mirror of
https://github.com/hwchase17/langchain.git
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docs: index pages, sidebars (#25316)
This commit is contained in:
Erick Friis
GitHub
parent
0478f7f5e4
commit
d5b548b4ce
@ -41,12 +41,8 @@ generate-files:
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cp -r $(SOURCE_DIR)/* $(INTERMEDIATE_DIR)
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mkdir -p $(INTERMEDIATE_DIR)/templates
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$(PYTHON) scripts/model_feat_table.py $(INTERMEDIATE_DIR)
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$(PYTHON) scripts/tool_feat_table.py $(INTERMEDIATE_DIR)
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$(PYTHON) scripts/document_loader_feat_table.py $(INTERMEDIATE_DIR)
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$(PYTHON) scripts/kv_store_feat_table.py $(INTERMEDIATE_DIR)
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$(PYTHON) scripts/partner_pkg_table.py $(INTERMEDIATE_DIR)
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@ -86,7 +82,7 @@ vercel-build: install-vercel-deps build generate-references
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rm -rf docs
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mv $(OUTPUT_NEW_DOCS_DIR) docs
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rm -rf build
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yarn run docusaurus build
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NODE_OPTIONS="--max-old-space-size=5000" yarn run docusaurus build
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mv build v0.2
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mkdir build
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mv v0.2 build
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30
docs/docs/integrations/chat/index.mdx
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30
docs/docs/integrations/chat/index.mdx
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@ -0,0 +1,30 @@
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---
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sidebar_position: 0
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sidebar_class_name: hidden
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keywords: [compatibility]
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---
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# Chat models
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:::info
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If you'd like to write your own chat model, see [this how-to](/docs/how_to/custom_chat_model/).
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If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
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:::
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## Featured Providers
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:::info
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While all these LangChain classes support the indicated advanced feature, you may have
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to open the provider-specific documentation to learn which hosted models or backends support
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the feature.
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:::
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import { CategoryTable, IndexTable } from "@theme/FeatureTables";
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<CategoryTable category="chat" />
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## All chat models
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<IndexTable />
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12
docs/docs/integrations/document_loaders/index.mdx
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12
docs/docs/integrations/document_loaders/index.mdx
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@ -0,0 +1,12 @@
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---
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sidebar_position: 0
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sidebar_class_name: hidden
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---
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# Document loaders
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import { CategoryTable, IndexTable } from "@theme/FeatureTables";
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## All document loaders
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<IndexTable />
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30
docs/docs/integrations/llms/index.mdx
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30
docs/docs/integrations/llms/index.mdx
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@ -0,0 +1,30 @@
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---
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sidebar_position: 0
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sidebar_class_name: hidden
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keywords: [compatibility]
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---
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# LLMs
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:::info
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If you'd like to write your own LLM, see [this how-to](/docs/how_to/custom_llm/).
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If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
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:::
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## Features (natively supported)
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All LLMs implement the Runnable interface, which comes with default implementations of all methods, ie. `ainvoke`, `batch`, `abatch`, `stream`, `astream`. This gives all LLMs basic support for async, streaming and batch, which by default is implemented as below:
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- *Async* support defaults to calling the respective sync method in asyncio's default thread pool executor. This lets other async functions in your application make progress while the LLM is being executed, by moving this call to a background thread.
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- *Streaming* support defaults to returning an `Iterator` (or `AsyncIterator` in the case of async streaming) of a single value, the final result returned by the underlying LLM provider. This obviously doesn't give you token-by-token streaming, which requires native support from the LLM provider, but ensures your code that expects an iterator of tokens can work for any of our LLM integrations.
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- *Batch* support defaults to calling the underlying LLM in parallel for each input by making use of a thread pool executor (in the sync batch case) or `asyncio.gather` (in the async batch case). The concurrency can be controlled with the `max_concurrency` key in `RunnableConfig`.
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Each LLM integration can optionally provide native implementations for async, streaming or batch, which, for providers that support it, can be more efficient. The table shows, for each integration, which features have been implemented with native support.
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import { CategoryTable, IndexTable } from "@theme/FeatureTables";
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<CategoryTable category="llms" />
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## All LLMs
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<IndexTable />
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@ -1,7 +1,7 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
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"id": "00a924a0-57e2-43fa-95dc-3ea48a56d3a5",
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"metadata": {},
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"source": [
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@ -17,8 +17,6 @@
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"source": [
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"# ArxivRetriever\n",
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"\n",
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"## Overview\n",
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"\n",
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">[arXiv](https://arxiv.org/) is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.\n",
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"\n",
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"This notebook shows how to retrieve scientific articles from Arxiv.org into the [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n",
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@ -27,9 +25,9 @@
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"\n",
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"### Integration details\n",
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"\n",
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"| Retriever | Source | Package |\n",
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"| :--- | :--- | :---: |\n",
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"[ArxivRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html) | Scholarly articles on [arxiv.org](https://arxiv.org/) | langchain_community |\n",
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"import {ItemTable} from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"external_retrievers\" item=\"ArxivRetriever\" />\n",
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"\n",
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"## Setup\n",
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"\n",
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@ -1,7 +1,7 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
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"id": "f9a62e19-b00b-4f6c-a700-1e500e4c290a",
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"metadata": {},
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"source": [
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@ -17,7 +17,6 @@
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"source": [
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"# AzureAISearchRetriever\n",
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"\n",
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"## Overview\n",
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"[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Cognitive Search`) is a Microsoft cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale.\n",
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"\n",
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"`AzureAISearchRetriever` is an integration module that returns documents from an unstructured query. It's based on the BaseRetriever class and it targets the 2023-11-01 stable REST API version of Azure AI Search, which means it supports vector indexing and queries.\n",
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@ -28,9 +27,9 @@
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"\n",
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"### Integration details\n",
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"\n",
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"| Retriever | Self-host | Cloud offering | Package |\n",
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"| :--- | :--- | :---: | :---: |\n",
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"[AzureAISearchRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html) | ❌ | ✅ | langchain_community |\n",
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"import {ItemTable} from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"document_retrievers\" item=\"AzureAISearchRetriever\" />\n",
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"\n",
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"\n",
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"## Setup\n",
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@ -1,7 +1,7 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
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"id": "b0872249-1af5-4d54-b816-1babad7a8c9e",
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"metadata": {},
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"source": [
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@ -17,8 +17,6 @@
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"source": [
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"# Bedrock (Knowledge Bases) Retriever\n",
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"\n",
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"## Overview\n",
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"\n",
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"This guide will help you getting started with the AWS Knowledge Bases [retriever](/docs/concepts/#retrievers).\n",
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"\n",
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"[Knowledge Bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.\n",
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@ -29,9 +27,9 @@
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"\n",
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"### Integration details\n",
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"\n",
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"| Retriever | Self-host | Cloud offering | Package |\n",
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"| :--- | :--- | :---: | :---: |\n",
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"[AmazonKnowledgeBasesRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html) | ❌ | ✅ | langchain_aws |\n"
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"import {ItemTable} from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"document_retrievers\" item=\"AmazonKnowledgeBasesRetriever\" />\n"
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]
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},
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{
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@ -1,7 +1,7 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
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"id": "41ccce84-f6d9-4ba0-8281-22cbf29f20d3",
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"metadata": {},
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"source": [
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@ -17,7 +17,6 @@
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"source": [
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"# ElasticsearchRetriever\n",
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"\n",
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"## Overview\n",
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">[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. It supports keyword search, vector search, hybrid search and complex filtering.\n",
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"\n",
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"The `ElasticsearchRetriever` is a generic wrapper to enable flexible access to all `Elasticsearch` features through the [Query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html). For most use cases the other classes (`ElasticsearchStore`, `ElasticsearchEmbeddings`, etc.) should suffice, but if they don't you can use `ElasticsearchRetriever`.\n",
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@ -26,9 +25,9 @@
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"\n",
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"### Integration details\n",
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"\n",
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"| Retriever | Self-host | Cloud offering | Package |\n",
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"| :--- | :--- | :---: | :---: |\n",
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"[ElasticsearchRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html) | ✅ | ✅ | langchain_elasticsearch |\n",
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"import {ItemTable} from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"document_retrievers\" item=\"ElasticsearchRetriever\" />\n",
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"\n",
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"\n",
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"## Setup\n",
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@ -1,7 +1,7 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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@ -15,8 +15,6 @@
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"source": [
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"# Google Vertex AI Search\n",
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"\n",
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"## Overview\n",
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"\n",
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">[Google Vertex AI Search](https://cloud.google.com/enterprise-search) (formerly known as `Enterprise Search` on `Generative AI App Builder`) is a part of the [Vertex AI](https://cloud.google.com/vertex-ai) machine learning platform offered by `Google Cloud`.\n",
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">\n",
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">`Vertex AI Search` lets organizations quickly build generative AI-powered search engines for customers and employees. It's underpinned by a variety of `Google Search` technologies, including semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user’s query input. Vertex AI Search also benefits from Google’s expertise in understanding how users search and factors in content relevance to order displayed results.\n",
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@ -29,9 +27,9 @@
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"\n",
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"### Integration details\n",
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"\n",
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"| Retriever | Self-host | Cloud offering | Package |\n",
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"| :--- | :--- | :---: | :---: |\n",
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"[VertexAISearchRetriever](https://api.python.langchain.com/en/latest/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html) | ❌ | ✅ | langchain_google_community |\n",
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"import {ItemTable} from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"document_retrievers\" item=\"VertexAISearchRetriever\" />\n",
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"\n",
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"\n",
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"## Setup\n",
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@ -3,6 +3,8 @@ sidebar_position: 0
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sidebar_class_name: hidden
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---
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import {CategoryTable, IndexTable} from '@theme/FeatureTables'
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# Retrievers
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A [retriever](/docs/concepts/#retrievers) is an interface that returns documents given an unstructured query.
