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Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.","zapabob\u002FSkills","zapabob","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130830376?v=4","01M6DBMYZAGRTFQDSQYW3W764P","峯岸 亮","01M6DBMZ3GKDSJ01NZ9XTZD7JD",2,"MIT","https:\u002F\u002Fgithub.com\u002Fzapabob\u002FSkills\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fchroma\u002Fvariants\u002Fhermes-agent-optional\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[5,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"open","embedding","filter","full","local","self","vector database","text","projects","macos","api","Transformers","open source","embeddings","vector","search","database","semantic search","rag","2026-11-10 10:12:30",false,"---\nname: chroma\ndescription: Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [chromadb, sentence-transformers]\nplatforms: [linux, macos, windows]\nmetadata:\n  hermes:\n    tags: [RAG, Chroma, Vector Database, Embeddings, Semantic Search, Open Source, Self-Hosted, Document Retrieval, Metadata Filtering]\n\n---\n\n# Chroma - Open-Source Embedding Database\n\nThe AI-native database for building LLM applications with memory.\n\n## When to use Chroma\n\n**Use Chroma when:**\n- Building RAG (retrieval-augmented generation) applications\n- Need local\u002Fself-hosted vector database\n- Want open-source solution (Apache 2.0)\n- Prototyping in notebooks\n- Semantic search over documents\n- Storing embeddings with metadata\n\n**Metrics**:\n- **24,300+ GitHub stars**\n- **1,900+ forks**\n- **v1.3.3** (stable, weekly releases)\n- **Apache 2.0 license**\n\n**Use alternatives instead**:\n- **Pinecone**: Managed cloud, auto-scaling\n- **FAISS**: Pure similarity search, no metadata\n- **Weaviate**: Production ML-native database\n- **Qdrant**: High performance, Rust-based\n\n## Quick start\n\n### Installation\n\n```bash\n# Python\npip install chromadb\n\n# JavaScript\u002FTypeScript\nnpm install chromadb @chroma-core\u002Fdefault-embed\n```\n\n### Basic usage (Python)\n\n```python\nimport chromadb\n\n# Create client\nclient = chromadb.Client()\n\n# Create collection\ncollection = client.create_collection(name=\"my_collection\")\n\n# Add documents\ncollection.add(\n    documents=[\"This is document 1\", \"This is document 2\"],\n    metadatas=[{\"source\": \"doc1\"}, {\"source\": \"doc2\"}],\n    ids=[\"id1\", \"id2\"]\n)\n\n# Query\nresults = collection.query(\n    query_texts=[\"document about topic\"],\n    n_results=2\n)\n\nprint(results)\n```\n\n## Core operations\n\n### 1. Create collection\n\n```python\n# Simple collection\ncollection = client.create_collection(\"my_docs\")\n\n# With custom embedding function\nfrom chromadb.utils import embedding_functions\n\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"text-embedding-3-small\"\n)\n\ncollection = client.create_collection(\n    name=\"my_docs\",\n    embedding_function=openai_ef\n)\n\n# Get existing collection\ncollection = client.get_collection(\"my_docs\")\n\n# Delete collection\nclient.delete_collection(\"my_docs\")\n```\n\n### 2. Add documents\n\n```python\n# Add with auto-generated IDs\ncollection.add(\n    documents=[\"Doc 1\", \"Doc 2\", \"Doc 3\"],\n    metadatas=[\n        {\"source\": \"web\", \"category\": \"tutorial\"},\n        {\"source\": \"pdf\", \"page\": 5},\n        {\"source\": \"api\", \"timestamp\": \"2025-01-01\"}\n    ],\n    ids=[\"id1\", \"id2\", \"id3\"]\n)\n\n# Add with custom embeddings\ncollection.add(\n    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],\n    documents=[\"Doc 1\", \"Doc 2\"],\n    ids=[\"id1\", \"id2\"]\n)\n```\n\n### 3. Query (similarity search)\n\n```python\n# Basic query\nresults = collection.query(\n    query_texts=[\"machine learning tutorial\"],\n    n_results=5\n)\n\n# Query with filters\nresults = collection.query(\n    query_texts=[\"Python programming\"],\n    n_results=3,\n    where={\"source\": \"web\"}\n)\n\n# Query with metadata filters\nresults = collection.query(\n    query_texts=[\"advanced topics\"],\n    where={\n        \"$and\": [\n            {\"category\": \"tutorial\"},\n            {\"difficulty\": {\"$gte\": 3}}\n        ]\n    }\n)\n\n# Access results\nprint(results[\"documents\"])      # List of matching documents\nprint(results[\"metadatas\"])      # Metadata for each doc\nprint(results[\"distances\"])      # Similarity scores\nprint(results[\"ids\"])            # Document IDs\n```\n\n### 4. Get documents\n\n```python\n# Get by IDs\ndocs = collection.get(\n    ids=[\"id1\", \"id2\"]\n)\n\n# Get with filters\ndocs = collection.get(\n    where={\"category\": \"tutorial\"},\n    limit=10\n)\n\n# Get all documents\ndocs = collection.get()\n```\n\n### 5. Update documents\n\n```python\n# Update document content\ncollection.update(\n    ids=[\"id1\"],\n    documents=[\"Updated content\"],\n    metadatas=[{\"source\": \"updated\"}]\n)\n```\n\n### 6. Delete documents\n\n```python\n# Delete by IDs\ncollection.delete(ids=[\"id1\", \"id2\"])\n\n# Delete with filter\ncollection.delete(\n    where={\"source\": \"outdated\"}\n)\n```\n\n## Persistent storage\n\n```python\n# Persist to disk\nclient = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\n\ncollection = client.create_collection(\"my_docs\")\ncollection.add(documents=[\"Doc 1\"], ids=[\"id1\"])\n\n# Data persisted automatically\n# Reload later with same path\nclient = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\ncollection = client.get_collection(\"my_docs\")\n```\n\n## Embedding functions\n\n### Default (Sentence Transformers)\n\n```python\n# Uses sentence-transformers by default\ncollection = client.create_collection(\"my_docs\")\n# Default model: all-MiniLM-L6-v2\n```\n\n### OpenAI\n\n```python\nfrom chromadb.utils import embedding_functions\n\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"text-embedding-3-small\"\n)\n\ncollection = client.create_collection(\n    name=\"openai_docs\",\n    embedding_function=openai_ef\n)\n```\n\n### HuggingFace\n\n```python\nhuggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\ncollection = client.create_collection(\n    name=\"hf_docs\",\n    embedding_function=huggingface_ef\n)\n```\n\n### Custom embedding function\n\n```python\nfrom chromadb import Documents, EmbeddingFunction, Embeddings\n\nclass MyEmbeddingFunction(EmbeddingFunction):\n    def __call__(self, input: Documents) -> Embeddings:\n        # Your embedding logic\n        return embeddings\n\nmy_ef = MyEmbeddingFunction()\ncollection = client.create_collection(\n    name=\"custom_docs\",\n    embedding_function=my_ef\n)\n```\n\n## Metadata filtering\n\n```python\n# Exact match\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"category\": \"tutorial\"}\n)\n\n# Comparison operators\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"page\": {\"$gt\": 10}}  # $gt, $gte, $lt, $lte, $ne\n)\n\n# Logical operators\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\n        \"$and\": [\n            {\"category\": \"tutorial\"},\n            {\"difficulty\": {\"$lte\": 3}}\n        ]\n    }  # Also: $or\n)\n\n# Contains\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"tags\": {\"$in\": [\"python\", \"ml\"]}}\n)\n```\n\n## LangChain integration\n\n```python\nfrom langchain_chroma import Chroma\nfrom langchain_openai import OpenAIEmbeddings\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\n\n# Split documents\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)\ndocs = text_splitter.split_documents(documents)\n\n# Create Chroma vector store\nvectorstore = Chroma.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    persist_directory=\".