diff --git a/docs/Makefile b/docs/Makefile index f8d5d96714d..107bb1feb4d 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -60,6 +60,7 @@ copy-infra: cp package.json $(OUTPUT_NEW_DIR) cp sidebars.js $(OUTPUT_NEW_DIR) cp -r static $(OUTPUT_NEW_DIR) + cp -r ../libs/cli/langchain_cli/integration_template $(OUTPUT_NEW_DIR)/src/theme cp yarn.lock $(OUTPUT_NEW_DIR) render: @@ -81,6 +82,7 @@ build: install-py-deps generate-files copy-infra render md-sync append-related vercel-build: install-vercel-deps build generate-references rm -rf docs mv $(OUTPUT_NEW_DOCS_DIR) docs + cp -r ../libs/cli/langchain_cli/integration_template src/theme rm -rf build mkdir static/api_reference git clone --depth=1 https://github.com/langchain-ai/langchain-api-docs-html.git diff --git a/docs/docs/contributing/how_to/integrations/package.mdx b/docs/docs/contributing/how_to/integrations/package.mdx index 2b5bb97865e..d016ff1bed4 100644 --- a/docs/docs/contributing/how_to/integrations/package.mdx +++ b/docs/docs/contributing/how_to/integrations/package.mdx @@ -26,81 +26,193 @@ Examples include [chat models](/docs/concepts/chat_models/), Your integration package will typically implement a subclass of at least one of these components. Expand the tabs below to see details on each. -
- Chat models +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import CodeBlock from '@theme/CodeBlock'; -Refer to the [Custom Chat Model Guide](/docs/how_to/custom_chat_model) guide for -detail on a starter chat model [implementation](/docs/how_to/custom_chat_model/#implementation). + -:::tip + + + Refer to the [Custom Chat Model Guide](/docs/how_to/custom_chat_model) guide for + detail on a starter chat model [implementation](/docs/how_to/custom_chat_model/#implementation). -The model from the [Custom Chat Model Guide](/docs/how_to/custom_chat_model) is tested -against the standard unit and integration tests in the LangChain Github repository. -You can also access that implementation directly from Github -[here](https://github.com/langchain-ai/langchain/blob/master/libs/standard-tests/tests/unit_tests/custom_chat_model.py). + :::tip -::: + The model from the [Custom Chat Model Guide](/docs/how_to/custom_chat_model) is tested + against the standard unit and integration tests in the LangChain Github repository. + You can also access that implementation directly from Github + [here](https://github.com/langchain-ai/langchain/blob/master/libs/standard-tests/tests/unit_tests/custom_chat_model.py). -
+ ::: -
- Vector stores +
+ Example chat model code -Your vector store implementation will depend on your chosen database technology. -`langchain-core` includes a minimal -[in-memory vector store](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html) -that we can use as a guide. You can access the code [here](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/vectorstores/in_memory.py). +import ChatModelSource from '../../../../src/theme/integration_template/integration_template/chat_models.py'; -All vector stores must inherit from the [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) -base class. This interface consists of methods for writing, deleting and searching -for documents in the vector store. + + { + ChatModelSource.replaceAll('__ModuleName__', 'ParrotLink') + .replaceAll('__package_name__', 'langchain-parrot-link') + .replaceAll('__MODULE_NAME__', 'PARROT_LINK') + .replaceAll('__module_name__', 'langchain_parrot_link') + } + -`VectorStore` supports a variety of synchronous and asynchronous search types (e.g., -nearest-neighbor or maximum marginal relevance), as well as interfaces for adding -documents to the store. See the [API Reference](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) -for all supported methods. The required methods are tabulated below: +
+ + + + + Your vector store implementation will depend on your chosen database technology. + `langchain-core` includes a minimal + [in-memory vector store](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html) + that we can use as a guide. You can access the code [here](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/vectorstores/in_memory.py). + + All vector stores must inherit from the [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) + base class. This interface consists of methods for writing, deleting and searching + for documents in the vector store. + + `VectorStore` supports a variety of synchronous and asynchronous search types (e.g., + nearest-neighbor or maximum marginal relevance), as well as interfaces for adding + documents to the store. See the [API Reference](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) + for all supported methods. The required methods are tabulated below: + + | Method/Property | Description | + |------------------------ |------------------------------------------------------| + | `add_documents` | Add documents to the vector store. | + | `delete` | Delete selected documents from vector store (by IDs) | + | `get_by_ids` | Get selected documents from vector store (by IDs) | + | `similarity_search` | Get documents most similar to a query. | + | `embeddings` (property) | Embeddings object for vector store. | + | `from_texts` | Instantiate vector store via adding texts. | + + Note that `InMemoryVectorStore` implements some optional search types, as well as + convenience methods for loading and dumping the object to a file, but this is not + necessary for all implementations. + + :::tip + + The [in-memory vector store](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/vectorstores/in_memory.py) + is tested against the standard tests in the LangChain Github repository. + + ::: + +
