From f9fe7318e6f10cfb7afc5e2a32a4ca081759e665 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Mon, 2 Aug 2021 15:32:04 +0100 Subject: [PATCH 01/19] docs: update Document cookbook --- .github/2.0/cookbooks/Document.md | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index 0aee314938332..678cf9d6f8ed2 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -85,7 +85,7 @@ A `Document` object has the following attributes, which can be put into the foll | | | |---|---| | Content attributes | `.buffer`, `.blob`, `.text`, `.uri`, `.content`, `.embedding` | -| Meta attributes | `.id`, `.weight`, `.mime_type`, `.location`, `.tags`, `.offset`, `.modality`, `siblings` | +| Meta attributes | `.id`, `.parent_id`, `.weight`, `.mime_type`, `.content_type`, `.tags`, `.modality` | | Recursive attributes | `.chunks`, `.matches`, `.granularity`, `.adjacency` | | Relevance attributes | `.score`, `.evaluations` | @@ -151,7 +151,7 @@ d4 = Document(content='https://static.jina.ai/logo/core/notext/light/logo.png') ```text - + ``` @@ -196,6 +196,7 @@ doc.convert_buffer_to_uri() doc.convert_text_to_uri() doc.convert_uri_to_text() ``` +You can use `convert_content_to_uri` to convert the content to URI. This will determine the used `content_type` and use the appropriate conversion method. You can convert a URI to a data URI (a data in-line URI scheme) using `doc.convert_uri_to_datauri()`. This will fetch the resource and make it inline. @@ -263,10 +264,10 @@ d4 = Document(blob=tf.SparseTensor(indices, values, dense_shape)) | --- | --- | | `doc.tags` | A structured data value, consisting of fields which map to dynamically typed values | | `doc.id` | A hexdigest that represents a unique Document ID | +| `doc.parent_id` | A hexdigest that ehe document's parent id | | `doc.weight` | The weight of the Document | | `doc.mime_type` | The mime type of the Document | -| `doc.location` | The position of the Document. This could be start and end index of a string; x,y (top, left) coordinates of an image crop; timestamp of an audio clip, etc | -| `doc.offset` | The offset of the Document in the previous granularity Document| +| `doc.content_type` | The content type of the Document | | `doc.modality` | An identifier of the modality the Document belongs to| You can assign multiple attributes in the constructor via: @@ -384,6 +385,7 @@ format: | `from_csv()` | Yield `Document` from a CSV file. Each line is a `Document` object | | `from_files()` | Yield `Document` from a glob files. Each file is a `Document` object | | `from_ndarray()` | Yield `Document` from a `ndarray`. Each row (depending on `axis`) is a `Document` object | +| `from_lines()` | Yield `Document` from lines, json and csv | Using a generator is sometimes less memory-demanding, as it does not load/build all Document objects in one shot. @@ -468,7 +470,7 @@ You can add **chunks** (sub-Document) and **matches** (neighbour-Document) to a d.matches.append(Document()) ``` -Note that both `doc.chunks` and `doc.matches` return `DocumentArray`, which we will introduce later. +Note that both `doc.chunks` and `doc.matches` return `ChunkArray` and `MatchArray`, which are sub-classes of `DocumentArray`. We will introduce `DocumentArray later. ### Represent `Document` as Dictionary or JSON @@ -596,7 +598,7 @@ d ``` ```text -``` +``` ``` Score information is often used jointly with `matches`. For example, you often see the indexer adding `matches` as @@ -614,7 +616,7 @@ q.matches.append(m) ``` ```text - + ``` These attributes (`scores` and `evaluations`) provide a dict-like interface that lets access all its elements: @@ -646,8 +648,9 @@ Methods supported by `DocumentArray`: | | | |--- |--- | -| Python `list`-like interface | `__getitem__`, `__setitem__`, `__delitem__`, `__len__`, `insert`, `append`, `reverse`, `extend`, `__iadd__`, `__add__`, `__iter__`, `clear`, `sort` | +| Python `list`-like interface | `__getitem__`, `__setitem__`, `__delitem__`, `__len__`, `insert`, `append`, `reverse`, `extend`, `__iadd__`, `__add__`, `__iter__`, `clear`, `sort`, `shuffle`, `sample` | | Persistence | `save`, `load` | +| Neural Search Operations | `match`, `visualize` | | Advanced getters | `get_attributes`, `get_attributes_with_docs`, `traverse_flat`, `traverse` | ### Construct `DocumentArray` @@ -965,7 +968,7 @@ The following image shows how `DocumentArrayA` finds `limit=5` matches from the More generally, given two `DocumentArray` objects `da_1` and `da_2` the function `da_1.match(da_2, metric=some_metric, normalization=(0, 1), limit=N)` finds for each document in `da_1` then `N` documents from `da_2` with the lowest metric values according to `some_metric`. -- `metric` can be `'cosine'`, `'euclidean'`, `'sqeuclidean'` +- `metric` can be `'cosine'`, `'euclidean'`, `'sqeuclidean'` or a callable that takes 2 `ndarray` parameters and returns an `ndarray` - `normalization` is a tuple [a, b] to be used with min-max normalization. The min distance will be rescaled to `a`, the max distance will be rescaled to `b`; all other values will be rescaled into range `[a, b]`. The following example find the 3 closest documents, according to the euclidean distance, for each element in `da_1` from the elements in `da_2`. From 6cd6f93718ea9ee043e4a3659c8c64788faaa2a9 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Wed, 4 Aug 2021 09:21:30 +0100 Subject: [PATCH 02/19] docs: update flow plots --- .github/2.0/chain-flow.svg | 2 +- .github/2.0/empty-flow.svg | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/2.0/chain-flow.svg b/.github/2.0/chain-flow.svg index 14e8cb3de0628..cfff71ce2dfee 100644 --- a/.github/2.0/chain-flow.svg +++ b/.github/2.0/chain-flow.svg @@ -1 +1 @@ -
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 \ No newline at end of file From 90be774ddadde430ef15319a03c38d0247cd5de1 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Wed, 4 Aug 2021 11:45:40 +0100 Subject: [PATCH 03/19] docs: update outputs of Flow.md --- .github/2.0/cookbooks/Flow.md | 70 ++++++++++---------------------- .github/2.0/parallel-explain.svg | 2 +- .github/simple-plot3.svg | 2 +- .github/simple-plot4.svg | 2 +- 4 files changed, 24 insertions(+), 52 deletions(-) diff --git a/.github/2.0/cookbooks/Flow.md b/.github/2.0/cookbooks/Flow.md index b0c9916d6362d..12da5da68010e 100644 --- a/.github/2.0/cookbooks/Flow.md +++ b/.github/2.0/cookbooks/Flow.md @@ -233,7 +233,7 @@ from jina import Executor class MyExecutor(Executor): - def __init__(self, bar): + def __init__(self, bar, *args, **kwargs): super().__init__() self.bar = bar @@ -540,7 +540,7 @@ with f: def post( self, on: str, - inputs: InputType, + inputs: Optional[InputType] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, @@ -570,14 +570,7 @@ def post( """ ``` -Compared to 1.x Client/Flow API, the three new arguments are: - -- `on`: the endpoint used for identifying the user-defined `request_type`, labeled by `@requests(on='/foo')` in - the `Executor` class; -- `parameters`: the kwargs that will be sent to the `Executor`; -- `target_peapod`: a regex string represent the certain peas/pods request targeted. - -Note, all 1.x CRUD methods (`index`, `search`, `update`, `delete`) are just sugary syntax of `post` with `on='/index'` +Note, you can also use CRUD methods (`index`, `search`, `update`, `delete`) which are just sugary syntax of `post` with `on='/index'` , `on='/search'`, etc. Precisely, they are defined as: ```python @@ -717,22 +710,13 @@ with f: ``` ```console - gateway@8869[I]:input tcp://0.0.0.0:53021 (ROUTER_BIND) output None (DEALER_CONNECT) control over ipc:///var/folders/89/wxpq1yjn44g26_kcbylqkcb40000gn/T/tmpdryull18 (PAIR_BIND) - gateway@8869[S]:GRPCRuntime is listening at: 0.0.0.0:53023 - gateway@8865[S]:ready and listening - Flow@8865[I]:1 Pods (i.e. 1 Peas) are running in this Flow - Flow@8865[S]:πŸŽ‰ Flow is ready to use! - Flow@8865[I]: + gateway@137489[L]:ready and listening + Flow@137489[I]:πŸŽ‰ Flow is ready to use! πŸ”— Protocol: GRPC - 🏠 Local access: 0.0.0.0:53023 - πŸ”’ Private network: 192.168.31.159:53023 - GRPCClient@8865[S]:connected to the gateway at 0.0.0.0:53023! + 🏠 Local access: 0.0.0.0:44249 + πŸ”’ Private network: 192.168.1.100:44249 + 🌐 Public address: 197.28.126.36:44249 - ... - - gateway@8869[I]:#sent: 10 #recv: 10 sent_size: 10.3 KB recv_size: 9.7 KB - gateway@8865[S]:terminated - Flow@8865[S]:flow is closed and all resources are released, current build level is 0 ``` To see that more clearly, you can turn on the progress-bar by `show_progress`. @@ -743,15 +727,13 @@ with f: ``` ```console - GRPCClient@8903[S]:connected to the gateway at 0.0.0.0:53061! - |β–ˆ | ⏳ 0 ⏱️ 0.0s 🐎 0 QPS ... gateway@8907[I]:prefetching 50 requests... - gateway@8907[W]:if this takes too long, you may want to take smaller "--prefetch" or ask client to reduce `request_size` - gateway@8907[I]:prefetching 50 requests takes 0 seconds (0.01s) - |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | ⏳ 10 ⏱️ 0.0s 🐎 769 QPS takes 0 seconds (0.01s) - βœ… done in ⏱ 0 seconds 🐎 757 QPS - gateway@8907[I]:#sent: 10 #recv: 10 sent_size: 10.3 KB recv_size: 9.7 KB - gateway@8903[S]:terminated - Flow@8903[S]:flow is closed and all resources are released, current build level is 0 + gateway@137489[L]:ready and listening + Flow@137489[I]:πŸŽ‰ Flow is ready to use! + πŸ”— Protocol: GRPC + 🏠 Local access: 0.0.0.0:59109 + πŸ”’ Private network: 192.168.1.100:59109 + 🌐 Public address: 197.28.126.36:59109 +⏳ |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | ⏱️ 0.0s 🐎 429.0 RPSβœ… 10 requests done in ⏱ 0 seconds 🐎 425.1 RPS ``` @@ -770,19 +752,12 @@ with f: ``` ```console - gateway@9003[I]:input tcp://0.0.0.0:53174 (ROUTER_BIND) output None (DEALER_CONNECT) control over ipc:///var/folders/89/wxpq1yjn44g26_kcbylqkcb40000gn/T/tmpgu5yqxyw (PAIR_BIND) - gateway@9003[S]:GRPCRuntime is listening at: 0.0.0.0:53176 - gateway@9000[S]:ready and listening - Flow@9000[I]:1 Pods (i.e. 1 Peas) are running in this Flow - Flow@9000[S]:πŸŽ‰ Flow is ready to use! - Flow@9000[I]: + gateway@137489[L]:ready and listening + Flow@137489[I]:πŸŽ‰ Flow is ready to use! πŸ”— Protocol: GRPC - 🏠 Local access: 0.0.0.0:53176 - πŸ”’ Private network: 192.168.31.159:53176 - GRPCClient@9000[S]:connected to the gateway at 0.0.0.0:53176! - gateway@9003[I]:prefetching 50 requests... - gateway@9003[W]:if this takes too long, you may want to take smaller "--prefetch" or ask client to reduce `request_size` - gateway@9003[I]:prefetching 50 requests takes 0 seconds (0.01s) + 🏠 Local access: 0.0.0.0:59275 + πŸ”’ Private network: 192.168.1.100:59275 + 🌐 Public address: 197.28.126.36:59275 [, , , @@ -793,9 +768,6 @@ with f: , , ] - gateway@9003[I]:#sent: 10 #recv: 10 sent_size: 10.3 KB recv_size: 9.7 KB - gateway@9000[S]:terminated - Flow@9000[S]:flow is closed and all resources are released, current build level is 0 ``` @@ -936,7 +908,7 @@ with f: on_done=print) ``` -You can also modify the Documents while merging, which was not feasible to do in 1.x: +You can also modify the Documents while merging: ```python class C(Executor): diff --git a/.github/2.0/parallel-explain.svg b/.github/2.0/parallel-explain.svg index 7bdb9c0248de5..8bbb775183ae6 100644 --- a/.github/2.0/parallel-explain.svg +++ b/.github/2.0/parallel-explain.svg @@ -1 +1 @@ -
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Create Executor -The resulted file structure should look like the following: - +To create a new executor, use `jina hub new` and follow the steps in order to customize the new executor. +This will generate an Executor project with the name and the configuration that you have provided. +After running the command, a project with the following structure will be generated: ```text MyExecutor/ β”œβ”€β”€ Dockerfile # Optional -β”œβ”€β”€ manifest.yml # Optional -β”œβ”€β”€ config.yml # Optional -β”œβ”€β”€ README.md # Optional -β”œβ”€β”€ requirements.txt # Optional -β”œβ”€β”€ __init__.py -β”œβ”€β”€ setup.py # Optional -└── tests/ # Optional - β”œβ”€β”€ test_MyAwesomeExecutor.py - └── __init__.py +β”œβ”€β”€ manifest.yml +β”œβ”€β”€ config.yml +β”œβ”€β”€ README.md +β”œβ”€β”€ requirements.txt +└── executor.py ``` -Link to the _**detailed guidelines**_ for creating an executor -is [here](https://github.com/jina-ai/executor-template/blob/main/.github/GUIDELINES.md). ## 2. Push and Pull CLI @@ -65,7 +60,7 @@ is [here](https://github.com/jina-ai/executor-template/blob/main/.github/GUIDELI $ jina hub push [--public/--private] --force --secret ``` _**Note**_: - - Without any visibility option, it will only update the content of exexited executor. + - Without any visibility option, it will only update the content of executor. - With `--public` option, the resulted executor will be **visible to public**. - With `--private` options, the resulted executor will be **invisible to public**. @@ -75,7 +70,7 @@ is [here](https://github.com/jina-ai/executor-template/blob/main/.github/GUIDELI ```bash $ jina hub pull jinahub+docker://[:] ``` -- Pull the executor's **source-code package** into `~/.jina/hub-executors` defaultly +- Pull the executor's **source-code package** into `~/.jina/hub-packages` defaultly ```bash $ jina hub pull jinahub://[:] ``` From 195aeacea68126d85102431871375875ba869831 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Thu, 5 Aug 2021 09:47:38 +0100 Subject: [PATCH 05/19] docs: update Daemon.md and Serving.md --- .github/2.0/cookbooks/Daemon.md | 2 +- .github/2.0/cookbooks/Serving.md | 87 +++++++++++++++----------------- 2 files changed, 41 insertions(+), 48 deletions(-) diff --git a/.github/2.0/cookbooks/Daemon.md b/.github/2.0/cookbooks/Daemon.md index 6e90775583ce3..7e7f92f7e9cf4 100644 --- a/.github/2.0/cookbooks/Daemon.md +++ b/.github/2.0/cookbooks/Daemon.md @@ -592,7 +592,7 @@ docker run --add-host host.docker.internal:host-gateway \ - `jinaai/jina:test-daemon` ? - All images created by JinaD during local tests use image with this name (hard-coded). Once 2.0 is released, it would be pulled from `docker hub` or a better naming would get used. + All images created by JinaD during local tests use image with this name (hard-coded). - `--env JINA_DAEMON_BUILD=DEVEL` ? diff --git a/.github/2.0/cookbooks/Serving.md b/.github/2.0/cookbooks/Serving.md index ec60b18d01013..0564fe4cd6356 100644 --- a/.github/2.0/cookbooks/Serving.md +++ b/.github/2.0/cookbooks/Serving.md @@ -106,25 +106,22 @@ with Flow(port_expose=12345) as f: ``` ```console - gateway@14736[I]:input tcp://0.0.0.0:56392 (PULL_CONNECT) output tcp://0.0.0.0:56392 (PUSH_BIND) control over ipc:///var/folders/89/wxpq1yjn44g26_kcbylqkcb40000gn/T/tmp5pe2snw1 (PAIR_BIND) - gateway@14736[S]:GRPCRuntime is listening at: 0.0.0.0:12345 - gateway@14733[S]:ready and listening - Flow@14733[I]:1 Pods (i.e. 1 Peas) are running in this Flow - Flow@14733[S]:πŸŽ‰ Flow is ready to use! - Flow@14733[I]: + gateway@153127[L]:ready and listening + Flow@153127[I]:πŸŽ‰ Flow is ready to use! πŸ”— Protocol: GRPC 🏠 Local access: 0.0.0.0:12345 - πŸ”’ Private network: 192.168.31.159:12345 + πŸ”’ Private network: 192.168.1.15:12345 + 🌐 Public address: 197.26.36.43:12345 ``` -Note that the host address is `192.168.31.159` and `port_expose` is `12345`. +Note that the host address is `192.168.1.15` and `port_expose` is `12345`. While keep this server open, let's create a client on a different machine: ```python from jina import Client -c = Client(host='192.168.31.159', port_expose=12345) +c = Client(host='192.168.1.15', port_expose=12345) c.post('/') ``` @@ -146,15 +143,12 @@ with f: ``` ```console - gateway@14550[I]:input tcp://0.0.0.0:56192 (PULL_CONNECT) output tcp://0.0.0.0:56192 (PUSH_BIND) control over ipc:///var/folders/89/wxpq1yjn44g26_kcbylqkcb40000gn/T/tmpwn67zk99 (PAIR_BIND) - gateway@14550[S]:WebSocketRuntime is listening at: 0.0.0.0:12345 - gateway@14547[S]:ready and listening - Flow@14547[I]:1 Pods (i.e. 1 Peas) are running in this Flow - Flow@14547[S]:πŸŽ‰ Flow is ready to use! - Flow@14547[I]: + gateway@153127[L]:ready and listening + Flow@153127[I]:πŸŽ‰ Flow is ready to use! πŸ”— Protocol: WEBSOCKET 🏠 Local access: 0.0.0.0:12345 - πŸ”’ Private network: 192.168.31.159:12345 + πŸ”’ Private network: 192.168.1.15:12345 + 🌐 Public address: 197.26.36.43:12345 ``` This will serve the Flow with WebSocket, so any Client connects to it should follow the WebSocket protocol as well. @@ -162,7 +156,7 @@ This will serve the Flow with WebSocket, so any Client connects to it should fol ```python from jina import Client -c = Client(protocol='websocket', host='192.168.31.159', port_expose=12345) +c = Client(protocol='websocket', host='192.168.1.15', port_expose=12345) c.post('/') ``` @@ -184,17 +178,14 @@ with f: ``` ```console - gateway@14786[I]:input tcp://0.0.0.0:56454 (PULL_CONNECT) output tcp://0.0.0.0:56454 (PUSH_BIND) control over ipc:///var/folders/89/wxpq1yjn44g26_kcbylqkcb40000gn/T/tmp_uqd9ifv (PAIR_BIND) - gateway@14786[S]:HTTPRuntime is listening at: 0.0.0.0:12345 - gateway@14783[S]:ready and listening - Flow@14783[I]:1 Pods (i.e. 1 Peas) are running in this Flow - Flow@14783[S]:πŸŽ‰ Flow is ready to use! - Flow@14783[I]: + gateway@153127[L]:ready and listening + Flow@153127[I]:πŸŽ‰ Flow is ready to use! πŸ”— Protocol: HTTP 🏠 Local access: 0.0.0.0:12345 - πŸ”’ Private network: 192.168.31.159:12345 + πŸ”’ Private network: 192.168.1.15:12345 + 🌐 Public address: 197.26.36.43:12345 πŸ’¬ Swagger UI: http://localhost:12345/docs - πŸ“š Redoc: http://localhost:12345/redoc + πŸ“š Redoc: http://localhost:12345/redoc ``` #### Use Swagger UI to Send HTTP Request @@ -208,53 +199,55 @@ You can navigate to the Swagger docs UI via `http://localhost:12345/docs`: Now you can send data request via `curl`/Postman: ```console -$ curl --request POST -d '{"exec_entrypoint":"index", "data": [{"text": "hello world"}]}' -H 'Content-Type: application/json' http://localhost:12345/post/ +$ curl --request POST 'http://localhost:12345/post' --header 'Content-Type: application/json' -d '{"data": [{"text": "hello world"}],"execEndpoint": "/index"}' { - "request_id": "1f52dae0-93a5-47b5-9fa0-522a75301d99", + "requestId": "e2978837-e5cb-45c6-a36d-588cf9b24309", "data": { "docs": [ { - "id": "28287a66-b86a-11eb-99c2-1e008a366d49", - "tags": {}, - "text": "hello world", - "content_hash": "", + "id": "84d9538e-f5be-11eb-8383-c7034ef3edd4", "granularity": 0, "adjacency": 0, - "parent_id": "", + "parentId": "", + "text": "hello world", "chunks": [], "weight": 0.0, - "siblings": 0, "matches": [], - "mime_type": "", + "mimeType": "", + "tags": { + "mimeType": "", + "parentId": "" + }, "location": [], "offset": 0, + "embedding": null, + "scores": {}, "modality": "", + "evaluations": {} } ], "groundtruths": [] }, "header": { - "exec_endpoint": "index", - "target_peapod": "", - "no_propagate": false + "execEndpoint": "/index", + "targetPeapod": "", + "noPropagate": false }, + "parameters": {}, "routes": [ { "pod": "gateway", - "pod_id": "5e4211d0-3916-4f33-8b9e-eec54be8ed9a", - "start_time": "2021-05-19T06:19:24.472050Z", - "end_time": "2021-05-19T06:19:24.473895Z" - }, - { - "pod": "gateway", - "pod_id": "83a7ad34-1042-4b5d-b065-3692e2fc691b", - "start_time": "2021-05-19T06:19:24.473831Z" + "podId": "5742d5dd-43f1-451f-88e7-ece0588b7557", + "startTime": "2021-08-05T07:26:58.636258+00:00", + "endTime": "2021-08-05T07:26:58.636910+00:00", + "status": null } ], "status": { - "code": "SUCCESS", - "description": "" + "code": 0, + "description": "", + "exception": null } } ``` From b4e1ad997c578425c5a826a873a3ee5c3bb97a87 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Thu, 5 Aug 2021 13:38:28 +0100 Subject: [PATCH 06/19] docs: add graph document to cookbooks --- .github/2.0/cookbooks/Document.md | 94 +++++++++++++++++++++++++++++++ .github/2.0/cookbooks/Executor.md | 6 +- jina/types/document/graph.py | 2 +- 3 files changed, 97 insertions(+), 5 deletions(-) diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index 678cf9d6f8ed2..e4c02e6a92a9e 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -639,6 +639,100 @@ for evaluation_key, evaluation_score in d.evaluations.items(): recall => recall at 10: 0.5 ``` +### `GraphDocument` +`GraphDocument` is a subclass of `Document`. It's a special type of `Document` that adds functionality to lets you work with a `Document` as a `directed graph` +Chunks of the document represent the nodes of the graph. `GraphDocument` adds graph-specific attributes (`nodes`, `adjacency` list, `edge_features`,...) and operations (`add_node`, `remove_node`, `add_edge`, `remove_edge`,...) + + +### `GraphDocument` Additional Attributes + +`GraphDocument` adds the following attributes to `Document`: + +| Attribute | Description | +|---|---| +| `edge_features` | The dictionary of edge features, indexed by `edge_id` | +| `adjacency` | Adjacency list | +| `undirected` | Type of the graph: undirected or directed | +| `num_nodes` | Number of nodes in the graph | +| `num_edges` | Number of edges in the graph | +| `nodes` | The list of nodes. Equivalent to `chunks` | + +### `GraphDocument` methods + +`GraphDocument` adds the following methods to `Document`: + +* `add_node`: adds a document to the graoh: +```python +from jina.types.document.graph import GraphDocument +from jina import Document +graph = GraphDocument() +graph.add_node(Document(text='hello world')) +graph.nodes[0] +``` + +```text + +``` + +* `add_edge`: Adds an edge between 2 documents. If a document does not exist in the graph, it is added. +You can also add dict features to the edge with parameter `features` + +```python +from jina import Document +from jina.types.document.graph import GraphDocument +graph = GraphDocument() +d1 = Document(id='1', text='hello world') +d2 = Document(id='2', text='goodbye world') +graph.add_edge(d1, d2, features={"text": "both documents are linked"}) +graph.nodes +``` + +```text + +``` + +You access the edge features using id1-id2 as key: +```python +graph.edge_features['1-2'] +``` + +```text + +``` + +* `remove_edge` and `remove_node` allows removing an edge (between 2 nodes) and removing a node respectively. + +* `GraphDocument` exposes methods that return node-specific information: + +| method | Description | +|---|---| +| `get_out_degree` | node outdegree | +| `get_in_degree` | node indegree | +| `get_outgoing_nodes` | Array of outgoing nodes for a given node | +| `get_incoming_nodes` | Array of incoming nodes for a given node | + +```python +from jina import Document +from jina.types.document.graph import GraphDocument +graph = GraphDocument() +d1 = Document(id='1', text='hello world') +d2 = Document(id='2', text='goodbye world') +d3 = Document(id='3') +graph.add_edge(d1, d2) +graph.add_edge(d1, d3) + +assert graph.get_out_degree(d1) == 2 + +graph.get_outgoing_nodes(d1) +``` + +```text + +``` + +* `to_dgl_graph`: returns a `dgl.DGLGraph` from the graph document. +* `load_from_dgl_graph`: returns a `GraphDocument` from a `dgl.DGLGraph`. + ## `DocumentArray` API A `DocumentArray` is a list of `Document` objects. You can construct, delete, insert, sort and traverse a `DocumentArray` diff --git a/.github/2.0/cookbooks/Executor.md b/.github/2.0/cookbooks/Executor.md index 21123dd58d9f0..215d94795ed2f 100644 --- a/.github/2.0/cookbooks/Executor.md +++ b/.github/2.0/cookbooks/Executor.md @@ -124,8 +124,6 @@ with f: Every new executor should be inherited directly from `jina.Executor`. -The 1.x inheritance tree is removed. `Executor` no longer has polymorphism. - You can name your executor class freely. ### `__init__` Constructor @@ -369,7 +367,7 @@ requests: - `jtype` is a string. Defines the class name, interchangeable with bang mark `!