{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-zilliztech--deep-searcher","slug":"zilliztech--deep-searcher","name":"deep-searcher","type":"repo","url":"https://zilliztech.github.io/deep-searcher/","page_url":"https://unfragile.ai/zilliztech--deep-searcher","categories":["research-search"],"tags":["agent","agentic-rag","claude","deep-research","deepseek","deepseek-r1","grok","grok3","llama4","llm","milvus","openai","qwen3","rag","reasoning-models","vector-database","zilliz"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-zilliztech--deep-searcher__cap_0","uri":"capability://planning.reasoning.multi.strategy.rag.agent.selection.with.automatic.strategy.routing","name":"multi-strategy rag agent selection with automatic strategy routing","description":"Implements three distinct RAG strategies (NaiveRAG, ChainOfRAG, DeepSearch) that can be selected via configuration or automatically routed based on query complexity. NaiveRAG performs single-pass retrieval-generation for simple queries; ChainOfRAG decomposes complex queries into sub-questions with iterative multi-hop reasoning and early stopping; DeepSearch executes parallel searches with LLM-based reranking and reflection loops for comprehensive research tasks. The agent selection is configuration-driven through the agent provider setting, enabling runtime strategy swapping without code changes.","intents":["I need different reasoning strategies for simple factual queries vs. complex research questions","I want to automatically route queries to the most efficient RAG strategy based on complexity","I need to compare performance across multiple RAG approaches on the same dataset","I want to use reasoning models like DeepSeek-R1 or Grok-3 for deep research tasks"],"best_for":["teams building enterprise Q&A systems with variable query complexity","researchers comparing RAG strategies on private datasets","organizations deploying reasoning models for deep research workflows"],"limitations":["ChainOfRAG and DeepSearch add latency due to multi-hop reasoning and reflection loops — typically 2-5x slower than NaiveRAG","Agent selection is static per configuration; no dynamic runtime routing based on query analysis","DeepSearch strategy requires higher token budgets due to parallel search and reranking overhead"],"requires":["Python 3.9+","At least one LLM provider configured (OpenAI, DeepSeek, Anthropic, etc.)","Vector database instance (Milvus, Zilliz Cloud, or alternative)","Embedding provider configured"],"input_types":["natural language query (string)"],"output_types":["structured answer with reasoning trace","comprehensive research report with citations"],"categories":["planning-reasoning","agentic-rag"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_1","uri":"capability://data.processing.analysis.private.data.ingestion.with.multi.format.file.loading.and.web.crawling","name":"private data ingestion with multi-format file loading and web crawling","description":"Provides pluggable file loader and web crawler implementations for ingesting diverse data sources into the vector database. Supports local file formats (PDF, text, markdown) and web content crawling through configurable loader and crawler provider classes. The offline_loading process orchestrates chunking, embedding generation via the configured embedding provider, and vector storage into Milvus or alternative vector databases. Data ingestion is decoupled from querying, enabling batch preprocessing of large document collections.","intents":["I need to index PDF documents, markdown files, and web content into a searchable vector database","I want to batch-process large document collections without blocking query operations","I need to support multiple file formats and crawling strategies through a unified interface","I want to keep all indexed data private and on-premises"],"best_for":["enterprises with large document repositories (internal wikis, PDFs, web content)","teams building knowledge management systems with strict data privacy requirements","organizations migrating from cloud-based RAG to on-premises solutions"],"limitations":["File loader implementations are limited to PDF, text, and markdown — no support for Word, Excel, or proprietary formats without custom loaders","Web crawler is basic and may not handle JavaScript-heavy sites or authentication-protected content","Chunking strategy is fixed; no adaptive chunking based on document structure or semantic boundaries","No incremental indexing — full re-indexing required for document updates"],"requires":["Python 3.9+","Embedding provider configured (cloud or local)","Vector database instance running (Milvus, Zilliz Cloud, or alternative)","File system access for local documents or network access for web crawling"],"input_types":["PDF files","plain text files","markdown files","web URLs"],"output_types":["vector embeddings stored in vector database","indexed document chunks with metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_10","uri":"capability://data.processing.analysis.offline.data.loading.pipeline.with.chunking.and.batch.embedding.generation","name":"offline data loading pipeline with chunking and batch embedding generation","description":"Implements the offline_loading process that orchestrates document ingestion, chunking, embedding generation, and vector storage. The pipeline loads documents using configured file loaders and web crawlers, chunks documents into fixed-size or semantic chunks, generates embeddings for each chunk using the configured embedding provider, and