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@ -22,20 +24,14 @@ This page lists custom retrievers, implemented via subclassing [BaseRetriever](/
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The below retrievers allow you to index and search a custom corpus of documents.
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| Retriever | Self-host | Cloud offering | Package |
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|-----------|-----------|----------------|---------|
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| [AmazonKnowledgeBasesRetriever](/docs/integrations/retrievers/bedrock) | ❌ | ✅ | [langchain_aws](https://api.python.langchain.com/en/latest/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html) |
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| [AzureAISearchRetriever](/docs/integrations/retrievers/azure_ai_search) | ❌ | ✅ | [langchain_community](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html) |
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| [ElasticsearchRetriever](/docs/integrations/retrievers/elasticsearch_retriever) | ✅ | ✅ | [langchain_elasticsearch](https://api.python.langchain.com/en/latest/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html) |
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| [MilvusCollectionHybridSearchRetriever](/docs/integrations/retrievers/milvus_hybrid_search) | ✅ | ❌ | [langchain_milvus](https://api.python.langchain.com/en/latest/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html) |
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| [VertexAISearchRetriever](/docs/integrations/retrievers/google_vertex_ai_search) | ❌ | ✅ | [langchain_google_community](https://api.python.langchain.com/en/latest/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html) |
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<CategoryTable category="document_retrievers" />
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## External index
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The below retrievers will search over an external index (e.g., constructed from Internet data or similar).
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| Retriever | Source | Package |
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|-----------|--------|---------|
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| [ArxivRetriever](/docs/integrations/retrievers/arxiv) | Scholarly articles on [arxiv.org](https://arxiv.org/) | [langchain_community](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html) |
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| [TavilySearchAPIRetriever](/docs/integrations/retrievers/tavily) | Internet search | [langchain_community](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html) |
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| [WikipediaRetriever](/docs/integrations/retrievers/wikipedia) | [Wikipedia](https://www.wikipedia.org/) articles | [langchain_community](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html) |
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<CategoryTable category="external_retrievers" />
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## All retrievers
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<IndexTable />
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@ -1,7 +1,7 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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@ -15,8 +15,6 @@
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"source": [
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"# Milvus Hybrid Search Retriever\n",
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"\n",
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"## Overview\n",
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"\n",
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"> [Milvus](https://milvus.io/docs) is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.\n",
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"\n",
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||||
"This will help you getting started with the Milvus Hybrid Search [retriever](/docs/concepts/#retrievers), which combines the strengths of both dense and sparse vector search. For detailed documentation of all `MilvusCollectionHybridSearchRetriever` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html).\n",
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@ -25,11 +23,9 @@
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"\n",
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"### Integration details\n",
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"\n",
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"| Retriever | Self-host | Cloud offering | Package |\n",
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"| :--- | :--- | :---: | :---: |\n",
|
||||
"[MilvusCollectionHybridSearchRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html) | ✅ | ❌ | langchain_milvus |\n",
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"\n",
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"import {ItemTable} from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"document_retrievers\" item=\"MilvusCollectionHybridSearchRetriever\" />\n",
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"\n",
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"## Setup\n",
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"\n",
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@ -1,7 +1,7 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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@ -15,16 +15,15 @@
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"source": [
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"# TavilySearchAPIRetriever\n",
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"\n",
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"## Overview\n",
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">[Tavily's Search API](https://tavily.com) is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.\n",
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"\n",
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"We can use this as a [retriever](/docs/how_to#retrievers). It will show functionality specific to this integration. After going through, it may be useful to explore [relevant use-case pages](/docs/how_to#qa-with-rag) to learn how to use this vectorstore as part of a larger chain.\n",
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"\n",
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"### Integration details\n",
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"\n",
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"| Retriever | Source | Package |\n",
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"| :--- | :--- | :---: |\n",
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||||
"[TavilySearchAPIRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html) | Internet search | langchain_community |\n",
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"import {ItemTable} from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"external_retrievers\" item=\"TavilySearchAPIRetriever\" />\n",
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"\n",
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||||
"## Setup"
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]
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@ -1,7 +1,7 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
|
||||
"id": "62727aaa-bcff-4087-891c-e539f824ee1f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
@ -24,9 +24,9 @@
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
"| Retriever | Source | Package |\n",
|
||||
"| :--- | :--- | :---: |\n",
|
||||
"[WikipediaRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html) | [Wikipedia](https://www.wikipedia.org/) articles | langchain_community |"
|
||||
"import {ItemTable} from \"@theme/FeatureTables\";\n",
|
||||
"\n",
|
||||
"<ItemTable category=\"external_retrievers\" item=\"WikipediaRetriever\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -5,110 +5,10 @@ sidebar_class_name: hidden
|
||||
|
||||
# Embedding models
|
||||
|
||||
**Embedding model** classes are implemented by inheriting the [Embeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_core.embeddings.Embeddings.html) class.
|
||||
import { CategoryTable, IndexTable } from "@theme/FeatureTables";
|
||||
|
||||
This table lists all 100 derived classes.
|
||||
<CategoryTable category="text_embedding" />
|
||||
|
||||
## All embedding models
|
||||
|
||||
| Namespace 🔻 | Class |
|
||||
|------------|---------|
|
||||
| langchain.chains.hyde.base | [HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html) |
|
||||
| langchain.embeddings.cache | [CacheBackedEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain.embeddings.cache.CacheBackedEmbeddings.html) |
|
||||
| langchain_ai21.embeddings | [AI21Embeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_ai21.embeddings.AI21Embeddings.html) |
|
||||
| langchain_aws.embeddings.bedrock | [BedrockEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_aws.embeddings.bedrock.BedrockEmbeddings.html) |
|
||||
| langchain_cohere.embeddings | [CohereEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html) |
|
||||
| langchain_community.embeddings.aleph_alpha | [AlephAlphaAsymmetricSemanticEmbedding](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding.html) |
|
||||
| langchain_community.embeddings.aleph_alpha | [AlephAlphaSymmetricSemanticEmbedding](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.aleph_alpha.AlephAlphaSymmetricSemanticEmbedding.html) |
|
||||
| langchain_community.embeddings.anyscale | [AnyscaleEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.anyscale.AnyscaleEmbeddings.html) |
|
||||
| langchain_community.embeddings.awa | [AwaEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.awa.AwaEmbeddings.html) |
|
||||
| langchain_community.embeddings.azure_openai | [AzureOpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.azure_openai.AzureOpenAIEmbeddings.html) |
|
||||
| langchain_community.embeddings.baichuan | [BaichuanTextEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.baichuan.BaichuanTextEmbeddings.html) |
|
||||
| langchain_community.embeddings.baidu_qianfan_endpoint | [QianfanEmbeddingsEndpoint](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.baidu_qianfan_endpoint.QianfanEmbeddingsEndpoint.html) |
|
||||
| langchain_community.embeddings.bedrock | [BedrockEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.bedrock.BedrockEmbeddings.html) |
|
||||
| langchain_community.embeddings.bookend | [BookendEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.bookend.BookendEmbeddings.html) |
|
||||
| langchain_community.embeddings.clarifai | [ClarifaiEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.clarifai.ClarifaiEmbeddings.html) |
|
||||
| langchain_community.embeddings.cloudflare_workersai | [CloudflareWorkersAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.cloudflare_workersai.CloudflareWorkersAIEmbeddings.html) |
|
||||
| langchain_community.embeddings.clova | [ClovaEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.clova.ClovaEmbeddings.html) |
|
||||
| langchain_community.embeddings.cohere | [CohereEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.cohere.CohereEmbeddings.html) |
|
||||
| langchain_community.embeddings.dashscope | [DashScopeEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.dashscope.DashScopeEmbeddings.html) |
|
||||
| langchain_community.embeddings.databricks | [DatabricksEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html) |
|
||||
| langchain_community.embeddings.deepinfra | [DeepInfraEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.deepinfra.DeepInfraEmbeddings.html) |
|
||||
| langchain_community.embeddings.edenai | [EdenAiEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.edenai.EdenAiEmbeddings.html) |
|
||||
| langchain_community.embeddings.elasticsearch | [ElasticsearchEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.elasticsearch.ElasticsearchEmbeddings.html) |
