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feat(vector-stores): Add support for Chroma VectorStore (#139)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,4 @@ | ||
| /// Chroma module for LangChain.dart. | ||
| /// LangChain.dart integration module for Chroma open-source embedding database. | ||
| library; | ||
|
|
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| export 'src/vector_stores/vector_stores.dart'; |
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packages/langchain_chroma/lib/src/vector_stores/chroma.dart
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| import 'package:chromadb/chromadb.dart'; | ||
| import 'package:http/http.dart' as http; | ||
| import 'package:langchain/langchain.dart'; | ||
| import 'package:uuid/uuid.dart'; | ||
|
|
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| import 'models/models.dart'; | ||
|
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| /// {@template chroma} | ||
| /// Vector store for Chroma open-source embedding database. | ||
| /// | ||
| /// Chroma documentation: | ||
| /// https://docs.trychroma.com | ||
| /// | ||
| /// This vector stores requires Chroma to be running in client/server mode. | ||
| /// | ||
| /// The server can run on your local computer via docker or be easily deployed | ||
| /// to any cloud provider. | ||
| /// | ||
| /// To run Chroma in client server mode, run the docker container: | ||
| /// ``` | ||
| /// docker-compose up -d --build | ||
| /// ``` | ||
| /// | ||
| /// By default, the Chroma client will connect to a server running on | ||
| /// `http://localhost:8000`. To connect to a different server, pass the | ||
| /// `host` parameter to the constructor. | ||
| /// | ||
| /// ### Collections | ||
| /// | ||
| /// Chroma lets you manage collections of embeddings, using the collection | ||
| /// primitive. | ||
| /// | ||
| /// You can configure the collection to use in the [collectionName] parameter. | ||
| /// | ||
| /// You can also configure the metadata to associate with the collection in the | ||
| /// [collectionMetadata] parameter. | ||
| /// | ||
| /// ### Changing the distance function | ||
| /// | ||
| /// You can change the distance function of the embedding space by setting the | ||
| /// value of `hnsw:space` in [collectionMetadata]. Valid options are "l2", | ||
| /// "ip", or "cosine". The default is "l2". | ||
| /// | ||
| /// ### Filtering | ||
| /// | ||
| /// Chroma supports filtering queries by metadata and document contents. | ||
| /// The `where` filter is used to filter by metadata, and the `whereDocument` | ||
| /// filter is used to filter by document contents. | ||
| /// | ||
| /// For example: | ||
| /// ```dart | ||
| /// final vectorStore = Chroma(...); | ||
| /// final res = await vectorStore.similaritySearch( | ||
| /// query: 'What should I feed my cat?', | ||
| /// config: ChromaSimilaritySearch( | ||
| /// k: 5, | ||
| /// scoreThreshold: 0.8, | ||
| /// where: {'class: 'cat'}, | ||
| /// ), | ||
| /// ); | ||
| /// ``` | ||
| /// | ||
| /// Chroma supports a wide range of operators for filtering. Check out the | ||
| /// filtering section of the Chroma docs for more info: | ||
| /// https://docs.trychroma.com/usage-guide?lang=js#using-where-filters | ||
| /// {@endtemplate} | ||
| class Chroma extends VectorStore { | ||
| /// {@macro chroma} | ||
| Chroma({ | ||
| this.collectionName = 'langchain', | ||
| this.collectionMetadata, | ||
| required super.embeddings, | ||
| final String? host, | ||
| final http.Client? client, | ||
| }) : _client = ChromaClient( | ||
| host: host ?? 'http://localhost:8000', | ||
| client: client, | ||
| ); | ||
|
|
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| /// Name of the collection to use. | ||
| final String collectionName; | ||
|
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| /// Metadata to associate with the collection. | ||
| final Map<String, dynamic>? collectionMetadata; | ||
|
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| /// The Chroma client. | ||
| final ChromaClient _client; | ||
|
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| /// A UUID generator. | ||
| final Uuid _uuid = const Uuid(); | ||
|
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| /// The collection to use. | ||
| Collection? _collection; | ||
|
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| @override | ||
| Future<List<String>> addVectors({ | ||
| required final List<List<double>> vectors, | ||
| required final List<Document> documents, | ||
| }) async { | ||
| assert(vectors.length == documents.length); | ||
|
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| final collection = await _getCollection(); | ||
|
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| final List<String> ids = []; | ||
| final List<Map<String, dynamic>> metadatas = []; | ||
| final List<String> docs = []; | ||
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| for (var i = 0; i < documents.length; i++) { | ||
| final doc = documents[i]; | ||
| final id = doc.id ?? _uuid.v4(); | ||
| ids.add(id); | ||
| metadatas.add(doc.metadata); | ||
| docs.add(doc.pageContent); | ||
| } | ||
|
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| await collection.upsert( | ||
| ids: ids, | ||
| embeddings: vectors, | ||
| metadatas: metadatas, | ||
| documents: docs, | ||
| ); | ||
| return ids; | ||
| } | ||
|
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| @override | ||
| Future<void> delete({ | ||
