Merge branch 'master' into feat/huggingface-inference

.github/CODEOWNERS

@@ -1,4 +1,5 @@
 * @wandb/weave-team
+/docs/ @wandb/docs-team @wandb/weave-team
 weave-js/src/common @wandb/fe-infra-reviewers
 weave-js/src/components @wandb/fe-infra-reviewers @wandb/weave-team
 weave-js/src/assets @wandb/fe-infra-reviewers @wandb/weave-team

.github/workflows/notify-wandb-core.yaml

@@ -6,14 +6,38 @@ name: Notify wandb/core
 on:
   push:
     branches:
-      - '**'
+      - "**"
   workflow_dispatch:
 
+permissions:
+  packages: write
+
 jobs:
+  publish-package:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+      - name: Configure npm for GitHub Packages
+        run: |
+          echo "//npm.pkg.github.com/:_authToken=${{ secrets.GITHUB_TOKEN }}" >> weave-js/.npmrc
+      - name: Publish package
+        run: |
+          cd weave-js
+          yarn install --frozen-lockfile
+          npm version 0.0.0-${{ github.sha }} --no-git-tag-version
+          yarn generate
+          cp package.json README.md .npmrc src/
+          cd src
+          if [ "${{ github.ref }}" = "refs/heads/master" ]; then
+            npm publish
+          else
+            npm publish --tag prerelease
+          fi
   check-which-tests-to-run:
     uses: ./.github/workflows/check-which-tests-to-run.yaml
   notify-wandb-core:
-    needs: check-which-tests-to-run
+    needs: [check-which-tests-to-run, publish-package]
     runs-on: ubuntu-latest
     steps:
       - name: Repository dispatch

.github/workflows/test.yaml

@@ -81,11 +81,16 @@ jobs:
         env:
           CI: 1
           WANDB_ENABLE_TEST_CONTAINER: true
+          LOGGING_ENABLED: true
         ports:
-          - '8080:8080'
-          - '8083:8083'
-          - '9015:9015'
-        options: --health-cmd "curl --fail http://localhost:8080/healthz || exit 1" --health-interval=5s --health-timeout=3s
+          - "8080:8080"
+          - "8083:8083"
+          - "9015:9015"
+        options: >-
+          --health-cmd "wget -q -O /dev/null http://localhost:8080/healthz || exit 1"
+          --health-interval=5s
+          --health-timeout=3s
+          --health-start-period=10s
     outputs:
       tests_should_run: ${{ steps.test_check.outputs.tests_should_run }}
     steps:
@@ -160,7 +165,10 @@ jobs:
       - uses: actions/setup-node@v1
         if: steps.check_run.outputs.should_lint_and_compile == 'true'
         with:
-          node-version: '18.x'
+          node-version: "18.x"
+      - name: Configure npm for GitHub Packages
+        run: |
+          echo "//npm.pkg.github.com/:_authToken=${{ secrets.GITHUB_TOKEN }}" >> .npmrc
       - name: Run WeaveJS Lint and Compile
         if: steps.check_run.outputs.should_lint_and_compile == 'true'
         run: |
@@ -213,37 +221,37 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version-major: ['3']
+        python-version-major: ["3"]
         python-version-minor: [
-            '9',
-            '10',
-            '11',
-            '12',
-            '13',
+            "9",
+            "10",
+            "11",
+            "12",
+            "13",
             #
           ]
         nox-shard:
           [
-            'trace',
-            'trace_server',
-            'anthropic',
-            'cerebras',
-            'cohere',
-            'dspy',
-            'groq',
-            'google_ai_studio',
-            'instructor',
-            'langchain',
-            'litellm',
-            'llamaindex',
-            'mistral0',
-            'mistral1',
-            'notdiamond',
-            'openai',
-            'vertexai',
-            'scorers_tests',
-            'pandas-test',
-            'huggingface',
+            "trace",
+            "trace_server",
+            "anthropic",
+            "cerebras",
+            "cohere",
+            "dspy",
+            "groq",
+            "google_ai_studio",
+            "instructor",
+            "langchain",
+            "litellm",
+            "llamaindex",
+            "mistral0",
+            "mistral1",
+            "notdiamond",
+            "openai",
+            "vertexai",
+            "scorers_tests",
+            "pandas-test",
+            "huggingface",
           ]
       fail-fast: false
     services:
@@ -255,19 +263,28 @@ jobs:
         env:
           CI: 1
           WANDB_ENABLE_TEST_CONTAINER: true
+          LOGGING_ENABLED: true
         ports:
-          - '8080:8080'
-          - '8083:8083'
-          - '9015:9015'
-        options: --health-cmd "curl --fail http://localhost:8080/healthz || exit 1" --health-interval=5s --health-timeout=3s
+          - "8080:8080"
+          - "8083:8083"
+          - "9015:9015"
+        options: >-
+          --health-cmd "wget -q -O /dev/null http://localhost:8080/healthz || exit 1"
+          --health-interval=5s
+          --health-timeout=3s
+          --health-start-period=10s
       weave_clickhouse:
         image: clickhouse/clickhouse-server
         ports:
-          - '8123:8123'
+          - "8123:8123"
         options: --health-cmd "wget -nv -O- 'http://localhost:8123/ping' || exit 1" --health-interval=5s --health-timeout=3s
     steps:
       - name: Checkout
         uses: actions/checkout@v3
+      - name: Enable debug logging
+        run: echo "ACTIONS_STEP_DEBUG=true" >> $GITHUB_ENV
+      - name: Install SQLite dev package
+        run: sudo apt update && sudo apt install -y libsqlite3-dev
       - name: Set up Python ${{ matrix.python-version-major }}.${{ matrix.python-version-minor }}
         uses: actions/setup-python@v5
         with:
@@ -294,6 +311,7 @@ jobs:
           WB_SERVER_HOST: http://wandbservice
           WF_CLICKHOUSE_HOST: weave_clickhouse
           WEAVE_SERVER_DISABLE_ECOSYSTEM: 1
+          WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
     hooks:
       - id: mypy
         additional_dependencies:
-          [types-pkg-resources==0.1.3, types-all, wandb>=0.15.5]
+          [types-pkg-resources==0.1.3, types-all, wandb>=0.15.5, wandb<0.19.0]
         # Note: You have to update pyproject.toml[tool.mypy] too!
         args: ["--config-file=pyproject.toml"]
         exclude: (.*pyi$)|(weave_query)|(tests)|(examples)

