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Docs/release 0.175.0 docs (#3441)
* docs(install): note OpenAI SDK requirement openai>=1.13.3 for 0.175.0 * docs(cli): document device-code login and config reset guidance; renumber sections * docs(flows): document conditional @start and resumable execution semantics * docs(tasks): move max_retries to deprecation note under attributes table * docs: provider-neutral RAG client config; entity memory batching; trigger payload note; tracing batch manager * docs(cli): fix duplicate numbering (renumber Login/API Keys/Configuration sections)
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@@ -24,6 +24,41 @@ For file-based Knowledge Sources, make sure to place your files in a `knowledge`
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Also, use relative paths from the `knowledge` directory when creating the source.
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</Tip>
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### Vector store (RAG) client configuration
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CrewAI exposes a provider-neutral RAG client abstraction for vector stores. The default provider is ChromaDB, and Qdrant is supported as well. You can switch providers using configuration utilities.
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Supported today:
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- ChromaDB (default)
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- Qdrant
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```python Code
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from crewai.rag.config.utils import set_rag_config, get_rag_client, clear_rag_config
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# ChromaDB (default)
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from crewai.rag.chromadb.config import ChromaDBConfig
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set_rag_config(ChromaDBConfig())
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chromadb_client = get_rag_client()
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# Qdrant
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from crewai.rag.qdrant.config import QdrantConfig
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set_rag_config(QdrantConfig())
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qdrant_client = get_rag_client()
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# Example operations (same API for any provider)
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client = qdrant_client # or chromadb_client
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client.create_collection(collection_name="docs")
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client.add_documents(
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collection_name="docs",
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documents=[{"id": "1", "content": "CrewAI enables collaborative AI agents."}],
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)
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results = client.search(collection_name="docs", query="collaborative agents", limit=3)
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clear_rag_config() # optional reset
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```
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This RAG client is separate from Knowledge’s built-in storage. Use it when you need direct vector-store control or custom retrieval pipelines.
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### Basic String Knowledge Example
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```python Code
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