`; - `with` is a map. Defines kwargs of the class `__init__` method -- `metas` is a map. Defines the meta information of that class. Compared to `1.x` it is reduced to the following fields: +- `metas` is a map. Defines the meta information of that class. It contains the following fields: - `name` is a string. Defines the name of the executor; - `description` is a string. Defines the description of this executor. It will be used in automatic docs UI; - `workspace` is a string. Defines the workspace of the executor; @@ -505,7 +503,7 @@ different purposes. In 2.0rc1, the following fields are valid for `metas` and `runtime_args`: -||| +| Attribute | Fields | | --- | --- | | `.metas` (static values from hard-coded values, YAML config) | `name`, `description`, `py_modules`, `workspace` | | `.runtime_args` (runtime values from its containers, e.g. `Runtime`, `Pea`, `Pod`) | `name`, `description`, `workspace`, `log_config`, `quiet`, `quiet_error`, `identity`, `port_ctrl`, `ctrl_with_ipc`, `timeout_ctrl`, `ssh_server`, `ssh_keyfile`, `ssh_password`, `uses`, `py_modules`, `port_in`, `port_out`, `host_in`, `host_out`, `socket_in`, `socket_out`, `memory_hwm`, `on_error_strategy`, `num_part`, `entrypoint`, `docker_kwargs`, `pull_latest`, `volumes`, `host`, `port_expose`, `quiet_remote_logs`, `upload_files`, `workspace_id`, `daemon`, `runtime_backend`, `runtime_cls`, `timeout_ready`, `env`, `expose_public`, `pea_id`, `pea_role`, `noblock_on_start`, `uses_before`, `uses_after`, `parallel`, `replicas`, `polling`, `scheduling`, `pod_role`, `peas_hosts` | diff --git a/jina/types/document/graph.py b/jina/types/document/graph.py index f20447e915158..9c03dcd503331 100644 --- a/jina/types/document/graph.py +++ b/jina/types/document/graph.py @@ -222,7 +222,7 @@ def _remove_edge_id(self, edge_id: int, edge_feature_key: str): def remove_edge(self, doc1: 'Document', doc2: 'Document'): """ - Remove a node from the graph along with the edges that may contain it + Remove the edge between doc1 and doc2 from the graph :param doc1: the starting node for this edge :param doc2: the ending node for this edge From 5569990111ddbdf91dfe3304d2ca11220e3ac69c Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Thu, 5 Aug 2021 13:46:40 +0100 Subject: [PATCH 07/19] docs: fill table headers --- .github/2.0/cookbooks/Document.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index e4c02e6a92a9e..b3d01e4eb20aa 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -82,7 +82,7 @@ d = Document() A `Document` object has the following attributes, which can be put into the following categories: -| | | +| Category | Attributes | |---|---| | Content attributes | `.buffer`, `.blob`, `.text`, `.uri`, `.content`, `.embedding` | | Meta attributes | `.id`, `.parent_id`, `.weight`, `.mime_type`, `.content_type`, `.tags`, `.modality` | @@ -128,7 +128,7 @@ d.pop('text', 'id', 'mime_type') ##### Content Attributes -| | | +| Attribute | Description | | --- | --- | | `doc.buffer` | The raw binary content of this Document | | `doc.blob` | The `ndarray` of the image/audio/video Document | From 29e23c65b731b5b2f83c0f5e4aaf16f3620c5fa1 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Fri, 6 Aug 2021 09:59:22 +0100 Subject: [PATCH 08/19] docs: add visualize to cookbooks --- .github/2.0/cookbooks/Document.md | 29 ++++++++++++++++++++--- .github/2.0/document-array-visualize.png | Bin 0 -> 24902 bytes 2 files changed, 26 insertions(+), 3 deletions(-) create mode 100644 .github/2.0/document-array-visualize.png diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index b3d01e4eb20aa..c64a6f43aeefb 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -640,11 +640,11 @@ for evaluation_key, evaluation_score in d.evaluations.items(): ``` ### `GraphDocument` -`GraphDocument` is a subclass of `Document`. It's a special type of `Document` that adds functionality to lets you work with a `Document` as a `directed graph` +`GraphDocument` is a subclass of `Document`. It's a special type of `Document` that adds functionality to let you work with a `Document` as a `directed graph`. Chunks of the document represent the nodes of the graph. `GraphDocument` adds graph-specific attributes (`nodes`, `adjacency` list, `edge_features`,...) and operations (`add_node`, `remove_node`, `add_edge`, `remove_edge`,...) -### `GraphDocument` Additional Attributes +#### `GraphDocument` Additional Attributes `GraphDocument` adds the following attributes to `Document`: @@ -657,7 +657,7 @@ Chunks of the document represent the nodes of the graph. `GraphDocument` adds gr | `num_edges` | Number of edges in the graph | | `nodes` | The list of nodes. Equivalent to `chunks` | -### `GraphDocument` methods +#### `GraphDocument` methods `GraphDocument` adds the following methods to `Document`: @@ -1227,6 +1227,29 @@ shuffled_da = da.shuffle() # shuffle the DocumentArray shuffled_da_with_seed = da.shuffle(seed=1) # shuffle the DocumentArray with seed. ``` +### Visualize Embeddings + +`DocumentArray` provides function `.visualize` to plot document embeddings in a 2D graph. `visualize` supports 2 methods to project in 2D space: `pca` and `tsne`. + +In the following example, we add 3 different distributions of embeddings and see 3 kinds of point clouds in the graph. +```python +import numpy as np +from jina import Document, DocumentArray + +da = DocumentArray( + [ + Document(embedding = np.random.normal(0, 1, 50)) for _ in range(500) + ] + [ + Document(embedding = np.random.normal(5, 2, 50)) for _ in range(500) + ] + [ + Document(embedding = np.random.normal(2, 5, 50)) for _ in range(500) + ] +) +da.visualize() + +``` + 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z8`DhAfYnrs(#ZF}j-CT<1D50qf(B~Xbgp^T9Htchg^WtjJ~F{y>@|8Dv)+^hdB z%D|_m?1V4Eo*lSkvML_k0ZhLa+F!%PyLf% z?HO`4KvR;y+CLh0b0GiCQvFEF-E}y4K#qDFYv#P{uFVVp!8)J=G(wJZ*-LZ@i|6}f z5JHm2yp+s18Vkjmd$gFv2lMRVluV@A8wwwP_&C;Q&xkJt1wx{K0eYXWX6N86yzI`< z;pPkv0P#YD1_>1c@fp9`1K`_#M0R2=5Mwy#CB}ajvNDj__*(=7_%eaE&4Sji>BY#n z6bCSF$>`*->|eBQkK!}t0$k~C1lCIlZ4+p8CJ?Lq8PPQ`be@owb_1H8KMob+$NJk>0B8dLBeDNqBNo_! zFh!191XvXU*qYGvdjVfzMv_t*;Et=J6qSTts$(C{kp#egTq+Ng+_}jhO;SAA6tv~{ z5PT|JJ~|aRp|9cp&S>j@3#_dBp91@3L(=6dV2o^J07S|mn{%syr~4nU4wBByK`ZB% z2x%b(q|#B6+{6+|B}T(YfM{jqNTIL!&l{laaZ}+wtcka1f=K=m%`ddG0|h6;<%EfV zl87@sn19!tOt%Lr1(4B#8}IC2vrq#X;eS5HG+71}esl#?dIDfRP6_`{^#d*CKNa%d zdYN=&V1kU^qz0g3Z}*Oc3~fI^0Y&w$1e4u)Vc o0IKXiOZI Date: Fri, 6 Aug 2021 11:52:05 +0100 Subject: [PATCH 09/19] docs: remove 1.x mentions in Executor.md --- .github/2.0/cookbooks/Executor.md | 64 +++++++++++++++++-------------- 1 file changed, 36 insertions(+), 28 deletions(-) diff --git a/.github/2.0/cookbooks/Executor.md b/.github/2.0/cookbooks/Executor.md index 215d94795ed2f..7a1f96e87571c 100644 --- a/.github/2.0/cookbooks/Executor.md +++ b/.github/2.0/cookbooks/Executor.md @@ -437,9 +437,39 @@ DocumentArray has 1 items: This is useful in debugging an Executor. +### Close Executor + +You might need to execute some logic when your executor's destructor is called. +For example, let's suppose you want to persist data to the disk (e.g in-memory indexed data, fine-tuned model,...). +To do so, you can overwrite the method `close` and add your logic. + +```python +from jina import Executor, requests, Document, DocumentArray + +class MyExec(Executor): + + @requests + def foo(self, docs, **kwargs): + for doc in docs: + print(doc.text) + + def close(self): + print("closing...") + +with MyExec() as executor: + executor.foo(DocumentArray([Document(text='hello world')])) +``` + +```text +hello world +closing... +``` + ## Executor Built-in Features -In Jina 2.0 the Executor class has fewer built-in features compared to 1.x. The design principles are (`user` here +In Jina 2.0 the Executor class is generic to all categories of executors (`encoders`, `indexers`, `segmenters`,...) to keep development simple. +We do not provide subclasses of `Executor` that are specific to each category. +The design principles are (`user` here means "Executor developer"): - **Do not surprise the user**: keep `Executor` class as Pythonic as possible. It should be as light and unintrusive as @@ -451,32 +481,10 @@ means "Executor developer"): interface while delivering just loosely-implemented features is bad for scaling the core framework. For example, `save`, `load`, `on_gpu`, etc. -We want to give programming freedom back to the user. If a user is a good Python programmer, they should pick -up `Executor` in no time - not spend extra time learning the implicit boilerplate as in 1.x. Plus, -subclassing `Executor` should be easy. - -### 1.x vs 2.0 +We want to give our users the freedom to customize their executors easily. If a user is a good Python programmer, they should pick +up `Executor` in no time. +It is as simple as subclassing `Executor` and adding an endpoint. -- ❌: Completely removed. Users have to implement it on their own. -- βœ…: Preserved. - -| 1.x | 2.0 | -| --- | --- | -| `.save_config()` | βœ… | -| `.load_config()` | βœ… | -| `.close()` | βœ… | -| `workspace` interface | βœ… [Refactored](#workspace). | -| `metas` config | Moved to `self.metas.xxx`. [Number of fields greatly reduced](#yaml-interface). | -| `._drivers` | Refactored and moved to `self.requests.xxx`. | -| `.save()` | ❌ | -| `.load()` | ❌ | -| `.logger` | ❌ | -| Pickle interface | ❌ | -| init boilerplates (`pre_init`, `post_init`) | ❌ | -| Context manager interface | ❌ | -| Inline `import` coding style | ❌ | - -![](1.xvs2.0%20BaseExecutor.svg) ### Workspace @@ -506,7 +514,7 @@ In 2.0rc1, the following fields are valid for `metas` and `runtime_args`: | Attribute | Fields | | --- | --- | | `.metas` (static values from hard-coded values, YAML config) | `name`, `description`, `py_modules`, `workspace` | -| `.runtime_args` (runtime values from its containers, e.g. `Runtime`, `Pea`, `Pod`) | `name`, `description`, `workspace`, `log_config`, `quiet`, `quiet_error`, `identity`, `port_ctrl`, `ctrl_with_ipc`, `timeout_ctrl`, `ssh_server`, `ssh_keyfile`, `ssh_password`, `uses`, `py_modules`, `port_in`, `port_out`, `host_in`, `host_out`, `socket_in`, `socket_out`, `memory_hwm`, `on_error_strategy`, `num_part`, `entrypoint`, `docker_kwargs`, `pull_latest`, `volumes`, `host`, `port_expose`, `quiet_remote_logs`, `upload_files`, `workspace_id`, `daemon`, `runtime_backend`, `runtime_cls`, `timeout_ready`, `env`, `expose_public`, `pea_id`, `pea_role`, `noblock_on_start`, `uses_before`, `uses_after`, `parallel`, `replicas`, `polling`, `scheduling`, `pod_role`, `peas_hosts` | +| `.runtime_args` (runtime values from its containers, e.g. `Runtime`, `Pea`, `Pod`) | `name`, `description`, `workspace`, `log_config`, `quiet`, `quiet_error`, `identity`, `port_ctrl`, `ctrl_with_ipc`, `timeout_ctrl`, `ssh_server`, `ssh_keyfile`, `ssh_password`, `uses`, `py_modules`, `port_in`, `port_out`, `host_in`, `host_out`, `socket_in`, `socket_out`, `memory_hwm`, `on_error_strategy`, `num_part`, `entrypoint`, `docker_kwargs`, `pull_latest`, `volumes`, `host`, `port_expose`, `quiet_remote_logs`, `upload_files`, `workspace_id`, `daemon`, `runtime_backend`, `runtime_cls`, `timeout_ready`, `env`, `expose_public`, `pea_id`, `pea_role`, `noblock_on_start`, `uses_before`, `uses_after`, `parallel`, `replicas`, `polling`, `scheduling`, `pod_role`, `peas_hosts`, `proxy`, `uses_metas`, `external`, `gpus`, `zmq_identity`, `hosts_in_connect`, `uses_with` | **Notes** @@ -571,7 +579,7 @@ with Flow().\ --- -## Migration in Practice +## Migrating from 1.x to 2.0 in Practice ### Encoder in `jina hello fashion` From 015428f4cb94c544fe8c7743d4d11983e461c55c Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Fri, 6 Aug 2021 12:07:50 +0100 Subject: [PATCH 10/19] docs: update TOC and remove 1.x mentions --- .github/2.0/cookbooks/Document.md | 15 ++++++++++++++- .github/2.0/cookbooks/Executor.md | 4 ++-- .github/2.0/cookbooks/Flow.md | 5 +++++ .github/2.0/cookbooks/Serving.md | 3 +++ 4 files changed, 24 insertions(+), 3 deletions(-) diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index c64a6f43aeefb..4d69ad82db6f1 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -27,11 +27,15 @@ Table of Contents - [`Document` Attributes](#document-attributes) - [Set & Unset Attributes](#set--unset-attributes) - [Construct `Document`](#construct-document) + - [Content Attributes](#content-attributes) - [Exclusivity of `doc.content`](#exclusivity-of-doccontent) - [Conversion between `doc.content`](#conversion-between-doccontent) + - [Set Embedding](#set-embedding) - [Support for Sparse arrays](#support-for-sparse-arrays) - [Construct with Multiple Attributes](#construct-with-multiple-attributes) + - [Meta Attributes](#meta-attributes) - [Construct from Dict or JSON String](#construct-from-dict-or-json-string) + - [Parsing Unrecognized Fields](#parsing-unrecognized-fields) - [Construct from Another `Document`](#construct-from-another-document) - [Construct from JSON, CSV, `ndarray` and Files](#construct-from-json-csv-ndarray-and-files) - [Serialize `Document`](#serialize-document) @@ -41,6 +45,10 @@ Table of Contents - [Visualize `Document`](#visualize-document) - [Add Relevancy to `Document`s](#add-relevancy-to-documents) - [Relevance Attributes](#relevance-attributes) + - [`GraphDocument`](#graphdocument) + - [`GraphDocument` Constructor](#graphdocument-constructor) + - [`GraphDocument` Additional Attributes](#graphdocument-additional-attributes) + - [`GraphDocument` methods](#graphdocument-methods) - [`DocumentArray` API](#documentarray-api) - [Construct `DocumentArray`](#construct-documentarray) - [Persistence via `save()`/`load()`](#persistence-via-saveload) @@ -56,6 +64,7 @@ Table of Contents - [Selecting a subset of Documents from a `DocumentArray` using `.find`](#selecting-a-subset-of-documents-from-a-documentarray-using-find) - [Random sample a subset of Documents from a `DocumentArray` using `sample`](#random-sample-a-subset-of-documents-from-a-documentarray-using-sample) - [Shuffle the `DocumentArray` using `shuffle`](#shuffle-the-documentarray-using-shuffle) + - [Visualize Embeddings](#visualize-embeddings) - [`DocumentArrayMemmap` API](#documentarraymemmap-api) - [Create `DocumentArrayMemmap` object](#create-documentarraymemmap-object) - [Add Documents to `DocumentArrayMemmap` object](#add-documents-to-documentarraymemmap-object) @@ -260,7 +269,7 @@ d4 = Document(blob=tf.SparseTensor(indices, values, dense_shape)) ##### Meta Attributes -| | | +| Attribute | Description | | --- | --- | | `doc.tags` | A structured data value, consisting of fields which map to dynamically typed values | | `doc.id` | A hexdigest that represents a unique Document ID | @@ -644,6 +653,10 @@ for evaluation_key, evaluation_score in d.evaluations.items(): Chunks of the document represent the nodes of the graph. `GraphDocument` adds graph-specific attributes (`nodes`, `adjacency` list, `edge_features`,...) and operations (`add_node`, `remove_node`, `add_edge`, `remove_edge`,...) +#### `GraphDocument` Constructor +`GraphDocument`'s constructor supports the same parameters as `Document`. +It mainly adds one parameter `force_undirected`. It's a boolean flag that, when set to `True`, forces the graph document to be undirected. + #### `GraphDocument` Additional Attributes `GraphDocument` adds the following attributes to `Document`: diff --git a/.github/2.0/cookbooks/Executor.md b/.github/2.0/cookbooks/Executor.md index 7a1f96e87571c..7f658f1cb93ac 100644 --- a/.github/2.0/cookbooks/Executor.md +++ b/.github/2.0/cookbooks/Executor.md @@ -34,13 +34,13 @@ Table of Contents - [YAML Interface](#yaml-interface) - [Load and Save Executor's YAML config](#load-and-save-executors-yaml-config) - [Use Executor out of the Flow](#use-executor-out-of-the-flow) + - [Close Executor](#close-executor) - [Executor Built-in Features](#executor-built-in-features) - - [1.x vs 2.0](#1x-vs-20) - [Workspace](#workspace) - [Metas](#metas) - [`.metas` & `.runtime_args`](#metas--runtime_args) - [Handle parameters](#handle-parameters) -- [Migration in Practice](#migration-in-practice) +- [Migrating from 1.x to 2.0 in Practice](#migrating-from-1x-to-20-in-practice) - [Encoder in `jina hello fashion`](#encoder-in-jina-hello-fashion) - [Executors in Action](#executors-in-action) - [Fastai](#fastai) diff --git a/.github/2.0/cookbooks/Flow.md b/.github/2.0/cookbooks/Flow.md index 12da5da68010e..2939f24cf2715 100644 --- a/.github/2.0/cookbooks/Flow.md +++ b/.github/2.0/cookbooks/Flow.md @@ -25,6 +25,10 @@ Table of Contents - [Add `Executor` to a Flow](#add-executor-to-a-flow) - [Chain `.add()`](#chain-add) - [Define What Executor to Use via `uses`](#define-what-executor-to-use-via-uses) + - [Add Executor via its Class Name](#add-executor-via-its-class-name) + - [Add Executor via YAML file](#add-executor-via-yaml-file) + - [Add Executor via `Dict`](#add-executor-via-dict) + - [Add Executor via Docker Image](#add-executor-via-docker-image) - [Intra Parallelism via `needs`](#intra-parallelism-via-needs) - [Inter Parallelism via `parallel`](#inter-parallelism-via-parallel) - [Add a Remote `Executor` via `host`](#add-a-remote-executor-via-host) @@ -36,6 +40,7 @@ Table of Contents - [Callback Functions](#callback-functions) - [Send Parameters](#send-parameters) - [Fine-grained Control on Request](#fine-grained-control-on-request) + - [Size of the Request](#size-of-the-request) - [Get All Responses](#get-all-responses) - [Asynchronous Flow](#asynchronous-flow) - [Remarks](#remarks) diff --git a/.github/2.0/cookbooks/Serving.md b/.github/2.0/cookbooks/Serving.md index 0564fe4cd6356..6919044ebde49 100644 --- a/.github/2.0/cookbooks/Serving.md +++ b/.github/2.0/cookbooks/Serving.md @@ -19,6 +19,9 @@ Table of Contents - [Use Python to Send HTTP Request](#use-python-to-send-http-request) - [Enable Cross-origin-resources-sharing (CORS)](#enable-cross-origin-resources-sharing-cors) - [Extend HTTP Interface](#extend-http-interface) + - [Hide CRUD and Debug Endpoints from HTTP Interface](#hide-crud-and-debug-endpoints-from-http-interface) + - [Expose Customized Endpoints to HTTP Interface](#expose-customized-endpoints-to-http-interface) + - [Add non-Jina Related Routes](#add-non-jina-related-routes) - [Switch Between Communication Protocols](#switch-between-communication-protocols) From cc4740707baf81c353b9ea385d571edcc81547ee Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Mon, 9 Aug 2021 07:18:07 +0100 Subject: [PATCH 11/19] docs: add jina hub new example usage --- .github/2.0/cookbooks/Hubble.md | 3 +++ .github/2.0/jina-hub-new.gif | Bin 0 -> 1060194 bytes 2 files changed, 3 insertions(+) create mode 100644 .github/2.0/jina-hub-new.gif diff --git a/.github/2.0/cookbooks/Hubble.md b/.github/2.0/cookbooks/Hubble.md index c2df4139756fa..20f8c4e6f8870 100644 --- a/.github/2.0/cookbooks/Hubble.md +++ b/.github/2.0/cookbooks/Hubble.md @@ -39,6 +39,9 @@ MyExecutor/ ``` +Example usage: + +![Demo](../jina-hub-new.gif) ## 2. 