\u002Fchroma_db\"\n)\n\n# Query\nresults = vectorstore.similarity_search(\"machine learning\", k=3)\n\n# As retriever\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.chroma import ChromaVectorStore\nfrom llama_index.core import VectorStoreIndex, StorageContext\nimport chromadb\n\n# Initialize Chroma\ndb = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\ncollection = db.get_or_create_collection(\"my_collection\")\n\n# Create vector store\nvector_store = ChromaVectorStore(chroma_collection=collection)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\n# Create index\nindex = VectorStoreIndex.from_documents(\n    documents,\n    storage_context=storage_context\n)\n\n# Query\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What is machine learning?\")\n```\n\n## Server mode\n\n```python\n# Run Chroma server\n# Terminal: chroma run --path .\u002Fchroma_db --port 8000\n\n# Connect to server\nimport chromadb\nfrom chromadb.config import Settings\n\nclient = chromadb.HttpClient(\n    host=\"localhost\",\n    port=8000,\n    settings=Settings(anonymized_telemetry=False)\n)\n\n# Use as normal\ncollection = client.get_or_create_collection(\"my_docs\")\n```\n\n## Best practices\n\n1. **Use persistent client** - Don't lose data on restart\n2. **Add metadata** - Enables filtering and tracking\n3. **Batch operations** - Add multiple docs at once\n4. **Choose right embedding model** - Balance speed\u002Fquality\n5. **Use filters** - Narrow search space\n6. **Unique IDs** - Avoid collisions\n7. **Regular backups** - Copy chroma_db directory\n8. **Monitor collection size** - Scale up if needed\n9. **Test embedding functions** - Ensure quality\n10. **Use server mode for production** - Better for multi-user\n\n## Performance\n\n| Operation | Latency | Notes |\n|-----------|---------|-------|\n| Add 100 docs | ~1-3s | With embedding |\n| Query (top 10) | ~50-200ms | Depends on collection size |\n| Metadata filter | ~10-50ms | Fast with proper indexing |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma ⭐ 24,300+\n- **Docs**: https:\u002F\u002Fdocs.trychroma.com\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FMMeYNTmh3x\n- **Version**: 1.3.3+\n- **License**: Apache 2.0\n\n\n","# Chroma - Open-Source Embedding Database\n\nThe AI-native database for building LLM applications with memory.\n\n## When to use Chroma\n\n**Use Chroma when:**\n- Building RAG (retrieval-augmented generation) applications\n- Need local\u002Fself-hosted vector database\n- Want open-source solution (Apache 2.0)\n- Prototyping in notebooks\n- Semantic search over documents\n- Storing embeddings with metadata\n\n**Metrics**:\n- **24,300+ GitHub stars**\n- **1,900+ forks**\n- **v1.3.3** (stable, weekly releases)\n- **Apache 2.0 license**\n\n**Use alternatives instead**:\n- **Pinecone**: Managed cloud, auto-scaling\n- **FAISS**: Pure similarity search, no metadata\n- **Weaviate**: Production ML-native database\n- **Qdrant**: High performance, Rust-based\n\n## Quick start\n\n### Installation\n\n```bash\n# Python\npip install chromadb\n\n# JavaScript\u002FTypeScript\nnpm install chromadb @chroma-core\u002Fdefault-embed\n```\n\n### Basic usage (Python)\n\n```python\nimport chromadb\n\n# Create client\nclient = chromadb.Client()\n\n# Create collection\ncollection = client.create_collection(name=\"my_collection\")\n\n# Add documents\ncollection.add(\n    documents=[\"This is document 1\", \"This is document 2\"],\n    metadatas=[{\"source\": \"doc1\"}, {\"source\": \"doc2\"}],\n    ids=[\"id1\", \"id2\"]\n)\n\n# Query\nresults = collection.query(\n    query_texts=[\"document about topic\"],\n    n_results=2\n)\n\nprint(results)\n```\n\n## Core operations\n\n### 1. Create collection\n\n```python\n# Simple collection\ncollection = client.create_collection(\"my_docs\")\n\n# With custom embedding function\nfrom chromadb.utils