|
||||
"""
|
||||
|
||||
memory_context = []
|
||||
|
||||
if not query:
|
||||
return memory_context
|
||||
|
||||
for memory in await self.get(query):
|
||||
if memory in memory_context:
|
||||
continue
|
||||
@@ -179,6 +206,10 @@ class MemoryAgent(Agent):
|
||||
|
||||
memory_context = []
|
||||
for query in queries:
|
||||
|
||||
if not query:
|
||||
continue
|
||||
|
||||
i = 0
|
||||
for memory in await self.get(formatter(query), limit=iterate, **where):
|
||||
if memory in memory_context:
|
||||
@@ -210,6 +241,10 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
|
||||
@property
|
||||
def ready(self):
|
||||
|
||||
if self.embeddings == "openai" and not self.openai_api_key:
|
||||
return False
|
||||
|
||||
if getattr(self, "db_client", None):
|
||||
return True
|
||||
return False
|
||||
@@ -218,10 +253,18 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
def status(self):
|
||||
if self.ready:
|
||||
return "active" if not getattr(self, "processing", False) else "busy"
|
||||
|
||||
if self.embeddings == "openai" and not self.openai_api_key:
|
||||
return "error"
|
||||
|
||||
return "waiting"
|
||||
|
||||
@property
|
||||
def agent_details(self):
|
||||
|
||||
if self.embeddings == "openai" and not self.openai_api_key:
|
||||
return "No OpenAI API key set"
|
||||
|
||||
return f"ChromaDB: {self.embeddings}"
|
||||
|
||||
@property
|
||||
@@ -266,6 +309,10 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
def db_name(self):
|
||||
return getattr(self, "collection_name", "<unnamed>")
|
||||
|
||||
@property
|
||||
def openai_api_key(self):
|
||||
return self.config.get("openai",{}).get("api_key")
|
||||
|
||||
def make_collection_name(self, scene):
|
||||
|
||||
if self.USE_OPENAI:
|
||||
@@ -286,17 +333,19 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
await asyncio.sleep(0)
|
||||
return self.db.count()
|
||||
|
||||
@set_processing
|
||||
async def set_db(self):
|
||||
await self.emit_status(processing=True)
|
||||
|
||||
loop = asyncio.get_running_loop()
|
||||
await loop.run_in_executor(None, self._set_db)
|
||||
|
||||
def _set_db(self):
|
||||
if not getattr(self, "db_client", None):
|
||||
log.info("chromadb agent", status="setting up db client to persistent db")
|
||||
self.db_client = chromadb.PersistentClient(
|
||||
settings=Settings(anonymized_telemetry=False)
|
||||
)
|
||||
|
||||
openai_key = self.config.get("openai").get("api_key") or os.environ.get("OPENAI_API_KEY")
|
||||
openai_key = self.openai_api_key
|
||||
|
||||
self.collection_name = collection_name = self.make_collection_name(self.scene)
|
||||
|
||||
@@ -341,8 +390,6 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
self.db = self.db_client.get_or_create_collection(collection_name)
|
||||
|
||||
self.scene._memory_never_persisted = self.db.count() == 0
|
||||
|
||||
await self.emit_status(processing=False)
|
||||
log.info("chromadb agent", status="db ready")
|
||||
|
||||
def clear_db(self):
|
||||
@@ -383,12 +430,10 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
|
||||
self.db = None
|
||||
|
||||
async def _add(self, text, character=None, uid=None, ts:str=None, **kwargs):
|
||||
def _add(self, text, character=None, uid=None, ts:str=None, **kwargs):
|
||||
metadatas = []
|
||||
ids = []
|
||||
|
||||
await self.emit_status(processing=True)
|
||||
|
||||
if character:
|
||||
meta = {"character": character.name, "source": "talemate"}
|
||||
if ts:
|
||||
@@ -414,16 +459,12 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
|
||||
self.db.upsert(documents=[text], metadatas=metadatas, ids=ids)
|
||||
|
||||
await self.emit_status(processing=False)
|
||||
|
||||
async def _add_many(self, objects: list[dict]):
|
||||
def _add_many(self, objects: list[dict]):
|
||||
|
||||
documents = []
|
||||
metadatas = []
|
||||
ids = []
|
||||
|
||||
await self.emit_status(processing=True)
|
||||
|
||||
for obj in objects:
|
||||
documents.append(obj["text"])
|
||||
meta = obj.get("meta", {})
|
||||
@@ -436,11 +477,7 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
ids.append(uid)
|
||||
self.db.upsert(documents=documents, metadatas=metadatas, ids=ids)
|
||||
|
||||
await self.emit_status(processing=False)
|
||||
|
||||
async def _get(self, text, character=None, limit:int=15, **kwargs):
|
||||
await self.emit_status(processing=True)
|
||||
|
||||
def _get(self, text, character=None, limit:int=15, **kwargs):
|
||||
where = {}
|
||||
where.setdefault("$and", [])
|
||||
|
||||
@@ -480,7 +517,7 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
if distance < 1:
|
||||
|
||||
try:
|
||||
log.debug("chromadb agent get", ts=ts, scene_ts=self.scene.ts)
|
||||
#log.debug("chromadb agent get", ts=ts, scene_ts=self.scene.ts)
|
||||
date_prefix = util.iso8601_diff_to_human(ts, self.scene.ts)
|
||||
except Exception as e:
|
||||
log.error("chromadb agent", error="failed to get date prefix", details=e, ts=ts, scene_ts=self.scene.ts)
|
||||
@@ -497,6 +534,4 @@ class ChromaDBMemoryAgent(MemoryAgent):
|
||||
if len(results) > limit:
|
||||
break
|
||||
|
||||
await self.emit_status(processing=False)
|
||||
|
||||
return results
|
||||
|
||||
@@ -68,6 +68,24 @@ class NarratorAgent(Agent):
|
||||
# agent actions
|
||||
|
||||
self.actions = {
|
||||
"generation_override": AgentAction(
|
||||
enabled = True,
|
||||
label = "Generation Override",
|
||||
description = "Override generation parameters",
|
||||
config = {
|
||||
"instructions": AgentActionConfig(
|
||||
type="text",
|
||||
label="Instructions",
|
||||
value="Never wax poetic.",
|
||||
description="Extra instructions to give to the AI for narrative generation.",
|
||||
),
|
||||
}
|
||||
),
|
||||
"auto_break_repetition": AgentAction(
|
||||
enabled = True,
|
||||
label = "Auto Break Repetition",
|
||||
description = "Will attempt to automatically break AI repetition.",
|
||||
),
|
||||
"narrate_time_passage": AgentAction(enabled=True, label="Narrate Time Passage", description="Whenever you indicate passage of time, narrate right after"),
|
||||
"narrate_dialogue": AgentAction(
|
||||
enabled=True,
|
||||
@@ -102,6 +120,12 @@ class NarratorAgent(Agent):
|
||||
),
|
||||
}
|
||||
|
||||
@property
|
||||
def extra_instructions(self):
|
||||
if self.actions["generation_override"].enabled:
|
||||
return self.actions["generation_override"].config["instructions"].value
|
||||
return ""
|
||||
|
||||
def clean_result(self, result):
|
||||
|
||||
"""
|
||||
@@ -159,16 +183,22 @@ class NarratorAgent(Agent):
|
||||
|
||||
if not self.actions["narrate_dialogue"].enabled:
|
||||
return
|
||||
narrate_on_ai_chance = self.actions["narrate_dialogue"].config["ai_dialog"].value
|
||||
narrate_on_player_chance = self.actions["narrate_dialogue"].config["player_dialog"].value
|
||||
narrate_on_ai = random.random() < narrate_on_ai_chance
|
||||
narrate_on_player = random.random() < narrate_on_player_chance
|
||||
log.debug(
|
||||
"narrate on dialog",
|
||||
narrate_on_ai=narrate_on_ai,
|
||||
narrate_on_ai_chance=narrate_on_ai_chance,
|
||||
narrate_on_player=narrate_on_player,
|
||||
narrate_on_player_chance=narrate_on_player_chance,
|
||||
)
|
||||
|
||||
narrate_on_ai_chance = random.random() < self.actions["narrate_dialogue"].config["ai_dialog"].value
|
||||
narrate_on_player_chance = random.random() < self.actions["narrate_dialogue"].config["player_dialog"].value
|
||||
|
||||
log.debug("narrate on dialog", narrate_on_ai_chance=narrate_on_ai_chance, narrate_on_player_chance=narrate_on_player_chance)
|
||||
|
||||
if event.actor.character.is_player and not narrate_on_player_chance:
|
||||
if event.actor.character.is_player and not narrate_on_player:
|
||||
return
|
||||
|
||||
if not event.actor.character.is_player and not narrate_on_ai_chance:
|
||||
if not event.actor.character.is_player and not narrate_on_ai:
|
||||
return
|
||||
|
||||
response = await self.narrate_after_dialogue(event.actor.character)
|
||||
@@ -189,6 +219,7 @@ class NarratorAgent(Agent):
|
||||
vars = {
|
||||
"scene": self.scene,
|
||||
"max_tokens": self.client.max_token_length,
|
||||
"extra_instructions": self.extra_instructions,
|
||||
}
|
||||
)
|
||||
|
||||
@@ -206,22 +237,11 @@ class NarratorAgent(Agent):
|
||||
"""
|
||||
|
||||
scene = self.scene
|
||||
director = scene.get_helper("director").agent
|
||||
pc = scene.get_player_character()
|
||||
npcs = list(scene.get_npc_characters())
|
||||
npc_names= ", ".join([npc.name for npc in npcs])
|
||||
|
||||
#summarized_history = await scene.summarized_dialogue_history(
|
||||
# budget = self.client.max_token_length - 300,
|
||||
# min_dialogue = 50,
|
||||
#)
|
||||
|
||||
#augmented_context = await self.augment_context()
|
||||
|
||||
if narrative_direction is None:
|
||||
#narrative_direction = await director.direct_narrative(
|
||||
# scene.context_history(budget=self.client.max_token_length - 500, min_dialogue=20),
|
||||
#)
|
||||
narrative_direction = "Slightly move the current scene forward."