| required final List<String> ids, | ||
| }) async { | ||
| final collection = await _getCollection(); | ||
| await collection.delete(ids: ids); | ||
| } | ||
|
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| @override | ||
| Future<List<(Document, double)>> similaritySearchByVectorWithScores({ | ||
| required final List<double> embedding, | ||
| final VectorStoreSimilaritySearch config = | ||
| const VectorStoreSimilaritySearch(), | ||
| }) async { | ||
| final collection = await _getCollection(); | ||
| final result = await collection.query( | ||
| queryEmbeddings: [embedding], | ||
| nResults: config.k, | ||
| where: config.filter, | ||
| whereDocument: | ||
| config is ChromaSimilaritySearch ? config.whereDocument : null, | ||
| include: const [ | ||
| Include.documents, | ||
| Include.metadatas, | ||
| Include.distances, | ||
| ], | ||
| ); | ||
| final ids = result.ids.first; | ||
| final metadatas = result.metadatas?.first; | ||
| final docs = result.documents?.first; | ||
| final distances = result.distances?.first; | ||
|
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| final List<(Document, double)> results = []; | ||
| for (var i = 0; i < ids.length; i++) { | ||
| final distance = distances?[i] ?? 0.0; | ||
| if (config.scoreThreshold != null && distance < config.scoreThreshold!) { | ||
| continue; | ||
| } | ||
|
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| final doc = Document( | ||
| id: ids[i], | ||
| metadata: metadatas?[i] ?? {}, | ||
| pageContent: docs?[i] ?? '', | ||
| ); | ||
| results.add((doc, distance)); | ||
| } | ||
| return results; | ||
| } | ||
|
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| Future<Collection> _getCollection() async { | ||
| if (_collection != null) { | ||
| return _collection!; | ||
| } | ||
|
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| final collection = await _client.getOrCreateCollection( | ||
| name: collectionName, | ||
| metadata: collectionMetadata, | ||
| ); | ||
|
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| _collection = collection; | ||
| return collection; | ||
| } | ||
| } |
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packages/langchain_chroma/lib/src/vector_stores/models/models.dart
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| @@ -0,0 +1,33 @@ | ||
| import 'package:langchain/langchain.dart'; | ||
|
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| /// {@template chroma_similarity_search} | ||
| /// Chroma similarity search config. | ||
| /// | ||
| /// Chroma supports filtering queries by metadata and document contents. | ||
| /// The [where] filter is used to filter by metadata, and the [whereDocument] | ||
| /// filter is used to filter by document contents. | ||
| /// | ||
| /// Check out the filtering section of the Chroma docs for more info: | ||
| /// https://docs.trychroma.com/usage-guide?lang=js#using-where-filters | ||
| /// | ||
| /// Example: | ||
| /// ```dart | ||
| /// ChromaSimilaritySearch( | ||
| /// k: 5, | ||
| /// where: {'style: 'style1'}, | ||
| /// scoreThreshold: 0.8, | ||
| /// ), | ||
| /// ``` | ||
| /// {@endtemplate} | ||
| class ChromaSimilaritySearch extends VectorStoreSimilaritySearch { | ||
| /// {@macro chroma_similarity_search} | ||
| const ChromaSimilaritySearch({ | ||
| super.k = 4, | ||
| final Map<String, dynamic>? where, | ||
| this.whereDocument, | ||
| super.scoreThreshold, | ||
| }) : super(filter: where); | ||
|
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| /// Optional query condition to filter results based on document content. | ||
| final Map<String, dynamic>? whereDocument; | ||
| } |
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packages/langchain_chroma/lib/src/vector_stores/vector_stores.dart
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| Original file line number | Diff line number | Diff line change |
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| export 'chroma.dart'; | ||
| export 'models/models.dart'; |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,18 +1,21 @@ | ||
| name: langchain_chroma | ||
| description: Chroma module for LangChain.dart. | ||
| description: LangChain.dart integration module for Chroma open-source embedding database. | ||
| version: 0.0.1-dev.1 | ||
| repository: https://github.com/davidmigloz/langchain_dart/tree/main/packages/langchain_chroma | ||
| issue_tracker: https://github.com/davidmigloz/langchain_dart/issues | ||
| homepage: https://github.com/davidmigloz/langchain_dart | ||
| documentation: https://langchaindart.com | ||
| publish_to: none # Remove when the package is ready to be published | ||
|
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| environment: | ||
| sdk: ">=3.0.0 <4.0.0" | ||
|
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| dependencies: | ||
| chromadb: ^0.0.1-dev.1 | ||
| http: ^1.1.0 | ||
| langchain: ^0.0.9 | ||
| meta: ^1.9.1 | ||
| uuid: ^3.0.7 | ||
|
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| dev_dependencies: | ||
| test: ^1.24.3 | ||
| langchain_openai: ^0.0.9 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,8 @@ | ||
| # melos_managed_dependency_overrides: langchain | ||
| # melos_managed_dependency_overrides: langchain,chromadb,langchain_openai | ||
| dependency_overrides: | ||
| chromadb: | ||
| path: ../chromadb | ||
| langchain: | ||
| path: ../langchain | ||
| langchain_openai: | ||
| path: ../langchain_openai |
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