import embedding_functions\n\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"text-embedding-3-small\"\n)\n\ncollection = client.create_collection(\n    name=\"my_docs\",\n    embedding_function=openai_ef\n)\n\n# Get existing collection\ncollection = client.get_collection(\"my_docs\")\n\n# Delete collection\nclient.delete_collection(\"my_docs\")\n```\n\n### 2. Add documents\n\n```python\n# Add with auto-generated IDs\ncollection.add(\n    documents=[\"Doc 1\", \"Doc 2\", \"Doc 3\"],\n    metadatas=[\n        {\"source\": \"web\", \"category\": \"tutorial\"},\n        {\"source\": \"pdf\", \"page\": 5},\n        {\"source\": \"api\", \"timestamp\": \"2025-01-01\"}\n    ],\n    ids=[\"id1\", \"id2\", \"id3\"]\n)\n\n# Add with custom embeddings\ncollection.add(\n    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],\n    documents=[\"Doc 1\", \"Doc 2\"],\n    ids=[\"id1\", \"id2\"]\n)\n```\n\n### 3. Query (similarity search)\n\n```python\n# Basic query\nresults = collection.query(\n    query_texts=[\"machine learning tutorial\"],\n    n_results=5\n)\n\n# Query with filters\nresults = collection.query(\n    query_texts=[\"Python programming\"],\n    n_results=3,\n    where={\"source\": \"web\"}\n)\n\n# Query with metadata filters\nresults = collection.query(\n    query_texts=[\"advanced topics\"],\n    where={\n        \"$and\": [\n            {\"category\": \"tutorial\"},\n            {\"difficulty\": {\"$gte\": 3}}\n        ]\n    }\n)\n\n# Access results\nprint(results[\"documents\"])      # List of matching documents\nprint(results[\"metadatas\"])      # Metadata for each doc\nprint(results[\"distances\"])      # Similarity scores\nprint(results[\"ids\"])            # Document IDs\n```\n\n### 4. Get documents\n\n```python\n# Get by IDs\ndocs = collection.get(\n    ids=[\"id1\", \"id2\"]\n)\n\n# Get with filters\ndocs = collection.get(\n    where={\"category\": \"tutorial\"},\n    limit=10\n)\n\n# Get all documents\ndocs = collection.get()\n```\n\n### 5. Update documents\n\n```python\n# Update document content\ncollection.update(\n    ids=[\"id1\"],\n    documents=[\"Updated content\"],\n    metadatas=[{\"source\": \"updated\"}]\n)\n```\n\n### 6. Delete documents\n\n```python\n# Delete by IDs\ncollection.delete(ids=[\"id1\", \"id2\"])\n\n# Delete with filter\ncollection.delete(\n    where={\"source\": \"outdated\"}\n)\n```\n\n## Persistent storage\n\n```python\n# Persist to disk\nclient = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\n\ncollection = client.create_collection(\"my_docs\")\ncollection.add(documents=[\"Doc 1\"], ids=[\"id1\"])\n\n# Data persisted automatically\n# Reload later with same path\nclient = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\ncollection = client.get_collection(\"my_docs\")\n```\n\n## Embedding functions\n\n### Default (Sentence Transformers)\n\n```python\n# Uses sentence-transformers by default\ncollection = client.create_collection(\"my_docs\")\n# Default model: all-MiniLM-L6-v2\n```\n\n### OpenAI\n\n```python\nfrom chromadb.utils import embedding_functions\n\nopenai_ef = embedding_functions.OpenAIEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"text-embedding-3-small\"\n)\n\ncollection = client.create_collection(\n    name=\"openai_docs\",\n    embedding_function=openai_ef\n)\n```\n\n### HuggingFace\n\n```python\nhuggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(\n    api_key=\"your-key\",\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\ncollection = client.create_collection(\n    name=\"hf_docs\",\n    embedding_function=huggingface_ef\n)\n```\n\n### Custom embedding function\n\n```python\nfrom chromadb import Documents, EmbeddingFunction, Embeddings\n\nclass MyEmbeddingFunction(EmbeddingFunction):\n    def __call__(self, input: Documents) -> Embeddings:\n        # Your embedding logic\n        