|
||||
|
||||
self.scene.log.info("narrative_direction", narrative_direction=narrative_direction)
|
||||
@@ -232,13 +252,12 @@ class NarratorAgent(Agent):
|
||||
"narrate",
|
||||
vars = {
|
||||
"scene": self.scene,
|
||||
#"summarized_history": summarized_history,
|
||||
#"augmented_context": augmented_context,
|
||||
"max_tokens": self.client.max_token_length,
|
||||
"narrative_direction": narrative_direction,
|
||||
"player_character": pc,
|
||||
"npcs": npcs,
|
||||
"npc_names": npc_names,
|
||||
"extra_instructions": self.extra_instructions,
|
||||
}
|
||||
)
|
||||
|
||||
@@ -269,6 +288,7 @@ class NarratorAgent(Agent):
|
||||
"query": query,
|
||||
"at_the_end": at_the_end,
|
||||
"as_narrative": as_narrative,
|
||||
"extra_instructions": self.extra_instructions,
|
||||
}
|
||||
)
|
||||
log.info("narrate_query", response=response)
|
||||
@@ -305,6 +325,7 @@ class NarratorAgent(Agent):
|
||||
"character": character,
|
||||
"max_tokens": self.client.max_token_length,
|
||||
"memory": memory_context,
|
||||
"extra_instructions": self.extra_instructions,
|
||||
}
|
||||
)
|
||||
|
||||
@@ -329,6 +350,7 @@ class NarratorAgent(Agent):
|
||||
vars = {
|
||||
"scene": self.scene,
|
||||
"max_tokens": self.client.max_token_length,
|
||||
"extra_instructions": self.extra_instructions,
|
||||
}
|
||||
)
|
||||
|
||||
@@ -349,6 +371,7 @@ class NarratorAgent(Agent):
|
||||
"max_tokens": self.client.max_token_length,
|
||||
"memory": memory_context,
|
||||
"questions": questions,
|
||||
"extra_instructions": self.extra_instructions,
|
||||
}
|
||||
)
|
||||
|
||||
@@ -374,6 +397,7 @@ class NarratorAgent(Agent):
|
||||
"max_tokens": self.client.max_token_length,
|
||||
"duration": duration,
|
||||
"narrative": narrative,
|
||||
"extra_instructions": self.extra_instructions,
|
||||
}
|
||||
)
|
||||
|
||||
@@ -399,7 +423,8 @@ class NarratorAgent(Agent):
|
||||
"scene": self.scene,
|
||||
"max_tokens": self.client.max_token_length,
|
||||
"character": character,
|
||||
"last_line": str(self.scene.history[-1])
|
||||
"last_line": str(self.scene.history[-1]),
|
||||
"extra_instructions": self.extra_instructions,
|
||||
}
|
||||
)
|
||||
|
||||
@@ -413,6 +438,22 @@ class NarratorAgent(Agent):
|
||||
if not allow_dialogue:
|
||||
response = response.split('"')[0].strip()
|
||||
response = response.replace("*", "")
|
||||
response = util.strip_partial_sentences(response)
|
||||
response = f"*{response}*"
|
||||
|
||||
return response
|
||||
|
||||
# LLM client related methods. These are called during or after the client
|
||||
|
||||
def inject_prompt_paramters(self, prompt_param: dict, kind: str, agent_function_name: str):
|
||||
log.debug("inject_prompt_paramters", prompt_param=prompt_param, kind=kind, agent_function_name=agent_function_name)
|
||||
character_names = [f"\n{c.name}:" for c in self.scene.get_characters()]
|
||||
if prompt_param.get("extra_stopping_strings") is None:
|
||||
prompt_param["extra_stopping_strings"] = []
|
||||
prompt_param["extra_stopping_strings"] += character_names
|
||||
|
||||
def allow_repetition_break(self, kind: str, agent_function_name: str, auto:bool=False):
|
||||
if auto and not self.actions["auto_break_repetition"].enabled:
|
||||
return False
|
||||
|
||||
return True
|
||||
@@ -15,7 +15,9 @@ from nltk.tokenize import sent_tokenize
|
||||
|
||||
import talemate.config as config
|
||||
import talemate.emit.async_signals
|
||||
import talemate.instance as instance
|
||||
from talemate.emit import emit
|
||||
from talemate.emit.signals import handlers
|
||||
from talemate.events import GameLoopNewMessageEvent
|
||||
from talemate.scene_message import CharacterMessage, NarratorMessage
|
||||
|
||||
@@ -198,6 +200,7 @@ class TTSAgent(Agent):
|
||||
}
|
||||
|
||||
self.actions["_config"].model_dump()
|
||||
handlers["config_saved"].connect(self.on_config_saved)
|
||||
|
||||
|
||||
@property
|
||||
@@ -324,6 +327,11 @@ class TTSAgent(Agent):
|
||||
super().connect(scene)
|
||||
talemate.emit.async_signals.get("game_loop_new_message").connect(self.on_game_loop_new_message)
|
||||
|
||||
def on_config_saved(self, event):
|
||||
config = event.data
|
||||
self.config = config
|
||||
instance.emit_agent_status(self.__class__, self)
|
||||
|
||||
async def on_game_loop_new_message(self, emission:GameLoopNewMessageEvent):
|
||||
"""
|
||||
Called when a conversation is generated
|
||||
@@ -377,14 +385,13 @@ class TTSAgent(Agent):
|
||||
|
||||
library = self.voices[self.api]
|
||||
|
||||
log.info("Listing voices", api=self.api, last_synced=library.last_synced)
|
||||
|
||||
# TODO: allow re-syncing voices
|
||||
if library.last_synced:
|
||||
return library.voices
|
||||
|
||||
list_fn = getattr(self, f"_list_voices_{self.api}")
|
||||
log.info("Listing voices", api=self.api)
|
||||
|
||||
library.voices = await list_fn()
|
||||
library.last_synced = time.time()
|
||||
|
||||
|
||||
@@ -17,7 +17,7 @@ import talemate.client.system_prompts as system_prompts
|
||||
import talemate.util as util
|
||||
from talemate.client.context import client_context_attribute
|
||||
from talemate.client.model_prompts import model_prompt
|
||||
|
||||
from talemate.agents.context import active_agent
|
||||
|
||||
# Set up logging level for httpx to WARNING to suppress debug logs.
|
||||
logging.getLogger('httpx').setLevel(logging.WARNING)
|
||||
@@ -37,10 +37,10 @@ class ClientBase:
|
||||
enabled: bool = True
|
||||
current_status: str = None
|
||||
max_token_length: int = 4096
|
||||
randomizable_inference_parameters: list[str] = ["temperature"]
|
||||
processing: bool = False
|
||||
connected: bool = False
|
||||
conversation_retries: int = 5
|
||||
auto_break_repetition_enabled: bool = True
|
||||
|
||||
client_type = "base"
|
||||
|
||||
@@ -74,6 +74,17 @@ class ClientBase:
|
||||
|
||||
return model_prompt(self.model_name, sys_msg, prompt)
|
||||
|
||||
def has_prompt_template(self):
|
||||
if not self.model_name:
|
||||
return False
|
||||
|
||||
return model_prompt.exists(self.model_name)
|
||||
|
||||
def prompt_template_example(self):
|
||||
if not self.model_name:
|
||||
return None
|
||||
return model_prompt(self.model_name, "sysmsg", "prompt<|BOT|>{LLM coercion}")
|
||||
|
||||
def reconfigure(self, **kwargs):
|
||||
|
||||
"""
|
||||
@@ -183,6 +194,10 @@ class ClientBase:
|
||||
id=self.name,
|
||||
details=model_name,
|
||||
status=status,
|
||||
data={
|
||||
"prompt_template_example": self.prompt_template_example(),
|
||||
"has_prompt_template": self.has_prompt_template(),
|
||||
}
|
||||
)
|
||||
|
||||
if status_change:
|
||||
@@ -247,6 +262,10 @@ class ClientBase:
|
||||
if fn_tune_kind:
|
||||
fn_tune_kind(parameters)
|
||||
|
||||
agent_context = active_agent.get()
|
||||
if agent_context.agent:
|
||||
agent_context.agent.inject_prompt_paramters(parameters, kind, agent_context.action)
|
||||
|
||||
def tune_prompt_parameters_conversation(self, parameters:dict):
|
||||
conversation_context = client_context_attribute("conversation")
|
||||
parameters["max_tokens"] = conversation_context.get("length", 96)
|
||||
@@ -277,7 +296,7 @@ class ClientBase:
|
||||
return ""
|
||||
|
||||
async def send_prompt(
|
||||
self, prompt: str, kind: str = "conversation", finalize: Callable = lambda x: x
|
||||
self, prompt: str, kind: str = "conversation", finalize: Callable = lambda x: x, retries:int=2
|
||||
) -> str:
|
||||
"""
|
||||
Send a prompt to the AI and return its response.