inserts embeddings into the vector database with metadata. This process is decoupled from query processing, enabling batch preprocessing of large document collections without blocking user queries. The pipeline is designed for one-time or periodic execution rather than real-time ingestion.","intents":["I want to batch-process large document collections into a searchable index","I need to chunk documents intelligently before embedding and indexing","I want to generate embeddings in parallel for faster indexing","I need to separate data preparation from query serving for better performance"],"best_for":["teams with large document repositories requiring batch indexing","organizations with periodic data updates (daily, weekly) rather than real-time ingestion","builders optimizing for query latency by pre-computing embeddings"],"limitations":["Offline loading is batch-only — no real-time document ingestion or updates","Chunking strategy is fixed — no adaptive chunking based on document structure","No incremental indexing — full re-indexing required for document updates","Embedding generation is sequential by default — parallel embedding requires custom implementation"],"requires":["Python 3.9+","File loaders configured for supported document types","Embedding provider configured","Vector database instance running","Sufficient disk space for document storage and embeddings"],"input_types":["document files (PDF, text, markdown)","web URLs"],"output_types":["indexed document chunks in vector database","embedding vectors with metadata","indexing statistics and logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_11","uri":"capability://text.generation.language.online.query.processing.with.context.retrieval.and.llm.based.answer.generation","name":"online query processing with context retrieval and llm-based answer generation","description":"Implements the online_query process that retrieves relevant context from the vector database and generates answers using the configured LLM. The process encodes the user query as a vector embedding, searches the vector database for similar documents, constructs a prompt with retrieved context and the original query, and calls the LLM to generate an answer. The LLM has access to retrieved context, enabling it to provide grounded answers with citations. This process is optimized for low-latency query serving and can be executed repeatedly without modifying indexed data.","intents":["I want to answer user queries using indexed documents as context","I need to generate grounded answers with citations to source documents","I want to support follow-up questions and conversational interactions","I need to serve queries with low latency after initial indexing"],"best_for":["teams building Q&A systems on top of indexed documents","applications requiring grounded answers with source citations","organizations serving queries to end users after batch indexing"],"limitations":["Query latency depends on vector database search speed and LLM response time — typically 1-5 seconds","Retrieved context is limited by vector database top-k parameter — may miss relevant documents","LLM may hallucinate or generate answers not supported by retrieved context","No built-in conversation memory — follow-up questions lose context from previous queries"],"requires":["Python 3.9+","Documents indexed in vector database with embeddings","Embedding provider configured for query encoding","LLM provider configured for answer generation"],"input_types":["user query (string)","optional conversation history (list of messages)"],"output_types":["generated answer (string)","retrieved context documents with relevance scores","source citations"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_12","uri":"capability://text.generation.language.streaming.response.generation.with.token.by.token.output","name":"streaming response generation with token-by-token output","description":"Implements streaming response generation that yields LLM output tokens one at a time rather than waiting for complete response generation. This capability is supported by LLM providers that implement streaming APIs (OpenAI, Anthropic, DeepSeek, etc.). Streaming enables real-time feedback to users, reduces perceived latency, and allows early termination if the user stops reading. The streaming interface is available through both the FastAPI web service (Server-Sent Events) and Python API (generator functions).","intents":["I want to show real-time streaming responses to users instead of waiting for complete generation","I need to reduce perceived latency by showing tokens as they are generated","I want to allow users to stop reading and terminate generation early","I need to support streaming in web applications via Server-Sent Events"],"best_for":["web applications requiring real-time user feedback","interactive applications where perceived latency matters","teams building chat-like interfaces on top of RAG"],"limitations":["Streaming is not supported by all LLM providers — requires provider-specific implementation","Streaming responses cannot be reranked or modified after generation starts","Token-by-token streaming adds overhead compared to batch generation","Server-Sent Events require persistent HTTP connections — not suitable for all deployment environments"],"requires":["Python 3.9+","LLM provider with streaming support (OpenAI, Anthropic, DeepSeek, etc.)","FastAPI for web service streaming (optional)"],"input_types":["user query (string)","context documents (strings)"],"output_types":["token