dev_docs/BaseObjectClasses.md → dev_docs/BuiltinObjectClasses.md

@@ -1,4 +1,4 @@
-# BaseObjectClasses
+# BuiltinObjectClasses
 
 ## Refresher on Objects and object storage
 
@@ -79,11 +79,11 @@ While many Weave Objects are free-form and user-defined, there is often a need f
 
 Here's how to define and use a validated base object:
 
-1. **Define your schema** (in `weave/trace_server/interface/base_object_classes/your_schema.py`):
+1. **Define your schema** (in `weave/trace_server/interface/builtin_object_classes/your_schema.py`):
 
 ```python
 from pydantic import BaseModel
-from weave.trace_server.interface.base_object_classes import base_object_def
+from weave.trace_server.interface.builtin_object_classes import base_object_def
 
 class NestedConfig(BaseModel):
     setting_a: int
@@ -116,7 +116,7 @@ curl -X POST 'https://trace.wandb.ai/obj/create' \
       "project_id": "user/project",
       "object_id": "my_config",
       "val": {...},
-      "set_base_object_class": "MyConfig"
+      "object_class": "MyConfig"
     }
   }'
 
@@ -154,38 +154,38 @@ Run `make synchronize-base-object-schemas` to ensure the frontend TypeScript typ
 
 ### Architecture Flow
 
-1. Define your schema in a python file in the `weave/trace_server/interface/base_object_classes/test_only_example.py` directory. See `weave/trace_server/interface/base_object_classes/test_only_example.py` as an example.
-2. Make sure to register your schemas in `weave/trace_server/interface/base_object_classes/base_object_registry.py` by calling `register_base_object`.
+1. Define your schema in a python file in the `weave/trace_server/interface/builtin_object_classes/test_only_example.py` directory. See `weave/trace_server/interface/builtin_object_classes/test_only_example.py` as an example.
+2. Make sure to register your schemas in `weave/trace_server/interface/builtin_object_classes/builtin_object_registry.py` by calling `register_base_object`.
 3. Run `make synchronize-base-object-schemas` to generate the frontend types.
-    * The first step (`make generate_base_object_schemas`) will run `weave/scripts/generate_base_object_schemas.py` to generate a JSON schema in `weave/trace_server/interface/base_object_classes/generated/generated_base_object_class_schemas.json`.
-    * The second step (yarn `generate-schemas`) will read this file and use it to generate the frontend types located in `weave-js/src/components/PagePanelComponents/Home/Browse3/pages/wfReactInterface/generatedBaseObjectClasses.zod.ts`.
+    * The first step (`make generate_base_object_schemas`) will run `weave/scripts/generate_base_object_schemas.py` to generate a JSON schema in `weave/trace_server/interface/builtin_object_classes/generated/generated_builtin_object_class_schemas.json`.
+    * The second step (yarn `generate-schemas`) will read this file and use it to generate the frontend types located in `weave-js/src/components/PagePanelComponents/Home/Browse3/pages/wfReactInterface/generatedBuiltinObjectClasses.zod.ts`.
 4. Now, each use case uses different parts:
     1. `Python Writing`. Users can directly import these classes and use them as normal Pydantic models, which get published with `weave.publish`. The python client correct builds the requisite payload.
     2. `Python Reading`. Users can `weave.ref().get()` and the weave python SDK will return the instance with the correct type. Note: we do some special handling such that the returned object is not a WeaveObject, but literally the exact pydantic class.