|
||||
@@ -301,7 +320,13 @@ class ClientBase:
|
||||
extra_stopping_strings = prompt_param.pop("extra_stopping_strings", [])
|
||||
|
||||
self.log.debug("send_prompt", token_length=token_length, max_token_length=self.max_token_length, parameters=prompt_param)
|
||||
response = await self.generate(finalized_prompt, prompt_param, kind)
|
||||
response = await self.generate(
|
||||
self.repetition_adjustment(finalized_prompt),
|
||||
prompt_param,
|
||||
kind
|
||||
)
|
||||
|
||||
response, finalized_prompt = await self.auto_break_repetition(finalized_prompt, prompt_param, response, kind, retries)
|
||||
|
||||
time_end = time.time()
|
||||
|
||||
@@ -327,6 +352,125 @@ class ClientBase:
|
||||
finally:
|
||||
self.emit_status(processing=False)
|
||||
|
||||
|
||||
async def auto_break_repetition(
|
||||
self,
|
||||
finalized_prompt:str,
|
||||
prompt_param:dict,
|
||||
response:str,
|
||||
kind:str,
|
||||
retries:int,
|
||||
pad_max_tokens:int=32,
|
||||
) -> str:
|
||||
|
||||
"""
|
||||
If repetition breaking is enabled, this will retry the prompt if its
|
||||
response is too similar to other messages in the prompt
|
||||
|
||||
This requires the agent to have the allow_repetition_break method
|
||||
and the jiggle_enabled_for method and the client to have the
|
||||
auto_break_repetition_enabled attribute set to True
|
||||
|
||||
Arguments:
|
||||
|
||||
- finalized_prompt: the prompt that was sent
|
||||
- prompt_param: the parameters that were used
|
||||
- response: the response that was received
|
||||
- kind: the kind of generation
|
||||
- retries: the number of retries left
|
||||
- pad_max_tokens: increase response max_tokens by this amount per iteration
|
||||
|
||||
Returns:
|
||||
|
||||
- the response
|
||||
"""
|
||||
|
||||
if not self.auto_break_repetition_enabled:
|
||||
return response, finalized_prompt
|
||||
|
||||
agent_context = active_agent.get()
|
||||
if self.jiggle_enabled_for(kind, auto=True):
|
||||
|
||||
# check if the response is a repetition
|
||||
# using the default similarity threshold of 98, meaning it needs
|
||||
# to be really similar to be considered a repetition
|
||||
|
||||
is_repetition, similarity_score, matched_line = util.similarity_score(
|
||||
response,
|
||||
finalized_prompt.split("\n"),
|
||||
)
|
||||
|
||||
if not is_repetition:
|
||||
|
||||
# not a repetition, return the response
|
||||
|
||||
self.log.debug("send_prompt no similarity", similarity_score=similarity_score)
|
||||
return response, finalized_prompt
|
||||
|
||||
while is_repetition and retries > 0:
|
||||
|
||||
# it's a repetition, retry the prompt with adjusted parameters
|
||||
|
||||
self.log.warn(
|
||||
"send_prompt similarity retry",
|
||||
agent=agent_context.agent.agent_type,
|
||||
similarity_score=similarity_score,
|
||||
retries=retries
|
||||
)
|
||||
|
||||
# first we apply the client's randomness jiggle which will adjust
|
||||
# parameters like temperature and repetition_penalty, depending
|
||||
# on the client
|
||||
#
|
||||
# this is a cumulative adjustment, so it will add to the previous
|
||||
# iteration's adjustment, this also means retries should be kept low
|
||||
# otherwise it will get out of hand and start generating nonsense
|
||||
|
||||
self.jiggle_randomness(prompt_param, offset=0.5)
|
||||
|
||||
# then we pad the max_tokens by the pad_max_tokens amount
|
||||
|
||||
prompt_param["max_tokens"] += pad_max_tokens
|
||||
|
||||
# send the prompt again
|
||||
# we use the repetition_adjustment method to further encourage
|
||||
# the AI to break the repetition on its own as well.
|
||||
|
||||
finalized_prompt = self.repetition_adjustment(finalized_prompt, is_repetitive=True)
|
||||
|
||||
response = retried_response = await self.generate(
|
||||
finalized_prompt,
|
||||
prompt_param,
|
||||
kind
|
||||
)
|
||||
|
||||
self.log.debug("send_prompt dedupe sentences", response=response, matched_line=matched_line)
|
||||
|
||||
# a lot of the times the response will now contain the repetition + something new
|
||||
# so we dedupe the response to remove the repetition on sentences level
|
||||
|
||||
response = util.dedupe_sentences(response, matched_line, similarity_threshold=85, debug=True)
|
||||
self.log.debug("send_prompt dedupe sentences (after)", response=response)
|
||||
|
||||
# deduping may have removed the entire response, so we check for that
|
||||
|
||||
if not util.strip_partial_sentences(response).strip():
|
||||
|
||||
# if the response is empty, we set the response to the original
|
||||
# and try again next loop
|
||||
|
||||
response = retried_response
|
||||
|
||||
# check if the response is a repetition again
|
||||
|
||||
is_repetition, similarity_score, matched_line = util.similarity_score(
|
||||
response,
|
||||
finalized_prompt.split("\n"),
|
||||
)
|
||||
retries -= 1
|
||||
|
||||
return response, finalized_prompt
|
||||
|
||||
def count_tokens(self, content:str):
|
||||
return util.count_tokens(content)
|
||||
|
||||
@@ -340,12 +484,35 @@ class ClientBase:
|
||||
min_offset = offset * 0.3
|
||||
prompt_config["temperature"] = random.uniform(temp + min_offset, temp + offset)
|
||||
|
||||
def jiggle_enabled_for(self, kind:str):
|
||||
def jiggle_enabled_for(self, kind:str, auto:bool=False) -> bool:
|
||||
|
||||
if kind in ["conversation", "story"]:
|
||||
return True
|
||||
|
||||
if kind.startswith("narrate"):
|
||||
return True
|
||||
agent_context = active_agent.get()
|
||||
agent = agent_context.agent
|
||||
|
||||
if not agent:
|
||||
return False
|
||||
|
||||
return agent.allow_repetition_break(kind, agent_context.action, auto=auto)
|
||||
|
||||
def repetition_adjustment(self, prompt:str, is_repetitive:bool=False):
|
||||
"""
|
||||
Breaks the prompt into lines and checkse each line for a match with
|
||||
[$REPETITION|{repetition_adjustment}].
|
||||
|
||||
On match and if is_repetitive is True, the line is removed from the prompt and
|
||||
replaced with the repetition_adjustment.
|
||||
|
||||
On match and if is_repetitive is False, the line is removed from the prompt.
|
||||
"""
|
||||
|
||||
lines = prompt.split("\n")
|
||||
new_lines = []
|
||||
|
||||
for line in lines:
|
||||
if line.startswith("[$REPETITION|"):
|
||||
if is_repetitive:
|
||||
new_lines.append(line.split("|")[1][:-1])
|
||||
else:
|
||||
new_lines.append(line)
|
||||
|
||||
return "\n".join(new_lines)
|
||||
@@ -39,6 +39,9 @@ class ModelPrompt:
|
||||
"set_response" : self.set_response
|
||||
})
|
||||
|
||||
def exists(self, model_name:str):
|
||||
return bool(self.get_template(model_name))
|
||||
|
||||
def set_response(self, prompt:str, response_str:str):
|
||||
|
||||
prompt = prompt.strip("\n").strip()
|
||||
|
||||
@@ -6,7 +6,10 @@ from openai import AsyncOpenAI
|
||||
from talemate.client.base import ClientBase
|
||||
from talemate.client.registry import register
|
||||
from talemate.emit import emit
|
||||
from talemate.emit.signals import handlers
|
||||
import talemate.emit.async_signals as async_signals
|
||||
from talemate.config import load_config
|
||||
import talemate.instance as instance
|
||||
import talemate.client.system_prompts as system_prompts
|
||||
import structlog
|
||||
import tiktoken
|
||||
@@ -75,39 +78,40 @@ class OpenAIClient(ClientBase):
|
||||
|
||||
client_type = "openai"
|
||||
conversation_retries = 0
|
||||
auto_break_repetition_enabled = False
|
||||
|
||||
def __init__(self, model="gpt-4-1106-preview", **kwargs):
|
||||
|
||||
self.model_name = model
|
||||
self.api_key_status = None
|
||||
self.config = load_config()
|
||||
super().__init__(**kwargs)
|
||||
|
||||
# if os.environ.get("OPENAI_API_KEY") is not set, look in the config file
|
||||
# and set it
|
||||
|
||||
if not os.environ.get("OPENAI_API_KEY"):
|
||||
if self.config.get("openai", {}).get("api_key"):
|
||||
os.environ["OPENAI_API_KEY"] = self.config["openai"]["api_key"]
|
||||
|
||||
self.set_client()
|
||||
|
||||
handlers["config_saved"].connect(self.on_config_saved)
|
||||
|
||||
|
||||
@property
|
||||
def openai_api_key(self):
|
||||
return os.environ.get("OPENAI_API_KEY")
|
||||
return self.config.get("openai",{}).get("api_key")
|
||||
|
||||
|
||||
def emit_status(self, processing: bool = None):
|
||||
if processing is not None:
|
||||
self.processing = processing
|
||||
|
||||
if os.environ.get("OPENAI_API_KEY"):
|
||||
if self.openai_api_key:
|
||||
status = "busy" if self.processing else "idle"
|
||||
model_name = self.model_name or "No model loaded"
|
||||
model_name = self.model_name
|
||||
else:
|
||||
status = "error"
|
||||
model_name = "No API key set"
|
||||
|
||||
if not self.model_name:
|
||||
status = "error"
|
||||
model_name = "No model loaded"
|
||||
|
||||
self.current_status = status
|
||||
|
||||
emit(
|
||||
@@ -121,12 +125,17 @@ class OpenAIClient(ClientBase):
|
||||
def set_client(self, max_token_length:int=None):
|
||||
|
||||
if not self.openai_api_key:
|
||||
self.client = AsyncOpenAI(api_key="sk-1111")
|
||||
log.error("No OpenAI API key set")
|
||||
if self.api_key_status:
|
||||
self.api_key_status = False
|
||||
emit('request_client_status')
|
||||
emit('request_agent_status')
|
||||
return
|
||||
|
||||
model = self.model_name
|
||||
|
||||
self.client = AsyncOpenAI()
|
||||
self.client = AsyncOpenAI(api_key=self.openai_api_key)
|
||||
if model == "gpt-3.5-turbo":
|
||||
self.max_token_length = min(max_token_length or 4096, 4096)
|
||||
elif model == "gpt-4":
|
||||
@@ -138,12 +147,27 @@ class OpenAIClient(ClientBase):
|
||||
else:
|
||||
self.max_token_length = max_token_length or 2048
|
||||
|
||||
if not self.api_key_status:
|
||||
if self.api_key_status is False:
|
||||
emit('request_client_status')
|
||||
emit('request_agent_status')
|
||||
self.api_key_status = True
|
||||
|
||||
log.info("openai set client")
|
||||
|
||||
def reconfigure(self, **kwargs):
|
||||
if "model" in kwargs:
|
||||
self.model_name = kwargs["model"]
|
||||
self.set_client(kwargs.get("max_token_length"))
|
||||
|
||||
def on_config_saved(self, event):
|
||||
config = event.data
|
||||
self.config = config
|
||||
self.set_client()
|
||||
|
||||
def count_tokens(self, content: str):
|
||||
if not self.model_name:
|
||||
return 0
|
||||
return num_tokens_from_messages([{"content": content}], model=self.model_name)
|
||||
|
||||
async def status(self):
|
||||
@@ -179,6 +203,9 @@ class OpenAIClient(ClientBase):
|
||||
Generates text from the given prompt and parameters.