return embeddings\n\nmy_ef = MyEmbeddingFunction()\ncollection = client.create_collection(\n    name=\"custom_docs\",\n    embedding_function=my_ef\n)\n```\n\n## Metadata filtering\n\n```python\n# Exact match\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"category\": \"tutorial\"}\n)\n\n# Comparison operators\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"page\": {\"$gt\": 10}}  # $gt, $gte, $lt, $lte, $ne\n)\n\n# Logical operators\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\n        \"$and\": [\n            {\"category\": \"tutorial\"},\n            {\"difficulty\": {\"$lte\": 3}}\n        ]\n    }  # Also: $or\n)\n\n# Contains\nresults = collection.query(\n    query_texts=[\"query\"],\n    where={\"tags\": {\"$in\": [\"python\", \"ml\"]}}\n)\n```\n\n## LangChain integration\n\n```python\nfrom langchain_chroma import Chroma\nfrom langchain_openai import OpenAIEmbeddings\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\n\n# Split documents\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)\ndocs = text_splitter.split_documents(documents)\n\n# Create Chroma vector store\nvectorstore = Chroma.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    persist_directory=\".\u002Fchroma_db\"\n)\n\n# Query\nresults = vectorstore.similarity_search(\"machine learning\", k=3)\n\n# As retriever\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.chroma import ChromaVectorStore\nfrom llama_index.core import VectorStoreIndex, StorageContext\nimport chromadb\n\n# Initialize Chroma\ndb = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\ncollection = db.get_or_create_collection(\"my_collection\")\n\n# Create vector store\nvector_store = ChromaVectorStore(chroma_collection=collection)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\n# Create index\nindex = VectorStoreIndex.from_documents(\n    documents,\n    storage_context=storage_context\n)\n\n# Query\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What is machine learning?\")\n```\n\n## Server mode\n\n```python\n# Run Chroma server\n# Terminal: chroma run --path .\u002Fchroma_db --port 8000\n\n# Connect to server\nimport chromadb\nfrom chromadb.config import Settings\n\nclient = chromadb.HttpClient(\n    host=\"localhost\",\n    port=8000,\n    settings=Settings(anonymized_telemetry=False)\n)\n\n# Use as normal\ncollection = client.get_or_create_collection(\"my_docs\")\n```\n\n## Best practices\n\n1. **Use persistent client** - Don't lose data on restart\n2. **Add metadata** - Enables filtering and tracking\n3. **Batch operations** - Add multiple docs at once\n4. **Choose right embedding model** - Balance speed\u002Fquality\n5. **Use filters** - Narrow search space\n6. **Unique IDs** - Avoid collisions\n7. **Regular backups** - Copy chroma_db directory\n8. **Monitor collection size** - Scale up if needed\n9. **Test embedding functions** - Ensure quality\n10. **Use server mode for production** - Better for multi-user\n\n## Performance\n\n| Operation | Latency | Notes |\n|-----------|---------|-------|\n| Add 100 docs | ~1-3s | With embedding |\n| Query (top 10) | ~50-200ms | Depends on collection size |\n| Metadata filter | ~10-50ms | Fast with proper indexing |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fchroma-core\u002Fchroma ⭐ 24,300+\n- **Docs**: https:\u002F\u002Fdocs.trychroma.com\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FMMeYNTmh3x\n- **Version**: 1.3.3+\n- **License**: Apache 2.0\n\n\n",{"name":5,"author":51,"license":15,"version":52,"metadata":53,"platforms":65,"description":7,"dependencies":68},"Orchestra Research","1.0.0",{"hermes":54},{"tags":55},[56,57,58,59,60,61,62,63,64],"RAG","Chroma","Vector Database","Embeddings","Semantic Search","Open Source","Self-Hosted","Document Retrieval","Metadata 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