stream (generator yielding strings)","Server-Sent Events stream (for web clients)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_13","uri":"capability://automation.workflow.production.deployment.with.docker.containerization.and.kubernetes.orchestration","name":"production deployment with docker containerization and kubernetes orchestration","description":"Provides Docker containerization and Kubernetes deployment patterns for production deployment of DeepSearcher. The system can be containerized with all dependencies (Python, LLM clients, embedding libraries, vector database clients) and deployed as microservices. Kubernetes manifests enable horizontal scaling of query processing, load balancing across instances, and automatic failover. The FastAPI web service is designed for containerized deployment with health checks and graceful shutdown.","intents":["I want to containerize DeepSearcher for consistent deployment across environments","I need to scale query processing horizontally using Kubernetes","I want to implement load balancing and failover for high availability","I need to manage secrets (API keys, database credentials) securely in production"],"best_for":["teams deploying RAG systems to production Kubernetes clusters","organizations requiring high availability and horizontal scaling","enterprises with containerization and orchestration infrastructure"],"limitations":["Docker containerization adds complexity — requires Docker and container registry setup","Kubernetes deployment requires cluster infrastructure and operational expertise","Vector database and LLM services must be accessible from containers — requires network configuration","Secrets management requires external tools (Kubernetes Secrets, HashiCorp Vault) — not built-in"],"requires":["Docker and Docker registry for image storage","Kubernetes cluster (1.20+) for orchestration","Persistent storage for vector database (if using Milvus)","Network access to LLM providers or local LLM servers"],"input_types":["Dockerfile configuration","Kubernetes manifests (YAML)"],"output_types":["Docker container image","Kubernetes deployments and services","Scaling and load balancing configuration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.17.provider.support","name":"multi-provider llm abstraction with 17+ provider support","description":"Provides a unified LLM provider interface that abstracts over 17+ language model providers including OpenAI, DeepSeek, Anthropic, Grok, Qwen, and local models. Each provider is implemented as a pluggable class (e.g., OpenAI, DeepSeek, AnthropicLLM, SiliconFlow, TogetherAI) with standardized method signatures for completion and streaming. Provider selection is configuration-driven via the llm_provider setting, enabling runtime swapping between cloud and local models without code changes. Supports both standard LLMs and specialized reasoning models (DeepSeek-R1, Grok-3).","intents":["I want to switch between OpenAI, DeepSeek, Anthropic, and local LLMs without changing application code","I need to use reasoning models like DeepSeek-R1 for complex research tasks","I want to compare model performance and costs across different providers","I need to run LLMs locally for data privacy while maintaining the same API"],"best_for":["teams evaluating multiple LLM providers for cost and performance","enterprises with strict data residency requirements needing local LLM fallbacks","builders prototyping with different reasoning models (DeepSeek-R1, Grok-3, Qwen3)"],"limitations":["Provider implementations vary in feature completeness — some providers may not support streaming, function calling, or advanced parameters","No automatic fallback or load balancing across providers — requires manual configuration changes","Local LLM integration requires separate model deployment (e.g., Ollama, vLLM) — not bundled","Token counting and cost estimation are provider-specific and not standardized"],"requires":["Python 3.9+","API keys for cloud providers (OpenAI, DeepSeek, Anthropic, etc.) OR local LLM server running (Ollama, vLLM)","Network access to cloud providers or localhost access to local LLM server"],"input_types":["prompt text (string)","system message (string)","conversation history (list of messages)"],"output_types":["completion text (string)","streaming token chunks","structured responses (JSON)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_3","uri":"capability://data.processing.analysis.multi.provider.embedding.abstraction.with.15.embedding.model.support","name":"multi-provider embedding abstraction with 15+ embedding model support","description":"Provides a unified embedding provider interface supporting 15+ embedding models from cloud providers (OpenAI, Cohere, Hugging Face) and local models (Sentence Transformers, Ollama). Each provider is implemented as a pluggable class with standardized embed() methods that return vector embeddings. Provider selection is configuration-driven via the embedding_provider setting, enabling runtime swapping between cloud and local embeddings. Embeddings are generated during offline_loading and used for semantic search during query processing.","intents":["I want to switch between OpenAI embeddings, Cohere, and local Sentence Transformers without code changes","I need to use domain-specific embedding models for specialized knowledge domains","I want to run embeddings locally for data privacy compliance","I need to compare embedding quality and latency across different providers"],"best_for":["teams with strict data