|
||||
"""
|
||||
|
||||
if not self.openai_api_key:
|
||||
raise Exception("No OpenAI API key set")
|
||||
|
||||
# only gpt-4-1106-preview supports json_object response coersion
|
||||
supports_json_object = self.model_name in ["gpt-4-1106-preview"]
|
||||
right = None
|
||||
@@ -208,5 +235,4 @@ class OpenAIClient(ClientBase):
|
||||
return response
|
||||
|
||||
except Exception as e:
|
||||
self.log.error("generate error", e=e)
|
||||
return ""
|
||||
raise
|
||||
@@ -6,6 +6,8 @@ import os
|
||||
from pydantic import BaseModel
|
||||
from typing import Optional, Dict, Union
|
||||
|
||||
from talemate.emit import emit
|
||||
|
||||
log = structlog.get_logger("talemate.config")
|
||||
|
||||
class Client(BaseModel):
|
||||
@@ -159,3 +161,5 @@ def save_config(config, file_path: str = "./config.yaml"):
|
||||
|
||||
with open(file_path, "w") as file:
|
||||
yaml.dump(config, file)
|
||||
|
||||
emit("config_saved", data=config)
|
||||
@@ -13,7 +13,9 @@ RequestInput = signal("request_input")
|
||||
ReceiveInput = signal("receive_input")
|
||||
|
||||
ClientStatus = signal("client_status")
|
||||
RequestClientStatus = signal("request_client_status")
|
||||
AgentStatus = signal("agent_status")
|
||||
RequestAgentStatus = signal("request_agent_status")
|
||||
ClientBootstraps = signal("client_bootstraps")
|
||||
PromptSent = signal("prompt_sent")
|
||||
|
||||
@@ -28,6 +30,8 @@ AudioQueue = signal("audio_queue")
|
||||
|
||||
MessageEdited = signal("message_edited")
|
||||
|
||||
ConfigSaved = signal("config_saved")
|
||||
|
||||
handlers = {
|
||||
"system": SystemMessage,
|
||||
"narrator": NarratorMessage,
|
||||
@@ -38,7 +42,9 @@ handlers = {
|
||||
"request_input": RequestInput,
|
||||
"receive_input": ReceiveInput,
|
||||
"client_status": ClientStatus,
|
||||
"request_client_status": RequestClientStatus,
|
||||
"agent_status": AgentStatus,
|
||||
"request_agent_status": RequestAgentStatus,
|
||||
"client_bootstraps": ClientBootstraps,
|
||||
"clear_screen": ClearScreen,
|
||||
"remove_message": RemoveMessage,
|
||||
@@ -49,4 +55,5 @@ handlers = {
|
||||
"message_edited": MessageEdited,
|
||||
"prompt_sent": PromptSent,
|
||||
"audio_queue": AudioQueue,
|
||||
"config_saved": ConfigSaved,
|
||||
}
|
||||
|
||||
@@ -1,10 +1,11 @@
|
||||
"""
|
||||
Keep track of clients and agents
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import talemate.agents as agents
|
||||
import talemate.client as clients
|
||||
from talemate.emit import emit
|
||||
from talemate.emit.signals import handlers
|
||||
import talemate.client.bootstrap as bootstrap
|
||||
|
||||
import structlog
|
||||
@@ -14,6 +15,8 @@ AGENTS = {}
|
||||
CLIENTS = {}
|
||||
|
||||
|
||||
|
||||
|
||||
def get_agent(typ: str, *create_args, **create_kwargs):
|
||||
agent = AGENTS.get(typ)
|
||||
|
||||
@@ -94,11 +97,19 @@ async def emit_clients_status():
|
||||
"""
|
||||
Will emit status of all clients
|
||||
"""
|
||||
|
||||
#log.debug("emit", type="client status")
|
||||
for client in CLIENTS.values():
|
||||
if client:
|
||||
await client.status()
|
||||
|
||||
def _sync_emit_clients_status(*args, **kwargs):
|
||||
"""
|
||||
Will emit status of all clients
|
||||
in synchronous mode
|
||||
"""
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(emit_clients_status())
|
||||
handlers["request_client_status"].connect(_sync_emit_clients_status)
|
||||
|
||||
def emit_client_bootstraps():
|
||||
emit(
|
||||
@@ -144,11 +155,13 @@ def emit_agent_status(cls, agent=None):
|
||||
)
|
||||
|
||||
|
||||
def emit_agents_status():
|
||||
def emit_agents_status(*args, **kwargs):
|
||||
"""
|
||||
Will emit status of all agents
|
||||
"""
|
||||
|
||||
#log.debug("emit", type="agent status")
|
||||
for typ, cls in agents.AGENT_CLASSES.items():
|
||||
agent = AGENTS.get(typ)
|
||||
emit_agent_status(cls, agent)
|
||||
|
||||
handlers["request_agent_status"].connect(emit_agents_status)
|
||||
@@ -343,7 +343,7 @@ class Prompt:
|
||||
parsed_text = env.from_string(prompt_text).render(self.vars)
|
||||
|
||||
if self.dedupe_enabled:
|
||||
parsed_text = dedupe_string(parsed_text, debug=True)
|
||||
parsed_text = dedupe_string(parsed_text, debug=False)
|
||||
|
||||
parsed_text = remove_extra_linebreaks(parsed_text)
|
||||
|
||||
@@ -395,7 +395,7 @@ class Prompt:
|
||||
f"Answer: " + loop.run_until_complete(memory.query(query, **kwargs)),
|
||||
])
|
||||
else:
|
||||
return loop.run_until_complete(memory.multi_query(query.split("\n"), **kwargs))
|
||||
return loop.run_until_complete(memory.multi_query([q for q in query.split("\n") if q.strip()], **kwargs))
|
||||
|
||||
def instruct_text(self, instruction:str, text:str):
|
||||
loop = asyncio.get_event_loop()
|
||||
|
||||
@@ -20,6 +20,7 @@ Use an informal and colloquial register with a conversational tone. Overall, the
|
||||
|
||||
Narration style should be that of a 90s point and click adventure game. You are omniscient and can describe the scene in detail.
|
||||
|
||||
Only generate new narration.
|
||||
Only generate new narration. {{ extra_instructions }}
|
||||
[$REPETITION|Narration is getting repetitive. Try to choose different words to break up the repetitive text.]
|
||||
<|CLOSE_SECTION|>
|
||||
{{ set_prepared_response('*') }}
|
||||
@@ -8,7 +8,7 @@ Last time we checked on {{ character.name }}:
|
||||
{% endfor %}
|
||||
<|CLOSE_SECTION|>
|
||||
|
||||
{% for scene_context in scene.context_history(budget=max_tokens-300, min_dialogue=30) -%}
|
||||
{% for scene_context in scene.context_history(budget=max_tokens-300, min_dialogue=20) -%}
|
||||
{{ scene_context }}
|
||||
{% endfor %}
|
||||
|
||||
@@ -25,5 +25,6 @@ Instruction: Answer the questions to describe {{ character.name }}'s appearance
|
||||
Content Context: This is a specific scene from {{ scene.context }}
|
||||
Narration style: point and click adventure game from the 90s
|
||||
Expected Answer: A brief summarized visual description of {{ character.name }}'s appearance at the end of the dialogue. NEVER break the fourth wall. (2 to 3 sentences)
|
||||
{{ extra_instructions }}
|
||||
<|CLOSE_SECTION|>
|
||||
Narrator answers: {{ bot_token }}At the end of the dialogue,
|
||||
{{ bot_token }}At the end of the dialogue,
|
||||
@@ -1,3 +1,4 @@
|
||||
{% block extra_context -%}
|
||||
<|SECTION:CONTEXT|>
|
||||
Scenario Premise: {{ scene.description }}
|
||||
|
||||
@@ -9,8 +10,8 @@ NPCs: {{ npc_names }}
|
||||
Player Character: {{ player_character.name }}
|
||||
Content Context: {{ scene.context }}
|
||||
<|CLOSE_SECTION|>
|
||||
|
||||
{% for scene_context in scene.context_history(budget=max_tokens-300, min_dialogue=30, sections=False, dialogue_negative_offset=10) -%}
|
||||
{% endblock -%}
|
||||
{% for scene_context in scene.context_history(budget=max_tokens-300, min_dialogue=20, sections=False) -%}
|
||||
{{ scene_context }}
|
||||
{% endfor %}
|
||||
<|SECTION:TASK|>
|
||||
@@ -25,11 +26,8 @@ Use an informal and colloquial register with a conversational tone. Overall, the
|
||||
Narration style should be that of a 90s point and click adventure game. You are omniscient and can describe the scene in detail.