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Date: Mon, 9 Aug 2021 07:26:18 +0100 Subject: [PATCH 12/19] docs: address comments --- .github/2.0/cookbooks/Document.md | 52 +++++++++++++++---------------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index 4d69ad82db6f1..764a0e2936994 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -87,7 +87,7 @@ d = Document() ## `Document` API -### `Document` Attributes +### `Document` attributes A `Document` object has the following attributes, which can be put into the following categories: @@ -98,7 +98,7 @@ A `Document` object has the following attributes, which can be put into the foll | Recursive attributes | `.chunks`, `.matches`, `.granularity`, `.adjacency` | | Relevance attributes | `.score`, `.evaluations` | -#### Set & Unset Attributes +#### Set & Unset attributes Set a attribute: @@ -135,7 +135,7 @@ d.pop('text', 'id', 'mime_type') ### Construct `Document` -##### Content Attributes +##### Content attributes | Attribute | Description | | --- | --- | @@ -219,7 +219,7 @@ doc.convert_image_uri_to_blob() doc.convert_image_datauri_to_blob() ``` -##### Set Embedding +##### Set embedding An embedding is a high-dimensional representation of a `Document`. You can assign any Numpy `ndarray` as a `Document`'s embedding. @@ -231,7 +231,7 @@ d1 = Document(embedding=np.array([1, 2, 3])) d2 = Document(embedding=np.array([[1, 2, 3], [4, 5, 6]])) ``` -#### Support for Sparse arrays +#### Support for sparse arrays Scipy sparse array (`coo_matrix, bsr_matrix, csr_matrix, csc_matrix`) are supported as both `embedding` or `blob` : @@ -265,15 +265,15 @@ d3 = Document(blob=torch.sparse_coo_tensor(indices, values, dense_shape)) d4 = Document(blob=tf.SparseTensor(indices, values, dense_shape)) ``` -#### Construct with Multiple Attributes +#### Construct with multiple attributes -##### Meta Attributes +##### Meta attributes | Attribute | Description | | --- | --- | | `doc.tags` | A structured data value, consisting of fields which map to dynamically typed values | | `doc.id` | A hexdigest that represents a unique Document ID | -| `doc.parent_id` | A hexdigest that ehe document's parent id | +| `doc.parent_id` | A hexdigest that represents the document's parent id | | `doc.weight` | The weight of the Document | | `doc.mime_type` | The mime type of the Document | | `doc.content_type` | The content type of the Document | @@ -295,7 +295,7 @@ d = Document(uri='https://jina.ai', ``` -#### Construct from Dict or JSON String +#### Construct from dict or JSON string You can build a `Document` from a `dict` or JSON string: @@ -310,7 +310,7 @@ d = json.dumps({'id': 'hello123', 'content': 'world'}) d2 = Document(d) ``` -##### Parsing Unrecognized Fields +##### Parsing unrecognized fields Unrecognized fields in a `dict`/JSON string are automatically put into the Document's `.tags` field: @@ -336,7 +336,7 @@ d1 = Document({'id': 'hello123', 'foo': 'bar'}, field_resolver={'foo': 'content' ``` -#### Construct from Another `Document` +#### Construct from another `Document` Assigning a `Document` object to another `Document` object will make a shallow copy: @@ -382,7 +382,7 @@ d.update(s, fields=['id']) d.update(s) ``` -#### Construct from JSON, CSV, `ndarray` and Files +#### Construct from JSON, CSV, `ndarray` and files The `jina.types.document.generators` module let you construct `Document` from common file types such as JSON, CSV, `ndarray` and text files. The following functions will give a generator of `Document`, where each `Document` object corresponds to a line/row in the original @@ -442,9 +442,9 @@ d.binary_str() b'\n$6a1c7f34-aef7-11eb-b075-1e008a366d48R\ntext/plainj\x0bhello world' ``` -### Add Recursion to `Document` +### Add recursion to `Document` -#### Recursive Attributes +#### Recursive attributes `Document` can be recursed both horizontally and vertically: @@ -481,7 +481,7 @@ You can add **chunks** (sub-Document) and **matches** (neighbour-Document) to a Note that both `doc.chunks` and `doc.matches` return `ChunkArray` and `MatchArray`, which are sub-classes of `DocumentArray`. We will introduce `DocumentArray later. -### Represent `Document` as Dictionary or JSON +### Represent `Document` as dictionary or JSON Any `Document` can be converted into a `Python dictionary` or into `Json string` by calling their `.dict()` or `.json()` methods. @@ -582,9 +582,9 @@ d0.plot() # simply `d0` on JupyterLab -### Add Relevancy to `Document`s +### Add relevancy to `Document`s -#### Relevance Attributes +#### Relevance attributes | | | | --- | --- | @@ -653,11 +653,11 @@ for evaluation_key, evaluation_score in d.evaluations.items(): Chunks of the document represent the nodes of the graph. `GraphDocument` adds graph-specific attributes (`nodes`, `adjacency` list, `edge_features`,...) and operations (`add_node`, `remove_node`, `add_edge`, `remove_edge`,...) -#### `GraphDocument` Constructor +#### `GraphDocument` constructor `GraphDocument`'s constructor supports the same parameters as `Document`. It mainly adds one parameter `force_undirected`. It's a boolean flag that, when set to `True`, forces the graph document to be undirected. -#### `GraphDocument` Additional Attributes +#### `GraphDocument` additional attributes `GraphDocument` adds the following attributes to `Document`: @@ -801,7 +801,7 @@ da.save('data.bin', file_format='binary') da1 = DocumentArray.load('data.bin', file_format='binary') ``` -### Access Element +### Access element You can access a `Document` in the `DocumentArray` via integer index, string `id` or `slice` indices: @@ -820,7 +820,7 @@ da[1:2] # ``` -### Traverse Elements +### Traverse elements The following graphic illustrates the recursive `Document` structure. Every `Document` can have multiple `Chunks` and `Matches`. `Chunks` and `Matches` are `Documents` as well. @@ -892,7 +892,7 @@ DocumentArray([ ``` -### Sort Elements +### Sort elements `DocumentArray` is a subclass of `MutableSequence`, therefore you can use built-in Python `sort` to sort elements in a `DocumentArray` object, e.g. @@ -922,7 +922,7 @@ To sort elements in `da` in-place, using `tags[id]` value in a descending manner {'id': '6a799190-b6b0-11eb-8a66-1e008a366d49', 'tags': {'id': 1.0}} ``` -### Filter Elements +### Filter elements You can use Python's [built-in `filter()`](https://docs.python.org/3/library/functions.html#filter) to filter elements in a `DocumentArray` object: @@ -985,7 +985,7 @@ for key, group in groups: ('1', 3) ``` -### Get Attributes in Bulk +### Get attributes in bulk `DocumentArray` implements powerful getters that lets you fetch multiple attributes from the Documents it contains in one-shot: @@ -1240,7 +1240,7 @@ shuffled_da = da.shuffle() # shuffle the DocumentArray shuffled_da_with_seed = da.shuffle(seed=1) # shuffle the DocumentArray with seed. ``` -### Visualize Embeddings +### Visualize embeddings `DocumentArray` provides function `.visualize` to plot document embeddings in a 2D graph. `visualize` supports 2 methods to project in 2D space: `pca` and `tsne`. @@ -1402,7 +1402,7 @@ da = DocumentArray(dam) ``` -### Maintaining Consistency via `.reload()` +### Maintaining consistency via `.reload()` Considering two `DocumentArrayMemmap` objects that share the same on-disk storage `./memmap` but sit in different processes/threads. After some writing ops, the consistency of the lookup table may be corrupted, as each `DocumentArrayMemmap` object has its own version of lookup table in memory. `.reload()` method is for solving this issue: From d4ab53ba18a795600d51c209399605d65e6ed290 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Mon, 9 Aug 2021 07:28:39 +0100 Subject: [PATCH 13/19] docs: update TOC --- .github/2.0/cookbooks/Document.md | 50 +++++++++++++++---------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index 764a0e2936994..d5e3a59411b86 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -24,47 +24,47 @@ Table of Contents - [Minimum working example](#minimum-working-example) - [`Document` API](#document-api) - - [`Document` Attributes](#document-attributes) - - [Set & Unset Attributes](#set--unset-attributes) + - [`Document` attributes](#document-attributes) + - [Set & Unset attributes](#set--unset-attributes) - [Construct `Document`](#construct-document) - - [Content Attributes](#content-attributes) + - [Content attributes](#content-attributes) - [Exclusivity of `doc.content`](#exclusivity-of-doccontent) - [Conversion between `doc.content`](#conversion-between-doccontent) - - [Set Embedding](#set-embedding) - - [Support for Sparse arrays](#support-for-sparse-arrays) - - [Construct with Multiple Attributes](#construct-with-multiple-attributes) - - [Meta Attributes](#meta-attributes) - - [Construct from Dict or JSON String](#construct-from-dict-or-json-string) - - [Parsing Unrecognized Fields](#parsing-unrecognized-fields) - - [Construct from Another `Document`](#construct-from-another-document) - - [Construct from JSON, CSV, `ndarray` and Files](#construct-from-json-csv-ndarray-and-files) + - [Set embedding](#set-embedding) + - [Support for sparse arrays](#support-for-sparse-arrays) + - [Construct with multiple attributes](#construct-with-multiple-attributes) + - [Meta attributes](#meta-attributes) + - [Construct from dict or JSON string](#construct-from-dict-or-json-string) + - [Parsing unrecognized fields](#parsing-unrecognized-fields) + - [Construct from another `Document`](#construct-from-another-document) + - [Construct from JSON, CSV, `ndarray` and files](#construct-from-json-csv-ndarray-and-files) - [Serialize `Document`](#serialize-document) - - [Add Recursion to `Document`](#add-recursion-to-document) - - [Recursive Attributes](#recursive-attributes) - - [Represent `Document` as Dictionary or JSON](#represent-document-as-dictionary-or-json) + - [Add recursion to `Document`](#add-recursion-to-document) + - [Recursive attributes](#recursive-attributes) + - [Represent `Document` as dictionary or JSON](#represent-document-as-dictionary-or-json) - [Visualize `Document`](#visualize-document) - - [Add Relevancy to `Document`s](#add-relevancy-to-documents) - - [Relevance Attributes](#relevance-attributes) + - [Add relevancy to `Document`s](#add-relevancy-to-documents) + - [Relevance attributes](#relevance-attributes) - [`GraphDocument`](#graphdocument) - - [`GraphDocument` Constructor](#graphdocument-constructor) - - [`GraphDocument` Additional Attributes](#graphdocument-additional-attributes) + - [`GraphDocument` constructor](#graphdocument-constructor) + - [`GraphDocument` additional attributes](#graphdocument-additional-attributes) - [`GraphDocument` methods](#graphdocument-methods) - [`DocumentArray` API](#documentarray-api) - [Construct `DocumentArray`](#construct-documentarray) - [Persistence via `save()`/`load()`](#persistence-via-saveload) - - [Access Element](#access-element) - - [Traverse Elements](#traverse-elements) - - [Sort Elements](#sort-elements) - - [Filter Elements](#filter-elements) + - [Access element](#access-element) + - [Traverse elements](#traverse-elements) + - [Sort elements](#sort-elements) + - [Filter elements](#filter-elements) - [Use `itertools` on `DocumentArray`](#use-itertools-on-documentarray) - - [Get Attributes in Bulk](#get-attributes-in-bulk) + - [Get attributes in bulk](#get-attributes-in-bulk) - [Access nested attributes from tags](#access-nested-attributes-from-tags) - [Finding closest documents between `DocumentArray` objects](#finding-closest-documents-between-documentarray-objects) - [Using Sparse arrays as embeddings](#using-sparse-arrays-as-embeddings) - [Selecting a subset of Documents from a `DocumentArray` using `.find`](#selecting-a-subset-of-documents-from-a-documentarray-using-find) - [Random sample a subset of Documents from a `DocumentArray` using `sample`](#random-sample-a-subset-of-documents-from-a-documentarray-using-sample) - [Shuffle the `DocumentArray` using `shuffle`](#shuffle-the-documentarray-using-shuffle) - - [Visualize Embeddings](#visualize-embeddings) + - [Visualize embeddings](#visualize-embeddings) - [`DocumentArrayMemmap` API](#documentarraymemmap-api) - [Create `DocumentArrayMemmap` object](#create-documentarraymemmap-object) - [Add Documents to `DocumentArrayMemmap` object](#add-documents-to-documentarraymemmap-object) @@ -73,7 +73,7 @@ Table of Contents - [Mutable sequence with "read-only" elements](#mutable-sequence-with-read-only-elements) - [Side-by-side vs. `DocumentArray`](#side-by-side-vs-documentarray) - [Convert between `DocumentArray` and `DocumentArrayMemmap`](#convert-between-documentarray-and-documentarraymemmap) - - [Maintaining Consistency via `.reload()`](#maintaining-consistency-via-reload) + - [Maintaining consistency via `.reload()`](#maintaining-consistency-via-reload) From a6e49b6c8ff0aa1b4bf6ab82a92e2abce07fd435 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Mon, 9 Aug 2021 07:41:29 +0100 Subject: [PATCH 14/19] docs: update links and use local import --- .github/2.0/cookbooks/Document.md | 6 +++--- .github/2.0/cookbooks/Flow.md | 10 +++++----- .github/2.0/cookbooks/Serving.md | 6 +++--- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index d5e3a59411b86..bb8c4913c0223 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -479,7 +479,7 @@ You can add **chunks** (sub-Document) and **matches** (neighbour-Document) to a d.matches.append(Document()) ``` -Note that both `doc.chunks` and `doc.matches` return `ChunkArray` and `MatchArray`, which are sub-classes of `DocumentArray`. We will introduce `DocumentArray later. +Note that both `doc.chunks` and `doc.matches` return `ChunkArray` and `MatchArray`, which are sub-classes of [`DocumentArray`](#documentarray-api). We will introduce `DocumentArray later. ### Represent `Document` as dictionary or JSON @@ -577,7 +577,7 @@ d0.plot() # simply `d0` on JupyterLab - + @@ -1071,7 +1071,7 @@ da.get_attributes('tags__dimensions__height', 'tags__dimensions__weight') The following image shows how `DocumentArrayA` finds `limit=5` matches from the documents in `DocumentArrayB`. By default, the cosine similarity is used to evaluate the score between documents. -![match_illustration_5](https://github.com/jina-ai/jina/blob/master/.github/images/match_illustration_5.svg) +![match_illustration_5](../../images/match_illustration_5.svg) More generally, given two `DocumentArray` objects `da_1` and `da_2` the function `da_1.match(da_2, metric=some_metric, normalization=(0, 1), limit=N)` finds for each document in `da_1` then `N` documents from `da_2` with the lowest metric values according to `some_metric`. diff --git a/.github/2.0/cookbooks/Flow.md b/.github/2.0/cookbooks/Flow.md index 2939f24cf2715..deac002956123 100644 --- a/.github/2.0/cookbooks/Flow.md +++ b/.github/2.0/cookbooks/Flow.md @@ -174,7 +174,7 @@ from jina import Flow f = Flow().add().plot('f.svg') ``` - + In Jupyter Lab/Notebook, the `Flow` object is rendered automatically without needing to call `plot()`. @@ -204,7 +204,7 @@ from jina import Flow f = Flow().add().add().add().add() ``` - + #### Define What Executor to Use via `uses` @@ -324,7 +324,7 @@ f = (Flow() .needs(['p1', 'p2', 'p3'], name='r1')) ``` - + `p1`, `p2`, `p3` now subscribe to `Gateway` and conduct their work in parallel. The last `.needs()` blocks all Executors until they finish their work. @@ -365,7 +365,7 @@ f = (Flow() .add(name='p2')) ``` - + Note, by default: @@ -393,7 +393,7 @@ f = (Flow() .needs(['p1', 'p3'], name='r1')) ``` - + #### Add a Remote `Executor` via `host` diff --git a/.github/2.0/cookbooks/Serving.md b/.github/2.0/cookbooks/Serving.md index 6919044ebde49..87b82b394d61b 100644 --- a/.github/2.0/cookbooks/Serving.md +++ b/.github/2.0/cookbooks/Serving.md @@ -70,7 +70,7 @@ A `Flow` _is_ a service by nature. Though implicitly, you are already using it a When you start a `Flow` and call `.post()` inside the context, a `jina.Client` object is created and used for communication. - +![](../implict-vs-explicit-service.svg?raw=true) Many times we need to use `Flow` & `Client` in a more explicit way, often due to one of the following reasons: @@ -95,7 +95,7 @@ Jina supports `grpc`, `websocket`, `http` three communication protocols between The protocol is controlled by `protocol=` argument in `Flow`/`Client`'s constructor. - + ### via gRPC @@ -195,7 +195,7 @@ with f: You can navigate to the Swagger docs UI via `http://localhost:12345/docs`: - + #### Use `curl` to Send HTTP Request From d7b73d7f6a1d7b7aaa08007e2aeecb6bccc6bfa9 Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Mon, 9 Aug 2021 09:39:51 +0100 Subject: [PATCH 15/19] docs: fix merge conflict in Hub.md --- .github/2.0/cookbooks/Hub.md | 187 +++++++++++++++++++ .github/2.0/cookbooks/Hubble.md | 140 --------------- cli/autocomplete.py | 2 + jina/hubble/hubio.py | 197 +++++++++++++-------- jina/parsers/hubble/push.py | 15 ++ tests/unit/hubble/test_hubio.py | 32 ++-- tests/unit/parsers/hubble/test_pushpull.py | 12 ++ 7 files changed, 354 insertions(+), 231 deletions(-) create mode 100644 .github/2.0/cookbooks/Hub.md delete mode 100644 .github/2.0/cookbooks/Hubble.md diff --git a/.github/2.0/cookbooks/Hub.md b/.github/2.0/cookbooks/Hub.md new file mode 100644 index 0000000000000..6d1448cf66f05 --- /dev/null +++ b/.github/2.0/cookbooks/Hub.md @@ -0,0 +1,187 @@ + + +Table of Contents + +- [Hubble CLI Guidelines](#hubble-cli-guidelines) + - [Prerequisites](#prerequisites) + - [1. Create Executor](#1-create-executor) + - [2. Push and Pull CLI](#2-push-and-pull-cli) + - [2.1 Distribute your executor](#21-distribute-your-executor) + - [2.2 Pull distributed executor](#22-pull-distributed-executor) + - [3. Use in Jina Flow](#3-use-in-jina-flow) + - [3.1 Using docker images](#31-using-docker-images) + - [3.2 Using source codes](#32-using-source-codes) + - [3.3 Override Default Parameters](#33-override-default-parameters) + + + +# JinaHub Guidelines + +## Prerequisites + +- [Docker](https://docs.docker.com/get-docker) installed. +- `jina[standard] >= 2.0.0` installed. + - Run `pip install -U "jina[standard]>=2.0.0"` to upgrade jina to correct version. + +## 1. Create Executor + +To create your Executor, you just need to run this command in your terminal: + +```terminal +jina hub new +``` + +A wizard will ask you some questions about the Executor. For the basic configuration, you will be asked two things: The Executor’s name and where it should be saved. The wizard will ask if you want to have a more advanced configuration, but it is unnecessary for most of user cases. + +After running the command, a project with the following structure will be generated: + +```text +MyExecutor/ +β”œβ”€β”€ Dockerfile # Advanced configuration will generate this file +β”œβ”€β”€ manifest.yml +β”œβ”€β”€ config.yml +β”œβ”€β”€ README.md +β”œβ”€β”€ requirements.txt +└── executor.py +``` + +- `manifest.yml` should contain the annotations of the Executor for getting better appealing on Jina Hub. +- `config.yml` is the config file of your Executor. You can define **__init__** arguments using **with** keyword in this config file. +- `requirements.txt` describes the Python dependencies of the Executor. +- `executor.py` should contain the main logic of your Executor. +- `README.md` should describe the usage of the Executor. + +Example usage: + +![Demo](../jina-hub-new.gif) + +## 2. Push Executor to JinaHub + +If you want to share your Executors to other persons or colleges, you can push your local Executor to JinaHub. + +### 2.1 Basic + +```bash +$ jina hub push [--public/--private] +``` + +It will return an **UUID** as well as a **SECRET**. You would need these two items when using or update the Executor later. **Please keep them carefully.** + +**Notes**: + +- When no `--public` or `--private` visibility options are provided. **By default, it's public.** +- There is only one difference between `--public` and `--private`. You can use public Executor once you know the `UUID`, but you must know `SECRET` and `UUID` if you want to use a private Executor. + +### 2.2 Advanced + +```bash +$ jina hub push -t TAG1 -t TAG2 -f +``` + +You can specify `-t` or `--tags` parameters to tag one Executor. In additional, you can specify `-f` or `--docker-file` parameters to use a specific docker file to build your Executor. + +## 3. Update Executor in JinaHub + +### 3.1 Basic + +Everything is iterating in Internet world. We also provide a way to update your existing Executor in JinaHub. To update one Executor, you must have both `UUID` and `SECRET`. + +```bash +$ jina hub push [--public/--private] --force --secret +``` + +**Notes**: +- With `--public` option, the resulted Executor will be **visible to public**. +- With `--private` options, the resulted Executor will be **invisible to public**. + +### 3.2 Advanced + +If you want to make a new tag on the existing Executor, you can also add the `-t` option here: + +```bash +$ jina hub push [--public/--private] --force --secret -t TAG +``` + +## 4. Pull Executor from JinaHub + +`Pull` means fetching the Executor content to your local machine. For each Executor, there are two different format content for user to pull. + +### 4.1 Docker Image + +```bash +$ jina hub pull jinahub+docker://[:] +``` + +You can find the Executor by running this command `docker images`. + +### 4.2 Source Code + +```bash +$ jina hub pull jinahub://[:] +``` + +It will store Executor source code to `~/.jina/hub-packages`. + +**Note**: + +- For public Executor, you can ignore the `SECRET` option. +- For private Executor, you must provide the `SECRET` option. + +## 5. Use in Jina Flow + +It will pull Executor automatically if you didn't pull it before. + +### 5.1 Using docker images + +Use the prebuilt images from `Hubble` in your python codes, + +```python +from jina import Flow + +# SECRET must be provided for private Executor +f = Flow().add(uses='jinahub+docker://[:][/]') +``` + +**Attention:** + +If you are a Mac user, please use `host.docker.internal` as your url when you want to connect a local port from Executor +docker container. + +For +example: [`jinahub+docker://PostgreSQLStorage`](https://github.com/jina-ai/executor-indexers/tree/main/jinahub/indexers/storage/PostgreSQLStorage) +will connect PostgreSQL server which was started at local. Then you must use it with: + +```python +from jina import Flow, Document + +f = Flow().add(uses='jinahub+docker://PostgreSQLStorage', + uses_with={'hostname': 'host.docker.internal'}) +with f: + resp = f.post(on='/index', inputs=Document(), return_results=True) + print(f'{resp}') +``` + +### 5.2 Using source codes + +Use the source codes from `Hubble` in your python codes, + +```python +from jina import Flow + +f = Flow().add(uses='jinahub://[:][/]') +``` + +### 5.3 Override Default Parameters + +It is possible that the default parameters of the published Executor may not be ideal for your use case. You can override +any of these parameters by passing `uses_with` and `uses_metas` as parameters. + +```python +from jina import Flow + +f = Flow().add(uses='jinahub://[:]', + uses_with={'param1': 'new_value'}, + uses_metas={'name': 'new_name'}) +``` + +This way, the Executor will work with the overrode parameters. diff --git a/.github/2.0/cookbooks/Hubble.md b/.github/2.0/cookbooks/Hubble.md deleted file mode 100644 index 20f8c4e6f8870..0000000000000 --- a/.github/2.0/cookbooks/Hubble.md +++ /dev/null @@ -1,140 +0,0 @@ - - -Table of Contents - -- [Hubble CLI Guidelines](#hubble-cli-guidelines) - - [Prerequisites](#prerequisites) - - [1. Create Executor](#1-create-executor) - - [2. Push and Pull CLI](#2-push-and-pull-cli) - - [2.1 Distribute your executor](#21-distribute-your-executor) - - [2.2 Pull distributed executor](#22-pull-distributed-executor) - - [3. Use in Jina Flow](#3-use-in-jina-flow) - - [3.1 Using docker images](#31-using-docker-images) - - [3.2 Using source codes](#32-using-source-codes) - - [3.3 Override Default Parameters](#33-override-default-parameters) - - - -# Hubble CLI Guidelines - -## Prerequisites - -- [Docker](https://docs.docker.com/get-docker) installed. -- `jina[standard] >= 2.0.0` installed. - - Run `pip install -U "jina[standard]>=2.0.0"` to upgrade jina to correct version. - -## 1. Create Executor - -To create a new executor, use `jina hub new` and follow the steps in order to customize the new executor. -This will generate an Executor project with the name and the configuration that you have provided. -After running the command, a project with the following structure will be generated: -```text -MyExecutor/ -β”œβ”€β”€ Dockerfile # Optional -β”œβ”€β”€ manifest.yml -β”œβ”€β”€ config.yml -β”œβ”€β”€ README.md -β”œβ”€β”€ requirements.txt -└── executor.py - -``` - -Example usage: - -![Demo](../jina-hub-new.gif) - -## 2. Push and Pull CLI - -### 2.1 Distribute your executor - -1. Push your local executor (without `--force`) - ```bash - $ jina hub push [--public/--private] - ``` - _**Note**_: - - When no `--public` or `--private` visibility options are provided. **By default, it's public.