-    3. `HTTP Writing`. In cases where the client/user does not want to add the special type information, users can publish base objects by setting the `set_base_object_class` setting on `POST obj/create` to the name of the class. The weave server will validate the object against the schema, update the metadata fields, and store the object.
+    3. `HTTP Writing`. In cases where the client/user does not want to add the special type information, users can publish builtin objects (set of weave.Objects provided by Weave) by setting the `builtin_object_class` setting on `POST obj/create` to the name of the class. The weave server will validate the object against the schema, update the metadata fields, and store the object.
     4. `HTTP Reading`. When querying for objects, the server will return the object with the correct type if the `base_object_class` metadata field is set.
-    5. `Frontend`. The frontend will read the zod schema from `weave-js/src/components/PagePanelComponents/Home/Browse3/pages/wfReactInterface/generatedBaseObjectClasses.zod.ts` and use that to provide compile time type safety when using `useBaseObjectInstances` and runtime type safety when using `useCreateBaseObjectInstance`.
+    5. `Frontend`. The frontend will read the zod schema from `weave-js/src/components/PagePanelComponents/Home/Browse3/pages/wfReactInterface/generatedBuiltinObjectClasses.zod.ts` and use that to provide compile time type safety when using `useBaseObjectInstances` and runtime type safety when using `useCreateBaseObjectInstance`.
 * Note: it is critical that all techniques produce the same digest for the same data - which is tested in the tests. This way versions are not thrashed by different clients/users.
 
 ```mermaid
 graph TD
     subgraph Schema Definition
         F["weave/trace_server/interface/<br>base_object_classes/your_schema.py"] --> |defines| P[Pydantic BaseObject]
-        P --> |register_base_object| R["base_object_registry.py"]
+        P --> |register_base_object| R["builtin_object_registry.py"]
     end
 
     subgraph Schema Generation
         M["make synchronize-base-object-schemas"] --> G["make generate_base_object_schemas"]
         G --> |runs| S["weave/scripts/<br>generate_base_object_schemas.py"]
         R --> |import registered classes| S
-        S --> |generates| J["generated_base_object_class_schemas.json"]
-        M --> |yarn generate-schemas| Z["generatedBaseObjectClasses.zod.ts"]
+        S --> |generates| J["generated_builtin_object_class_schemas.json"]
+        M --> |yarn generate-schemas| Z["generatedBuiltinObjectClasses.zod.ts"]
         J --> Z
     end
 
     subgraph "Trace Server"
         subgraph "HTTP API"
-            R --> |validates using| HW["POST obj/create<br>set_base_object_class"]
+            R --> |validates using| HW["POST obj/create<br>object_class"]
             HW --> DB[(Weave Object Store)]
             HR["POST objs/query<br>base_object_classes"] --> |Filters base_object_class| DB
         end
@@ -203,7 +203,7 @@ graph TD
         Z --> |import| UBI["useBaseObjectInstances"]
         Z --> |import| UCI["useCreateBaseObjectInstance"]
         UBI --> |Filters base_object_class| HR
-        UCI --> |set_base_object_class| HW
+        UCI --> |object_class| HW
         UI[React UI] --> UBI
         UI --> UCI
     end

dev_docs/RELEASE.md

@@ -6,7 +6,8 @@ This document outlines how to publish a new Weave release to our public [PyPI pa
 
 2. You should also run through this [sample notebook](https://colab.research.google.com/drive/1DmkLzhFCFC0OoN-ggBDoG1nejGw2jQZy#scrollTo=29hJrcJQA7jZ) remember to install from master. You can also just run the [quickstart](http://wandb.me/weave_colab).
 