|
||||
|
||||
Only generate new narration. Avoid including any character's internal thoughts or dialogue.
|
||||
Write 2 to 4 sentences. {{ extra_instructions }}
|
||||
<|CLOSE_SECTION|>
|
||||
{{ bot_token }}
|
||||
{% for row in scene.history[-10:] -%}
|
||||
{{ row }}
|
||||
{% endfor %}
|
||||
{{
|
||||
set_prepared_response_random(
|
||||
npc_names.split(", ") + [
|
||||
|
||||
@@ -22,5 +22,6 @@ When evaluating both story and context, story is more important. You can fill in
|
||||
{% endif -%}
|
||||
Content Context: This is a specific scene from {{ scene.context }}
|
||||
Your answer should be in the style of short, concise narration that fits the context of the scene. (1 to 2 sentences)
|
||||
{{ extra_instructions }}
|
||||
<|CLOSE_SECTION|>
|
||||
Narrator answers: {% if at_the_end %}{{ bot_token }}At the end of the dialogue, {% endif %}
|
||||
{% if at_the_end %}{{ bot_token }}At the end of the dialogue, {% endif %}
|
||||
@@ -8,5 +8,6 @@ Scenario Premise: {{ scene.description }}
|
||||
<|CLOSE_SECTION|>
|
||||
<|SECTION:TASK|>
|
||||
Provide a visual description of what is currently happening in the scene. Don't progress the scene.
|
||||
{{ extra_instructions }}
|
||||
<|CLOSE_SECTION|>
|
||||
{{ bot_token }}At the end of the scene we currently see:
|
||||
@@ -12,5 +12,6 @@ Content Context: {{ scene.context }}
|
||||
<|SECTION:TASK|>
|
||||
Narrate the passage of time that just occured, subtly move the story forward, and set up the next scene.
|
||||
Write 1 to 3 sentences.
|
||||
{{ extra_instructions }}
|
||||
<|CLOSE_SECTION|>
|
||||
{{ bot_token }}{{ narrative }}:
|
||||
@@ -6,10 +6,11 @@ import textwrap
|
||||
import structlog
|
||||
import isodate
|
||||
import datetime
|
||||
from typing import List
|
||||
from typing import List, Union
|
||||
from thefuzz import fuzz
|
||||
from colorama import Back, Fore, Style, init
|
||||
from PIL import Image
|
||||
from nltk.tokenize import sent_tokenize
|
||||
|
||||
from talemate.scene_message import SceneMessage
|
||||
log = structlog.get_logger("talemate.util")
|
||||
@@ -733,12 +734,91 @@ def extract_json(s):
|
||||
json_object = json.loads(json_string)
|
||||
return json_string, json_object
|
||||
|
||||
def similarity_score(line: str, lines: list[str], similarity_threshold: int = 95) -> tuple[bool, int, str]:
|
||||
"""
|
||||
Checks if a line is similar to any of the lines in the list of lines.
|
||||
|
||||
Arguments:
|
||||
line (str): The line to check.
|
||||
lines (list): The list of lines to check against.
|
||||
threshold (int): The similarity threshold to use when comparing lines.
|
||||
|
||||
Returns:
|
||||
bool: Whether a similar line was found.
|
||||
int: The similarity score of the line. If no similar line was found, the highest similarity score is returned.
|
||||
str: The similar line that was found. If no similar line was found, None is returned.
|
||||
"""
|
||||
|
||||
highest_similarity = 0
|
||||
|
||||
for existing_line in lines:
|
||||
similarity = fuzz.ratio(line, existing_line)
|
||||
highest_similarity = max(highest_similarity, similarity)
|
||||
#print("SIMILARITY", similarity, existing_line[:32]+"...")
|
||||
if similarity >= similarity_threshold:
|
||||
return True, similarity, existing_line
|
||||
|
||||
return False, highest_similarity, None
|
||||
|
||||
def dedupe_sentences(line_a:str, line_b:str, similarity_threshold:int=95, debug:bool=False, split_on_comma:bool=True) -> str:
|
||||
"""
|
||||
Will split both lines into sentences and then compare each sentence in line_a
|
||||
against similar sentences in line_b. If a similar sentence is found, it will be
|
||||
removed from line_a.
|
||||
|
||||
The similarity threshold is used to determine if two sentences are similar.
|
||||
|
||||
Arguments:
|
||||
line_a (str): The first line.
|
||||
line_b (str): The second line.
|
||||
similarity_threshold (int): The similarity threshold to use when comparing sentences.
|
||||
debug (bool): Whether to log debug messages.
|
||||
split_on_comma (bool): Whether to split line_b sentences on commas as well.
|
||||
|
||||
Returns:
|
||||
str: the cleaned line_a.
|
||||
"""
|
||||
|
||||
line_a_sentences = sent_tokenize(line_a)
|
||||
line_b_sentences = sent_tokenize(line_b)
|
||||
|
||||
cleaned_line_a_sentences = []
|
||||
|
||||
if split_on_comma:
|
||||
# collect all sentences from line_b that contain a comma
|
||||
line_b_sentences_with_comma = []
|
||||
for line_b_sentence in line_b_sentences:
|
||||
if "," in line_b_sentence:
|
||||
line_b_sentences_with_comma.append(line_b_sentence)
|
||||
|
||||
# then split all sentences in line_b_sentences_with_comma on the comma
|
||||
# and extend line_b_sentences with the split sentences, making sure
|
||||
# to strip whitespace from the beginning and end of each sentence
|
||||
|
||||
for line_b_sentence in line_b_sentences_with_comma:
|
||||
line_b_sentences.extend([s.strip() for s in line_b_sentence.split(",")])
|
||||
|
||||
|
||||
for line_a_sentence in line_a_sentences:
|
||||
similar_found = False
|
||||
for line_b_sentence in line_b_sentences:
|
||||
similarity = fuzz.ratio(line_a_sentence, line_b_sentence)
|
||||
if similarity >= similarity_threshold:
|
||||
if debug:
|
||||
log.debug("DEDUPE SENTENCE", similarity=similarity, line_a_sentence=line_a_sentence, line_b_sentence=line_b_sentence)
|
||||
similar_found = True
|
||||
break
|
||||
if not similar_found:
|
||||
cleaned_line_a_sentences.append(line_a_sentence)
|
||||
|
||||
return " ".join(cleaned_line_a_sentences)
|
||||
|
||||
def dedupe_string(s: str, min_length: int = 32, similarity_threshold: int = 95, debug: bool = False) -> str:
|
||||
|
||||
"""
|
||||
Removes duplicate lines from a string.
|
||||
|
||||
Parameters:
|
||||
Arguments:
|
||||
s (str): The input string.
|
||||
min_length (int): The minimum length of a line to be checked for duplicates.
|
||||
similarity_threshold (int): The similarity threshold to use when comparing lines.