** - - Returns a **UUID8** as well as a **SECRET**. You can use it in `Jina flow` - via `.add(uses='jinahub://')` or `.add(uses='jinahub+docker://')` - - For public executor, you have read access if you have `UUID8`, you have write access if you have `SECRET` - - -2. Update your local executor (with `--force`) - ```bash - $ jina hub push [--public/--private] --force --secret - ``` - _**Note**_: - - Without any visibility option, it will only update the content of executor. - - With `--public` option, the resulted executor will be **visible to public**. - - With `--private` options, the resulted executor will be **invisible to public**. - -### 2.2 Pull distributed executor - -- Pull the executor's **docker image** - ```bash - $ jina hub pull jinahub+docker://[:] - ``` -- Pull the executor's **source-code package** into `~/.jina/hub-packages` defaultly - ```bash - $ jina hub pull jinahub://[:] - ``` - - _**Note**_: - - For public executor, you can ignore the `SECRET` option. - - For private executor, you must provide the `SECRET` option. - -## 3. Use in Jina Flow - -### 3.1 Using docker images - -Use the prebuilt images from `Hubble` in your python codes, - -```python -from jina import Flow - -# SECRET must be provided for private executor -f = Flow().add(uses='jinahub+docker://[:]') -``` - -**Attention:** - -If you are a Mac user, please use `host.docker.internal` as your url when you want to connect a local port from executor -docker container. - -For -example: [`jinahub+docker://PostgreSQLStorage`](https://github.com/jina-ai/executor-indexers/tree/main/jinahub/indexers/storage/PostgreSQLStorage) -will connect PostgreSQL server which was started at local. Then you must use it with: - -```python -from jina import Flow, Document - -f = Flow().add(uses='jinahub+docker://PostgreSQLStorage', - uses_with={'hostname': 'host.docker.internal'}) -with f: - resp = f.post(on='/index', inputs=Document(), return_results=True) - print(f'{resp}') -``` - -### 3.2 Using source codes - -Use the source codes from `Hubble` in your python codes, - -```python -from jina import Flow - -f = Flow().add(uses='jinahub://[:]') -``` - -### 3.3 Override Default Parameters - -It is possible that the default parameters of the published executor may not be ideal for your usecase. You can override -any of these parameters by passing `uses_with` and `uses_metas` as parameters. - -```python -from jina import Flow - -f = Flow().add(uses='jinahub://[:]', - uses_with={'param1': 'new_value'}, - uses_metas={'name': 'new_name'}) -``` - -This way, the executor will work with the overriden parameters. diff --git a/cli/autocomplete.py b/cli/autocomplete.py index 6cee182943e5f..f2057352f3379 100644 --- a/cli/autocomplete.py +++ b/cli/autocomplete.py @@ -264,6 +264,8 @@ 'hub push': [ '--help', '--no-usage', + '--docker-file', + '--tag', '--force', '--secret', '--public', diff --git a/jina/hubble/hubio.py b/jina/hubble/hubio.py index f374b3fc3586d..0a47eea1a50a5 100644 --- a/jina/hubble/hubio.py +++ b/jina/hubble/hubio.py @@ -64,9 +64,10 @@ def __init__(self, args: Optional[argparse.Namespace] = None, **kwargs): def _load_docker_client(self): with ImportExtensions(required=True): import docker - from docker import DockerClient + from docker import APIClient - self._client: DockerClient = docker.from_env() + # low-level client + self._raw_client = APIClient(base_url='unix://var/run/docker.sock') @staticmethod def _get_request_header() -> Dict: @@ -327,13 +328,31 @@ def push(self) -> None: from rich.console import Console + work_path = Path(self.args.path) + + exec_tags = None + if self.args.tag: + exec_tags = ','.join(self.args.tag) + + dockerfile = None + if self.args.docker_file: + dockerfile = Path(self.args.docker_file) + if not dockerfile.exists(): + raise Exception(f'The given Dockerfile `{dockerfile}` does not exist!') + if dockerfile.parent != work_path: + raise Exception( + f'The Dockerfile must be placed at the given folder `{work_path}`' + ) + + dockerfile = dockerfile.relative_to(work_path) + console = Console() - with console.status(f'Pushing `{self.args.path}`...') as st: + with console.status(f'Pushing `{self.args.path}` ...') as st: req_header = self._get_request_header() try: - st.update(f'Packaging {self.args.path}...') + st.update(f'Packaging {self.args.path} ...') md5_hash = hashlib.md5() - bytesio = archive_package(Path(self.args.path)) + bytesio = archive_package(work_path) content = bytesio.getvalue() md5_hash.update(content) md5_digest = md5_hash.hexdigest() @@ -346,7 +365,14 @@ def push(self) -> None: else 'False', 'md5sum': md5_digest, } - uuid8, secret = load_secret(Path(self.args.path)) + + if exec_tags: + form_data['tags'] = exec_tags + + if dockerfile: + form_data['dockerfile'] = str(dockerfile) + + uuid8, secret = load_secret(work_path) if self.args.force or uuid8: form_data['force'] = self.args.force or uuid8 if self.args.secret or secret: @@ -354,11 +380,11 @@ def push(self) -> None: method = 'put' if ('force' in form_data) else 'post' - st.update(f'Connecting Hubble...') + st.update(f'Connecting Hubble ...') hubble_url = get_hubble_url() - # upload the archived executor to Jina Hub - st.update(f'Uploading...') + # upload the archived executor to Jina Hub + st.update(f'Uploading ...') resp = upload_file( hubble_url, 'filename', @@ -372,8 +398,11 @@ def push(self) -> None: result = None for stream_line in resp.iter_lines(): stream_msg = json.loads(stream_line) + if 'stream' in stream_msg: - st.update(stream_msg['stream']) + console.print(f'=> {stream_msg["stream"]}') + elif 'status' in stream_msg: + st.update(f'{stream_msg["status"]}') elif 'result' in stream_msg: result = stream_msg['result'] break @@ -385,7 +414,7 @@ def push(self) -> None: elif 200 <= result['statusCode'] < 300: new_uuid8, new_secret = self._prettyprint_result(console, result) if new_uuid8 != uuid8 or new_secret != secret: - dump_secret(Path(self.args.path), new_uuid8, new_secret) + dump_secret(work_path, new_uuid8, new_secret) elif result['message']: raise Exception(result['message']) elif resp.text: @@ -555,16 +584,14 @@ def pull(self) -> str: :return: the `uses` string """ from rich.console import Console - - with ImportExtensions(required=True): - import docker + from rich.progress import Progress, DownloadColumn, BarColumn console = Console() cached_zip_file = None usage = None - with console.status(f'Pulling {self.args.uri}...') as st: - try: + try: + with console.status(f'Pulling {self.args.uri}...') as st: scheme, name, tag, secret = parse_hub_uri(self.args.uri) st.update(f'Fetching meta data of {name}...') @@ -575,49 +602,81 @@ def pull(self) -> str: else f'{executor.uuid}:{secret}' ) - if scheme == 'jinahub+docker': - self._load_docker_client() - st.update(f'Pulling {executor.image_name}...') + if scheme == 'jinahub+docker': + self._load_docker_client() + + with Progress( + "[progress.description]{task.description}", + BarColumn(), + DownloadColumn(), + "[progress.percentage]{task.percentage:>3.0f}%", + console=console, + transient=True, + ) as progress: + try: - self._client.images.pull(executor.image_name) - except docker.errors.APIError as api_exc: - try: - self._client.images.get(executor.image_name) - except docker.errors.ImageNotFound: - raise api_exc - - return f'docker://{executor.image_name}' - elif scheme == 'jinahub': - import filelock - - with filelock.FileLock(get_lockfile(), timeout=-1): - try: - pkg_path, pkg_dist_path = resolve_local(executor) - # check serial number to upgrade - sn_file_path = pkg_dist_path / f'PKG-SN-{executor.sn or 0}' - if not sn_file_path.exists() and any( - True for _ in pkg_dist_path.glob('PKG-SN-*') - ): - raise FileNotFoundError( - f'{pkg_path} need to be upgraded' - ) - if self.args.install_requirements: - requirements_file = pkg_dist_path / 'requirements.txt' - if requirements_file.exists(): - install_requirements(requirements_file) - return f'{pkg_path / "config.yml"}' - except FileNotFoundError: - pass # have not been downloaded yet, download for the first time - # download the package - cache_dir = Path( - os.environ.get( - 'JINA_HUB_CACHE_DIR', - Path.home().joinpath('.cache', 'jina'), + tasks = {} + for log in self._raw_client.pull( + executor.image_name, stream=True, decode=True + ): + if 'status' not in log: + continue + status = log['status'] + status_id = log.get('id', None) + pg_detail = log.get('progressDetail', None) + + if (pg_detail is None) or (status_id is None): + console.print(status) + continue + + task = tasks.get( + status_id, progress.add_task(status, total=0) ) + current = pg_detail['current'] + total = pg_detail['total'] + progress.update( + task, + completed=current, + total=total, + description=status, + ) + + if total and current == total: + progress.stop_task(task) + + except Exception as ex: + raise ex + + return f'docker://{executor.image_name}' + elif scheme == 'jinahub': + import filelock + + with filelock.FileLock(get_lockfile(), timeout=-1): + try: + pkg_path, pkg_dist_path = resolve_local(executor) + # check serial number to upgrade + sn_file_path = pkg_dist_path / f'PKG-SN-{executor.sn or 0}' + if (not sn_file_path.exists()) and any( + pkg_dist_path.glob('PKG-SN-*') + ): + raise FileNotFoundError(f'{pkg_path} need to be upgraded') + if self.args.install_requirements: + requirements_file = pkg_dist_path / 'requirements.txt' + if requirements_file.exists(): + install_requirements(requirements_file) + return f'{pkg_path / "config.yml"}' + except FileNotFoundError: + pass # have not been downloaded yet, download for the first time + # download the package + cache_dir = Path( + os.environ.get( + 'JINA_HUB_CACHE_DIR', + Path.home().joinpath('.cache', 'jina'), ) - cache_dir.mkdir(parents=True, exist_ok=True) + ) + cache_dir.mkdir(parents=True, exist_ok=True) - st.update(f'Downloading {name}...') + with console.status(f'Downloading {name} ...') as st: cached_zip_file = download_with_resume( executor.archive_url, cache_dir, @@ -625,7 +684,7 @@ def pull(self) -> str: md5sum=executor.md5sum, ) - st.update(f'Unpacking {name}...') + st.update(f'Unpacking {name} ...') install_local( cached_zip_file, executor, @@ -633,16 +692,16 @@ def pull(self) -> str: ) pkg_path, _ = resolve_local(executor) - return f'{pkg_path / "config.yml"}' - else: - raise ValueError(f'{self.args.uri} is not a valid scheme') - except Exception as e: - self.logger.error(f'Error while pulling {self.args.uri}: \n{e!r}') - raise e - finally: - # delete downloaded zip package if existed - if cached_zip_file is not None: - cached_zip_file.unlink() - - if not self.args.no_usage and usage: - self._get_prettyprint_usage(console, usage) + return f'{pkg_path / "config.yml"}' + else: + raise ValueError(f'{self.args.uri} is not a valid scheme') + except Exception as e: + self.logger.error(f'Error while pulling {self.args.uri}: \n{e!r}') + raise e + finally: + # delete downloaded zip package if existed + if cached_zip_file is not None: + cached_zip_file.unlink() + + if not self.args.no_usage and usage: + self._get_prettyprint_usage(console, usage) diff --git a/jina/parsers/hubble/push.py b/jina/parsers/hubble/push.py index 100e62cd76b21..8372a00e716dc 100644 --- a/jina/parsers/hubble/push.py +++ b/jina/parsers/hubble/push.py @@ -26,6 +26,21 @@ def dir_path(string): - a directory containing Dockerfile, manifest.yml, README.md, zero or more yaml config, zero or more Python file. ''', ) + + gp.add_argument( + '-f', + '--docker-file', + metavar='PATH', + help='Name of the Dockerfile (Default is `path/Dockerfile`)', + ) + + gp.add_argument( + '-t', + '--tag', + action='append', + help='Name and optionally a list of tags', + ) + gp.add_argument( '--force', type=str, diff --git a/tests/unit/hubble/test_hubio.py b/tests/unit/hubble/test_hubio.py index 814874ef14d6c..7e3f37bb3cf2c 100644 --- a/tests/unit/hubble/test_hubio.py +++ b/tests/unit/hubble/test_hubio.py @@ -181,27 +181,15 @@ def _mock_head(url): HubIO(args).pull() -class MockImageCollection: - def __init__(self, fail_pull: bool, fail_get: bool): +class MockDockerClient: + def __init__(self, fail_pull: bool = True): self.fail_pull = fail_pull - self.fail_get = fail_get - def pull(self, repository: str): + def pull(self, repository: str, stream: bool = True, decode: bool = True): if self.fail_pull: - raise docker.errors.APIError('Failed pullingdocker image') - else: - return - - def get(self, repository: str): - if self.fail_get: - raise docker.errors.ImageNotFound("Image not found") + raise docker.errors.APIError('Failed pulling docker image') else: - return - - -class MockDockerClient: - def __init__(self, fail_pull: bool = True, fail_get: bool = True): - self.images = MockImageCollection(fail_pull, fail_get) + yield {} def test_offline_pull(test_envs, mocker, monkeypatch, tmpfile): @@ -225,9 +213,9 @@ def _mock_fetch(name, tag=None, secret=None): archive_url=None, ) - def _gen_load_docker_client(fail_pull: bool, fail_get: bool): + def _gen_load_docker_client(fail_pull: bool): def _load_docker_client(obj): - obj._client = MockDockerClient(fail_pull=fail_pull, fail_get=fail_get) + obj._raw_client = MockDockerClient(fail_pull=fail_pull) return _load_docker_client @@ -235,7 +223,7 @@ def _load_docker_client(obj): monkeypatch.setattr( HubIO, '_load_docker_client', - _gen_load_docker_client(fail_pull=True, fail_get=True), + _gen_load_docker_client(fail_pull=True), ) monkeypatch.setattr(HubIO, '_fetch_meta', _mock_fetch) @@ -252,7 +240,7 @@ def _load_docker_client(obj): monkeypatch.setattr( HubIO, '_load_docker_client', - _gen_load_docker_client(fail_pull=False, fail_get=True), + _gen_load_docker_client(fail_pull=False), ) assert HubIO(args).pull() == 'docker://jinahub/pod.dummy_mwu_encoder' @@ -261,6 +249,6 @@ def _load_docker_client(obj): monkeypatch.setattr( HubIO, '_load_docker_client', - _gen_load_docker_client(fail_pull=True, fail_get=False), + _gen_load_docker_client(fail_pull=False), ) assert HubIO(args).pull() == 'docker://jinahub/pod.dummy_mwu_encoder' diff --git a/tests/unit/parsers/hubble/test_pushpull.py b/tests/unit/parsers/hubble/test_pushpull.py index 9ceec6d39f7f3..42239b735bc8b 100644 --- a/tests/unit/parsers/hubble/test_pushpull.py +++ b/tests/unit/parsers/hubble/test_pushpull.py @@ -18,6 +18,18 @@ def test_push_parser(): assert not hasattr(args, 'public') assert not hasattr(args, 'private') + args = parser.parse_args(['/tmp', '-t', 'v1', '-t', 'v2']) + assert args.tag == ['v1', 'v2'] + + args = parser.parse_args(['/tmp', '--tag', 'v1', '--tag', 'v2']) + assert args.tag == ['v1', 'v2'] + + args = parser.parse_args(['/tmp', '-f', 'Dockerfile']) + assert args.docker_file == 'Dockerfile' + + args = parser.parse_args(['/tmp', '--docker-file', 'Dockerfile']) + assert args.docker_file == 'Dockerfile' + args = parser.parse_args(['/tmp', '--public']) assert args.public is True assert not hasattr(args, 'private') From ce4b9dec7f5b5dda3f81a60f104d90998135730d Mon Sep 17 00:00:00 2001 From: Alaeddine Abdessalem Date: Mon, 9 Aug 2021 13:37:32 +0100 Subject: [PATCH 16/19] docs: add math cookbook --- .github/2.0/cookbooks/Flow.md | 8 +++ .github/2.0/cookbooks/Hub.md | 22 +++--- .github/2.0/cookbooks/Math.md | 126 ++++++++++++++++++++++++++++++++++ 3 files changed, 148 insertions(+), 8 deletions(-) create mode 100644 .github/2.0/cookbooks/Math.md diff --git a/.github/2.0/cookbooks/Flow.md b/.github/2.0/cookbooks/Flow.md index deac002956123..45ba4ef7c1384 100644 --- a/.github/2.0/cookbooks/Flow.md +++ b/.github/2.0/cookbooks/Flow.md @@ -34,6 +34,14 @@ Table of Contents - [Add a Remote `Executor` via `host`](#add-a-remote-executor-via-host) - [Commonly used arguments for deployment in `.add`](#commonly-used-arguments-for-deployment-in-add) - [Commonly used patterns for `.add`](#commonly-used-patterns-for-add) + - [Local native Executor via `.py` file:](#local-native-executor-via-py-file) + - [Local native Executor via `.yml` file:](#local-native-executor-via-yml-file) + - [Local Docker Executor:](#local-docker-executor) + - [Local Docker Executor from Jina Hub:](#local-docker-executor-from-jina-hub) + - [Using an already spawned Executor:](#using-an-already-spawned-executor) + - [Remote Executor](#remote-executor) + - [Setting the Gateway data port and host](#setting-the-gateway-data-port-and-host) + - [Forcing an Executor in the remote-local configuration](#forcing-an-executor-in-the-remote-local-configuration) - [Send data request via `post`](#send-data-request-via-post) - [Function Signature](#function-signature) - [Define Data via `inputs`](#define-data-via-inputs) diff --git a/.github/2.0/cookbooks/Hub.md b/.github/2.0/cookbooks/Hub.md index 6d1448cf66f05..82a2b862ea1ac 100644 --- a/.github/2.0/cookbooks/Hub.md +++ b/.github/2.0/cookbooks/Hub.md @@ -2,16 +2,22 @@ Table of Contents -- [Hubble CLI Guidelines](#hubble-cli-guidelines) +- [JinaHub Guidelines](#jinahub-guidelines) - [Prerequisites](#prerequisites) - [1. Create Executor](#1-create-executor) - - [2. Push and Pull CLI](#2-push-and-pull-cli) - - [2.1 Distribute your executor](#21-distribute-your-executor) - - [2.2 Pull distributed executor](#22-pull-distributed-executor) - - [3. Use in Jina Flow](#3-use-in-jina-flow) - - [3.1 Using docker images](#31-using-docker-images) - - [3.2 Using source codes](#32-using-source-codes) - - [3.3 Override Default Parameters](#33-override-default-parameters) + - [2. Push Executor to JinaHub](#2-push-executor-to-jinahub) + - [2.1 Basic](#21-basic) + - [2.2 Advanced](#22-advanced) + - [3. Update Executor in JinaHub](#3-update-executor-in-jinahub) + - [3.1 Basic](#31-basic) + - [3.2 Advanced](#32-advanced) + - [4. Pull Executor from JinaHub](#4-pull-executor-from-jinahub) + - [4.1 Docker Image](#41-docker-image) + - [4.2 Source Code](#42-source-code) + - [5. Use in Jina Flow](#5-use-in-jina-flow) + - [5.1 Using docker images](#51-using-docker-images) + - [5.2 Using source codes](#52-using-source-codes) + - [5.3 Override Default Parameters](#53-override-default-parameters) diff --git a/.github/2.0/cookbooks/Math.md b/.github/2.0/cookbooks/Math.md new file mode 100644 index 0000000000000..dd7535ce26df5 --- /dev/null +++ b/.github/2.0/cookbooks/Math.md @@ -0,0 +1,126 @@ +# Jina Math + +Jina offers several Math helpers used by it's core logic and that users can use for their own purposes (for example, in their executors). + + + + +Table of Contents + +- [Distances](#distances) +- [Dimension Reduction](#dimension-reduction) +- [Other helpers](#other-helpers) + - [Min-max normalization](#min-max-normalization) + - [Top K:](#top-k) + + + +## Distances +You can compute distances between ndarrays within a single matrix using `pdist` or between 2 matrices using `cdist`. + +```python +import numpy as np +from jina.math.distance import pdist +embeddings = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]]) +pdist(embeddings, metric='euclidean') +``` + +```text +array([[0., 1., 2.], + [1., 0., 1.], + [2., 1., 0.]]) +``` + +```python +import numpy as np +from jina.math.distance import cdist +embeddings_1 = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]]) +embeddings_2 = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]) +cdist(embeddings_1, embeddings_2, metric='euclidean') +``` + +```text +array([[1.41421356, 1.41421356, 0. ], + [2.23606798, 2.23606798, 1. ], + [3.16227766, 3.16227766, 2. ]]) +``` + +These functions support the following metrics: +* `cosine`: cosine similarity +* `sqeuclidean`: euclidean square distance +* `euclidean`: euclidean distance + +You can also specify whether computation should be done with sparse data by setting `is_sparse` to `True`. + +## Dimension Reduction + +Dimension reduction is offered with class `PCA`. It performs Principal Component Analysis given a number of components. +To reduce reduce the dimensions of a dataset, you can either call `PCA.fit` and then `PCA.transform` or directly call `PCA.fit_transform`. +`fit` computes the projection matrix and stores it while transform uses the projection matrix to apply reduction. + +```python +import numpy as np +from jina.math.dimensionality_reduction import PCA + +embeddings = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]]) +pca = PCA(n_components=2) +pca.fit(embeddings) +pca.transform(embeddings) +``` + +```text +array([[1., 0.], + [2., 0.], + [3., 0.]]) +``` + +Or you can just do: + +```python +import numpy as np +from jina.math.dimensionality_reduction import PCA + +embeddings = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]]) +pca = PCA(n_components=2) +pca.fit_transform(embeddings) +``` + +## Other helpers + +### Min-max normalization +The function `minmax_normalize` normalizes the input `ndarray` so that values fit in `t_range` (by default `(0, 1)`): + +```python +import numpy as np +from jina.math.helper import minmax_normalize +a = np.array([1, 2, 3]) +minmax_normalize(a) +``` + +```text +array([0. , 0.5, 1. ]) +``` + +### Top K: +Using function `top_k`, you can extract the top k values along with their indices from a list of vectors. +The function returns a tuple of arrays: the selected distances and the indices array: + +```python +from jina.math.helper import top_k +import numpy as np + +dist, idx = top_k(np.array([[1, 0, 2], [2, 0, 10]]), 2) +print("distances") +print(dist) +print("indices") +print(idx) +``` + +```text +distances +[[0 1] + [0 2]] +indices +[[1 0] + [1 0]] +``` From a3b9f9595f124512662f871f67c010a92bc76407 Mon Sep 17 00:00:00 2001 From: AlaeddineAbdessalem Date: Mon, 9 Aug 2021 15:34:50 +0100 Subject: [PATCH 17/19] chore: apply review suggestions Co-authored-by: cristian --- .github/2.0/cookbooks/Executor.md | 4 ++-- .github/2.0/cookbooks/Math.