|
||||
| langchain_community.embeddings.embaas | [EmbaasEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.embaas.EmbaasEmbeddings.html) |
|
||||
| langchain_community.embeddings.ernie | [ErnieEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ernie.ErnieEmbeddings.html) |
|
||||
| langchain_community.embeddings.fake | [DeterministicFakeEmbedding](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.fake.DeterministicFakeEmbedding.html) |
|
||||
| langchain_community.embeddings.fake | [FakeEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.fake.FakeEmbeddings.html) |
|
||||
| langchain_community.embeddings.fastembed | [FastEmbedEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.fastembed.FastEmbedEmbeddings.html) |
|
||||
| langchain_community.embeddings.gigachat | [GigaChatEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gigachat.GigaChatEmbeddings.html) |
|
||||
| langchain_community.embeddings.google_palm | [GooglePalmEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.google_palm.GooglePalmEmbeddings.html) |
|
||||
| langchain_community.embeddings.gpt4all | [GPT4AllEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gpt4all.GPT4AllEmbeddings.html) |
|
||||
| langchain_community.embeddings.gradient_ai | [GradientEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gradient_ai.GradientEmbeddings.html) |
|
||||
| langchain_community.embeddings.huggingface | [HuggingFaceBgeEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface.HuggingFaceBgeEmbeddings.html) |
|
||||
| langchain_community.embeddings.huggingface | [HuggingFaceEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface.HuggingFaceEmbeddings.html) |
|
||||
| langchain_community.embeddings.huggingface | [HuggingFaceInferenceAPIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface.HuggingFaceInferenceAPIEmbeddings.html) |
|
||||
| langchain_community.embeddings.huggingface | [HuggingFaceInstructEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface.HuggingFaceInstructEmbeddings.html) |
|
||||
| langchain_community.embeddings.huggingface_hub | [HuggingFaceHubEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface_hub.HuggingFaceHubEmbeddings.html) |
|
||||
| langchain_community.embeddings.infinity | [InfinityEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.infinity.InfinityEmbeddings.html) |
|
||||
| langchain_community.embeddings.infinity_local | [InfinityEmbeddingsLocal](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.infinity_local.InfinityEmbeddingsLocal.html) |
|
||||
| langchain_community.embeddings.ipex_llm | [IpexLLMBgeEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html) |
|
||||
| langchain_community.embeddings.itrex | [QuantizedBgeEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.itrex.QuantizedBgeEmbeddings.html) |
|
||||
| langchain_community.embeddings.javelin_ai_gateway | [JavelinAIGatewayEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings.html) |
|
||||
| langchain_community.embeddings.jina | [JinaEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.jina.JinaEmbeddings.html) |
|
||||
| langchain_community.embeddings.johnsnowlabs | [JohnSnowLabsEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings.html) |
|
||||
| langchain_community.embeddings.laser | [LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html) |
|
||||
| langchain_community.embeddings.llamacpp | [LlamaCppEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llamacpp.LlamaCppEmbeddings.html) |
|
||||
| langchain_community.embeddings.llamafile | [LlamafileEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llamafile.LlamafileEmbeddings.html) |
|
||||
| langchain_community.embeddings.llm_rails | [LLMRailsEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llm_rails.LLMRailsEmbeddings.html) |
|
||||
| langchain_community.embeddings.localai | [LocalAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.localai.LocalAIEmbeddings.html) |
|
||||
| langchain_community.embeddings.minimax | [MiniMaxEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.minimax.MiniMaxEmbeddings.html) |
|
||||
| langchain_community.embeddings.mlflow | [MlflowCohereEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.mlflow.MlflowCohereEmbeddings.html) |
|
||||
| langchain_community.embeddings.mlflow | [MlflowEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.mlflow.MlflowEmbeddings.html) |
|
||||
| langchain_community.embeddings.mlflow_gateway | [MlflowAIGatewayEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.mlflow_gateway.MlflowAIGatewayEmbeddings.html) |
|
||||
| langchain_community.embeddings.modelscope_hub | [ModelScopeEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.modelscope_hub.ModelScopeEmbeddings.html) |
|
||||
| langchain_community.embeddings.mosaicml | [MosaicMLInstructorEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.mosaicml.MosaicMLInstructorEmbeddings.html) |
|
||||
| langchain_community.embeddings.nemo | [NeMoEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nemo.NeMoEmbeddings.html) |
|
||||
| langchain_community.embeddings.nlpcloud | [NLPCloudEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings.html) |
|
||||
| langchain_community.embeddings.oci_generative_ai | [OCIGenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.oci_generative_ai.OCIGenAIEmbeddings.html) |
|
||||
| langchain_community.embeddings.octoai_embeddings | [OctoAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.octoai_embeddings.OctoAIEmbeddings.html) |
|
||||
| langchain_community.embeddings.ollama | [OllamaEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ollama.OllamaEmbeddings.html) |
|
||||
| langchain_community.embeddings.openai | [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openai.OpenAIEmbeddings.html) |
|
||||
| langchain_community.embeddings.openvino | [OpenVINOBgeEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openvino.OpenVINOBgeEmbeddings.html) |
|
||||
| langchain_community.embeddings.openvino | [OpenVINOEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openvino.OpenVINOEmbeddings.html) |
|
||||
| langchain_community.embeddings.optimum_intel | [QuantizedBiEncoderEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings.html) |
|
||||
| langchain_community.embeddings.oracleai | [OracleEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.oracleai.OracleEmbeddings.html) |
|
||||
| langchain_community.embeddings.ovhcloud | [OVHCloudEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ovhcloud.OVHCloudEmbeddings.html) |
|
||||
| langchain_community.embeddings.premai | [PremAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.premai.PremAIEmbeddings.html) |
|
||||
| langchain_community.embeddings.sagemaker_endpoint | [SagemakerEndpointEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings.html) |
|
||||
| langchain_community.embeddings.sambanova | [SambaStudioEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sambanova.SambaStudioEmbeddings.html) |
|
||||
| langchain_community.embeddings.self_hosted | [SelfHostedEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.self_hosted.SelfHostedEmbeddings.html) |
|
||||
| langchain_community.embeddings.self_hosted_hugging_face | [SelfHostedHuggingFaceEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceEmbeddings.html) |
|
||||
| langchain_community.embeddings.self_hosted_hugging_face | [SelfHostedHuggingFaceInstructEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceInstructEmbeddings.html) |
|
||||
| langchain_community.embeddings.solar | [SolarEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.solar.SolarEmbeddings.html) |
|
||||
| langchain_community.embeddings.spacy_embeddings | [SpacyEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.spacy_embeddings.SpacyEmbeddings.html) |
|
||||
| langchain_community.embeddings.sparkllm | [SparkLLMTextEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sparkllm.SparkLLMTextEmbeddings.html) |
|
||||
| langchain_community.embeddings.tensorflow_hub | [TensorflowHubEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings.html) |
|
||||
| langchain_community.embeddings.text2vec | [Text2vecEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.text2vec.Text2vecEmbeddings.html) |
|
||||
| langchain_community.embeddings.titan_takeoff | [TitanTakeoffEmbed](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.titan_takeoff.TitanTakeoffEmbed.html) |
|
||||
| langchain_community.embeddings.vertexai | [VertexAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.vertexai.VertexAIEmbeddings.html) |
|
||||
| langchain_community.embeddings.volcengine | [VolcanoEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.volcengine.VolcanoEmbeddings.html) |
|
||||
| langchain_community.embeddings.voyageai | [VoyageEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.voyageai.VoyageEmbeddings.html) |
|
||||
| langchain_community.embeddings.xinference | [XinferenceEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.xinference.XinferenceEmbeddings.html) |
|
||||
| langchain_community.embeddings.yandex | [YandexGPTEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.yandex.YandexGPTEmbeddings.html) |
|
||||
| langchain_community.embeddings.zhipuai | [ZhipuAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html) |
|
||||
| langchain_core.embeddings.fake | [DeterministicFakeEmbedding](https://api.python.langchain.com/en/latest/embeddings/langchain_core.embeddings.fake.DeterministicFakeEmbedding.html) |
|
||||
| langchain_core.embeddings.fake | [FakeEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_core.embeddings.fake.FakeEmbeddings.html) |
|
||||
| langchain_elasticsearch.embeddings | [ElasticsearchEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_elasticsearch.embeddings.ElasticsearchEmbeddings.html) |
|
||||
| langchain_fireworks.embeddings | [FireworksEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html) |
|
||||
| langchain_google_genai.embeddings | [GoogleGenerativeAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_google_genai.embeddings.GoogleGenerativeAIEmbeddings.html) |
|
||||
| langchain_google_genai.google_vector_store | [ServerSideEmbedding](https://api.python.langchain.com/en/latest/google_vector_store/langchain_google_genai.google_vector_store.ServerSideEmbedding.html) |
|
||||
| langchain_google_vertexai.embeddings | [VertexAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |
|
||||
| langchain_huggingface.embeddings.huggingface | [HuggingFaceEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_huggingface.embeddings.huggingface.HuggingFaceEmbeddings.html) |
|
||||
| langchain_huggingface.embeddings.huggingface_endpoint | [HuggingFaceEndpointEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_huggingface.embeddings.huggingface_endpoint.HuggingFaceEndpointEmbeddings.html) |
|
||||
| langchain_ibm.embeddings | [WatsonxEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_ibm.embeddings.WatsonxEmbeddings.html) |
|
||||
| langchain_mistralai.embeddings | [MistralAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html) |
|
||||
| langchain_nomic.embeddings | [NomicEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html) |
|
||||
| langchain_nvidia_ai_endpoints.embeddings | [NVIDIAEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings.html) |
|
||||
| langchain_together.embeddings | [TogetherEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html) |
|
||||
| langchain_upstage.embeddings | [UpstageEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_upstage.embeddings.UpstageEmbeddings.html) |
|
||||
| langchain_voyageai.embeddings | [VoyageAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_voyageai.embeddings.VoyageAIEmbeddings.html) |
|
||||
<IndexTable />
|
||||
|
||||
@ -1,29 +1,15 @@
|
||||
---
|
||||
sidebar_position: 0
|
||||
sidebar_class_name: hidden
|
||||
keywords: [compatibility]
|
||||
custom_edit_url:
|
||||
---
|
||||
|
||||
# Vectorstores
|
||||
|
||||
## Features
|
||||
import { CategoryTable, IndexTable } from "@theme/FeatureTables";
|
||||
|
||||
The table below lists the features for some of our most popular vector stores.