|
||||
|
||||
@@ -19,7 +19,7 @@
|
||||
<v-chip v-if="agent.data.experimental" color="warning" size="x-small">experimental</v-chip>
|
||||
</v-list-item>
|
||||
</v-list>
|
||||
<AgentModal :dialog="dialog" :formTitle="formTitle" @save="saveAgent" @update:dialog="updateDialog"></AgentModal>
|
||||
<AgentModal :dialog="state.dialog" :formTitle="state.formTitle" @save="saveAgent" @update:dialog="updateDialog"></AgentModal>
|
||||
</div>
|
||||
</template>
|
||||
|
||||
|
||||
@@ -21,7 +21,7 @@
|
||||
{{ client.type }}
|
||||
<v-chip label size="x-small" variant="outlined" class="ml-1">ctx {{ client.max_token_length }}</v-chip>
|
||||
</v-list-item-subtitle>
|
||||
<v-list-item-content density="compact">
|
||||
<div density="compact">
|
||||
<v-slider
|
||||
hide-details
|
||||
v-model="client.max_token_length"
|
||||
@@ -32,9 +32,15 @@
|
||||
@click.stop
|
||||
density="compact"
|
||||
></v-slider>
|
||||
</v-list-item-content>
|
||||
</div>
|
||||
<v-list-item-subtitle class="text-center">
|
||||
|
||||
<v-tooltip text="No LLM prompt template for this model. Using default. Templates can be added in ./templates/llm-prompt" v-if="client.status === 'idle' && client.data && !client.data.has_prompt_template" max-width="200">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-icon x-size="14" class="mr-1" v-bind="props" color="orange">mdi-alert</v-icon>
|
||||
</template>
|
||||
</v-tooltip>
|
||||
|
||||
<v-tooltip text="Edit client">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn size="x-small" class="mr-1" v-bind="props" variant="tonal" density="comfortable" rounded="sm" @click.stop="editClient(index)" icon="mdi-cogs"></v-btn>
|
||||
@@ -56,7 +62,7 @@
|
||||
</v-list-item-subtitle>
|
||||
</v-list-item>
|
||||
</v-list>
|
||||
<ClientModal :dialog="dialog" :formTitle="formTitle" @save="saveClient" @error="propagateError" @update:dialog="updateDialog"></ClientModal>
|
||||
<ClientModal :dialog="state.dialog" :formTitle="state.formTitle" @save="saveClient" @error="propagateError" @update:dialog="updateDialog"></ClientModal>
|
||||
<v-alert type="warning" variant="tonal" v-if="state.clients.length === 0">You have no LLM clients configured. Add one.</v-alert>
|
||||
<v-btn @click="openModal" prepend-icon="mdi-plus-box">Add client</v-btn>
|
||||
</div>
|
||||
@@ -81,6 +87,9 @@ export default {
|
||||
apiUrl: '',
|
||||
model_name: '',
|
||||
max_token_length: 2048,
|
||||
data: {
|
||||
has_prompt_template: false,
|
||||
}
|
||||
}, // Add a new field to store the model name
|
||||
formTitle: ''
|
||||
}
|
||||
@@ -90,7 +99,6 @@ export default {
|
||||
'getWebsocket',
|
||||
'registerMessageHandler',
|
||||
'isConnected',
|
||||
'chekcingStatus',
|
||||
'getAgents',
|
||||
],
|
||||
provide() {
|
||||
@@ -123,6 +131,9 @@ export default {
|
||||
apiUrl: 'http://localhost:5000',
|
||||
model_name: '',
|
||||
max_token_length: 4096,
|
||||
data: {
|
||||
has_prompt_template: false,
|
||||
}
|
||||
};
|
||||
this.state.formTitle = 'Add Client';
|
||||
this.state.dialog = true;
|
||||
@@ -181,6 +192,7 @@ export default {
|
||||
client.status = data.status;
|
||||
client.max_token_length = data.max_token_length;
|
||||
client.apiUrl = data.apiUrl;
|
||||
client.data = data.data;
|
||||
} else {
|
||||
console.log("Adding new client", data);
|
||||
this.state.clients.push({
|
||||
@@ -190,6 +202,7 @@ export default {
|
||||
status: data.status,
|
||||
max_token_length: data.max_token_length,
|
||||
apiUrl: data.apiUrl,
|
||||
data: data.data,
|
||||
});
|
||||
// sort the clients by name
|
||||
this.state.clients.sort((a, b) => (a.name > b.name) ? 1 : -1);
|
||||
|
||||
@@ -1,73 +1,209 @@
|
||||
<template>
|
||||
<v-dialog v-model="dialog" scrollable max-width="50%">
|
||||
<v-dialog v-model="dialog" scrollable max-width="960px">
|
||||
<v-card v-if="app_config !== null">
|
||||
|
||||
<v-card-title><v-icon class="mr-1">mdi-cog</v-icon>Settings</v-card-title>
|
||||
<v-tabs color="primary" v-model="tab">
|
||||
<v-tab value="game">
|
||||
<v-icon start>mdi-gamepad-square</v-icon>
|
||||
Game
|
||||
</v-tab>
|
||||
<v-tab value="application">
|
||||
<v-icon start>mdi-application</v-icon>
|
||||
Application
|
||||
</v-tab>
|
||||
<v-tab value="creator">
|
||||
<v-icon start>mdi-palette-outline</v-icon>
|
||||
Creator
|
||||
</v-tab>
|
||||
</v-tabs>
|
||||
<v-window v-model="tab">
|
||||
|
||||
<!-- GAME -->
|
||||
|
||||
<v-window-item value="game">
|
||||
<v-card flat>
|
||||
<v-card-title>
|
||||
Default player character
|
||||
<v-tooltip location="top" max-width="500" text="This will be default player character that will be added to a game if the game does not come with a defined player character. Essentially this is relevant for when you load character-cards that aren't in the talemate scene format.">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-icon size="x-small" v-bind="props" v-on="on">mdi-help</v-icon>
|
||||
</template>
|
||||
</v-tooltip>
|
||||
</v-card-title>
|
||||
<v-card-text>
|
||||
<v-row>
|
||||
<v-col cols="4">
|
||||
<v-list>
|
||||
<v-list-item @click="gamePageSelected=item.value" :prepend-icon="item.icon" v-for="(item, index) in navigation.game" :key="index">
|
||||
<v-list-item-title>{{ item.title }}</v-list-item-title>
|
||||
</v-list-item>
|
||||
</v-list>
|
||||
</v-col>
|
||||
<v-col cols="8">
|
||||
<div v-if="gamePageSelected === 'character'">
|
||||
<v-alert color="white" variant="text" icon="mdi-human-edit" density="compact">
|
||||
<v-alert-title>Default player character</v-alert-title>
|
||||
<div class="text-grey">
|
||||
This will be default player character that will be added to a game if the game does not come with a defined player character. Essentially this is relevant for when you load character-cards that aren't in the talemate scene format.
|
||||
|
||||
</div>
|
||||
</v-alert>
|
||||
<v-divider class="mb-2"></v-divider>
|
||||
<v-row>
|
||||
<v-col cols="6">
|
||||
<v-text-field v-model="app_config.game.default_player_character.name" label="Name"></v-text-field>
|
||||
<v-text-field v-model="app_config.game.default_player_character.name"
|
||||
label="Name"></v-text-field>
|
||||
</v-col>
|
||||
<v-col cols="6">
|
||||
<v-text-field v-model="app_config.game.default_player_character.gender" label="Gender"></v-text-field>
|
||||
<v-text-field v-model="app_config.game.default_player_character.gender"
|
||||
label="Gender"></v-text-field>
|
||||
</v-col>
|
||||
</v-row>
|
||||
<v-row>
|
||||
<v-col>
|
||||
<v-textarea v-model="app_config.game.default_player_character.description" auto-grow label="Description"></v-textarea>
|
||||
<v-col cols="12">
|
||||
<v-textarea v-model="app_config.game.default_player_character.description"
|
||||
auto-grow label="Description"></v-textarea>
|
||||
</v-col>
|
||||
<v-col>
|
||||
<v-color-picker v-model="app_config.game.default_player_character.color" hide-inputs label="Color" elevation="0"></v-color-picker>
|
||||
|
||||
</v-row>
|
||||
</div>
|
||||
</v-col>
|
||||
|
||||
</v-row>
|
||||
</v-card-text>
|
||||
</v-card>
|
||||
</v-window-item>
|
||||
<v-window-item value="creator">
|
||||
|
||||
<!-- APPLICATION -->
|
||||
|
||||
<v-window-item value="application">
|
||||
<v-card flat>
|
||||
<v-card-title>
|
||||
Content context
|
||||
<v-tooltip location="top" max-width="500" text="Available choices when generating characters or scenarios within talemate.">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-icon size="x-small" v-bind="props" v-on="on">mdi-help</v-icon>
|
||||
</template>
|
||||
</v-tooltip>
|
||||
</v-card-title>
|
||||
<v-card-text style="max-height:600px; overflow-y:scroll;">
|
||||
<v-list density="compact">
|
||||
<v-list-item v-for="(value, index) in app_config.creator.content_context" :key="index">
|
||||
<v-list-item-title><v-icon color="red">mdi-delete</v-icon>{{ value }}</v-list-item-title>
|
||||
<v-card-text>
|
||||
<v-row>
|
||||
<v-col cols="4">
|
||||
<v-list>
|
||||
<v-list-subheader>Third Party APIs</v-list-subheader>
|
||||
<v-list-item @click="applicationPageSelected=item.value" :prepend-icon="item.icon" v-for="(item, index) in navigation.application" :key="index">
|
||||
<v-list-item-title>{{ item.title }}</v-list-item-title>
|
||||
</v-list-item>
|
||||
</v-list>
|
||||
<v-text-field v-model="content_context_input" label="Add content context" @keyup.enter="app_config.creator.content_context.push(content_context_input); app_config.creator.content_context_input = ''"></v-text-field>
|
||||
</v-col>
|
||||
<v-col cols="8">
|
||||
|
||||
<!-- OPENAI API -->
|
||||
<div v-if="applicationPageSelected === 'openai_api'">
|
||||
<v-alert color="white" variant="text" icon="mdi-api" density="compact">
|
||||
<v-alert-title>OpenAI</v-alert-title>
|
||||
<div class="text-grey">
|
||||
Configure your OpenAI API key here. You can get one from <a href="https://platform.openai.com/" target="_blank">https://platform.openai.com/</a>
|
||||
</div>
|
||||
</v-alert>
|
||||
<v-divider class="mb-2"></v-divider>
|
||||
<v-row>
|
||||
<v-col cols="12">
|
||||
<v-text-field type="password" v-model="app_config.openai.api_key"
|
||||
label="OpenAI API Key"></v-text-field>
|
||||
</v-col>
|
||||
</v-row>
|
||||
</div>
|
||||
|
||||
<!-- ELEVENLABS API -->
|
||||
<div v-if="applicationPageSelected === 'elevenlabs_api'">
|
||||
<v-alert color="white" variant="text" icon="mdi-api" density="compact">
|
||||
<v-alert-title>ElevenLabs</v-alert-title>
|
||||
<div class="text-grey">
|
||||
<p class="mb-1">Generate realistic speech with the most advanced AI voice model ever.</p>
|
||||
Configure your ElevenLabs API key here. You can get one from <a href="https://elevenlabs.io/?from=partnerewing2048" target="_blank">https://elevenlabs.io</a> <span class="text-caption">(affiliate link)</span>
|
||||
</div>
|
||||
</v-alert>
|
||||
<v-divider class="mb-2"></v-divider>
|
||||
<v-row>
|
||||
<v-col cols="12">
|
||||
<v-text-field type="password" v-model="app_config.elevenlabs.api_key"
|
||||
label="ElevenLabs API Key"></v-text-field>
|
||||
</v-col>
|
||||
</v-row>
|
||||
</div>
|
||||
|
||||
<!-- COQUI API -->
|
||||
<div v-if="applicationPageSelected === 'coqui_api'">
|
||||
<v-alert color="white" variant="text" icon="mdi-api" density="compact">
|
||||
<v-alert-title>Coqui Studio</v-alert-title>
|
||||
<div class="text-grey">
|
||||
<p class="mb-1">Realistic, emotive text-to-speech through generative AI.</p>
|
||||
Configure your Coqui API key here. You can get one from <a href="https://app.coqui.ai/account" target="_blank">https://app.coqui.ai/account</a>
|
||||
</div>
|
||||
</v-alert>
|
||||
<v-divider class="mb-2"></v-divider>
|
||||
<v-row>
|
||||
<v-col cols="12">
|
||||
<v-text-field type="password" v-model="app_config.coqui.api_key"
|
||||
label="Coqui API Key"></v-text-field>
|
||||
</v-col>
|
||||
</v-row>
|
||||
</div>
|
||||
|
||||
<!-- RUNPOD API -->
|
||||
<div v-if="applicationPageSelected === 'runpod_api'">
|
||||
<v-alert color="white" variant="text" icon="mdi-api" density="compact">
|
||||
<v-alert-title>RunPod</v-alert-title>
|
||||
<div class="text-grey">
|
||||
<p class="mb-1">Launch a GPU instance in seconds.</p>
|
||||
Configure your RunPod API key here. You can get one from <a href="https://runpod.io?ref=gma8kdu0" target="_blank">https://runpod.io/</a> <span class="text-caption">(affiliate link)</span>
|
||||
</div>
|
||||
</v-alert>
|
||||
<v-divider class="mb-2"></v-divider>
|
||||
<v-row>
|
||||
<v-col cols="12">
|
||||
<v-text-field type="password" v-model="app_config.runpod.api_key"
|
||||
label="RunPod API Key"></v-text-field>
|
||||
</v-col>
|
||||
</v-row>
|
||||
</div>
|
||||
|
||||
</v-col>
|
||||
</v-row>
|
||||
</v-card-text>
|
||||
</v-card>
|
||||
</v-window-item>
|
||||
|
||||
<!-- CREATOR -->
|
||||
|
||||
<v-window-item value="creator">
|
||||
<v-card flat>
|
||||
<v-card-text>
|
||||
<v-row>
|
||||
<v-col cols="4">
|
||||
<v-list>
|
||||
<v-list-item @click="creatorPageSelected=item.value" :prepend-icon="item.icon" v-for="(item, index) in navigation.creator" :key="index">
|
||||
<v-list-item-title>{{ item.title }}</v-list-item-title>
|
||||
</v-list-item>
|
||||
</v-list>
|
||||
</v-col>
|
||||
<v-col cols="8">
|
||||
<div v-if="creatorPageSelected === 'content_context'">
|
||||
<!-- Content for Content context will go here -->
|
||||
<v-alert color="white" variant="text" icon="mdi-cube-scan" density="compact">
|
||||
<v-alert-title>Content context</v-alert-title>
|
||||
<div class="text-grey">
|
||||
Available content-context choices when generating characters or scenarios. This can strongly influence the content that is generated.