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/2.0/cookbooks/Executor.md b/.github/2.0/cookbooks/Executor.md index 7f658f1cb93ac..5015890be511b 100644 --- a/.github/2.0/cookbooks/Executor.md +++ b/.github/2.0/cookbooks/Executor.md @@ -367,7 +367,7 @@ requests: - `jtype` is a string. Defines the class name, interchangeable with bang mark `!`; - `with` is a map. Defines kwargs of the class `__init__` method -- `metas` is a map. Defines the meta information of that class. It contains the following fields: +- `metas` is a dictionary. It defines the meta information of that class. It contains the following fields: - `name` is a string. Defines the name of the executor; - `description` is a string. Defines the description of this executor. It will be used in automatic docs UI; - `workspace` is a string. Defines the workspace of the executor; @@ -902,4 +902,4 @@ class ONNXBertExecutor(Executor): output, pooled = self.session.run(None, inputs) # assign the encoding results to ``embedding`` doc.embedding = pooled[0] -``` \ No newline at end of file +``` diff --git a/.github/2.0/cookbooks/Math.md b/.github/2.0/cookbooks/Math.md index dd7535ce26df5..379986d83bebf 100644 --- a/.github/2.0/cookbooks/Math.md +++ b/.github/2.0/cookbooks/Math.md @@ -1,6 +1,6 @@ # Jina Math -Jina offers several Math helpers used by it's core logic and that users can use for their own purposes (for example, in their executors). +Jina offers several Math helpers used by its core logic that users can use for their own purposes (for example, in their Executors). From fe001175f9591306bc4ad9d3497d06cdb1503b6f Mon Sep 17 00:00:00 2001 From: Han Xiao Date: Tue, 10 Aug 2021 09:32:27 +0200 Subject: [PATCH 18/19] docs(cookbook): restructure & update the content --- .github/2.0/cookbooks/Executor.md | 174 +++++++-------- .github/2.0/cookbooks/Flow.md | 358 ++++++++++++++---------------- 2 files changed, 246 insertions(+), 286 deletions(-) diff --git a/.github/2.0/cookbooks/Executor.md b/.github/2.0/cookbooks/Executor.md index 5015890be511b..88ef9b0c4aee6 100644 --- a/.github/2.0/cookbooks/Executor.md +++ b/.github/2.0/cookbooks/Executor.md @@ -15,42 +15,42 @@ Document, Executor, and Flow are the three fundamental concepts in Jina. Table of Contents - [Minimum working example](#minimum-working-example) - - [Pure Python](#pure-python) - - [With YAML](#with-yaml) + - [Pure Python](#pure-python) + - [With YAML](#with-yaml) - [Executor API](#executor-api) - - [Inheritance](#inheritance) - - [`__init__` Constructor](#__init__-constructor) - - [Method naming](#method-naming) - - [`@requests` decorator](#requests-decorator) - - [Default binding: `@requests` without `on=`](#default-binding-requests-without-on) - - [Multiple bindings: `@requests(on=[...])`](#multiple-bindings-requestson) - - [No binding](#no-binding) - - [Method Signature](#method-signature) - - [Method Arguments](#method-arguments) - - [Method Returns](#method-returns) - - [Example 1: Embed Documents `blob`](#example-1-embed-documents-blob) - - [Example 2: Add Chunks by Segmenting Document](#example-2-add-chunks-by-segmenting-document) - - [Example 3: Preserve Document `id` Only](#example-3-preserve-document-id-only) - - [YAML Interface](#yaml-interface) - - [Load and Save Executor's YAML config](#load-and-save-executors-yaml-config) - - [Use Executor out of the Flow](#use-executor-out-of-the-flow) - - [Close Executor](#close-executor) + - [Inheritance](#inheritance) + - [`__init__` Constructor](#__init__-constructor) + - [Method naming](#method-naming) + - [`@requests` decorator](#requests-decorator) + - [Default binding: `@requests` without `on=`](#default-binding-requests-without-on) + - [Multiple bindings: `@requests(on=[...])`](#multiple-bindings-requestson) + - [No binding](#no-binding) + - [Method Signature](#method-signature) + - [Method Arguments](#method-arguments) + - [Method Returns](#method-returns) + - [Example 1: Embed Documents `blob`](#example-1-embed-documents-blob) + - [Example 2: Add Chunks by Segmenting Document](#example-2-add-chunks-by-segmenting-document) + - [Example 3: Preserve Document `id` Only](#example-3-preserve-document-id-only) + - [YAML Interface](#yaml-interface) + - [Load and Save Executor's YAML config](#load-and-save-executors-yaml-config) + - [Use Executor out of the Flow](#use-executor-out-of-the-flow) + - [Close Executor](#close-executor) - [Executor Built-in Features](#executor-built-in-features) - - [Workspace](#workspace) - - [Metas](#metas) - - [`.metas` & `.runtime_args`](#metas--runtime_args) - - [Handle parameters](#handle-parameters) + - [Workspace](#workspace) + - [Metas](#metas) + - [`.metas` & `.runtime_args`](#metas--runtime_args) + - [Handle parameters](#handle-parameters) - [Migrating from 1.x to 2.0 in Practice](#migrating-from-1x-to-20-in-practice) - - [Encoder in `jina hello fashion`](#encoder-in-jina-hello-fashion) + - [Encoder in `jina hello fashion`](#encoder-in-jina-hello-fashion) - [Executors in Action](#executors-in-action) - - [Fastai](#fastai) - - [Pytorch Lightning](#pytorch-lightning) - - [Paddle](#paddle) - - [Tensorflow](#tensorflow) - - [MindSpore](#mindspore) - - [Scikit-learn](#scikit-learn) - - [PyTorch](#pytorch) - - [ONNX-Runtime](#onnx-runtime) + - [Fastai](#fastai) + - [Pytorch Lightning](#pytorch-lightning) + - [Paddle](#paddle) + - [Tensorflow](#tensorflow) + - [MindSpore](#mindspore) + - [Scikit-learn](#scikit-learn) + - [PyTorch](#pytorch) + - [ONNX-Runtime](#onnx-runtime) @@ -439,23 +439,25 @@ This is useful in debugging an Executor. ### Close Executor -You might need to execute some logic when your executor's destructor is called. -For example, let's suppose you want to persist data to the disk (e.g in-memory indexed data, fine-tuned model,...). -To do so, you can overwrite the method `close` and add your logic. +You might need to execute some logic when your executor's destructor is called. For example, let's suppose you want to +persist data to the disk (e.g in-memory indexed data, fine-tuned model,...). To do so, you can overwrite the +method `close` and add your logic. ```python from jina import Executor, requests, Document, DocumentArray + class MyExec(Executor): @requests def foo(self, docs, **kwargs): for doc in docs: - print(doc.text) - + print(doc.text) + def close(self): - print("closing...") - + print("closing...") + + with MyExec() as executor: executor.foo(DocumentArray([Document(text='hello world')])) ``` @@ -467,10 +469,9 @@ closing... ## Executor Built-in Features -In Jina 2.0 the Executor class is generic to all categories of executors (`encoders`, `indexers`, `segmenters`,...) to keep development simple. -We do not provide subclasses of `Executor` that are specific to each category. -The design principles are (`user` here -means "Executor developer"): +In Jina 2.0 the Executor class is generic to all categories of executors (`encoders`, `indexers`, `segmenters`,...) to +keep development simple. We do not provide subclasses of `Executor` that are specific to each category. The design +principles are (`user` here means "Executor developer"): - **Do not surprise the user**: keep `Executor` class as Pythonic as possible. It should be as light and unintrusive as a `mixin` class: @@ -481,10 +482,8 @@ means "Executor developer"): interface while delivering just loosely-implemented features is bad for scaling the core framework. For example, `save`, `load`, `on_gpu`, etc. -We want to give our users the freedom to customize their executors easily. If a user is a good Python programmer, they should pick -up `Executor` in no time. -It is as simple as subclassing `Executor` and adding an endpoint. - +We want to give our users the freedom to customize their executors easily. If a user is a good Python programmer, they +should pick up `Executor` in no time. It is as simple as subclassing `Executor` and adding an endpoint. ### Workspace @@ -516,21 +515,25 @@ In 2.0rc1, the following fields are valid for `metas` and `runtime_args`: | `.metas` (static values from hard-coded values, YAML config) | `name`, `description`, `py_modules`, `workspace` | | `.runtime_args` (runtime values from its containers, e.g. `Runtime`, `Pea`, `Pod`) | `name`, `description`, `workspace`, `log_config`, `quiet`, `quiet_error`, `identity`, `port_ctrl`, `ctrl_with_ipc`, `timeout_ctrl`, `ssh_server`, `ssh_keyfile`, `ssh_password`, `uses`, `py_modules`, `port_in`, `port_out`, `host_in`, `host_out`, `socket_in`, `socket_out`, `memory_hwm`, `on_error_strategy`, `num_part`, `entrypoint`, `docker_kwargs`, `pull_latest`, `volumes`, `host`, `port_expose`, `quiet_remote_logs`, `upload_files`, `workspace_id`, `daemon`, `runtime_backend`, `runtime_cls`, `timeout_ready`, `env`, `expose_public`, `pea_id`, `pea_role`, `noblock_on_start`, `uses_before`, `uses_after`, `parallel`, `replicas`, `polling`, `scheduling`, `pod_role`, `peas_hosts`, `proxy`, `uses_metas`, `external`, `gpus`, `zmq_identity`, `hosts_in_connect`, `uses_with` | -**Notes** +**Notes** - the YAML API will ignore `.runtime_args` during save and load as they are not statically stored -- for any other parametrization of the Executor, you can still access its constructor arguments (defined in the class `__init__`) and the request `parameters` +- for any other parametrization of the Executor, you can still access its constructor arguments (defined in the + class `__init__`) and the request `parameters` - `workspace` will be retrieved from either `metas` or `runtime_args`, in that order ### Handle parameters -Parameters are passed to executors via `request.parameters` with `Flow.post(..., parameters=)`. This way all the `executors` will receive -`parameters` as an argument to their `methods`. These `parameters` can be used to pass extra information or tune the `executor` behavior for a -given request without updating the general configuration. + +Parameters are passed to executors via `request.parameters` with `Flow.post(..., parameters=)`. This way all +the `executors` will receive +`parameters` as an argument to their `methods`. These `parameters` can be used to pass extra information or tune +the `executor` behavior for a given request without updating the general configuration. ```python from typing import Optional from jina import Executor, requests, DocumentArray, Flow + class MyExecutor(Executor): def __init__(self, default_param: int = 1, *args, **kwargs): super().__init__(*args, **kwargs) @@ -542,19 +545,21 @@ class MyExecutor(Executor): # param may be overriden for this specific request assert param == 5 + with Flow().add(uses=MyExecutor) as f: f.post(on='/endpoint', inputs=DocumentArray([]), parameters={'param': 5}) ``` -However, this can be a problem when the user wants different executors to have different values of the same parameters. -In that case one can specify specific parameters for the specific `executor` by adding a `dictionary` inside parameters with -the `executor` name as `key`. Jina will then take all these specific parameters and copy to the root of the parameters dictionary before -calling the executor `method`. +However, this can be a problem when the user wants different executors to have different values of the same parameters. +In that case one can specify specific parameters for the specific `executor` by adding a `dictionary` inside parameters +with the `executor` name as `key`. Jina will then take all these specific parameters and copy to the root of the +parameters dictionary before calling the executor `method`. ```python from typing import Optional from jina import Executor, requests, DocumentArray, Flow + class MyExecutor(Executor): def __init__(self, default_param: int = 1, *args, **kwargs): super().__init__(*args, **kwargs) @@ -571,37 +576,12 @@ class MyExecutor(Executor): assert param == 5 -with Flow().\ - add(uses={'jtype': 'MyExecutor', 'metas': {'name': 'my-executor-1'}}).\ - add(uses={'jtype': 'MyExecutor', 'metas': {'name': 'my-executor-2'}}) as f: +with (Flow(). + add(uses={'jtype': 'MyExecutor', 'metas': {'name': 'my-executor-1'}}). + add(uses={'jtype': 'MyExecutor', 'metas': {'name': 'my-executor-2'}})) as f: f.post(on='/endpoint', inputs=DocumentArray([]), parameters={'param': 5, 'my-executor-1': {'param': 10}}) ``` ---- - -## Migrating from 1.x to 2.0 in Practice - -### Encoder in `jina hello fashion` - -Left is 1.x, right is 2.0: - -![img.png](../migration-fashion.png?raw=true) - -Line number corresponds to the 1.x code: - -- `L5`: change imports to top-level namespace `jina`; -- `L8`: all executors now subclass from `Executor` class; -- `L13-14`: there is no need to inherit from `__init__`, no signature is enforced; -- `L20`: `.touch()` is removed; for this particular encoder as long as the seed is fixed there is no need to store; -- `L22`: adding `@requests` to decorate the core method, changing signature to `docs, **kwargs`; -- `L32`: - - content extraction and embedding assignment are now done manually; - - replacing previous `Blob2PngURI` and `ExcludeQL` driver logic using `Document` built-in - methods `convert_blob_to_uri` and `pop` - - there is nothing to return, as the change is done in-place. - ---- - ## Executors in Action ### Fastai @@ -611,8 +591,8 @@ by [fastai](https://github.com/fastai/fastai). The `encode` function of this executor generates a feature vector for each image in each `Document` of the input `DocumentArray`. The feature vector generated is the output activations of the neural network (a vector of 1000 -components). Note the embedding of each text is performed in a joined operation (all embeddings are created for all images -in a single function call) to achieve higher performance. +components). Note the embedding of each text is performed in a joined operation (all embeddings are created for all +images in a single function call) to achieve higher performance. As a result each `Document` in the input `DocumentArray` _docs_ will have an `embedding` after `encode()` has completed. @@ -674,15 +654,14 @@ class PLMwuAutoEncoder(Executor): ### Paddle -The example below use PaddlePaddle [Ernie](https://github.com/PaddlePaddle/ERNIE) model as encoder. -The Executor load pre-trained Ernie family of tokenizer and model. -Convert Jina Document ``doc.text`` into Paddle Tensor and encode it as embedding. -As a result, each `Document` in the `DocumentArray` will have an `embedding` after `encode()` has completed. +The example below use PaddlePaddle [Ernie](https://github.com/PaddlePaddle/ERNIE) model as encoder. The Executor load +pre-trained Ernie family of tokenizer and model. Convert Jina Document ``doc.text`` into Paddle Tensor and encode it as +embedding. As a result, each `Document` in the `DocumentArray` will have an `embedding` after `encode()` has completed. ```python -import paddle as P # paddle==2.1.0 +import paddle as P # paddle==2.1.0 import numpy as np -from ernie.modeling_ernie import ErnieModel # paddle-ernie 0.2.0.dev1 +from ernie.modeling_ernie import ErnieModel # paddle-ernie 0.2.0.dev1 from ernie.tokenizing_ernie import ErnieTokenizer from jina import Executor, requests @@ -692,7 +671,7 @@ class PaddleErineExecutor(Executor): def __init__(self, **kwargs): super().__init__() self.tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') - self.model = ErnieModel.from_pretrained('ernie-1.0') + self.model = ErnieModel.from_pretrained('ernie-1.0') self.model.eval() @requests @@ -827,9 +806,9 @@ class TFIDFTextEncoder(Executor): ### PyTorch -The code snippet below takes ``docs`` as input and perform feature extraction with ``modelnet v2``. It -leverages Pytorch's ``Tensor`` conversion and operation. Finally, the ``embedding`` of each document will be set as the -extracted features. +The code snippet below takes ``docs`` as input and perform feature extraction with ``modelnet v2``. It leverages +Pytorch's ``Tensor`` conversion and operation. Finally, the ``embedding`` of each document will be set as the extracted +features. ```python import torch # 1.8.1 @@ -854,7 +833,8 @@ class PytorchMobilNetExecutor(Executor): ### ONNX-Runtime -The code snippet bellow converts a `Pytorch` model to the `ONNX` and leverage `onnxruntime` to run inference tasks on models from `hugging-face transformers`. +The code snippet bellow converts a `Pytorch` model to the `ONNX` and leverage `onnxruntime` to run inference tasks on +models from `hugging-face transformers`. ```python from pathlib import Path diff --git a/.github/2.0/cookbooks/Flow.md b/.github/2.0/cookbooks/Flow.md index 45ba4ef7c1384..d3a02611b2c8d 100644 --- a/.github/2.0/cookbooks/Flow.md +++ b/.github/2.0/cookbooks/Flow.md @@ -15,50 +15,50 @@ Document, Executor, and Flow are the three fundamental concepts in Jina. Table of Contents - [Minimum working example](#minimum-working-example) - - [Pure Python: All-in-one Style](#pure-python-all-in-one-style) - - [Pure Python: Flow-as-a-Service Style](#pure-python-flow-as-a-service-style) - - [With YAML](#with-yaml) + - [Pure Python: All-in-one Style](#pure-python-all-in-one-style) + - [Pure Python: Flow-as-a-Service Style](#pure-python-flow-as-a-service-style) + - [With YAML](#with-yaml) - [`Flow` API](#flow-api) - - [Create a Flow](#create-a-flow) - - [Use a Flow](#use-a-flow) - - [Visualize a Flow](#visualize-a-flow) - - [Add `Executor` to a Flow](#add-executor-to-a-flow) - - [Chain `.add()`](#chain-add) - - [Define What Executor to Use via `uses`](#define-what-executor-to-use-via-uses) - - [Add Executor via its Class Name](#add-executor-via-its-class-name) - - [Add Executor via YAML file](#add-executor-via-yaml-file) - - [Add Executor via `Dict`](#add-executor-via-dict) - - [Add Executor via Docker Image](#add-executor-via-docker-image) - - [Intra Parallelism via `needs`](#intra-parallelism-via-needs) - - [Inter Parallelism via `parallel`](#inter-parallelism-via-parallel) - - [Add a Remote `Executor` via `host`](#add-a-remote-executor-via-host) - - [Commonly used arguments for deployment in `.add`](#commonly-used-arguments-for-deployment-in-add) - - [Commonly used patterns for `.add`](#commonly-used-patterns-for-add) - - [Local native Executor via `.py` file:](#local-native-executor-via-py-file) - - [Local native Executor via `.yml` file:](#local-native-executor-via-yml-file) - - [Local Docker Executor:](#local-docker-executor) - - [Local Docker Executor from Jina Hub:](#local-docker-executor-from-jina-hub) - - [Using an already spawned Executor:](#using-an-already-spawned-executor) - - [Remote Executor](#remote-executor) - - [Setting the Gateway data port and host](#setting-the-gateway-data-port-and-host) - - [Forcing an Executor in the remote-local configuration](#forcing-an-executor-in-the-remote-local-configuration) - - [Send data request via `post`](#send-data-request-via-post) - - [Function Signature](#function-signature) - - [Define Data via `inputs`](#define-data-via-inputs) - - [Callback Functions](#callback-functions) - - [Send Parameters](#send-parameters) - - [Fine-grained Control on Request](#fine-grained-control-on-request) - - [Size of the Request](#size-of-the-request) - - [Get All Responses](#get-all-responses) - - [Asynchronous Flow](#asynchronous-flow) + - [Create a Flow](#create-a-flow) + - [Use a Flow](#use-a-flow) + - [Visualize a Flow](#visualize-a-flow) + - [Add `Executor` to a Flow](#add-executor-to-a-flow) + - [Chain `.add()`](#chain-add) + - [Define What Executor to Use via `uses`](#define-what-executor-to-use-via-uses) + - [Add Executor via its Class Name](#add-executor-via-its-class-name) + - [Add Executor via YAML file](#add-executor-via-yaml-file) + - [Add Executor via `Dict`](#add-executor-via-dict) + - [Add Executor via Docker Image](#add-executor-via-docker-image) + - [Intra Parallelism via `needs`](#intra-parallelism-via-needs) + - [Inter Parallelism via `parallel`](#inter-parallelism-via-parallel) + - [Add a Remote `Executor` via `host`](#add-a-remote-executor-via-host) + - [Commonly used arguments for deployment in `.add`](#commonly-used-arguments-for-deployment-in-add) + - [Commonly used patterns for `.add`](#commonly-used-patterns-for-add) + - [Local native Executor via `.py` file:](#local-native-executor-via-py-file) + - [Local native Executor via `.yml` file:](#local-native-executor-via-yml-file) + - [Local Docker Executor:](#local-docker-executor) + - [Local Docker Executor from Jina Hub:](#local-docker-executor-from-jina-hub) + - [Using an already spawned Executor:](#using-an-already-spawned-executor) + - [Remote Executor](#remote-executor) + - [Setting the Gateway data port and host](#setting-the-gateway-data-port-and-host) + - [Forcing an Executor in the remote-local configuration](#forcing-an-executor-in-the-remote-local-configuration) + - [Send data request via `post`](#send-data-request-via-post) + - [Function Signature](#function-signature) + - [Define Data via `inputs`](#define-data-via-inputs) + - [Callback Functions](#callback-functions) + - [Send Parameters](#send-parameters) + - [Fine-grained Control on Request](#fine-grained-control-on-request) + - [Size of the Request](#size-of-the-request) + - [Get All Responses](#get-all-responses) + - [Asynchronous Flow](#asynchronous-flow) - [Remarks](#remarks) - - [Joining/Merging](#joiningmerging) + - [Joining/Merging](#joiningmerging) ## Minimum working example -### Pure Python: All-in-one Style +### Pure Python: All-in-one Style ```python from jina import Flow, Document, Executor, requests @@ -80,6 +80,7 @@ with f: ### Pure Python: Flow-as-a-Service Style Server: + ```python from jina import Flow, Executor, requests @@ -98,6 +99,7 @@ with f: ``` Client: + ```python from jina import Client, Document @@ -105,7 +107,6 @@ c = Client(port_expose=12345) c.post(on='/bar', inputs=Document(), on_done=print) ``` - ### With YAML `my.yml`: @@ -128,9 +129,9 @@ with f: ## `Flow` API -- Flow is how Jina streamlines and scales Executors. +- Flow is how Jina streamlines and scales Executors. - Flow is a service, allowing multiple clients to access it via gRPC/REST/WebSocket from the public/private network. - + A `Flow` object has the following common methods: | | | @@ -188,18 +189,35 @@ In Jupyter Lab/Notebook, the `Flow` object is rendered automatically without nee ### Add `Executor` to a Flow -`.add()` is the core method to add an Executor to a `Flow` object. Each `add` creates a new Executor, and these Executors -can be run as a local thread/process, a remote process, inside a Docker container, or even inside a remote Docker -container. + +`.add()` is the core method to add an Executor to a `Flow` object. Each `add` creates a new Executor, and these +Executors can be run as a local thread/process, a remote process, inside a Docker container, or even inside a remote +Docker container. + + +##### Quick lookup: commonly used patterns for `.add` + +| Description | Usage (`f = Flow(...)`) | +| --- | --- | +| Local native Executor in the context | `f.add(uses=MyExecutor)` | +| Local native Executor from a YAML | `f.add(uses='mwu_encoder.yml')` | +| Executor from Jina Hub | `f.add(uses='jinahub://MyExecutor')` | +| Dockerized Executor from Jina Hub | `f.add(uses='jinahub+docker://MyExecutor')` | +| Generalized dockerized Executor | `f.add(uses='docker://MyExecutor')` | +| Existing remote Executor | `f.add(host='123.45.67.89', port_in=12345, external=True)` | +| Spawn Remote Executor (via `jinad` on Remote) | `f.add(uses='mwu_encoder.yml', host='123.45.67.89', port_in=12345, port_expose=8080)` | + `.add()` accepts the following common arguments: | | | |---|---| |Define Executor| `uses`| +|Define Executor's parameters | `uses_with`, `uses_metas`, `uses_requests`| |Define Dependencies | `needs` | |Parallelization | `parallel`, `polling` | + For a full list of arguments, please check `jina executor --help`. #### Chain `.add()` @@ -306,17 +324,91 @@ f = Flow().add( }) ``` -##### Add Executor via Docker Image +##### Add an already spawned Executor + +A Flow does not have to be local-only. You can use any Executor on remote(s). + +The external Executor in the following two use-cases could have been spawned + +- either by another Flow +- or by the `jina executor` CLI command + +```python +f.add(host='localhost', port_in=12345, external=True) +f.add(host='123.45.67.89', port_in=12345, external=True) +``` + +##### Add & spawn a Remote `Executor` via `jinad` -To add an Executor from a Docker image tag `myexec:latest`, use: +In the example below, the Executor with +the `host` +keyword `gpu-exec`, is put to a remote machine for parallelization, whereas other Executors stay local. Extra file +dependencies that need to be uploaded are specified via the `upload_files` keyword. + + + + + + + + + + +
123.45.67.89 + +```bash +# have docker installed +docker run --name=jinad --network=host -v /var/run/docker.sock:/var/run/docker.sock jinaai/jina:latest-daemon --port-expose 8000 +# stop the docker container +docker rm -f jinad +``` + +
+ Local + ```python from jina import Flow -f = Flow().add(uses='docker://myexec:latest') +f = (Flow() + .add() + .add(name='gpu_exec', + uses='mwu_encoder.yml', + host='123.45.67.89:8000', + parallel=2, + upload_files=['mwu_encoder.py']) + .add()) +``` + +
+ +###### Commonly used arguments for deployment in `.add` + +| Name | default | Description | +| --- | --- | --- | +| `host` | `0.0.0.0` | The host of the machine. Can be an ip address or DNS name (e.g. `0.0.0.0`, `my_encoder.jina.ai`) | +| `port_expose` | randomly initialized | Port of JinaD on the remote machine. | +| `port_in` | randomly initialized | Port for incoming traffic for the Executor. | +| `port_out` | randomly initialized | Port for outgoing traffic for the Executor. This is only used in the remote-local use-case described below. | +| `connect_to_predecessor` | `False` | Forces a Head to connect to the previous Tail. This is only used in the remote-local use-case described below. | +| `external` | `False` | Stops `Flow` from context managing an Executor. This allows spawning of an external Executor and reusing across multiple Flows. | +| `uses`, `uses_before` and `uses_after` prefix | No prefix | When prefixing one of the `uses` arguments with `docker` or `jinahub+docker`, the Executor does not run natively, but is spawned inside a container. | + + +##### Forcing an Executor in the remote-local configuration + +Sometimes you want to use a remote Executor in your local Flow (e.g. using an expensive encoder on a remote GPU). Then +the remote cannot talk back to the next local Executor directly. This is similar to a server that cannot talk to a +client before the client has opened a connection. The Flow inside Jina has an auto-detection mechanism for such cases. +Anyhow, in some networking setups this mechanism fails. Then you can force this by hand by setting +the `connect_to_predecessor` argument and `port_out` to the Executor in front. + +```python +f.add(name='remote', host='123.45.67.89', port_out=23456).add(name='local', connect_to_predecessor=True) ``` -Once built, it will start a Docker container. + +### Parallelization #### Intra Parallelism via `needs` @@ -403,133 +495,7 @@ f = (Flow() -#### Add a Remote `Executor` via `host` - -A Flow does not have to be local-only. You can put any Executor to remote(s). In the example below, the Executor with the `host` -keyword `gpu-exec`, is put to a remote machine for parallelization, whereas other Executors stay local. Extra file -dependencies that need to be uploaded are specified via the `upload_files` keyword. - - - - - - - - - - -
123.45.67.89 - -```bash -# have docker installed -docker run --name=jinad --network=host -v /var/run/docker.sock:/var/run/docker.sock jinaai/jina:latest-daemon --port-expose 8000 -# stop the docker container -docker rm -f jinad -``` - -
- Local - - -```python -from jina import Flow - -f = (Flow() - .add() - .add(name='gpu_exec', - uses='mwu_encoder.yml', - host='123.45.67.89:8000', - parallel=2, - upload_files=['mwu_encoder.py']) - .add()) -``` - -
- -#### Commonly used arguments for deployment in `.add` - -| Name | default | Description | -| --- | --- | --- | -| `host` | `0.0.0.0` | The host of the machine. Can be an ip address or DNS name (e.g. `0.0.0.0`, `my_encoder.jina.ai`) | -| `port_expose` | randomly initialized | Port of JinaD on the remote machine. | -| `port_in` | randomly initialized | Port for incoming traffic for the Executor. | -| `port_out` | randomly initialized | Port for outgoing traffic for the Executor. This is only used in the remote-local use-case described below. | -| `connect_to_predecessor` | `False` | Forces a Head to connect to the previous Tail. This is only used in the remote-local use-case described below. | -| `external` | `False` | Stops `Flow` from context managing an Executor. This allows spawning of an external Executor and reusing across multiple Flows. | -| `uses`, `uses_before` and `uses_after` prefix | No prefix | When prefixing one of the `uses` arguments with `docker` or `jinahub+docker`, the Executor does not run natively, but is spawned inside a container. | - -#### Commonly used patterns for `.add` - -##### Local native Executor via `.py` file: - -```python -class MyExecutor():... - -f.add(uses=MyExecutor) # passing the Executor class -f.add(uses='MyExecutor') # passing the name of the Executor class -``` - -##### Local native Executor via `.yml` file: - -```python -f.add(uses='mwu_encoder.yml') -``` - -##### Local Docker Executor: -```python -f.add(uses='docker://MyExecutor') -``` - -##### Local Docker Executor from Jina Hub: - -```python -f.add(uses='jinahub+docker://MyExecutor') -``` - -##### Using an already spawned Executor: - -The external Executor in the following two use-cases could have been spawned - -- either by another Flow -- or by the `jina executor` CLI command - -```python -f.add(host='localhost', port_in=12345, external=True) -f.add(host='123.45.67.89', port_in=12345, external=True) -``` - -##### Remote Executor - -In any of the following remote patterns without `external`, JinaD must be running on the remote machine. -Furthermore, JinaD must be setup to listen on `port_expose`. - -```python -f.add(uses='mwu_encoder.yml', host='123.45.67.89', port_in=12345, port_expose=8080) -``` - -`uses` can take any argument that would be used in the local case (e.g. `jinahub+docker://...`). - -##### Setting the Gateway data port and host - -If you need to change the networking of the Gateway, the respective arguments go directly into the Flow initialization. - -```python -Flow(host='123.45.67.89', port_in=12345) -``` - -##### Forcing an Executor in the remote-local configuration - -Sometimes you want to use a remote Executor in your local Flow (e.g. using an expensive encoder on a remote GPU). -Then the remote cannot talk back to the next local Executor directly. -This is similar to a server that cannot talk to a client before the client has opened a connection. -The Flow inside Jina has an auto-detection mechanism for such cases. -Anyhow, in some networking setups this mechanism fails. -Then you can force this by hand by setting the `connect_to_predecessor` argument and `port_out` to the Executor in front. - -```python -Flow().add(name='remote', host='123.45.67.89', port_out=23456).add(name='local', connect_to_predecessor=True) -``` ### Send data request via `post` @@ -583,7 +549,8 @@ def post( """ ``` -Note, you can also use CRUD methods (`index`, `search`, `update`, `delete`) which are just sugary syntax of `post` with `on='/index'` +Note, you can also use CRUD methods (`index`, `search`, `update`, `delete`) which are just sugary syntax of `post` +with `on='/index'` , `on='/search'`, etc. Precisely, they are defined as: ```python @@ -626,7 +593,7 @@ with Flow() as f: `Document` module provides some methods that lets you build `Document` generator, e.g. [`from_csv` , `from_files`, `from_ndarray`, `from_ndjson`](Document.md#construct-from-json-csv-ndarray-and-files). They can be used - in conjunction with `.post()`, e.g. +in conjunction with `.post()`, e.g. ```python from jina import Flow @@ -641,22 +608,24 @@ with f, open('my.csv') as fp: #### Callback Functions Once a request is returned, callback functions are fired. Jina Flow implements a Promise-like interface. You can add -callback functions `on_done`, `on_error`, `on_always` to hook different events. +callback functions `on_done`, `on_error`, `on_always` to hook different events. -In Jina, callback function's first argument is a `jina.types.request.Response` object. Hence, you can annotate the callback function via: +In Jina, callback function's first argument is a `jina.types.request.Response` object. Hence, you can annotate the +callback function via: ```python from jina.types.request import Response + def my_callback(rep: Response): ... ``` -`Response` object has many attributes, probably the most popular one is `Response.docs`, where you can access all `Document` as an `DocumentArray`. +`Response` object has many attributes, probably the most popular one is `Response.docs`, where you can access +all `Document` as an `DocumentArray`. -In the example below, our Flow passes -the message then prints the result when successful. If something goes wrong, it beeps. Finally, the result is written -to `output.txt`. +In the example below, our Flow passes the message then prints the result when successful. If something goes wrong, it +beeps. Finally, the result is written to `output.txt`. ```python from jina import Document, Flow @@ -706,12 +675,12 @@ with f: This is useful to control `Executor` objects in the runtime. - #### Fine-grained Control on Request ##### Size of the Request -You can control how many `Documents` in each request by `request_size`. Say your `inputs` has length of 100, whereas you `request_size` is set to `10`. Then `f.post` will send ten requests and return 10 responses: +You can control how many `Documents` in each request by `request_size`. Say your `inputs` has length of 100, whereas +you `request_size` is set to `10`. Then `f.post` will send ten requests and return 10 responses: ```python from jina import Flow, Document @@ -752,7 +721,8 @@ with f: #### Get All Responses -In some scenarios (e.g. testing), you may want to get the all responses in bulk and then process them; instead of processing responses on the fly. To do that, you can turn on `return_results`: +In some scenarios (e.g. testing), you may want to get the all responses in bulk and then process them; instead of +processing responses on the fly. To do that, you can turn on `return_results`: ```python from jina import Flow, Document @@ -784,9 +754,10 @@ with f: ``` -Note, turning on `return_results` breaks the streaming of the system. If you are sending 1000 requests, then `return_results=True` means you will get nothing until the 1000th response returns. Moreover, if each response takes 10MB memory, it means you will consume upto 10GB memory! On contrary, with callback and `return_results=False`, your memory usage will stay constant at 10MB. - - +Note, turning on `return_results` breaks the streaming of the system. If you are sending 1000 requests, +then `return_results=True` means you will get nothing until the 1000th response returns. Moreover, if each response +takes 10MB memory, it means you will consume upto 10GB memory! On contrary, with callback and `return_results=False`, +your memory usage will stay constant at 10MB. ### Asynchronous Flow @@ -877,10 +848,19 @@ if __name__ == '__main__': ## Remarks +### Setting the Flow's port and host + +If you need to change the networking of the Flow, the respective arguments go directly into the Flow initialization. + +```python +Flow(host='123.45.67.89', port_in=12345) +``` + + ### Joining/Merging -Combining `docs` from multiple requests is already done by the `ZEDRuntime` before feeding them to the Executor's function. -Hence, simple joining is just returning this `docs`. Complicated joining should be implemented at `Document` +Combining `docs` from multiple requests is already done by the `ZEDRuntime` before feeding them to the Executor's +function. Hence, simple joining is just returning this `docs`. Complicated joining should be implemented at `Document` /`DocumentArray` ```python From 90d31d7f92a41b3ec414b2e1164938b4c4c50a64 Mon Sep 17 00:00:00 2001 From: Han Xiao Date: Tue, 10 Aug 2021 09:51:47 +0200 Subject: [PATCH 19/19] docs(cookbook): restructure & update the content --- .github/2.0/cookbooks/Document.md | 765 +++++++++++++++--------------- 1 file changed, 389 insertions(+), 376 deletions(-) diff --git a/.github/2.0/cookbooks/Document.md b/.github/2.0/cookbooks/Document.md index bb8c4913c0223..98e12524b97c1 100644 --- a/.github/2.0/cookbooks/Document.md +++ b/.github/2.0/cookbooks/Document.md @@ -13,8 +13,8 @@ Document, Executor, and Flow are the three fundamental concepts in Jina. `Document` is the basic data type that Jina operates with. Text, picture, video, audio, image or 3D mesh: They are all `Document`s in Jina. -`DocumentArray` is a sequence container of `Document`s. It is the first-class citizen of `Executor`, serving as the Executor's input -and output. +`DocumentArray` is a sequence container of `Document`s. It is the first-class citizen of `Executor`, serving as the +Executor's input and output. You could say `Document` is to Jina is what `np.float` is to Numpy, and `DocumentArray` is similar to `np.ndarray`. @@ -24,56 +24,56 @@ Table of Contents - [Minimum working example](#minimum-working-example) - [`Document` API](#document-api) - - [`Document` attributes](#document-attributes) - - [Set & Unset attributes](#set--unset-attributes) - - [Construct `Document`](#construct-document) - - [Content attributes](#content-attributes) - - [Exclusivity of `doc.content`](#exclusivity-of-doccontent) - - [Conversion between `doc.content`](#conversion-between-doccontent) - - [Set embedding](#set-embedding) - - [Support for sparse arrays](#support-for-sparse-arrays) - - [Construct with multiple attributes](#construct-with-multiple-attributes) - - [Meta attributes](#meta-attributes) - - [Construct from dict or JSON string](#construct-from-dict-or-json-string) - - [Parsing unrecognized fields](#parsing-unrecognized-fields) - - [Construct from another `Document`](#construct-from-another-document) - - [Construct from JSON, CSV, `ndarray` and files](#construct-from-json-csv-ndarray-and-files) - - [Serialize `Document`](#serialize-document) - - [Add recursion to `Document`](#add-recursion-to-document) - - [Recursive attributes](#recursive-attributes) - - [Represent `Document` as dictionary or JSON](#represent-document-as-dictionary-or-json) - - [Visualize `Document`](#visualize-document) - - [Add relevancy to `Document`s](#add-relevancy-to-documents) - - [Relevance attributes](#relevance-attributes) - - [`GraphDocument`](#graphdocument) - - [`GraphDocument` constructor](#graphdocument-constructor) - - [`GraphDocument` additional attributes](#graphdocument-additional-attributes) - - [`GraphDocument` methods](#graphdocument-methods) + - [`Document` attributes](#document-attributes) + - [Set & Unset attributes](#set--unset-attributes) + - [Construct `Document`](#construct-document) + - [Content attributes](#content-attributes) + - [Exclusivity of `doc.content`](#exclusivity-of-doccontent) + - [Conversion between `doc.content`](#conversion-between-doccontent) + - [Set embedding](#set-embedding) + - [Support for sparse arrays](#support-for-sparse-arrays) + - [Construct with multiple attributes](#construct-with-multiple-attributes) + - [Meta attributes](#meta-attributes) + - [Construct from dict or JSON string](#construct-from-dict-or-json-string) + - [Parsing unrecognized fields](#parsing-unrecognized-fields) + - [Construct from another `Document`](#construct-from-another-document) + - [Construct from JSON, CSV, `ndarray` and files](#construct-from-json-csv-ndarray-and-files) + - [Serialize `Document`](#serialize-document) + - [Add recursion to `Document`](#add-recursion-to-document) + - [Recursive attributes](#recursive-attributes) + - [Represent `Document` as dictionary or JSON](#represent-document-as-dictionary-or-json) + - [Visualize `Document`](#visualize-document) + - [Add relevancy to `Document`s](#add-relevancy-to-documents) + - [Relevance attributes](#relevance-attributes) + - [`GraphDocument`](#graphdocument) + - [`GraphDocument` constructor](#graphdocument-constructor) + - [`GraphDocument` additional attributes](#graphdocument-additional-attributes) + - [`GraphDocument` methods](#graphdocument-methods) - [`DocumentArray` API](#documentarray-api) - - [Construct `DocumentArray`](#construct-documentarray) - - [Persistence via `save()`/`load()`](#persistence-via-saveload) - - [Access element](#access-element) - - [Traverse elements](#traverse-elements) - - [Sort elements](#sort-elements) - - [Filter elements](#filter-elements) - - [Use `itertools` on `DocumentArray`](#use-itertools-on-documentarray) - - [Get attributes in bulk](#get-attributes-in-bulk) - - [Access nested attributes from