|
||||
<CategoryTable category="vectorstores" />
|
||||
|
||||
Vectorstore|Delete by ID|Filtering|Search by Vector|Search with score|Async|Passes Standard Tests|Multi Tenancy|Local/Cloud|IDs in add Documents
|
||||
:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:
|
||||
AstraDBVectorStore|✅|✅|✅|✅|✅|❌|❌|❌|Local|✅
|
||||
Chroma|✅|✅|✅|✅|✅|❌|❌|❌|Local|✅
|
||||
Clickhouse|✅|✅|❌|✅|❌|❌|❌|❌|Local|✅
|
||||
CouchbaseVectorStore|✅|✅|❌|✅|✅|❌|❌|❌|Local|✅
|
||||
ElasticsearchStore|✅|✅|✅|❌|✅|❌|❌|❌|Local|✅
|
||||
FAISS|✅|✅|✅|✅|✅|❌|❌|❌|Local|✅
|
||||
InMemoryVectorStore|✅|✅|❌|✅|✅|❌|❌|❌|Local|✅
|
||||
Milvus|✅|✅|❌|✅|✅|❌|❌|❌|Local|✅
|
||||
MongoDBAtlasVectorSearch|✅|✅|❌|❌|✅|❌|❌|❌|Local|✅
|
||||
PGVector|✅|✅|✅|✅|✅|❌|❌|❌|Local|✅
|
||||
PineconeVectorStore|✅|✅|✅|❌|✅|❌|❌|❌|Local|✅
|
||||
QdrantVectorStore|✅|✅|✅|✅|✅|❌|❌|❌|Local|✅
|
||||
Redis|✅|✅|✅|✅|✅|❌|❌|❌|Local|✅
|
||||
## All Vectorstores
|
||||
|
||||
<IndexTable />
|
||||
|
||||
|
||||
@ -1,312 +0,0 @@
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from langchain_community import llms
|
||||
from langchain_core.language_models.llms import LLM, BaseLLM
|
||||
|
||||
LLM_IGNORE = ("FakeListLLM", "OpenAIChat", "PromptLayerOpenAIChat")
|
||||
LLM_FEAT_TABLE_CORRECTION = {
|
||||
"TextGen": {"_astream": False, "_agenerate": False},
|
||||
"Ollama": {
|
||||
"_stream": False,
|
||||
},
|
||||
"PromptLayerOpenAI": {"batch_generate": False, "batch_agenerate": False},
|
||||
}
|
||||
CHAT_MODEL_IGNORE = ("FakeListChatModel", "HumanInputChatModel")
|
||||
|
||||
CHAT_MODEL_FEAT_TABLE = {
|
||||
"ChatAnthropic": {
|
||||
"tool_calling": True,
|
||||
"multimodal": True,
|
||||
"package": "langchain-anthropic",
|
||||
"link": "/docs/integrations/chat/anthropic/",
|
||||
},
|
||||
"ChatMistralAI": {
|
||||
"tool_calling": True,
|
||||
"json_model": True,
|
||||
"package": "langchain-mistralai",
|
||||
"link": "/docs/integrations/chat/mistralai/",
|
||||
},
|
||||
"ChatFireworks": {
|
||||
"tool_calling": True,
|
||||
"json_mode": True,
|
||||
"package": "langchain-fireworks",
|
||||
"link": "/docs/integrations/chat/fireworks/",
|
||||
},
|
||||
"AzureChatOpenAI": {
|
||||
"tool_calling": True,
|
||||
"json_mode": True,
|
||||
"multimodal": True,
|
||||
"package": "langchain-openai",
|
||||
"link": "/docs/integrations/chat/azure_chat_openai/",
|
||||
},
|
||||
"ChatOpenAI": {
|
||||
"tool_calling": True,
|
||||
"json_mode": True,
|
||||
"multimodal": True,
|
||||
"package": "langchain-openai",
|
||||
"link": "/docs/integrations/chat/openai/",
|
||||
},
|
||||
"ChatTogether": {
|
||||
"tool_calling": True,
|
||||
"json_mode": True,
|
||||
"package": "langchain-together",
|
||||
"link": "/docs/integrations/chat/together/",
|
||||
},
|
||||
"ChatVertexAI": {
|
||||
"tool_calling": True,
|
||||
"multimodal": True,
|
||||
"package": "langchain-google-vertexai",
|
||||
"link": "/docs/integrations/chat/google_vertex_ai_palm/",
|
||||
},
|
||||
"ChatGoogleGenerativeAI": {
|
||||
"tool_calling": True,
|
||||
"multimodal": True,
|
||||
"package": "langchain-google-genai",
|
||||
"link": "/docs/integrations/chat/google_generative_ai/",
|
||||
},
|
||||
"ChatGroq": {
|
||||
"tool_calling": True,
|
||||
"json_mode": True,
|
||||
"package": "langchain-groq",
|
||||
"link": "/docs/integrations/chat/groq/",
|
||||
},
|
||||
"ChatCohere": {
|
||||
"tool_calling": True,
|
||||
"package": "langchain-cohere",
|
||||
"link": "/docs/integrations/chat/cohere/",
|
||||
},
|
||||
"ChatBedrock": {
|
||||
"tool_calling": True,
|
||||
"package": "langchain-aws",
|
||||
"link": "/docs/integrations/chat/bedrock/",
|
||||
},
|
||||
"ChatHuggingFace": {
|
||||
"tool_calling": True,
|
||||
"local": True,
|
||||
"package": "langchain-huggingface",
|
||||
"link": "/docs/integrations/chat/huggingface/",
|
||||
},
|
||||
"ChatNVIDIA": {
|
||||
"tool_calling": True,
|
||||
"json_mode": False,
|
||||
"local": True,
|
||||
"multimodal": False,
|
||||
"package": "langchain-nvidia-ai-endpoints",
|
||||
"link": "/docs/integrations/chat/nvidia_ai_endpoints/",
|
||||
},
|
||||
"ChatOllama": {
|
||||
"tool_calling": True,
|
||||
"local": True,
|
||||
"json_mode": True,
|
||||
"package": "langchain-ollama",
|
||||
"link": "/docs/integrations/chat/ollama/",
|
||||
},
|
||||
"vLLM Chat (via ChatOpenAI)": {
|
||||
"local": True,
|
||||
"package": "langchain-openai",
|
||||
"link": "/docs/integrations/chat/vllm/",
|
||||
},
|
||||
"ChatLlamaCpp": {
|
||||
"tool_calling": True,
|
||||
"local": True,
|
||||
"package": "langchain-community",
|
||||
"link": "/docs/integrations/chat/llamacpp",
|
||||
},
|
||||
"ChatAI21": {
|
||||
"tool_calling": True,
|
||||
"package": "langchain-ai21",
|
||||
"link": "/docs/integrations/chat/ai21",
|
||||
},
|
||||
"ChatWatsonx": {
|
||||
"tool_calling": True,
|
||||
"package": "langchain-ibm",
|
||||
"link": "/docs/integrations/chat/ibm_watsonx",
|
||||
},
|
||||
"ChatUpstage": {
|
||||
"tool_calling": True,
|
||||
"package": "langchain-upstage",
|
||||
"link": "/docs/integrations/chat/upstage",
|
||||
},
|
||||
}
|
||||
|
||||
for feats in CHAT_MODEL_FEAT_TABLE.values():
|
||||
feats["structured_output"] = feats.get("tool_calling", False)
|
||||
|
||||
|
||||
LLM_TEMPLATE = """\
|
||||
---
|
||||
sidebar_position: 1
|
||||
sidebar_class_name: hidden
|
||||
keywords: [compatibility]
|
||||
custom_edit_url:
|
||||
---
|
||||
|
||||
# LLMs
|
||||
|
||||
:::info
|
||||
|
||||
If you'd like to write your own LLM, see [this how-to](/docs/how_to/custom_llm/).