-3. To prepare a PATCH release, go to GitHub Actions and run the `bump-python-sdk-version` workflow on master. This will:
+3. To prepare a PATCH release, go to GitHub Actions and run the [bump-python-sdk-version](https://github.com/wandb/weave/actions/workflows/bump_version.yaml) workflow on master. This will:
+
    - Create a new patch version by dropping the pre-release (e.g., `x.y.z-dev0` -> `x.y.z`) and tag this commit with `x.y.z`
    - Create a new dev version by incrementing the dev version (e.g., `x.y.z` -> `x.y.(z+1)-dev0`) and commit this to master
    - Both of these commits will be pushed to master
@@ -16,6 +17,6 @@ This document outlines how to publish a new Weave release to our public [PyPI pa
 
 5. Verify the new version of Weave exists in [PyPI](https://pypi.org/project/weave/) once it is complete.
 
-6. Go to GitHub, click the release tag, and click `Draft a New Release`. Select the new tag, and click generate release notes. Publish the release.
+6. Go to the [GitHub new release page](https://github.com/wandb/weave/releases/new). Select the new tag, and click "Generate release notes". Publish the release.
 
 7. Finally, announce that the merge freeze is over.

docs/docs/guides/core-types/datasets.md

@@ -13,7 +13,7 @@ This guide will show you how to:
 
 ## Sample code
 
-<Tabs groupId="programming-language">
+<Tabs groupId="programming-language" queryString>
   <TabItem value="python" label="Python" default>
     ```python
     import weave

docs/docs/guides/core-types/env-vars.md

@@ -0,0 +1,28 @@
+# Environment variables
+
+Weave provides a set of environment variables to configure and optimize its behavior. You can set these variables in your shell or within scripts to control specific functionality.
+
+```bash
+# Example of setting environment variables in the shell
+WEAVE_PARALLELISM=10  # Controls the number of parallel workers
+WEAVE_PRINT_CALL_LINK=false  # Disables call link output
+```
+
+```python
+# Example of setting environment variables in Python
+import os
+
+os.environ["WEAVE_PARALLELISM"] = "10"
+os.environ["WEAVE_PRINT_CALL_LINK"] = "false"
+```
+
+## Environment variables reference 
+
+| Variable Name            | Description                                                     |
+|--------------------------|-----------------------------------------------------------------|
+| WEAVE_CAPTURE_CODE      | Disable code capture for `weave.op` if set to `false`.                                    |
+| WEAVE_DEBUG_HTTP        | If set to `1`, turns on HTTP request and response logging for debugging.  |
+| WEAVE_DISABLED          | If set to `true`, all tracing to Weave is disabled.      |
+| WEAVE_PARALLELISM       | In evaluations, the number of examples to evaluate in parallel. `1` runs examples sequentially. Default value is `20`.    |
+| WEAVE_PRINT_CALL_LINK   | If set to `false`, call URL printing is suppressed. Default value is `false`.                            |
+| WEAVE_TRACE_LANGCHAIN   | When set to `false`,  explicitly disable global tracing for LangChain.  |                                                              |

docs/docs/guides/core-types/media.md

@@ -9,7 +9,7 @@ Weave supports logging and displaying multiple first class media types. Log imag
 
 Logging type: `PIL.Image.Image`. Here is an example of logging an image with the OpenAI DALL-E API:
 
-<Tabs groupId="programming-language">
+<Tabs groupId="programming-language" queryString>
   <TabItem value="python" label="Python" default>
 
     ```python
@@ -83,7 +83,7 @@ This image will be logged to weave and automatically displayed in the UI. The fo
 
 Logging type: `wave.Wave_read`. Here is an example of logging an audio file using openai's speech generation API.
 
-<Tabs groupId="programming-language">
+<Tabs groupId="programming-language" queryString>
   <TabItem value="python" label="Python" default>
 
     ```python

docs/docs/guides/core-types/models.md

@@ -3,7 +3,7 @@ import TabItem from '@theme/TabItem';
 
 # Models
 
-<Tabs groupId="programming-language">
+<Tabs groupId="programming-language" queryString>
   <TabItem value="python" label="Python" default>
     A `Model` is a combination of data (which can include configuration, trained model weights, or other information) and code that defines how the model operates. By structuring your code to be compatible with this API, you benefit from a structured way to version your application so you can more systematically keep track of your experiments.