privacy requirements needing local embedding models","organizations comparing embedding quality across providers","builders working with specialized domains (legal, medical, scientific) requiring domain-specific embeddings"],"limitations":["Embedding dimension varies by provider (OpenAI: 1536, Cohere: 4096, local models: 384-768) — requires vector database support for variable dimensions","No automatic re-embedding when switching providers — requires full re-indexing of document collections","Local embedding models require separate deployment (Ollama, Hugging Face Inference Server) — not bundled","Batch embedding API is not standardized across providers — some providers have rate limits"],"requires":["Python 3.9+","API keys for cloud embedding providers (OpenAI, Cohere, Hugging Face) OR local embedding server running (Ollama, Hugging Face Inference Server)","Vector database configured to support embedding dimensions of selected provider"],"input_types":["text chunks (strings)","document passages"],"output_types":["vector embeddings (float arrays)","embedding dimension metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_4","uri":"capability://memory.knowledge.flexible.vector.database.abstraction.with.milvus.zilliz.cloud.and.alternative.support","name":"flexible vector database abstraction with milvus, zilliz cloud, and alternative support","description":"Provides a pluggable vector database provider interface supporting Milvus (open-source), Zilliz Cloud (managed), and alternative vector databases. The base VectorDB class defines standardized methods for insert, search, and delete operations. Provider implementations handle connection management, index creation, and similarity search. Vector database selection is configuration-driven via the vector_db_provider setting, enabling runtime swapping between on-premises Milvus and managed Zilliz Cloud without code changes. Supports semantic search queries during online_query processing.","intents":["I want to switch between self-hosted Milvus and managed Zilliz Cloud without code changes","I need to scale vector search from development (Milvus) to production (Zilliz Cloud) seamlessly","I want to use alternative vector databases (Weaviate, Pinecone, Qdrant) with the same RAG pipeline","I need to manage vector indexes and perform similarity search at scale"],"best_for":["enterprises scaling from development to production vector search infrastructure","teams with data residency requirements needing on-premises Milvus","organizations evaluating multiple vector database providers"],"limitations":["Vector database provider implementations are not fully interchangeable — schema and index configuration vary by provider","No automatic index migration between providers — requires manual data export/import","Alternative vector database support (beyond Milvus/Zilliz) requires custom provider implementation","Search performance and cost vary significantly by provider — no automatic optimization"],"requires":["Python 3.9+","Milvus instance running (Docker, Kubernetes, or standalone) OR Zilliz Cloud account with API credentials","Network connectivity to vector database instance"],"input_types":["vector embeddings (float arrays)","document metadata (strings, integers)","search queries (vector embeddings)"],"output_types":["search results with similarity scores","retrieved document chunks with metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_5","uri":"capability://automation.workflow.configuration.driven.provider.ecosystem.with.runtime.swapping","name":"configuration-driven provider ecosystem with runtime swapping","description":"Implements a centralized Configuration class and config.yaml file that manages provider selection across LLMs, embeddings, vector databases, file loaders, and web crawlers. The init_config() and set_provider_config() methods enable runtime provider changes without code modifications. Configuration is loaded at startup and can be updated dynamically. This design pattern decouples provider implementations from application logic, enabling teams to swap entire technology stacks (e.g., OpenAI→DeepSeek, Milvus→Zilliz Cloud) through configuration changes alone.","intents":["I want to change LLM providers without modifying application code","I need to manage different provider configurations for development, staging, and production","I want to enable non-technical users to switch between providers via configuration files","I need to A/B test different provider combinations (LLM + embedding + vector DB)"],"best_for":["teams managing multiple deployment environments with different provider requirements","organizations enabling non-technical operators to manage provider selection","builders prototyping with multiple provider combinations"],"limitations":["Configuration changes require application restart for some providers — no hot-reloading","No validation of provider compatibility (e.g., embedding dimension vs. vector DB schema) at configuration time","Configuration file format is YAML — no schema validation or IDE autocomplete support","Provider credentials are stored in config.yaml — requires secure secret management in production"],"requires":["Python 3.9+","config.yaml file in application directory","API keys or connection strings for selected providers in environment variables or config file"],"input_types":["YAML configuration file","environment variables"],"output_types":["initialized provider instances","runtime configuration