tags](#access-nested-attributes-from-tags) - - [Finding closest documents between `DocumentArray` objects](#finding-closest-documents-between-documentarray-objects) - - [Using Sparse arrays as embeddings](#using-sparse-arrays-as-embeddings) - - [Selecting a subset of Documents from a `DocumentArray` using `.find`](#selecting-a-subset-of-documents-from-a-documentarray-using-find) - - [Random sample a subset of Documents from a `DocumentArray` using `sample`](#random-sample-a-subset-of-documents-from-a-documentarray-using-sample) - - [Shuffle the `DocumentArray` using `shuffle`](#shuffle-the-documentarray-using-shuffle) - - [Visualize embeddings](#visualize-embeddings) + - [Construct `DocumentArray`](#construct-documentarray) + - [Persistence via `save()`/`load()`](#persistence-via-saveload) + - [Access element](#access-element) + - [Traverse elements](#traverse-elements) + - [Sort elements](#sort-elements) + - [Filter elements](#filter-elements) + - [Use `itertools` on `DocumentArray`](#use-itertools-on-documentarray) + - [Get attributes in bulk](#get-attributes-in-bulk) + - [Access nested attributes from tags](#access-nested-attributes-from-tags) + - [Finding closest documents between `DocumentArray` objects](#finding-closest-documents-between-documentarray-objects) + - [Using Sparse arrays as embeddings](#using-sparse-arrays-as-embeddings) + - [Selecting a subset of Documents from a `DocumentArray` using `.find`](#selecting-a-subset-of-documents-from-a-documentarray-using-find) + - [Random sample a subset of Documents from a `DocumentArray` using `sample`](#random-sample-a-subset-of-documents-from-a-documentarray-using-sample) + - [Shuffle the `DocumentArray` using `shuffle`](#shuffle-the-documentarray-using-shuffle) + - [Visualize embeddings](#visualize-embeddings) - [`DocumentArrayMemmap` API](#documentarraymemmap-api) - - [Create `DocumentArrayMemmap` object](#create-documentarraymemmap-object) - - [Add Documents to `DocumentArrayMemmap` object](#add-documents-to-documentarraymemmap-object) - - [Clear a `DocumentArrayMemmap` object](#clear-a-documentarraymemmap-object) - - [Pruning](#pruning) - - [Mutable sequence with "read-only" elements](#mutable-sequence-with-read-only-elements) - - [Side-by-side vs. `DocumentArray`](#side-by-side-vs-documentarray) - - [Convert between `DocumentArray` and `DocumentArrayMemmap`](#convert-between-documentarray-and-documentarraymemmap) - - [Maintaining consistency via `.reload()`](#maintaining-consistency-via-reload) + - [Create `DocumentArrayMemmap` object](#create-documentarraymemmap-object) + - [Add Documents to `DocumentArrayMemmap` object](#add-documents-to-documentarraymemmap-object) + - [Clear a `DocumentArrayMemmap` object](#clear-a-documentarraymemmap-object) + - [Pruning](#pruning) + - [Mutable sequence with "read-only" elements](#mutable-sequence-with-read-only-elements) + - [Side-by-side vs. `DocumentArray`](#side-by-side-vs-documentarray) + - [Convert between `DocumentArray` and `DocumentArrayMemmap`](#convert-between-documentarray-and-documentarraymemmap) + - [Maintaining consistency via `.reload()`](#maintaining-consistency-via-reload) @@ -133,6 +133,51 @@ d.pop('text', 'id', 'mime_type') ``` +#### Access nested attributes from tags + +`Document` contains the `tags` field that can hold a map-like structure that can map arbitrary values. + +```python +from jina import Document + +doc = Document(tags={'dimensions': {'height': 5.0, 'weight': 10.0}}) + +doc.tags['dimensions'] +``` + +```text +{'weight': 10.0, 'height': 5.0} +``` + +In order to provide easy access to nested fields, the `Document` allows to access attributes by composing the attribute +qualified name with interlaced `__` symbols: + +```python +from jina import Document + +doc = Document(tags={'dimensions': {'height': 5.0, 'weight': 10.0}}) + +doc.tags__dimensions__weight +``` + +```text +10.0 +``` + +This also allows to access nested metadata attributes in `bulk` from a `DocumentArray`. + +```python +from jina import Document, DocumentArray + +da = DocumentArray([Document(tags={'dimensions': {'height': 5.0, 'weight': 10.0}}) for _ in range(10)]) + +da.get_attributes('tags__dimensions__height', 'tags__dimensions__weight') +``` + +```text +[[5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0], [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]] +``` + ### Construct `Document` ##### Content attributes @@ -165,8 +210,8 @@ d4 = Document(content='https://static.jina.ai/logo/core/notext/light/logo.png') ``` -The content will be automatically assigned to either the `text`, `buffer`, `blob`, or `uri` fields. `id` and `mime_type` are -auto-generated when not given. +The content will be automatically assigned to either the `text`, `buffer`, `blob`, or `uri` fields. `id` and `mime_type` +are auto-generated when not given. You can get a visualization of a `Document` object in Jupyter Notebook or by calling `.plot()`. @@ -205,10 +250,12 @@ doc.convert_buffer_to_uri() doc.convert_text_to_uri() doc.convert_uri_to_text() ``` -You can use `convert_content_to_uri` to convert the content to URI. This will determine the used `content_type` and use the appropriate conversion method. -You can convert a URI to a data URI (a data in-line URI scheme) using `doc.convert_uri_to_datauri()`. This will fetch the -resource and make it inline. +You can use `convert_content_to_uri` to convert the content to URI. This will determine the used `content_type` and use +the appropriate conversion method. + +You can convert a URI to a data URI (a data in-line URI scheme) using `doc.convert_uri_to_datauri()`. This will fetch +the resource and make it inline. In particular, when you work with an image `Document`, there are some extra helpers that enable more conversion: @@ -219,9 +266,10 @@ doc.convert_image_uri_to_blob() doc.convert_image_datauri_to_blob() ``` -##### Set embedding +#### Set embedding -An embedding is a high-dimensional representation of a `Document`. You can assign any Numpy `ndarray` as a `Document`'s embedding. +An embedding is a high-dimensional representation of a `Document`. You can assign any Numpy `ndarray` as a `Document`'s +embedding. ```python import numpy as np @@ -231,22 +279,22 @@ d1 = Document(embedding=np.array([1, 2, 3])) d2 = Document(embedding=np.array([[1, 2, 3], [4, 5, 6]])) ``` -#### Support for sparse arrays +##### Support for sparse arrays - Scipy sparse array (`coo_matrix, bsr_matrix, csr_matrix, csc_matrix`) are supported as both `embedding` or `blob` : +Scipy sparse array (`coo_matrix, bsr_matrix, csr_matrix, csc_matrix`) are supported as both `embedding` or `blob` : ```python -import scipy.sparse as sp +import scipy.sparse as sp -d1 = Document(embedding=sp.coo_matrix([0,0,0,1,0])) -d2 = Document(embedding=sp.csr_matrix([0,0,0,1,0])) -d3 = Document(embedding=sp.bsr_matrix([0,0,0,1,0])) -d4 = Document(embedding=sp.csc_matrix([0,0,0,1,0])) +d1 = Document(embedding=sp.coo_matrix([0, 0, 0, 1, 0])) +d2 = Document(embedding=sp.csr_matrix([0, 0, 0, 1, 0])) +d3 = Document(embedding=sp.bsr_matrix([0, 0, 0, 1, 0])) +d4 = Document(embedding=sp.csc_matrix([0, 0, 0, 1, 0])) -d5 = Document(blob=sp.coo_matrix([0,0,0,1,0])) -d6 = Document(blob=sp.csr_matrix([0,0,0,1,0])) -d7 = Document(blob=sp.bsr_matrix([0,0,0,1,0])) -d8 = Document(blob=sp.csc_matrix([0,0,0,1,0])) +d5 = Document(blob=sp.coo_matrix([0, 0, 0, 1, 0])) +d6 = Document(blob=sp.csr_matrix([0, 0, 0, 1, 0])) +d7 = Document(blob=sp.bsr_matrix([0, 0, 0, 1, 0])) +d8 = Document(blob=sp.csc_matrix([0, 0, 0, 1, 0])) ``` Tensorflow and Pytorch sparse arrays are also supported @@ -384,9 +432,9 @@ d.update(s) #### Construct from JSON, CSV, `ndarray` and files -The `jina.types.document.generators` module let you construct `Document` from common file types such as JSON, CSV, `ndarray` and text files. The following -functions will give a generator of `Document`, where each `Document` object corresponds to a line/row in the original -format: +The `jina.types.document.generators` module let you construct `Document` from common file types such as JSON, +CSV, `ndarray` and text files. The following functions will give a generator of `Document`, where each `Document` object +corresponds to a line/row in the original format: | | | | --- | --- | @@ -407,42 +455,7 @@ from jina.types.document.generators import from_files DocumentArray(from_files('/*.png')) ``` -### Serialize `Document` - -You can serialize a `Document` into JSON string or Python dict or binary string: - -```python -from jina import Document - -d = Document(content='hello, world') -d.json() -``` - -```json -{ - "id": "6a1c7f34-aef7-11eb-b075-1e008a366d48", - "mimeType": "text/plain", - "text": "hello world" -} -``` - -```python -d.dict() -``` - -``` -{'id': '6a1c7f34-aef7-11eb-b075-1e008a366d48', 'mimeType': 'text/plain', 'text': 'hello world'} -``` - -```python -d.binary_str() -``` - -``` -b'\n$6a1c7f34-aef7-11eb-b075-1e008a366d48R\ntext/plainj\x0bhello world' -``` - -### Add recursion to `Document` +### Construct Recursive `Document` #### Recursive attributes @@ -479,110 +492,118 @@ You can add **chunks** (sub-Document) and **matches** (neighbour-Document) to a d.matches.append(Document()) ``` -Note that both `doc.chunks` and `doc.matches` return `ChunkArray` and `MatchArray`, which are sub-classes of [`DocumentArray`](#documentarray-api). We will introduce `DocumentArray later. +Note that both `doc.chunks` and `doc.matches` return `ChunkArray` and `MatchArray`, which are sub-classes +of [`DocumentArray`](#documentarray-api). We will introduce `DocumentArray later. -### Represent `Document` as dictionary or JSON -Any `Document` can be converted into a `Python dictionary` or into `Json string` by calling their `.dict()` or `.json()` methods. +### Construct `GraphDocument` -```python -import pprint -import numpy as np +`GraphDocument` is a subclass of `Document`. It's a special type of `Document` that adds functionality to let you work +with a `Document` as a `directed graph`. Chunks of the document represent the nodes of the graph. `GraphDocument` adds +graph-specific attributes (`nodes`, `adjacency` list, `edge_features`,...) and operations (`add_node`, `remove_node` +, `add_edge`, `remove_edge`,...) +#### `GraphDocument` constructor + +`GraphDocument`'s constructor supports the same parameters as `Document`. It mainly adds one +parameter `force_undirected`. It's a boolean flag that, when set to `True`, forces the graph document to be undirected. + +#### `GraphDocument` additional attributes + +`GraphDocument` adds the following attributes to `Document`: + +| Attribute | Description | +|---|---| +| `edge_features` | The dictionary of edge features, indexed by `edge_id` | +| `adjacency` | Adjacency list | +| `undirected` | Type of the graph: undirected or directed | +| `num_nodes` | Number of nodes in the graph | +| `num_edges` | Number of edges in the graph | +| `nodes` | The list of nodes. Equivalent to `chunks` | + +#### `GraphDocument` methods + +`GraphDocument` adds the following methods to `Document`: + +* `add_node`: adds a document to the graoh: + +```python +from jina.types.document.graph import GraphDocument from jina import Document -d0 = Document(id='🐲identifier', text='I am a Jina Document', tags={'cool': True}, embedding=np.array([0, 0])) -pprint.pprint(d0.dict()) -pprint.pprint(d0.json()) +graph = GraphDocument() +graph.add_node(Document(text='hello world')) +graph.nodes[0] ``` ```text -{'embedding': {'dense': {'buffer': 'AAAAAAAAAAAAAAAAAAAAAA==', - 'dtype': ' +``` -```python -import pprint -import numpy as np +* `add_edge`: Adds an edge between 2 documents. If a document does not exist in the graph, it is added. You can also add + dict features to the edge with parameter `features` +```python from jina import Document +from jina.types.document.graph import GraphDocument -d0 = Document(id='🐲identifier', text='I am a Jina Document', tags={'cool': True}, embedding=np.array([0, 0])) -pprint.pprint(d0.dict(prettify_ndarrays=True)) -pprint.pprint(d0.json(prettify_ndarrays=True)) +graph = GraphDocument() +d1 = Document(id='1', text='hello world') +d2 = Document(id='2', text='goodbye world') +graph.add_edge(d1, d2, features={"text": "both documents are linked"}) +graph.nodes ``` ```text -{'embedding': [0, 0], - 'id': '🐲identifier', - 'mime_type': 'text/plain', - 'tags': {'cool': True}, - 'text': 'I am a Jina Document'} + +``` -('{"embedding": [0, 0], "id": "identifier", "mime_type": ' - '"text/plain", "tags": {"cool": true}, "text": "I am a Jina Document"}') +You access the edge features using id1-id2 as key: + +```python +graph.edge_features['1-2'] ``` -This can be useful to understand the contents of the `Document` and to send to backends that can process vectors as `lists` of values. +```text + +``` -### Visualize `Document` +* `remove_edge` and `remove_node` allows removing an edge (between 2 nodes) and removing a node respectively. -To better see the Document's recursive structure, you can use `.plot()` function. If you are using JupyterLab/Notebook, -all `Document` objects will be auto-rendered: +* `GraphDocument` exposes methods that return node-specific information: - - - - - -
+| method | Description | +|---|---| +| `get_out_degree` | node outdegree | +| `get_in_degree` | node indegree | +| `get_outgoing_nodes` | Array of outgoing nodes for a given node | +| `get_incoming_nodes` | Array of incoming nodes for a given node | ```python -import numpy as np from jina import Document +from jina.types.document.graph import GraphDocument -d0 = Document(id='🐲', embedding=np.array([0, 0])) -d1 = Document(id='🐦', embedding=np.array([1, 0])) -d2 = Document(id='🐒', embedding=np.array([0, 1])) -d3 = Document(id='🐯', embedding=np.array([1, 1])) +graph = GraphDocument() +d1 = Document(id='1', text='hello world') +d2 = Document(id='2', text='goodbye world') +d3 = Document(id='3') +graph.add_edge(d1, d2) +graph.add_edge(d1, d3) -d0.chunks.append(d1) -d0.chunks[0].chunks.append(d2) -d0.matches.append(d3) +assert graph.get_out_degree(d1) == 2 -d0.plot() # simply `d0` on JupyterLab +graph.get_outgoing_nodes(d1) ``` - - -
+```text + +``` + +* `to_dgl_graph`: returns a `dgl.DGLGraph` from the graph document. +* `load_from_dgl_graph`: returns a `GraphDocument` from a `dgl.DGLGraph`. + -### Add relevancy to `Document`s +### Add relevancy to `Document` #### Relevance attributes @@ -629,6 +650,7 @@ q.matches.append(m) ``` These attributes (`scores` and `evaluations`) provide a dict-like interface that lets access all its elements: + ```python from jina import Document @@ -648,107 +670,113 @@ for evaluation_key, evaluation_score in d.evaluations.items(): recall => recall at 10: 0.5 ``` -### `GraphDocument` -`GraphDocument` is a subclass of `Document`. It's a special type of `Document` that adds functionality to let you work with a `Document` as a `directed graph`. -Chunks of the document represent the nodes of the graph. `GraphDocument` adds graph-specific attributes (`nodes`, `adjacency` list, `edge_features`,...) and operations (`add_node`, `remove_node`, `add_edge`, `remove_edge`,...) +### Serialize `Document` to binary/`Dict`/JSON -#### `GraphDocument` constructor -`GraphDocument`'s constructor supports the same parameters as `Document`. -It mainly adds one parameter `force_undirected`. It's a boolean flag that, when set to `True`, forces the graph document to be undirected. - -#### `GraphDocument` additional attributes +You can serialize a `Document` into JSON string or Python dict or binary string: -`GraphDocument` adds the following attributes to `Document`: +```python +from jina import Document -| Attribute | Description | -|---|---| -| `edge_features` | The dictionary of edge features, indexed by `edge_id` | -| `adjacency` | Adjacency list | -| `undirected` | Type of the graph: undirected or directed | -| `num_nodes` | Number of nodes in the graph | -| `num_edges` | Number of edges in the graph | -| `nodes` | The list of nodes. Equivalent to `chunks` | +d = Document(content='hello, world') +``` -#### `GraphDocument` methods +```python +d.binary_str() +``` -`GraphDocument` adds the following methods to `Document`: +``` +b'\n$6a1c7f34-aef7-11eb-b075-1e008a366d48R\ntext/plainj\x0bhello world' +``` -* `add_node`: adds a document to the graoh: ```python -from jina.types.document.graph import GraphDocument -from jina import Document -graph = GraphDocument() -graph.add_node(Document(text='hello world')) -graph.nodes[0] +d.json() ``` -```text - +```json +{ + "id": "6a1c7f34-aef7-11eb-b075-1e008a366d48", + "mimeType": "text/plain", + "text": "hello world" +} ``` -* `add_edge`: Adds an edge between 2 documents. If a document does not exist in the graph, it is added. -You can also add dict features to the edge with parameter `features` ```python -from jina import Document -from jina.types.document.graph import GraphDocument -graph = GraphDocument() -d1 = Document(id='1', text='hello world') -d2 = Document(id='2', text='goodbye world') -graph.add_edge(d1, d2, features={"text": "both documents are linked"}) -graph.nodes +d.dict() ``` -```text - +``` +{'id': '6a1c7f34-aef7-11eb-b075-1e008a366d48', 'mimeType': 'text/plain', 'text': 'hello world'} ``` -You access the edge features using id1-id2 as key: + +In order to have a nicer representation of +the `embeddings` and any `ndarray` field, you can call `dict` and `json` with the option `prettify_ndarrays=True`. + ```python -graph.edge_features['1-2'] +import pprint +import numpy as np + +from jina import Document + +d0 = Document(id='🐲identifier', text='I am a Jina Document', tags={'cool': True}, embedding=np.array([0, 0])) +pprint.pprint(d0.dict(prettify_ndarrays=True)) +pprint.pprint(d0.json(prettify_ndarrays=True)) ``` ```text - +{'embedding': [0, 0], + 'id': '🐲identifier', + 'mime_type': 'text/plain', + 'tags': {'cool': True}, + 'text': 'I am a Jina Document'} + +('{"embedding": [0, 0], "id": "identifier", "mime_type": ' + '"text/plain", "tags": {"cool": true}, "text": "I am a Jina Document"}') ``` -* `remove_edge` and `remove_node` allows removing an edge (between 2 nodes) and removing a node respectively. +This can be useful to understand the contents of the `Document` and to send to backends that can process vectors +as `lists` of values. -* `GraphDocument` exposes methods that return node-specific information: -| method | Description | -|---|---| -| `get_out_degree` | node outdegree | -| `get_in_degree` | node indegree | -| `get_outgoing_nodes` | Array of outgoing nodes for a given node | -| `get_incoming_nodes` | Array of incoming nodes for a given node | +### Visualize `Document` + +To better see the Document's recursive structure, you can use `.plot()` function. If you are using JupyterLab/Notebook, +all `Document` objects will be auto-rendered: + + + + + + +
```python +import numpy as np from jina import Document -from jina.types.document.graph import GraphDocument -graph = GraphDocument() -d1 = Document(id='1', text='hello world') -d2 = Document(id='2', text='goodbye world') -d3 = Document(id='3') -graph.add_edge(d1, d2) -graph.add_edge(d1, d3) -assert graph.get_out_degree(d1) == 2 +d0 = Document(id='🐲', embedding=np.array([0, 0])) +d1 = Document(id='🐦', embedding=np.array([1, 0])) +d2 = Document(id='🐒', embedding=np.array([0, 1])) +d3 = Document(id='🐯', embedding=np.array([1, 1])) -graph.get_outgoing_nodes(d1) -``` +d0.chunks.append(d1) +d0.chunks[0].chunks.append(d2) +d0.matches.append(d3) -```text - +d0.plot() # simply `d0` on JupyterLab ``` -* `to_dgl_graph`: returns a `dgl.DGLGraph` from the graph document. -* `load_from_dgl_graph`: returns a `GraphDocument` from a `dgl.DGLGraph`. + + +