|
||||
If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
|
||||
|
||||
:::
|
||||
|
||||
## Features (natively supported)
|
||||
All LLMs implement the Runnable interface, which comes with default implementations of all methods, ie. `ainvoke`, `batch`, `abatch`, `stream`, `astream`. This gives all LLMs basic support for async, streaming and batch, which by default is implemented as below:
|
||||
- *Async* support defaults to calling the respective sync method in asyncio's default thread pool executor. This lets other async functions in your application make progress while the LLM is being executed, by moving this call to a background thread.
|
||||
- *Streaming* support defaults to returning an `Iterator` (or `AsyncIterator` in the case of async streaming) of a single value, the final result returned by the underlying LLM provider. This obviously doesn't give you token-by-token streaming, which requires native support from the LLM provider, but ensures your code that expects an iterator of tokens can work for any of our LLM integrations.
|
||||
- *Batch* support defaults to calling the underlying LLM in parallel for each input by making use of a thread pool executor (in the sync batch case) or `asyncio.gather` (in the async batch case). The concurrency can be controlled with the `max_concurrency` key in `RunnableConfig`.
|
||||
|
||||
Each LLM integration can optionally provide native implementations for async, streaming or batch, which, for providers that support it, can be more efficient. The table shows, for each integration, which features have been implemented with native support.
|
||||
|
||||
{table}
|
||||
|
||||
"""
|
||||
|
||||
CHAT_MODEL_TEMPLATE = """\
|
||||
---
|
||||
sidebar_position: 0
|
||||
sidebar_class_name: hidden
|
||||
keywords: [compatibility]
|
||||
custom_edit_url:
|
||||
hide_table_of_contents: true
|
||||
---
|
||||
|
||||
# Chat models
|
||||
|
||||
:::info
|
||||
|
||||
If you'd like to write your own chat model, see [this how-to](/docs/how_to/custom_chat_model/).
|
||||
If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
|
||||
|
||||
:::
|
||||
|
||||
## Advanced features
|
||||
|
||||
The following table shows all the chat model classes that support one or more advanced features.
|
||||
|
||||
:::info
|
||||
While all these LangChain classes support the indicated advanced feature, you may have
|
||||
to open the provider-specific documentation to learn which hosted models or backends support
|
||||
the feature.
|
||||
:::
|
||||
|
||||
{table}
|
||||
|
||||
"""
|
||||
|
||||
|
||||
def get_llm_table():
|
||||
llm_feat_table = {}
|
||||
for cm in llms.__all__:
|
||||
llm_feat_table[cm] = {}
|
||||
cls = getattr(llms, cm)
|
||||
if issubclass(cls, LLM):
|
||||
for feat in ("_stream", "_astream", ("_acall", "_agenerate")):
|
||||
if isinstance(feat, tuple):
|
||||
feat, name = feat
|
||||
else:
|
||||
feat, name = feat, feat
|
||||
llm_feat_table[cm][name] = getattr(cls, feat) != getattr(LLM, feat)
|
||||
else:
|
||||
for feat in [
|
||||
"_stream",
|
||||
"_astream",
|
||||
("_generate", "batch_generate"),
|
||||
"_agenerate",
|
||||
("_agenerate", "batch_agenerate"),
|
||||
]:
|
||||
if isinstance(feat, tuple):
|
||||
feat, name = feat
|
||||
else:
|
||||
feat, name = feat, feat
|
||||
llm_feat_table[cm][name] = getattr(cls, feat) != getattr(BaseLLM, feat)
|
||||
final_feats = {
|
||||
k: v
|
||||
for k, v in {**llm_feat_table, **LLM_FEAT_TABLE_CORRECTION}.items()
|
||||
if k not in LLM_IGNORE
|
||||
}
|
||||
|
||||
header = [
|
||||
"model",
|
||||
"_agenerate",
|
||||
"_stream",
|
||||
"_astream",
|
||||
"batch_generate",
|
||||
"batch_agenerate",
|
||||
]
|
||||
title = [
|
||||
"Model",
|
||||
"Invoke",
|
||||
"Async invoke",
|
||||
"Stream",
|
||||
"Async stream",
|
||||
"Batch",
|
||||
"Async batch",
|
||||
]
|
||||
rows = [title, [":-"] + [":-:"] * (len(title) - 1)]
|
||||
for llm, feats in sorted(final_feats.items()):
|
||||
rows += [[llm, "✅"] + ["✅" if feats.get(h) else "❌" for h in header[1:]]]
|
||||
return "\n".join(["|".join(row) for row in rows])
|
||||
|
||||
|
||||
def get_chat_model_table() -> str:
|
||||
"""Get the table of chat models."""
|
||||
header = [
|
||||
"model",
|
||||
"tool_calling",
|
||||
"structured_output",
|
||||
"json_mode",
|
||||
"local",
|
||||
"multimodal",
|
||||
"package",
|
||||
]
|
||||
title = [
|
||||
"Model",
|
||||
"[Tool calling](/docs/how_to/tool_calling)",
|
||||
"[Structured output](/docs/how_to/structured_output/)",
|
||||
"JSON mode",
|
||||
"Local",
|
||||
"[Multimodal](/docs/how_to/multimodal_inputs/)",
|
||||
"Package",
|
||||
]
|
||||
rows = [title, [":-"] + [":-:"] * (len(title) - 1)]
|
||||
for llm, feats in sorted(CHAT_MODEL_FEAT_TABLE.items()):
|
||||
# Fields are in the order of the header
|
||||
row = [
|
||||
f"[{llm}]({feats['link']})",
|
||||
]
|
||||
for h in header[1:]:
|
||||
value = feats.get(h)
|
||||
if h == "package":
|
||||
value = value or "langchain-community"
|
||||
name = value[len("langchain-") :]
|
||||
link = f"https://api.python.langchain.com/en/latest/{name}_api_reference.html"
|
||||
value = f"[{value}]({link})"
|
||||
row.append(value)
|
||||
else:
|
||||
if value == "partial":
|
||||
row.append("🟡")
|
||||
elif value is True:
|
||||
row.append("✅")
|
||||
else:
|
||||
row.append("❌")
|
||||
rows.append(row)
|
||||
return "\n".join(["|".join(row) for row in rows])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
output_dir = Path(sys.argv[1])
|
||||
output_integrations_dir = output_dir / "integrations"
|
||||
output_integrations_dir_llms = output_integrations_dir / "llms"
|
||||
output_integrations_dir_chat = output_integrations_dir / "chat"
|
||||
output_integrations_dir_llms.mkdir(parents=True, exist_ok=True)
|
||||
output_integrations_dir_chat.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
llm_page = LLM_TEMPLATE.format(table=get_llm_table())
|
||||
|
||||
with open(output_integrations_dir / "llms" / "index.mdx", "w") as f:
|
||||
f.write(llm_page)
|
||||
chat_model_page = CHAT_MODEL_TEMPLATE.format(table=get_chat_model_table())
|
||||
with open(output_integrations_dir / "chat" / "index.mdx", "w") as f:
|
||||
f.write(chat_model_page)
|
||||
196
docs/sidebars.js
196
docs/sidebars.js
@ -146,11 +146,12 @@ module.exports = {
|
||||
{
|
||||
type: "category",
|
||||
label: "Chat models",
|
||||
collapsed: true,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/chat",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
@ -161,11 +162,12 @@ module.exports = {
|
||||
{
|
||||
type: "category",
|
||||
label: "LLMs",
|
||||
collapsed: true,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/llms",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
@ -176,11 +178,12 @@ module.exports = {
|
||||
{
|
||||
type: "category",
|
||||
label: "Embedding models",
|
||||
collapsed: true,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/text_embedding",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
@ -191,11 +194,12 @@ module.exports = {
|
||||
{
|
||||
type: "category",
|
||||
label: "Document loaders",
|
||||
collapsed: true,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/document_loaders",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
@ -203,44 +207,31 @@ module.exports = {
|
||||
id: "integrations/document_loaders/index",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Document transformers",
|
||||
collapsed: true,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/document_transformers",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/document_transformers",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Vector stores",
|
||||
collapsed: true,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/vectorstores",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/vectorstores",
|
||||
type: "doc",
|
||||
id: "integrations/vectorstores/index",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Retrievers",
|
||||
collapsed: true,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/retrievers",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
@ -251,11 +242,12 @@ module.exports = {
|
||||
{
|
||||
type: "category",
|
||||
label: "Tools/Toolkits",
|
||||
collapsed: true,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/tools",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
@ -266,11 +258,12 @@ module.exports = {
|
||||
{
|
||||
type: "category",
|
||||
label: "Key-value stores",
|
||||