state"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_6","uri":"capability://tool.use.integration.multi.interface.access.with.cli.fastapi.web.service.and.python.api","name":"multi-interface access with cli, fastapi web service, and python api","description":"Provides three distinct usage interfaces: (1) CLI via the deepsearcher command for command-line workflows, (2) FastAPI web service for HTTP-based access with REST endpoints, and (3) Python library API for programmatic integration. All interfaces share the same underlying core engines (offline_loading, online_query) and RAG agents, enabling consistent behavior across access methods. This design enables diverse deployment patterns: CLI for batch processing, FastAPI for web applications, and Python API for integration into larger systems.","intents":["I want to index documents from the command line without writing code","I need to expose RAG capabilities as HTTP endpoints for web applications","I want to integrate DeepSearcher into my Python application as a library","I need to support multiple access patterns (CLI, web, programmatic) from a single codebase"],"best_for":["teams supporting multiple deployment patterns (CLI, web, programmatic)","organizations building web applications on top of RAG","developers integrating RAG into larger Python systems"],"limitations":["CLI interface is limited to basic operations — complex workflows require Python API","FastAPI web service requires separate deployment and scaling — not bundled with CLI","No authentication or authorization built into FastAPI service — requires external API gateway","Interface consistency is not enforced — different interfaces may have different behavior if not carefully maintained"],"requires":["Python 3.9+","FastAPI and Uvicorn for web service (pip install fastapi uvicorn)","All provider dependencies (LLM, embedding, vector DB)"],"input_types":["CLI arguments and flags","HTTP POST/GET requests with JSON payloads","Python function calls with native types"],"output_types":["CLI text output and exit codes","HTTP JSON responses","Python objects and data structures"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_7","uri":"capability://planning.reasoning.iterative.multi.hop.reasoning.with.chainofrag.sub.question.decomposition","name":"iterative multi-hop reasoning with chainofrag sub-question decomposition","description":"Implements the ChainOfRAG agent that decomposes complex queries into sub-questions, iteratively retrieves relevant context for each sub-question, and synthesizes answers with early stopping logic. The agent uses the configured LLM to generate sub-questions, performs semantic search for each sub-question in the vector database, and combines results into a comprehensive answer. Early stopping logic terminates iteration when sufficient information is retrieved or a maximum iteration count is reached. This strategy is optimized for multi-hop reasoning tasks that require breaking down complex information needs.","intents":["I need to answer complex questions that require reasoning across multiple documents","I want to decompose a research question into sub-questions automatically","I need to retrieve context iteratively based on intermediate answers","I want to avoid over-fetching documents by using early stopping logic"],"best_for":["teams answering complex research questions requiring multi-hop reasoning","applications where query complexity varies and early stopping saves costs","builders implementing question-answering systems for knowledge bases"],"limitations":["Sub-question generation quality depends on LLM capability — weaker models may generate poor decompositions","Early stopping logic is heuristic-based — may stop prematurely or continue unnecessarily","Iterative retrieval adds latency — typically 3-5x slower than NaiveRAG","No mechanism to detect and avoid redundant sub-questions across iterations"],"requires":["Python 3.9+","LLM provider configured with sufficient reasoning capability","Vector database with semantic search capability","Embedding provider for query encoding"],"input_types":["complex natural language query (string)"],"output_types":["structured answer with reasoning trace","list of sub-questions and their answers","iteration count and early stopping reason"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_8","uri":"capability://planning.reasoning.comprehensive.parallel.search.with.llm.based.reranking.and.reflection.loops","name":"comprehensive parallel search with llm-based reranking and reflection loops","description":"Implements the DeepSearch agent that executes parallel semantic searches, applies LLM-based reranking to retrieved documents, and performs reflection loops to evaluate answer quality and iterate if needed. The agent retrieves multiple candidate documents in parallel, uses the configured LLM to score and rerank results based on relevance to the query, and generates reflection prompts to assess answer completeness. If reflection indicates insufficient information, the agent performs additional searches with refined queries. This strategy is optimized for comprehensive research tasks requiring high-quality answers.","intents":["I need to perform comprehensive research on a topic with high-quality answers","I want to rerank retrieved documents using LLM judgment rather than just similarity scores","I need to evaluate answer quality and iterate if information is insufficient","I want to use reasoning models like DeepSeek-R1 for deep research tasks"],"best_for":["teams performing deep research requiring comprehensive, high-quality answers","applications