+ ## `DocumentArray` API -A `DocumentArray` is a list of `Document` objects. You can construct, delete, insert, sort and traverse a `DocumentArray` +A `DocumentArray` is a list of `Document` objects. You can construct, delete, insert, sort and traverse +a `DocumentArray` like a Python `list`. Methods supported by `DocumentArray`: @@ -821,23 +849,25 @@ da[1:2] ``` ### Traverse elements -The following graphic illustrates the recursive `Document` structure. -Every `Document` can have multiple `Chunks` and `Matches`. + +The following graphic illustrates the recursive `Document` structure. Every `Document` can have multiple `Chunks` +and `Matches`. `Chunks` and `Matches` are `Documents` as well. -In most of the cases, you want to iterate through a certain level of documents. -`DocumentArray.traverse` can be used for that by providing custom paths. -As return value you get a generator which generates `DocumentArrays` matching the provided traversal paths. -Let's assume you have the following `Document` structure: +In most of the cases, you want to iterate through a certain level of documents. +`DocumentArray.traverse` can be used for that by providing custom paths. As return value you get a generator which +generates `DocumentArrays` matching the provided traversal paths. Let's assume you have the following `Document` +structure: + ```python from jina import DocumentArray, Document da = DocumentArray([ Document(id='r1', chunks=[ Document(id='c1', matches=[ - Document(id='c1c1m1'), + Document(id='c1c1m1'), ]), Document(id='c2', chunks=[ Document(id='c2c1', matches=[ @@ -851,9 +881,11 @@ da = DocumentArray([ Document(id='r2') ]) ``` -When calling `da.traverse(['cm', 'ccm'])` you get a generator over two `DocumentArrays`. -The first `DocumentArray` contains the `Matches` of the `Chunks` and the second `DocumentArray` contains the `Matches` of the `Chunks` of the `Chunks`. -The following `DocumentArrays` are emitted from the generator: + +When calling `da.traverse(['cm', 'ccm'])` you get a generator over two `DocumentArrays`. The first `DocumentArray` +contains the `Matches` of the `Chunks` and the second `DocumentArray` contains the `Matches` of the `Chunks` of +the `Chunks`. The following `DocumentArrays` are emitted from the generator: + ```python from jina import Document from jina.types.arrays import MatchArray @@ -861,15 +893,17 @@ from jina.types.arrays import MatchArray MatchArray([Document(id='c1c1m1', adjacency=1)], reference_doc=da['r1'].chunks['c1']) MatchArray([], reference_doc=da['r1'].chunks['c2']) MatchArray([], reference_doc=da['r1'].chunks['c3']) -MatchArray([Document(id='c2c1m1', adjacency=1),Document(id='c2c1m2', adjacency=1)], reference_doc=da['r1'].chunks['c2'].chunks['c2c1']) +MatchArray([Document(id='c2c1m1', adjacency=1), Document(id='c2c1m2', adjacency=1)], + reference_doc=da['r1'].chunks['c2'].chunks['c2c1']) MatchArray([], reference_doc=da['r1'].chunks['c2'].chunks['c2c2']) ``` -`DocumentArray.traverse_flat` is doing the same but flattens all `DocumentArrays` in the generator. -When calling `da.traverse_flat(['cm', 'ccm'])` the result in our example will be the following: +`DocumentArray.traverse_flat` is doing the same but flattens all `DocumentArrays` in the generator. When +calling `da.traverse_flat(['cm', 'ccm'])` the result in our example will be the following: ```python from jina import Document, DocumentArray + assert da.traverse_flat(['cm', 'ccm']) == DocumentArray([ Document(id='c1c1m1', adjacency=1), Document(id='c2c1m1', adjacency=1), @@ -877,11 +911,13 @@ assert da.traverse_flat(['cm', 'ccm']) == DocumentArray([ ]) ``` -`DocumentArray.traverse_flat_per_path` is a further method for `Document` traversal. -It works like `DocumentArray.traverse_flat` but groups the `Documents` into `DocumentArrays` based on the traversal path. -When calling `da.traverse_flat_per_path(['cm', 'ccm'])`, the resulting generator emits the following `DocumentArrays`: +`DocumentArray.traverse_flat_per_path` is a further method for `Document` traversal. It works +like `DocumentArray.traverse_flat` but groups the `Documents` into `DocumentArrays` based on the traversal path. When +calling `da.traverse_flat_per_path(['cm', 'ccm'])`, the resulting generator emits the following `DocumentArrays`: + ```python from jina import Document, DocumentArray + DocumentArray([ Document(id='c1c1m1', adjacency=1), ]) @@ -891,7 +927,6 @@ DocumentArray([ ]) ``` - ### Sort elements `DocumentArray` is a subclass of `MutableSequence`, therefore you can use built-in Python `sort` to sort elements in @@ -912,7 +947,7 @@ da.sort(key=lambda d: d.tags['id'], reverse=True) print(da) ``` -To sort elements in `da` in-place, using `tags[id]` value in a descending manner: +To sort elements in `da` in-place, using `tags[id]` value in a descending manner: ```text @@ -924,7 +959,8 @@ To sort elements in `da` in-place, using `tags[id]` value in a descending manner ### Filter elements -You can use Python's [built-in `filter()`](https://docs.python.org/3/library/functions.html#filter) to filter elements in a `DocumentArray` object: +You can use Python's [built-in `filter()`](https://docs.python.org/3/library/functions.html#filter) to filter elements +in a `DocumentArray` object: ```python from jina import DocumentArray, Document @@ -987,8 +1023,8 @@ for key, group in groups: ### Get attributes in bulk -`DocumentArray` implements powerful getters that lets you fetch multiple attributes from the Documents it contains -in one-shot: +`DocumentArray` implements powerful getters that lets you fetch multiple attributes from the Documents it contains in +one-shot: ```python import numpy as np @@ -1020,86 +1056,48 @@ np.stack(da.get_attributes('embedding')) [7 8 9]] ``` -### Access nested attributes from tags - -`Document` contains the `tags` field that can hold a map-like structure that can map arbitrary values. - -```python -from jina import Document - -doc = Document(tags={'dimensions': {'height': 5.0, 'weight': 10.0}}) - -doc.tags['dimensions'] -``` - -```text -{'weight': 10.0, 'height': 5.0} -``` - -In order to provide easy access to nested fields, the `Document` allows to access attributes by composing the attribute -qualified name with interlaced `__` symbols: - -```python -from jina import Document - -doc = Document(tags={'dimensions': {'height': 5.0, 'weight': 10.0}}) - -doc.tags__dimensions__weight -``` - -```text -10.0 -``` - -This also allows to access nested metadata attributes in `bulk` from a `DocumentArray`. - -```python -from jina import Document, DocumentArray - -da = DocumentArray([Document(tags={'dimensions': {'height': 5.0, 'weight': 10.0}}) for _ in range(10)]) - -da.get_attributes('tags__dimensions__height', 'tags__dimensions__weight') -``` - -```text -[[5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0], [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]] -``` ### Finding closest documents between `DocumentArray` objects -`DocumentArray` provides a`.match` function that finds the closest documents between two `DocumentArray` objects. This function requires all documents to be compared have an `embedding` and all embeddings to have the same length. +`DocumentArray` provides a`.match` function that finds the closest documents between two `DocumentArray` objects. This +function requires all documents to be compared have an `embedding` and all embeddings to have the same length. -The following image shows how `DocumentArrayA` finds `limit=5` matches from the documents in `DocumentArrayB`. By default, the cosine similarity is used to evaluate the score between documents. +The following image shows how `DocumentArrayA` finds `limit=5` matches from the documents in `DocumentArrayB`. By +default, the cosine similarity is used to evaluate the score between documents. ![match_illustration_5](../../images/match_illustration_5.svg) -More generally, given two `DocumentArray` objects `da_1` and `da_2` the function `da_1.match(da_2, metric=some_metric, normalization=(0, 1), limit=N)` finds for each document in `da_1` then `N` documents from `da_2` with the lowest metric values according to `some_metric`. +More generally, given two `DocumentArray` objects `da_1` and `da_2` the +function `da_1.match(da_2, metric=some_metric, normalization=(0, 1), limit=N)` finds for each document in `da_1` +then `N` documents from `da_2` with the lowest metric values according to `some_metric`. -- `metric` can be `'cosine'`, `'euclidean'`, `'sqeuclidean'` or a callable that takes 2 `ndarray` parameters and returns an `ndarray` -- `normalization` is a tuple [a, b] to be used with min-max normalization. The min distance will be rescaled to `a`, the max distance will be rescaled to `b`; all other values will be rescaled into range `[a, b]`. +- `metric` can be `'cosine'`, `'euclidean'`, `'sqeuclidean'` or a callable that takes 2 `ndarray` parameters and + returns an `ndarray` +- `normalization` is a tuple [a, b] to be used with min-max normalization. The min distance will be rescaled to `a`, the + max distance will be rescaled to `b`; all other values will be rescaled into range `[a, b]`. -The following example find the 3 closest documents, according to the euclidean distance, for each element in `da_1` from the elements in `da_2`. +The following example find the 3 closest documents, according to the euclidean distance, for each element in `da_1` from +the elements in `da_2`. ```python from jina import Document, DocumentArray import numpy as np -d1 = Document(embedding=np.array([0,0,0,0,1])) -d2 = Document(embedding=np.array([1,0,0,0,0])) -d3 = Document(embedding=np.array([1,1,1,1,0])) -d4 = Document(embedding=np.array([1,2,2,1,0])) +d1 = Document(embedding=np.array([0, 0, 0, 0, 1])) +d2 = Document(embedding=np.array([1, 0, 0, 0, 0])) +d3 = Document(embedding=np.array([1, 1, 1, 1, 0])) +d4 = Document(embedding=np.array([1, 2, 2, 1, 0])) -d1_m = Document(embedding=np.array([0,0.1,0,0,0])) -d2_m = Document(embedding=np.array([1,0.1,0,0,0])) -d3_m = Document(embedding=np.array([1,1.2,1,1,0])) -d4_m = Document(embedding=np.array([1,2.2,2,1,0])) -d5_m = Document(embedding=np.array([4,5.2,2,1,0])) +d1_m = Document(embedding=np.array([0, 0.1, 0, 0, 0])) +d2_m = Document(embedding=np.array([1, 0.1, 0, 0, 0])) +d3_m = Document(embedding=np.array([1, 1.2, 1, 1, 0])) +d4_m = Document(embedding=np.array([1, 2.2, 2, 1, 0])) +d5_m = Document(embedding=np.array([4, 5.2, 2, 1, 0])) da_1 = DocumentArray([d1, d2, d3, d4]) da_2 = DocumentArray([d1_m, d2_m, d3_m, d4_m, d5_m]) - -da_1.match(da_2, metric='euclidean', limit=3) +da_1.match(da_2, metric='euclidean', limit=3) query = da_1[2] print(f'query emb = {query.embedding}') for m in query.matches: @@ -1113,8 +1111,6 @@ match emb = [1. 2.2 2. 1. 0. ] score = 1.5620499849319458 match emb = [1. 0.1 0. 0. 0. ] score = 1.6763054132461548 ``` - - #### Using Sparse arrays as embeddings We can use sparse embeddings and do the `.match` using `is_sparse=True` @@ -1123,16 +1119,16 @@ We can use sparse embeddings and do the `.match` using `is_sparse=True` from jina import Document, DocumentArray import scipy.sparse as sp -d1 = Document(embedding=sp.csr_matrix([0,0,0,0,1])) -d2 = Document(embedding=sp.csr_matrix([1,0,0,0,0])) -d3 = Document(embedding=sp.csr_matrix([1,1,1,1,0])) -d4 = Document(embedding=sp.csr_matrix([1,2,2,1,0])) +d1 = Document(embedding=sp.csr_matrix([0, 0, 0, 0, 1])) +d2 = Document(embedding=sp.csr_matrix([1, 0, 0, 0, 0])) +d3 = Document(embedding=sp.csr_matrix([1, 1, 1, 1, 0])) +d4 = Document(embedding=sp.csr_matrix([1, 2, 2, 1, 0])) -d1_m = Document(embedding=sp.csr_matrix([0,0.1,0,0,0])) -d2_m = Document(embedding=sp.csr_matrix([1,0.1,0,0,0])) -d3_m = Document(embedding=sp.csr_matrix([1,1.2,1,1,0])) -d4_m = Document(embedding=sp.csr_matrix([1,2.2,2,1,0])) -d5_m = Document(embedding=sp.csr_matrix([4,5.2,2,1,0])) +d1_m = Document(embedding=sp.csr_matrix([0, 0.1, 0, 0, 0])) +d2_m = Document(embedding=sp.csr_matrix([1, 0.1, 0, 0, 0])) +d3_m = Document(embedding=sp.csr_matrix([1, 1.2, 1, 1, 0])) +d4_m = Document(embedding=sp.csr_matrix([1, 2.2, 2, 1, 0])) +d5_m = Document(embedding=sp.csr_matrix([4, 5.2, 2, 1, 0])) da_1 = DocumentArray([d1, d2, d3, d4]) da_2 = DocumentArray([d1_m, d2_m, d3_m, d4_m, d5_m]) @@ -1151,27 +1147,29 @@ match emb = [[1. 2.2 2. 1. 0. ]] score = 1.5620499849319458 match emb = [[1. 0.1 0. 0. 0. ]] score = 1.6763054132461548 ``` +### Filter a subset of `DocumentArray` using `.find` +`DocumentArray` provides function `.find` that finds the documents in the `DocumentArray` whose tag values match a +dictionary of user provided regular expressions. Since a `Document` can have many tags, the function expects one regular +expression for each tag that a user wants to consider. +The simplest way to use this function is to provide only the `regexes` dict. In this case, documents will be selected if +all regular expressions passed are matched. Sometimes, a user might want to be less restrictive and might want to select +documents only if a subset of the regular expressions is verified. In this case, `threshold` can be used to set the +number of regular expressions that need to be matched in order to select a document. - -### Selecting a subset of Documents from a `DocumentArray` using `.find` - -`DocumentArray` provides function `.find` that finds the documents in the `DocumentArray` whose tag values match a dictionary of user provided regular expressions. Since a `Document` can have many tags, the function expects one regular expression for each tag that a user wants to consider. - -The simplest way to use this function is to provide only the `regexes` dict. In this case, documents will be selected if all regular expressions passed are matched. Sometimes, a user might want to be less restrictive and might want to select documents only if a subset of the regular expressions is verified. In this case, `threshold` can be used to set the number of regular expressions that need to be matched in order to select a document. - -Let us consider the following example, where we want to select documents form a `DocumentArray` if the `city` tag contains a city that starts with `'B'`. +Let us consider the following example, where we want to select documents form a `DocumentArray` if the `city` tag +contains a city that starts with `'B'`. ```python -d1 = Document(tags={'city': 'Barcelona', 'phone':'None'}) -d2 = Document(tags={'city': 'Berlin','phone':'648907348'}) +d1 = Document(tags={'city': 'Barcelona', 'phone': 'None'}) +d2 = Document(tags={'city': 'Berlin', 'phone': '648907348'}) d3 = Document(tags={'city': 'Paris', 'phone': 'None'}) d4 = Document(tags={'city': 'Brussels', 'phone': 'None'}) docarray = DocumentArray([d1, d2, d3, d4]) -regexes = {'city':r'B.*'} +regexes = {'city': r'B.*'} docarray_filtered = docarray.find(regexes=regexes) print(f'len(docarray_filtered)={len(docarray_filtered)}') for d in docarray_filtered: @@ -1181,16 +1179,17 @@ for d in docarray_filtered: Will print ```python -len(docarray_filtered)=3 -dict(d.tags)={'phone': 'None', 'city': 'Barcelona'} -dict(d.tags)={'phone': '648907348', 'city': 'Berlin'} -dict(d.tags)={'phone': 'None', 'city': 'Brussels'} +len(docarray_filtered) = 3 +dict(d.tags) = {'phone': 'None', 'city': 'Barcelona'} +dict(d.tags) = {'phone': '648907348', 'city': 'Berlin'} +dict(d.tags) = {'phone': 'None', 'city': 'Brussels'} ``` -We can consider more conditions, for example a subset of the previous documents that have `None` in the `'phone'` tag. We could do this as follows: +We can consider more conditions, for example a subset of the previous documents that have `None` in the `'phone'` tag. +We could do this as follows: ```python -regexes = {'city':r'B.*', 'phone':'None' } +regexes = {'city': r'B.*', 'phone': 'None'} docarray_filtered = docarray.find(regexes=regexes) print(f'len(docarray_filtered)={len(docarray_filtered)}') for d in docarray_filtered: @@ -1205,11 +1204,12 @@ dict(d.tags)={'city': 'Barcelona', 'phone': 'None'} dict(d.tags)={'phone': 'None', 'city': 'Brussels'} ``` -### Random sample a subset of Documents from a `DocumentArray` using `sample` +### Sample a subset of `DocumentArray` using `sample` -`DocumentArray` provides function `.sample` that sample `k` elements without replacement. -It accepts 2 parameters, `k` and `seed`. `k` is used to define the number of elements to sample, and `seed` -helps you generate pseudo random results. It should be noted that `k` should always less or equal than the length of the document array. +`DocumentArray` provides function `.sample` that sample `k` elements without replacement. It accepts 2 parameters, `k` +and `seed`. `k` is used to define the number of elements to sample, and `seed` +helps you generate pseudo random results. It should be noted that `k` should always less or equal than the length of the +document array. To make use of the function: @@ -1223,10 +1223,10 @@ sampled_da = da.sample(k=10) # sample 10 documents sampled_da_with_seed = da.sample(k=10, seed=1) # sample 10 documents with seed. ``` -### Shuffle the `DocumentArray` using `shuffle` +### Shuffle a `DocumentArray` using `shuffle` -`DocumentArray` provides function `.shuffle` that shuffle the entire `DocumentArray`. -It accepts the parameter `seed`. `seed` helps you generate pseudo random results. By default, `seed` is None. +`DocumentArray` provides function `.shuffle` that shuffle the entire `DocumentArray`. It accepts the parameter `seed` +. `seed` helps you generate pseudo random results. By default, `seed` is None. To make use of the function: @@ -1240,22 +1240,24 @@ shuffled_da = da.shuffle() # shuffle the DocumentArray shuffled_da_with_seed = da.shuffle(seed=1) # shuffle the DocumentArray with seed. ``` -### Visualize embeddings +### Visualize the embeddings of a `DocumentArray` -`DocumentArray` provides function `.visualize` to plot document embeddings in a 2D graph. `visualize` supports 2 methods to project in 2D space: `pca` and `tsne`. +`DocumentArray` provides function `.visualize` to plot document embeddings in a 2D graph. `visualize` supports 2 methods +to project in 2D space: `pca` and `tsne`. In the following example, we add 3 different distributions of embeddings and see 3 kinds of point clouds in the graph. + ```python import numpy as np from jina import Document, DocumentArray da = DocumentArray( [ - Document(embedding = np.random.normal(0, 1, 50)) for _ in range(500) + Document(embedding=np.random.normal(0, 1, 50)) for _ in range(500) ] + [ - Document(embedding = np.random.normal(5, 2, 50)) for _ in range(500) + Document(embedding=np.random.normal(5, 2, 50)) for _ in range(500) ] + [ - Document(embedding = np.random.normal(2, 5, 50)) for _ in range(500) + Document(embedding=np.random.normal(2, 5, 50)) for _ in range(500) ] ) da.visualize() @@ -1266,7 +1268,11 @@ da.visualize() ## `DocumentArrayMemmap` API -When your `DocumentArray` object contains a large number of `Document`, holding it in memory can be very demanding. You may want to use `DocumentArrayMemmap` to alleviate this issue. A `DocumentArrayMemmap` stores all Documents directly on the disk, while only keeps a small lookup table in memory. This lookup table contains the offset and length of each `Document`, hence it is much smaller than the full `DocumentArray`. Elements are loaded on-demand to memory during the access. +When your `DocumentArray` object contains a large number of `Document`, holding it in memory can be very demanding. You +may want to use `DocumentArrayMemmap` to alleviate this issue. A `DocumentArrayMemmap` stores all Documents directly on +the disk, while only keeps a small lookup table in memory. This lookup table contains the offset and length of +each `Document`, hence it is much smaller than the full `DocumentArray`. Elements are loaded on-demand to memory during +the access. The next table show the speed and memory consumption when writing and reading 50,000 `Documents`. @@ -1277,7 +1283,7 @@ The next table show the speed and memory consumption when writing and reading 50 |Memory usage | 20MB | 342MB | |Disk storage | 14.3MB | 12.6MB | -### Create `DocumentArrayMemmap` object +### Create `DocumentArrayMemmap` ```python from jina.types.arrays.memmap import DocumentArrayMemmap @@ -1285,7 +1291,7 @@ from jina.types.arrays.memmap import DocumentArrayMemmap dam = DocumentArrayMemmap('./my-memmap') ``` -### Add Documents to `DocumentArrayMemmap` object +### Add Documents to `DocumentArrayMemmap` ```python from jina.types.arrays.memmap import DocumentArrayMemmap @@ -1298,19 +1304,22 @@ dam = DocumentArrayMemmap('./my-memmap') dam.extend([d1, d2]) ``` -The `dam` object stores all future Documents into `./my-memmap`, there is no need to manually call `save`/`load`. In fact, `save`/`load` methods are not available in `DocumentArrayMemmap`. +The `dam` object stores all future Documents into `./my-memmap`, there is no need to manually call `save`/`load`. In +fact, `save`/`load` methods are not available in `DocumentArrayMemmap`. -### Clear a `DocumentArrayMemmap` object +### Clear a `DocumentArrayMemmap` To clear all contents in a `DocumentArrayMemmap` object, simply call `.clear()`. It will clean all content on disk. #### Pruning -One may notice another method `.prune()` that shares similar semantics. `.prune()` method is designed for "post-optimizing" the on-disk data structure of `DocumentArrayMemmap` object. It can reduce the on-disk usage. +One may notice another method `.prune()` that shares similar semantics. `.prune()` method is designed for " +post-optimizing" the on-disk data structure of `DocumentArrayMemmap` object. It can reduce the on-disk usage. ### Mutable sequence with "read-only" elements -The biggest caveat in `DocumentArrayMemmap` is that you can **not** modify element's attribute inplace. Though the `DocumentArrayMemmap` is mutable, each of its element is not. For example: +The biggest caveat in `DocumentArrayMemmap` is that you can **not** modify element's attribute inplace. Though +the `DocumentArrayMemmap` is mutable, each of its element is not. For example: ```python from jina.types.arrays.memmap import DocumentArrayMemmap @@ -1358,7 +1367,9 @@ for d in dam: ### Side-by-side vs. `DocumentArray` -Accessing elements in `DocumentArrayMemmap` is _almost_ the same as `DocumentArray`, you can use integer/string index to access element; you can loop over a `DocumentArrayMemmap` to get all `Document`; you can use `get_attributes` or `traverse_flat` to achieve advanced traversal or getter. +Accessing elements in `DocumentArrayMemmap` is _almost_ the same as `DocumentArray`, you can use integer/string index to +access element; you can loop over a `DocumentArrayMemmap` to get all `Document`; you can use `get_attributes` +or `traverse_flat` to achieve advanced traversal or getter. This table summarizes the interfaces of `DocumentArrayMemmap` and `DocumentArray`: @@ -1401,10 +1412,12 @@ dam.extend(da) da = DocumentArray(dam) ``` - ### Maintaining consistency via `.reload()` -Considering two `DocumentArrayMemmap` objects that share the same on-disk storage `./memmap` but sit in different processes/threads. After some writing ops, the consistency of the lookup table may be corrupted, as each `DocumentArrayMemmap` object has its own version of lookup table in memory. `.reload()` method is for solving this issue: +Considering two `DocumentArrayMemmap` objects that share the same on-disk storage `./memmap` but sit in different +processes/threads. After some writing ops, the consistency of the lookup table may be corrupted, as +each `DocumentArrayMemmap` object has its own version of lookup table in memory. `.reload()` method is for solving this +issue: ```python from jina.types.arrays.memmap import DocumentArrayMemmap