collapsed: true,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/stores",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
@ -278,82 +271,115 @@ module.exports = {
|
||||
id: "integrations/stores/index",
|
||||
},
|
||||
},
|
||||
"integrations/llm_caching",
|
||||
{
|
||||
type: "category",
|
||||
label: "Graphs",
|
||||
label: "Other",
|
||||
collapsed: true,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/graphs",
|
||||
type: "category",
|
||||
label: "Document transformers",
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/document_transformers",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/document_transformers",
|
||||
},
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/graphs",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Memory",
|
||||
collapsed: true,
|
||||
items: [
|
||||
"integrations/llm_caching",
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/memory",
|
||||
type: "category",
|
||||
label: "Graphs",
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/graphs",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/graphs",
|
||||
},
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/memory",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Callbacks",
|
||||
collapsed: true,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/callbacks",
|
||||
type: "category",
|
||||
label: "Memory",
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/memory",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/memory",
|
||||
},
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/callbacks",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Chat loaders",
|
||||
collapsed: true,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/chat_loaders",
|
||||
type: "category",
|
||||
label: "Callbacks",
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/callbacks",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/callbacks",
|
||||
},
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/chat_loaders",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Adapters",
|
||||
collapsed: true,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/adapters",
|
||||
type: "category",
|
||||
label: "Chat loaders",
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/chat_loaders",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/chat_loaders",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Adapters",
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: "autogenerated",
|
||||
dirName: "integrations/adapters",
|
||||
className: "hidden",
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/adapters",
|
||||
},
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
slug: "integrations/adapters",
|
||||
},
|
||||
// link: {
|
||||
// type: "generated-index",
|
||||
// slug: "integrations/providers",
|
||||
// },
|
||||
},
|
||||
|
||||
],
|
||||
link: {
|
||||
type: "generated-index",
|
||||
|
||||
682
docs/src/theme/FeatureTables.js
Normal file
682
docs/src/theme/FeatureTables.js
Normal file
@ -0,0 +1,682 @@
|
||||
import React from "react";
|
||||
import {useCurrentSidebarCategory} from '@docusaurus/theme-common';
|
||||
import {
|
||||
useDocById,
|
||||
} from '@docusaurus/theme-common/internal';
|
||||
|
||||
const FEATURE_TABLES = {
|
||||
chat: {
|
||||
link: "/docs/integrations/chat",
|
||||
columns: [
|
||||
{title: "Provider", formatter: (item) => <a href={item.link}>{item.name}</a>},
|
||||
{title: <a href="/docs/how_to/tool_calling">Tool calling</a>, formatter: (item) => item.tool_calling ? "✅" : "❌"},
|
||||
{title: <a href="/docs/how_to/structured_output/">Structured output</a>, formatter: (item) => item.structured_output ? "✅" : "❌"},
|
||||
{title: "JSON mode", formatter: (item) => item.json_mode ? "✅" : "❌"},
|
||||
{title: "Local", formatter: (item) => item.local ? "✅" : "❌"},
|
||||
{title: <a href="/docs/how_to/multimodal_inputs/">Multimodal</a>, formatter: (item) => item.multimodal ? "✅" : "❌"},
|
||||
{title: "Package", formatter: (item) => <a href={item.apiLink}>{item.package}</a>},
|
||||
],
|
||||
items: [
|
||||
{
|
||||
"name": "ChatAnthropic",
|
||||
"package": "langchain-anthropic",
|
||||
"link": "anthropic/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": true,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html#langchain_anthropic.chat_models.ChatAnthropic"
|
||||
},
|
||||
{
|
||||
"name": "ChatMistralAI",
|
||||
"package": "langchain-mistralai",
|
||||
"link": "mistralai/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": false,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html#langchain_mistralai.chat_models.ChatMistralAI"
|
||||
},
|
||||
{
|
||||
"name": "ChatFireworks",
|
||||
"package": "langchain-fireworks",
|
||||
"link": "fireworks/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": true,
|
||||
"multimodal": false,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_fireworks.chat_models.ChatFireworks.html#langchain_fireworks.chat_models.ChatFireworks"
|
||||
},
|
||||
{
|
||||
"name": "AzureChatOpenAI",
|
||||
"package": "langchain-openai",
|
||||
"link": "azure_chat_openai/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": true,
|
||||
"multimodal": true,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html#langchain_openai.chat_models.azure.AzureChatOpenAI"
|
||||
},
|
||||
{
|
||||
"name": "ChatOpenAI",
|
||||
"package": "langchain-openai",
|
||||
"link": "openai/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": true,
|
||||
"multimodal": true,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI"
|
||||
},
|
||||
{
|
||||
"name": "ChatTogether",
|
||||
"package": "langchain-together",
|
||||
"link": "together/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": true,
|
||||
"multimodal": false,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_together.chat_models.ChatTogether.html#langchain_together.chat_models.ChatTogether"
|
||||
},
|
||||
{
|
||||
"name": "ChatVertexAI",
|
||||
"package": "langchain-google-vertexai",
|
||||
"link": "google_vertex_ai_palm/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": true,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html#langchain_google_vertexai.chat_models.ChatVertexAI"
|
||||
},
|
||||
{
|
||||
"name": "ChatGoogleGenerativeAI",
|
||||
"package": "langchain-google-genai",
|
||||
"link": "google_generative_ai/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": true,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html#langchain_google_genai.chat_models.ChatGoogleGenerativeAI"
|
||||
},
|
||||
{
|
||||
"name": "ChatGroq",
|
||||
"package": "langchain-groq",
|
||||
"link": "groq/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": true,
|
||||
"multimodal": false,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_groq.chat_models.ChatGroq.html#langchain_groq.chat_models.ChatGroq"
|
||||
},
|
||||
{
|
||||
"name": "ChatCohere",
|
||||
"package": "langchain-cohere",
|
||||
"link": "cohere/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": false,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_cohere.chat_models.ChatCohere.html#langchain_cohere.chat_models.ChatCohere"
|
||||
},
|
||||
{
|
||||
"name": "ChatBedrock",
|
||||
"package": "langchain-aws",
|
||||
"link": "bedrock/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": false,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html#langchain_aws.chat_models.bedrock.ChatBedrock"
|
||||
},
|
||||
{
|
||||
"name": "ChatHuggingFace",
|
||||
"package": "langchain-huggingface",
|
||||
"link": "huggingface/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": false,
|
||||
"local": true,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html#langchain_huggingface.chat_models.huggingface.ChatHuggingFace",
|
||||
},
|
||||
{
|
||||
"name": "ChatNVIDIA",
|
||||
"package": "langchain-nvidia-ai-endpoints",
|
||||
"link": "nvidia_ai_endpoints/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": false,
|
||||
"local": true,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html#langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA"
|
||||
},
|
||||
{
|
||||
"name": "ChatOllama",
|
||||
"package": "langchain-ollama",
|
||||
"link": "ollama/",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": true,
|
||||
"multimodal": false,
|
||||
"local": true,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_ollama.chat_models.ChatOllama.html#langchain_ollama.chat_models.ChatOllama"
|
||||
},
|
||||
{
|
||||
"name": "ChatLlamaCpp",
|
||||
"package": "langchain-community",
|
||||