using reasoning models (DeepSeek-R1, Grok-3) for complex analysis","organizations where answer quality is more important than latency"],"limitations":["DeepSearch is computationally expensive — parallel searches, reranking, and reflection loops require many LLM calls","Reflection loops add significant latency — typically 5-10x slower than NaiveRAG","Reflection logic is heuristic-based — may iterate unnecessarily or miss information gaps","Requires high-capability LLM for effective reranking and reflection — weaker models may not improve answer quality"],"requires":["Python 3.9+","High-capability LLM provider (OpenAI GPT-4, DeepSeek-R1, Anthropic Claude, etc.)","Vector database with parallel search capability","Embedding provider for query encoding","Sufficient token budget for multiple LLM calls per query"],"input_types":["research query (string)"],"output_types":["comprehensive research report with citations","reranked document list with relevance scores","reflection analysis and iteration history"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--deep-searcher__cap_9","uri":"capability://search.retrieval.semantic.search.with.vector.embeddings.and.similarity.scoring","name":"semantic search with vector embeddings and similarity scoring","description":"Implements semantic search by encoding queries and documents as vector embeddings using the configured embedding provider, storing embeddings in the vector database, and retrieving documents based on cosine similarity or other distance metrics. During offline_loading, document chunks are embedded and indexed. During online_query, the user query is embedded and used to search the vector database, returning top-k most similar documents. This approach enables semantic understanding beyond keyword matching, allowing retrieval of documents with similar meaning even if they use different terminology.","intents":["I want to find documents semantically similar to a query, not just keyword matches","I need to retrieve relevant context for RAG without explicit keyword indexing","I want to support natural language queries without requiring users to know exact terminology","I need to handle synonyms and paraphrases in document retrieval"],"best_for":["teams building semantic search into RAG systems","applications with large document collections where keyword search is insufficient","organizations supporting natural language queries from non-expert users"],"limitations":["Semantic search quality depends on embedding model quality — poor embeddings lead to irrelevant results","Embedding generation adds latency during indexing and query time","Vector database similarity search is approximate — may miss relevant documents with low similarity scores","No support for hybrid search combining semantic and keyword matching (requires separate implementation)"],"requires":["Python 3.9+","Embedding provider configured (cloud or local)","Vector database with similarity search capability","Document collection indexed with embeddings"],"input_types":["query text (string)","document chunks (strings)"],"output_types":["ranked list of similar documents","similarity scores (0-1 range)","document metadata and content"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","At least one LLM provider configured (OpenAI, DeepSeek, Anthropic, etc.)","Vector database instance (Milvus, Zilliz Cloud, or alternative)","Embedding provider configured","Embedding provider configured (cloud or local)","Vector database instance running (Milvus, Zilliz Cloud, or alternative)","File system access for local documents or network access for web crawling","File loaders configured for supported document types","Vector database instance running","Sufficient disk space for document storage and embeddings"],"failure_modes":["ChainOfRAG and DeepSearch add latency due to multi-hop reasoning and reflection loops — typically 2-5x slower than NaiveRAG","Agent selection is static per configuration; no dynamic runtime routing based on query analysis","DeepSearch strategy requires higher token budgets due to parallel search and reranking overhead","File loader implementations are limited to PDF, text, and markdown — no support for Word, Excel, or proprietary formats without custom loaders","Web crawler is basic and may not handle JavaScript-heavy sites or authentication-protected content","Chunking strategy is fixed; no adaptive chunking based on document structure or semantic boundaries","No incremental indexing — full re-indexing required for document updates","Offline loading is batch-only — no real-time document ingestion or updates","Chunking strategy is fixed — no adaptive chunking based on document structure","Embedding generation is sequential by default — parallel embedding requires custom implementation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6341890795399054,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2025-11-19T06:04:16Z"},"community":{"stars":7808,"forks":755,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=zilliztech--deep-searcher","compare_url":"https://unfragile.ai/compare?artifact=zilliztech--deep-searcher"}},"signature":"/NN6yYQ1mr3wOOxrVUJF+UHrS6XJTu4QaxH2zXYarrFJO1iQ+b1TFYQdxjtecoyi4W+66IVJNt6TOMZAZ1sGDQ==","signedAt":"2026-06-20T00:41:08.154Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zilliztech--deep-searcher","artifact":"https://unfragile.ai/zilliztech--deep-searcher","verify":"https://unfragile.ai/api/v1/verify?slug=zilliztech--deep-searcher","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}