|
||||
</div>
|
||||
</v-alert>
|
||||
<v-divider class="mb-2"></v-divider>
|
||||
<v-row>
|
||||
<v-col cols="12">
|
||||
<v-list density="compact">
|
||||
<v-list-item v-for="(value, index) in app_config.creator.content_context" :key="index">
|
||||
<v-list-item-title><v-icon color="red" class="mr-2" @click="contentContextRemove(index)">mdi-delete</v-icon>{{ value }}</v-list-item-title>
|
||||
</v-list-item>
|
||||
</v-list>
|
||||
<v-divider></v-divider>
|
||||
<v-text-field v-model="content_context_input" label="Add content context (Press enter to add)"
|
||||
@keyup.enter="app_config.creator.content_context.push(content_context_input); content_context_input = ''"></v-text-field>
|
||||
</v-col>
|
||||
</v-row>
|
||||
|
||||
|
||||
</div>
|
||||
</v-col>
|
||||
</v-row>
|
||||
</v-card-text>
|
||||
</v-card>
|
||||
</v-window-item>
|
||||
</v-window>
|
||||
<v-card-actions>
|
||||
<v-btn color="primary" text @click="saveConfig">Save</v-btn>
|
||||
<v-spacer></v-spacer>
|
||||
<v-btn color="primary" text @click="saveConfig" prepend-icon="mdi-check-circle-outline">Save</v-btn>
|
||||
</v-card-actions>
|
||||
</v-card>
|
||||
<v-card v-else>
|
||||
@@ -90,6 +226,23 @@ export default {
|
||||
dialog: false,
|
||||
app_config: null,
|
||||
content_context_input: '',
|
||||
navigation: {
|
||||
game: [
|
||||
{title: 'Default Character', icon: 'mdi-human-edit', value: 'character'},
|
||||
],
|
||||
application: [
|
||||
{title: 'OpenAI', icon: 'mdi-api', value: 'openai_api'},
|
||||
{title: 'ElevenLabs', icon: 'mdi-api', value: 'elevenlabs_api'},
|
||||
{title: 'Coqui Studio', icon: 'mdi-api', value: 'coqui_api'},
|
||||
{title: 'RunPod', icon: 'mdi-api', value: 'runpod_api'},
|
||||
],
|
||||
creator: [
|
||||
{title: 'Content Context', icon: 'mdi-cube-scan', value: 'content_context'},
|
||||
]
|
||||
},
|
||||
gamePageSelected: 'character',
|
||||
applicationPageSelected: 'openai_api',
|
||||
creatorPageSelected: 'content_context',
|
||||
}
|
||||
},
|
||||
inject: ['getWebsocket', 'registerMessageHandler', 'setWaitingForInput', 'requestSceneAssets', 'requestAppConfig'],
|
||||
@@ -104,6 +257,10 @@ export default {
|
||||
this.dialog = false
|
||||
},
|
||||
|
||||
contentContextRemove(index) {
|
||||
this.app_config.creator.content_context.splice(index, 1);
|
||||
},
|
||||
|
||||
handleMessage(message) {
|
||||
if (message.type == "app_config") {
|
||||
this.app_config = message.data;
|
||||
@@ -138,5 +295,4 @@ export default {
|
||||
|
||||
</script>
|
||||
|
||||
<style scoped>
|
||||
</style>
|
||||
<style scoped></style>
|
||||
@@ -44,10 +44,10 @@
|
||||
<v-window-item value="details">
|
||||
<v-card-text style="max-height:600px; overflow-y:scroll;">
|
||||
<v-list-item v-for="(value, key) in base_attributes" :key="key">
|
||||
<v-list-item-content>
|
||||
<div>
|
||||
<v-list-item-title>{{ key }}</v-list-item-title>
|
||||
<v-list-item-subtitle>{{ value }}</v-list-item-subtitle>
|
||||
</v-list-item-content>
|
||||
</div>
|
||||
</v-list-item>
|
||||
</v-card-text>
|
||||
</v-window-item>
|
||||
|
||||
@@ -23,9 +23,20 @@
|
||||
</v-col>
|
||||
</v-row>
|
||||
<v-row>
|
||||
<v-col cols="6">
|
||||
<v-col cols="4">
|
||||
<v-text-field v-model="client.max_token_length" v-if="isLocalApiClient(client)" type="number" label="Context Length"></v-text-field>
|
||||
</v-col>
|
||||
<v-col cols="8" v-if="!typeEditable() && client.data && client.data.prompt_template_example !== null">
|
||||
<v-card elevation="3" :color="(client.data.has_prompt_template ? 'primary' : 'warning')" variant="tonal">
|
||||
<v-card-title>Prompt Template</v-card-title>
|
||||
|
||||
<v-card-text>
|
||||
<div class="text-caption" v-if="!client.data.has_prompt_template">No matching LLM prompt template found. Using default.</div>
|
||||
<pre>{{ client.data.prompt_template_example }}</pre>
|
||||
</v-card-text>
|
||||
</v-card>
|
||||
|
||||
</v-col>
|
||||
</v-row>
|
||||
</v-container>
|
||||
</v-card-text>
|
||||
@@ -101,6 +112,7 @@ export default {
|
||||
i++;
|
||||
}
|
||||
this.client.name = name;
|
||||
this.client.data = {};
|
||||
}
|
||||
},
|
||||
validateName() {
|
||||
|
||||
@@ -7,9 +7,9 @@
|
||||
<v-list-subheader class="text-uppercase" v-else>
|
||||
<v-progress-circular indeterminate color="primary" size="20"></v-progress-circular> Waiting for config...