+ Example vector store code + +import VectorstoreSource from '../../../../src/theme/integration_template/integration_template/vectorstores.py'; + + + { + VectorstoreSource.replaceAll('__ModuleName__', 'ParrotLink') + .replaceAll('__package_name__', 'langchain-parrot-link') + .replaceAll('__MODULE_NAME__', 'PARROT_LINK') + .replaceAll('__module_name__', 'langchain_parrot_link') + } + + +
+ + + + +
+ Example embeddings code + +Embeddings are used to convert `str` objects from `Document.page_content` fields +into a vector representation (represented as a list of floats). + +The `Embeddings` class must inherit from the [Embeddings](https://python.langchain.com/api_reference/core/embeddings/langchain_core.embeddings.embeddings.Embeddings.html#langchain_core.embeddings.embeddings.Embeddings) +base class. This interface has 5 methods that can be implemented. | Method/Property | Description | |------------------------ |------------------------------------------------------| -| `add_documents` | Add documents to the vector store. | -| `delete` | Delete selected documents from vector store (by IDs) | -| `get_by_ids` | Get selected documents from vector store (by IDs) | -| `similarity_search` | Get documents most similar to a query. | -| `embeddings` (property) | Embeddings object for vector store. | -| `from_texts` | Instantiate vector store via adding texts. | +| `__init__` | Initialize the embeddings object. (optional) | +| `embed_query` | Embed a list of texts. (required) | +| `embed_documents` | Embed a list of documents. (required) | +| `aembed_query` | Asynchronously embed a list of texts. (optional) | +| `aembed_documents` | Asynchronously embed a list of documents. (optional) | -Note that `InMemoryVectorStore` implements some optional search types, as well as -convenience methods for loading and dumping the object to a file, but this is not -necessary for all implementations. +### Constructor -:::tip +The `__init__` constructor is optional but common, but can be used to set up any necessary attributes +that a user can pass in when initializing the embeddings object. Common attributes include -The [in-memory vector store](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/vectorstores/in_memory.py) -is tested against the standard tests in the LangChain Github repository. +- `model` - the id of the model to use for embeddings -::: +### Embedding queries vs documents -
+The `embed_query` and `embed_documents` methods are required. These methods both operate +on string inputs (the accessing of `Document.page_content` attributes) is handled +by the VectorStore using the embedding model for legacy reasons. - +
+ + + + +
+ Example tool code + +import ToolSource from '/src/theme/integration_template/integration_template/tools.py'; + + + { + ToolSource.replaceAll('__ModuleName__', 'ParrotLink') + .replaceAll('__package_name__', 'langchain-parrot-link') + .replaceAll('__MODULE_NAME__', 'PARROT_LINK') + .replaceAll('__module_name__', 'langchain_parrot_link') + } + + +
+ + + + +
+ Example retriever code + +import RetrieverSource from '/src/theme/integration_template/integration_template/retrievers.py'; + + + { + RetrieverSource.replaceAll('__ModuleName__', 'ParrotLink') + .replaceAll('__package_name__', 'langchain-parrot-link') + .replaceAll('__MODULE_NAME__', 'PARROT_LINK') + .replaceAll('__module_name__', 'langchain_parrot_link') + } + + +
+ + + + +--- ## (Optional) bootstrapping a new integration package diff --git a/docs/package.json b/docs/package.json index b82fda1c4c0..b02d589e268 100644 --- a/docs/package.json +++ b/docs/package.json @@ -35,6 +35,7 @@ "json-loader": "^0.5.7", "prism-react-renderer": "^2.1.0", "process": "^0.11.10", + "raw-loader": "^4.0.2", "react": "^18", "react-dom": "^18", "typescript": "^5.2.2", diff --git a/docs/yarn.lock b/docs/yarn.lock index 94436d8e058..aad5a86bb52 100644 --- a/docs/yarn.lock +++ b/docs/yarn.lock @@ -9043,6 +9043,14 @@ raw-body@2.5.2: iconv-lite "0.4.24" unpipe "1.0.0" +raw-loader@^4.0.2: + version "4.0.2" + resolved "https://registry.yarnpkg.com/raw-loader/-/raw-loader-4.0.2.tgz#1aac6b7d1ad1501e66efdac1522c73e59a584eb6" + integrity sha512-ZnScIV3ag9A4wPX/ZayxL/jZH+euYb6FcUinPcgiQW0+UBtEv0O6Q3lGd3cqJ+GHH+rksEv3Pj99oxJ3u3VIKA== + dependencies: + loader-utils "^2.0.0" + schema-utils "^3.0.0" + rc@1.2.8: version "1.2.8" resolved "https://registry.yarnpkg.com/rc/-/rc-1.2.8.tgz#cd924bf5200a075b83c188cd6b9e211b7fc0d3ed" diff --git a/libs/standard-tests/poetry.lock b/libs/standard-tests/poetry.lock index 0bb01c5870e..55d58b9c2d6 100644 --- a/libs/standard-tests/poetry.lock +++ b/libs/standard-tests/poetry.lock @@ -13,24 +13,24 @@ files = [ [[package]] name = "anyio" -version = "4.6.2.post1" +version = "4.7.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.9" files = [ - 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