"link": "llamacpp",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": false,
|
||||
"local": true,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html#langchain_community.chat_models.llamacpp.ChatLlamaCpp"
|
||||
},
|
||||
{
|
||||
"name": "ChatAI21",
|
||||
"package": "langchain-ai21",
|
||||
"link": "ai21",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": false,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_ai21.chat_models.ChatAI21.html#langchain_ai21.chat_models.ChatAI21"
|
||||
},
|
||||
{
|
||||
"name": "ChatUpstage",
|
||||
"package": "langchain-upstage",
|
||||
"link": "upstage",
|
||||
"structured_output": true,
|
||||
"tool_calling": true,
|
||||
"json_mode": false,
|
||||
"multimodal": false,
|
||||
"local": false,
|
||||
"apiLink": "https://api.python.langchain.com/en/latest/chat_models/langchain_upstage.chat_models.ChatUpstage.html#langchain_upstage.chat_models.ChatUpstage"
|
||||
}
|
||||
],
|
||||
},
|
||||
llms: {
|
||||
link: "/docs/integrations/llms",
|
||||
columns: [
|
||||
{title: "Provider", formatter: (item) => <a href={
|
||||
item.link
|
||||
}>{item.name}</a>},
|
||||
{title: "Package", formatter: (item) => <a href={
|
||||
item.apiLink
|
||||
}>{item.package}</a>},
|
||||
],
|
||||
items: [
|
||||
{
|
||||
name: "AI21LLM",
|
||||
link: "ai21",
|
||||
package: "langchain-ai21",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_ai21.llms.AI21LLM.html#langchain_ai21.llms.AI21LLM"
|
||||
},
|
||||
{
|
||||
name: "AnthropicLLM",
|
||||
link: "anthropic",
|
||||
package: "langchain-anthropic",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_anthropic.llms.AnthropicLLM.html#langchain_anthropic.llms.AnthropicLLM"
|
||||
},
|
||||
{
|
||||
name: "AzureOpenAI",
|
||||
link: "azure_openai",
|
||||
package: "langchain-openai",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_openai.llms.azure.AzureOpenAI.html#langchain_openai.llms.azure.AzureOpenAI"
|
||||
},
|
||||
{
|
||||
name: "BedrockLLM",
|
||||
link: "bedrock",
|
||||
package: "langchain-aws",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_aws.llms.bedrock.BedrockLLM.html#langchain_aws.llms.bedrock.BedrockLLM"
|
||||
},
|
||||
{
|
||||
name: "CohereLLM",
|
||||
link: "cohere",
|
||||
package: "langchain-cohere",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_cohere.llms.Cohere.html#langchain_cohere.llms.Cohere"
|
||||
},
|
||||
{
|
||||
name: "FireworksLLM",
|
||||
link: "fireworks",
|
||||
package: "langchain-fireworks",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks"
|
||||
},
|
||||
{
|
||||
name: "OllamaLLM",
|
||||
link: "ollama",
|
||||
package: "langchain-ollama",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_ollama.llms.OllamaLLM.html#langchain_ollama.llms.OllamaLLM"
|
||||
},
|
||||
{
|
||||
name: "OpenAILLM",
|
||||
link: "openai",
|
||||
package: "langchain-openai",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_openai.llms.base.OpenAI.html#langchain_openai.llms.base.OpenAI"
|
||||
},
|
||||
{
|
||||
name: "TogetherLLM",
|
||||
link: "together",
|
||||
package: "langchain-together",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_together.llms.Together.html#langchain_together.llms.Together"
|
||||
},
|
||||
{
|
||||
name: "VertexAILLM",
|
||||
link: "google_vertexai",
|
||||
package: "langchain-google_vertexai",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/llms/langchain_google_vertexai.llms.VertexAI.html#langchain_google_vertexai.llms.VertexAI"
|
||||
},
|
||||
],
|
||||
},
|
||||
text_embedding: {
|
||||
link: "/docs/integrations/text_embedding",
|
||||
columns: [
|
||||
{title: "Provider", formatter: (item) => <a href={item.link}>{item.name}</a>},
|
||||
{title: "Package", formatter: (item) => <a href={item.apiLink}>{item.package}</a>},
|
||||
],
|
||||
items:[
|
||||
{
|
||||
name: "AzureOpenAI",
|
||||
link: "azureopenai",
|
||||
package: "langchain-openai",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html#langchain_openai.embeddings.azure.AzureOpenAIEmbeddings"
|
||||
},
|
||||
{
|
||||
name: "Ollama",
|
||||
link: "ollama",
|
||||
package: "langchain-ollama",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html#langchain_ollama.embeddings.OllamaEmbeddings"
|
||||
},
|
||||
{
|
||||
name: "AI21",
|
||||
link: "ai21",
|
||||
package: "langchain-ai21",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_ai21.embeddings.AI21Embeddings.html#langchain_ai21.embeddings.AI21Embeddings"
|
||||
},
|
||||
{
|
||||
name: "Fake",
|
||||
link: "fake",
|
||||
package: "langchain-core",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_core.embeddings.fake.FakeEmbeddings.html#langchain_core.embeddings.fake.FakeEmbeddings"
|
||||
},
|
||||
{
|
||||
name: "OpenAI",
|
||||
link: "openai",
|
||||
package: "langchain-openai",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI"
|
||||
},
|
||||
{
|
||||
name: "Together",
|
||||
link: "together",
|
||||
package: "langchain-together",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html#langchain_together.embeddings.TogetherEmbeddings"
|
||||
},
|
||||
{
|
||||
name: "Fireworks",
|
||||
link: "fireworks",
|
||||
package: "langchain-fireworks",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html#langchain_fireworks.embeddings.FireworksEmbeddings"
|
||||
},
|
||||
{
|
||||
name: "MistralAI",
|
||||
link: "mistralai",
|
||||
package: "langchain-mistralai",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html#langchain_mistralai.embeddings.MistralAIEmbeddings"
|
||||
},
|
||||
{
|
||||
name: "Cohere",
|
||||
link: "cohere",
|
||||
package: "langchain-cohere",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html#langchain_cohere.embeddings.CohereEmbeddings"
|
||||
},
|
||||
]
|
||||
},
|
||||
document_retrievers: {
|
||||
link: 'docs/integrations/retrievers',
|
||||
columns: [
|
||||
{title: "Retriever", formatter: (item) => <a href={item.link}>{item.name}</a>},
|
||||
{title: "Self-host", formatter: (item) => item.selfHost ? "✅" : "❌"},
|
||||
{title: "Cloud offering", formatter: (item) => item.cloudOffering ? "✅" : "❌"},
|
||||
{title: "Package", formatter: (item) => <a href={item.apiLink}>{item.package}</a>},
|
||||
],
|
||||
items: [
|
||||
{
|
||||
name: "AmazonKnowledgeBasesRetriever",
|
||||
link: "bedrock",
|
||||
selfHost: false,
|
||||
cloudOffering: true,
|
||||
apiLink: "https://api.python.langchain.com/en/latest/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html",
|
||||
package: "langchain_aws"
|
||||
},
|
||||
{
|
||||
name: "AzureAISearchRetriever",
|
||||
link: "azure_ai_search",
|
||||
selfHost: false,
|
||||
cloudOffering: true,
|
||||
apiLink: "https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html",
|
||||
package: "langchain_community"
|
||||
},
|
||||
{
|
||||
name: "ElasticsearchRetriever",
|
||||
link: "elasticsearch_retriever",
|
||||
selfHost: true,
|
||||
cloudOffering: true,
|
||||
apiLink: "https://api.python.langchain.com/en/latest/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html",
|
||||
package: "langchain_elasticsearch"
|
||||
},
|
||||
{
|
||||
name: "MilvusCollectionHybridSearchRetriever",
|
||||
link: "milvus_hybrid_search",
|
||||
selfHost: true,
|
||||
cloudOffering: false,
|
||||
apiLink: "https://api.python.langchain.com/en/latest/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html",
|
||||
package: "langchain_milvus"
|
||||
},
|
||||
{
|
||||
name: "VertexAISearchRetriever",
|
||||
link: "google_vertex_ai_search",
|
||||
selfHost: false,
|
||||
cloudOffering: true,
|
||||
apiLink: "https://api.python.langchain.com/en/latest/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html",
|
||||
package: "langchain_google_community"
|
||||
}
|
||||
],
|
||||
},
|
||||
external_retrievers: {
|
||||
link: 'docs/integrations/retrievers',
|
||||
columns: [
|
||||
{title: "Retriever", formatter: (item) => <a href={
|
||||
item.link
|
||||
}>{item.name}</a>},
|
||||
{title: "Source", formatter: (item) => item.source},
|
||||
{title: "Package", formatter: (item) => <a href={
|
||||
item.apiLink
|
||||
}>{item.package}</a>},
|
||||
],
|
||||
items: [
|
||||
{
|
||||
name: "ArxivRetriever",
|
||||
link: "arxiv",
|
||||
source: (<>Scholarly articles on <a href="https://arxiv.org/">arxiv.org</a></>),
|
||||
apiLink: "https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html",
|
||||
package: "langchain_community"
|
||||
},
|
||||
{
|
||||
name: "TavilySearchAPIRetriever",
|
||||
link: "tavily",
|
||||
source: "Internet search",
|
||||