|
||||
</v-list-subheader>
|
||||
<v-list-item-group v-if="!loading && isConnected() && expanded && !configurationRequired() && appConfig !== null">
|
||||
<div v-if="!loading && isConnected() && expanded && !configurationRequired() && appConfig !== null">
|
||||
<v-list-item>
|
||||
<v-list-item-content class="mb-3">
|
||||
<div class="mb-3">
|
||||
<!-- Toggle buttons for switching between file upload and path input -->
|
||||
<v-btn-toggle density="compact" class="mb-3" v-model="inputMethod" mandatory>
|
||||
<v-btn value="file">
|
||||
@@ -40,9 +40,9 @@
|
||||
<v-icon left>mdi-palette-outline</v-icon>
|
||||
Creative Mode
|
||||
</v-btn>
|
||||
</v-list-item-content>
|
||||
</div>
|
||||
</v-list-item>
|
||||
</v-list-item-group>
|
||||
</div>
|
||||
<div v-else-if="configurationRequired()">
|
||||
<v-alert type="warning" variant="tonal">You need to configure a Talemate client before you can load scenes.</v-alert>
|
||||
</div>
|
||||
@@ -72,6 +72,9 @@ export default {
|
||||
appConfig: null, // Store the app configuration
|
||||
}
|
||||
},
|
||||
emits: {
|
||||
loading: null,
|
||||
},
|
||||
inject: ['getWebsocket', 'registerMessageHandler', 'isConnected', 'configurationRequired'],
|
||||
methods: {
|
||||
// Method to show the DefaultCharacter modal
|
||||
|
||||
@@ -18,7 +18,7 @@
|
||||
<v-tooltip v-if="isEnvironment('scene')" :disabled="isInputDisabled()" location="top"
|
||||
text="Redo most recent AI message">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!rerun')" color="primary" icon>
|
||||
<v-icon>mdi-refresh</v-icon>
|
||||
</v-btn>
|
||||
@@ -28,7 +28,7 @@
|
||||
<v-tooltip v-if="isEnvironment('scene')" :disabled="isInputDisabled()" location="top"
|
||||
text="Redo most recent AI message (Nuke Option - use this to attempt to break out of repetition)">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!rerun:0.5')" color="primary" icon>
|
||||
<v-icon>mdi-nuke</v-icon>
|
||||
</v-btn>
|
||||
@@ -39,7 +39,7 @@
|
||||
<v-tooltip v-if="commandActive" location="top"
|
||||
text="Abort / end action.">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mr-3" v-bind="props" v-on="on" :disabled="!isWaitingForInput()"
|
||||
<v-btn class="hotkey mr-3" v-bind="props" :disabled="!isWaitingForInput()"
|
||||
@click="sendHotButtonMessage('!abort')" color="primary" icon>
|
||||
<v-icon>mdi-cancel</v-icon>
|
||||
|
||||
@@ -56,7 +56,7 @@
|
||||
<v-card-actions>
|
||||
<v-tooltip :disabled="isInputDisabled()" location="top" text="Narrate: Progress Story">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!narrate_progress')" color="primary" icon>
|
||||
<v-icon>mdi-script-text-play</v-icon>
|
||||
</v-btn>
|
||||
@@ -64,7 +64,7 @@
|
||||
</v-tooltip>
|
||||
<v-tooltip :disabled="isInputDisabled()" location="top" text="Narrate: Scene">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!narrate')" color="primary" icon>
|
||||
<v-icon>mdi-script-text</v-icon>
|
||||
</v-btn>
|
||||
@@ -72,7 +72,7 @@
|
||||
</v-tooltip>
|
||||
<v-tooltip :disabled="isInputDisabled()" location="top" text="Narrate: Character">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!narrate_c')" color="primary" icon>
|
||||
<v-icon>mdi-account-voice</v-icon>
|
||||
</v-btn>
|
||||
@@ -80,7 +80,7 @@
|
||||
</v-tooltip>
|
||||
<v-tooltip :disabled="isInputDisabled()" location="top" text="Narrate: Query">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!narrate_q')" color="primary" icon>
|
||||
<v-icon>mdi-crystal-ball</v-icon>
|
||||
</v-btn>
|
||||
@@ -103,7 +103,7 @@
|
||||
<v-divider vertical></v-divider>
|
||||
<v-tooltip :disabled="isInputDisabled()" location="top" text="Direct a character">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!director')" color="primary" icon>
|
||||
<v-icon>mdi-bullhorn</v-icon>
|
||||
</v-btn>
|
||||
@@ -118,7 +118,7 @@
|
||||
|
||||
<v-tooltip :disabled="isInputDisabled()" location="top" text="Save">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!save')" color="primary" icon>
|
||||
<v-icon>mdi-content-save</v-icon>
|
||||
</v-btn>
|
||||
@@ -127,7 +127,7 @@
|
||||
|
||||
<v-tooltip :disabled="isInputDisabled()" location="top" text="Save As">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!save_as')" color="primary" icon>
|
||||
<v-icon>mdi-content-save-all</v-icon>
|
||||
</v-btn>
|
||||
@@ -136,7 +136,7 @@
|
||||
|
||||
<v-tooltip v-if="isEnvironment('scene')" :disabled="isInputDisabled()" location="top" text="Switch to creative mode">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!setenv_creative')" color="primary" icon>
|
||||
<v-icon>mdi-palette-outline</v-icon>
|
||||
</v-btn>
|
||||
@@ -145,7 +145,7 @@
|
||||
|
||||
<v-tooltip v-else-if="isEnvironment('creative')" :disabled="isInputDisabled()" location="top" text="Switch to game mode">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn class="hotkey mx-3" v-bind="props" v-on="on" :disabled="isInputDisabled()"
|
||||
<v-btn class="hotkey mx-3" v-bind="props" :disabled="isInputDisabled()"
|
||||
@click="sendHotButtonMessage('!setenv_scene')" color="primary" icon>
|
||||
<v-icon>mdi-gamepad-square</v-icon>
|
||||
</v-btn>
|
||||
|
||||
@@ -37,18 +37,14 @@
|
||||
<v-list>
|
||||
<v-list-subheader class="text-uppercase"><v-icon>mdi-network-outline</v-icon>
|
||||
Clients</v-list-subheader>
|
||||
<v-list-item-group>
|
||||
<v-list-item>
|
||||
<AIClient ref="aiClient" @save="saveClients" @error="uxErrorHandler" @clients-updated="saveClients" @client-assigned="saveAgents"></AIClient>
|
||||
</v-list-item>
|
||||
</v-list-item-group>
|
||||
<v-divider></v-divider>
|
||||
<v-list-subheader class="text-uppercase"><v-icon>mdi-transit-connection-variant</v-icon> Agents</v-list-subheader>
|
||||
<v-list-item-group>
|
||||
<v-list-item>
|
||||
<AIAgent ref="aiAgent" @save="saveAgents" @agents-updated="saveAgents"></AIAgent>
|
||||
</v-list-item>
|
||||
</v-list-item-group>
|
||||
<!-- More sections can be added here -->
|
||||
</v-list>
|
||||
</v-navigation-drawer>
|
||||
@@ -97,13 +93,12 @@
|
||||
<v-chip size="x-small" v-else-if="scene.environment === 'scene'" class="ml-1"><v-icon text="Play" size="14"
|
||||
class="mr-1">mdi-gamepad-square</v-icon>Game Mode</v-chip>
|
||||
|
||||
<v-btn v-if="scene.environment === 'scene'" class="ml-1" @click="openSceneHistory()"><v-icon size="14"
|
||||
class="mr-1">mdi-playlist-star</v-icon>History</v-btn>
|
||||
|
||||
<v-chip size="x-small" v-if="scene.scene_time !== undefined">
|
||||
<v-icon>mdi-clock</v-icon>
|
||||
{{ scene.scene_time }}
|
||||
</v-chip>
|
||||
<v-tooltip :text="scene.scene_time" v-if="scene.scene_time !== undefined">
|
||||
<template v-slot:activator="{ props }">
|
||||
<v-btn v-bind="props" v-if="scene.environment === 'scene'" class="ml-1" @click="openSceneHistory()"><v-icon size="14"
|
||||
class="mr-1">mdi-clock</v-icon>History</v-btn>
|
||||
</template>
|
||||
</v-tooltip>
|
||||
|
||||
</v-toolbar-title>
|
||||
<v-toolbar-title v-else>
|
||||
|
||||
@@ -2,10 +2,8 @@
|
||||
<v-list-subheader class="text-uppercase">
|
||||
<v-icon class="mr-1">mdi-earth</v-icon>World
|
||||
<v-progress-circular class="ml-1 mr-3" size="14" v-if="requesting" indeterminate color="primary"></v-progress-circular>
|
||||
<v-btn v-else size="x-small" class="mr-1" v-bind="props" variant="tonal" density="comfortable" rounded="sm" @click.stop="refresh()" icon="mdi-refresh"></v-btn>
|
||||
|
||||
<v-btn v-else :disabled="isInputDisabled()" size="x-small" class="mr-1" variant="tonal" density="comfortable" rounded="sm" @click.stop="refresh()" icon="mdi-refresh"></v-btn>
|
||||
</v-list-subheader>
|
||||
|
||||
<div ref="charactersContainer">
|
||||
|
||||
<v-expansion-panels density="compact" v-for="(character,name) in characters" :key="name">
|
||||
@@ -85,6 +83,7 @@ export default {
|
||||
items: {},
|
||||
location: null,
|
||||
requesting: false,
|
||||
sceneTime: null,
|
||||
}
|
||||
},
|
||||
|
||||
@@ -94,6 +93,7 @@ export default {
|
||||
'setWaitingForInput',
|
||||
'openCharacterSheet',
|
||||
'characterSheet',
|
||||
'isInputDisabled',
|
||||
],
|
||||
|
||||
methods: {
|
||||
@@ -127,6 +127,8 @@ export default {
|
||||
this.items = data.data.items;
|
||||
this.location = data.data.location;
|
||||
this.requesting = (data.status==="requested")
|
||||
} else if (data.type == "scene_status") {
|
||||
this.sceneTime = data.data.scene_time;
|
||||
}
|
||||
},
|
||||
},
|
||||
|
||||
4
templates/llm-prompt/Noromaid.jinja2
Normal file
4
templates/llm-prompt/Noromaid.jinja2
Normal file
@@ -0,0 +1,4 @@
|
||||
{{ system_message }}
|
||||
|
||||
### Instruction:
|
||||
{{ set_response(prompt, "\n\n### Response:\n") }}
|
||||
4
templates/llm-prompt/OpenHermes-2.5-neural-chat.jinja2
Normal file
4
templates/llm-prompt/OpenHermes-2.5-neural-chat.jinja2
Normal file
@@ -0,0 +1,4 @@
|
||||
<|im_start|>system
|
||||
{{ system_message }}<|im_end|>
|
||||
<|im_start|>user
|
||||
{{ set_response(prompt, "<|im_end|>\n<|im_start|>assistant\n") }}
|
||||
2
templates/llm-prompt/RpBird-Yi-34B.jinja2
Normal file
2
templates/llm-prompt/RpBird-Yi-34B.jinja2
Normal file
@@ -0,0 +1,2 @@
|
||||
SYSTEM: {{ system_message }}
|
||||
USER: {{ set_response(prompt, "\nASSISTANT: ") }}
|
||||
2
templates/llm-prompt/Tess.jinja2
Normal file
2
templates/llm-prompt/Tess.jinja2
Normal file
@@ -0,0 +1,2 @@
|
||||
SYSTEM: {{ system_message }}
|
||||
USER: {{ set_response(prompt, "\nASSISTANT: ") }}
|
||||
Reference in New Issue
Block a user