apiLink: "https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html",
|
||||
package: "langchain_community"
|
||||
},
|
||||
{
|
||||
name: "WikipediaRetriever",
|
||||
link: "wikipedia",
|
||||
source: (<><a href="https://www.wikipedia.org/">Wikipedia</a> articles</>),
|
||||
apiLink: "https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html",
|
||||
package: "langchain_community"
|
||||
}
|
||||
]
|
||||
|
||||
},
|
||||
document_loaders: {
|
||||
link: 'docs/integrations/loaders',
|
||||
columns: [],
|
||||
items: [],
|
||||
},
|
||||
vectorstores: {
|
||||
link: 'docs/integrations/vectorstores',
|
||||
columns: [
|
||||
{title: "Vectorstore", formatter: (item) => <a href={
|
||||
item.link
|
||||
}>{item.name}</a>},
|
||||
{title: "Delete by ID", formatter: (item) => item.deleteById ? "✅" : "❌"},
|
||||
{title: "Filtering", formatter: (item) => item.filtering ? "✅" : "❌"},
|
||||
{title: "Search by Vector", formatter: (item) => item.searchByVector ? "✅" : "❌"},
|
||||
{title: "Search with score", formatter: (item) => item.searchWithScore ? "✅" : "❌"},
|
||||
{title: "Async", formatter: (item) => item.async ? "✅" : "❌"},
|
||||
{title: "Passes Standard Tests", formatter: (item) => item.passesStandardTests ? "✅" : "❌"},
|
||||
{title: "Multi Tenancy", formatter: (item) => item.multiTenancy ? "✅" : "❌"},
|
||||
{title: "IDs in add Documents", formatter: (item) => item.idsInAddDocuments ? "✅" : "❌"},
|
||||
{title: "Local/Cloud", formatter: (item) => item.local ? "Local" : "Cloud"},
|
||||
],
|
||||
items: [
|
||||
{
|
||||
name: "AstraDBVectorStore",
|
||||
link: "astradb",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: true,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "Chroma",
|
||||
link: "chroma",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: true,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "Clickhouse",
|
||||
link: "clickhouse",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: false,
|
||||
searchWithScore: true,
|
||||
async: false,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "CouchbaseVectorStore",
|
||||
link: "couchbase",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: false,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "ElasticsearchStore",
|
||||
link: "elasticsearch",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: true,
|
||||
searchWithScore: false,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "FAISS",
|
||||
link: "faiss",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: true,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "InMemoryVectorStore",
|
||||
link: "in_memory",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: false,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "Milvus",
|
||||
link: "milvus",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: false,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "MongoDBAtlasVectorSearch",
|
||||
link: "mongodb_atlas",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: false,
|
||||
searchWithScore: false,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "PGVector",
|
||||
link: "pg_vector",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: true,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "PineconeVectorStore",
|
||||
link: "pinecone",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: true,
|
||||
searchWithScore: false,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "QdrantVectorStore",
|
||||
link: "qdrant",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: true,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
},
|
||||
{
|
||||
name: "Redis",
|
||||
link: "redis",
|
||||
deleteById: true,
|
||||
filtering: true,
|
||||
searchByVector: true,
|
||||
searchWithScore: true,
|
||||
async: true,
|
||||
passesStandardTests: false,
|
||||
multiTenancy: false,
|
||||
local: true,
|
||||
idsInAddDocuments: false,
|
||||
}
|
||||
],
|
||||
}
|
||||
};
|
||||
|
||||
function toTable(columns, items) {
|
||||
const headers = columns.map((col) => col.title);
|
||||
return (
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
{headers.map((header, i) => <th key={`header-${i}`}>{header}</th>)}
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
{items.map((item, i) => (
|
||||
<tr key={`row-${i}`}>
|
||||
{columns.map((col, j) => <td key={`cell-${i}-${j}`}>{col.formatter(item)}</td>)}
|
||||
</tr>
|
||||
))}
|
||||
</tbody>
|
||||
</table>
|
||||
);
|
||||
}
|
||||
|
||||
export function CategoryTable({ category }) {
|
||||
const cat = FEATURE_TABLES[category];
|
||||
const rtn = toTable(cat.columns, cat.items);
|
||||
return rtn;
|
||||
}
|
||||
|
||||
export function ItemTable({ category, item }) {
|
||||
const cat = FEATURE_TABLES[category];
|
||||
const row = cat.items.find((i) => i.name === item);
|
||||
if (!row) {
|
||||
throw new Error(`Item ${item} not found in category ${category}`);
|
||||
}
|
||||
const rtn = toTable(cat.columns, [row]);
|
||||
return rtn;
|
||||
}
|
||||
|
||||
function truncate(str, n) {
|
||||
return (str.length > n) ? str.substring(0, n-1) + '...' : str;
|
||||
}
|
||||
|
||||
export function IndexTable() {
|
||||
const { items } = useCurrentSidebarCategory();
|
||||
|
||||
const rows = items.filter(item => !item.docId?.endsWith?.('/index')).map(item => ({
|
||||
...item,
|
||||
description: useDocById(item.docId ?? undefined)?.description,
|
||||
}));
|
||||
const rtn = toTable(
|
||||
[
|
||||
{ title: "Label", formatter: (item) => <a href={item.href}>{item.label}</a> },
|
||||
{ title: "Description", formatter: (item) => truncate(item.description ?? "", 70) },
|
||||
],
|
||||
rows,
|
||||
);
|
||||
return rtn;
|
||||
}
|
||||
@ -1,76 +0,0 @@
|
||||
import React from "react";
|
||||
|
||||
interface Column {
|
||||
title: string | React.ReactNode;
|
||||
formatter: (item: any) => React.ReactNode;
|
||||
}
|
||||
interface Category {
|
||||
link: string;
|
||||
columns: Column[];
|
||||
items: any[];
|
||||
}
|
||||
|
||||
const FeatureTables: Record<string, Category> = {
|
||||
llms: {
|
||||
link: "/docs/integrations/llms",
|
||||
columns: [
|
||||
{title: "Provider", formatter: (item) => <a href={item.link}>{item.name}</a>},
|
||||
{title: "Package", formatter: (item) => <a href={`https://pypi.org/project/${item.package}/`}>{item.package}</a>},
|
||||
],
|
||||
items:[
|
||||
{
|
||||
name: "Anthropic",
|
||||
link: "anthropic.ipynb",
|
||||
package: "langchain-anthropic",
|
||||
}
|
||||
]
|
||||
},
|
||||
text_embedding: {
|
||||
link: "/docs/integrations/text_embedding",
|
||||
columns: [
|
||||
{title: "Provider", formatter: (item) => <a href={item.link}>{item.name}</a>},
|
||||
{title: "Package", formatter: (item) => <a href={`https://pypi.org/project/${item.package}/`}>{item.package}</a>},
|
||||
],
|
||||
items:[
|
||||
{
|
||||
name: "Cohere",
|
||||
link: "cohere.ipynb",
|
||||
package: "langchain-cohere",
|
||||
}
|
||||
]
|
||||
},
|
||||
};
|
||||
|
||||
function toTable(columns: Column[], items: any[]) {
|
||||
const headers = columns.map((col) => col.title);
|
||||
return (
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
{headers.map((header) => <th>{header}</th>)}
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
{items.map((item) => (
|
||||
<tr>
|
||||
{columns.map((col) => <td>{col.formatter(item)}</td>)}
|
||||
</tr>
|
||||
))}
|
||||
</tbody>
|
||||
</table>
|
||||
);
|
||||
}
|
||||
|
||||
export function CategoryTable({category}: {category: string}) {
|
||||
const cat = FeatureTables[category];
|
||||
return toTable(cat.columns, cat.items);
|
||||
}
|
||||
|
||||
export function ItemTable({category, item}: {category: string, item: string}) {
|
||||
const cat = FeatureTables[category];
|
||||
const row = cat.items.find((i) => i.name === item);
|
||||
if (!row) {
|
||||
throw new Error(`Item ${item} not found in category ${category}`);
|
||||
}
|
||||
return toTable(cat.columns, [row]);
|
||||
}
|
||||
@ -17,7 +17,6 @@
|
||||
"source": [
|
||||
"# __ModuleName__Retriever\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"- TODO: Make sure API reference link is correct.\n",
|
||||
"\n",
|
||||
"This will help you getting started with the __ModuleName__ [retriever](/docs/concepts/#retrievers). For detailed documentation of all __ModuleName__Retriever features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/retrievers/__module_name__.retrievers.__ModuleName__.__ModuleName__Retriever.html).\n",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user