> ## Documentation Index > Fetch the complete documentation index at: https://docs.jinba.io/llms.txt > Use this file to discover all available pages before exploring further. # Vector Search > Semantic search for knowledge bases using vector embeddings ## Overview Jinba Vector Search enables semantic search capabilities for knowledge bases using vector embeddings. This tool uses OpenAI's text-embedding-3-large to vectorize queries and retrieve relevant data from your knowledge base based on similarity scores, providing powerful RAG (Retrieval-Augmented Generation) functionality. ## Key Features * **Semantic Search**: Find conceptually similar content, not just exact matches * **Knowledge Base Integration**: Search across uploaded documents and files * **Similarity Scoring**: Filter results by similarity threshold * **Configurable Results**: Control the number of results returned * **RAG Support**: Perfect for building question-answering and information retrieval workflows ## Authentication This tool requires a Jinba API Token to access knowledge bases. **Required Configuration:** * `token` (string): Your Jinba API Token (stored as a secret) ## Input Parameters | Parameter | Type | Required | Default | Description | | ----------------- | ------ | -------- | ------- | ------------------------------------------------ | | `query` | string | Yes | - | Search query for semantic search | | `knowledgeBaseId` | string | Yes | - | ID of the knowledge base to search | | `topK` | number | No | 3 | Number of top results to return (1-50) | | `threshold` | number | No | 0.3 | Similarity threshold for filtering results (0-1) | ## Output Structure Returns an array of search results with: | Field | Type | Description | | -------------- | ------ | ----------------------------------------------------- | | `chunk` | object | Content chunk with ID, file ID, content, and metadata | | `score` | number | Similarity score (0-1, higher is more similar) | | `file` | object | File information including filename and content type | | `query` | string | The original search query | | `totalResults` | number | Total number of results found | ## Example: Document Q\&A System ```yaml theme={null} - id: search_knowledge_base name: search_knowledge_base tool: JINBA_VECTOR_SEARCH config: - name: token value: "{{secrets.JINBA_API_TOKEN}}" input: - name: query value: "What is our company's return policy?" - name: knowledgeBaseId value: "{{secrets.KNOWLEDGE_BASE_ID}}" - name: topK value: 5 - name: threshold value: 0.4 - id: generate_answer name: generate_answer tool: OPENAI_INVOKE config: - name: version value: gpt-4 input: - name: prompt value: | Based on the following search results from our knowledge base, answer the user's question about our return policy. Search Results: {{#each steps.search_knowledge_base.results}} **From: {{file.filename}}** Content: {{chunk.content}} Similarity: {{score}} {{/each}} Question: {{steps.search_knowledge_base.query}} Please provide a comprehensive answer based on the search results above. ``` ## Example: Research Assistant ```yaml theme={null} - id: research_query name: research_query tool: INPUT_TEXT input: - name: description value: "Enter your research question" - id: search_research_base name: search_research_base tool: JINBA_VECTOR_SEARCH config: - name: token value: "{{secrets.JINBA_API_TOKEN}}" input: - name: query value: "{{steps.research_query.result}}" - name: knowledgeBaseId value: "{{secrets.RESEARCH_KB_ID}}" - name: topK value: 10 - name: threshold value: 0.3 - id: analyze_findings name: analyze_findings tool: PYTHON_SANDBOX_RUN input: - name: code value: | import json results = {{steps.search_research_base.results}} print("=== Research Findings ===") print(f"Query: {results['query']}") print(f"Total Results: {results['totalResults']}") print() # Group results by source file sources = {} for result in results['results']: filename = result['file']['filename'] if filename not in sources: sources[filename] = [] sources[filename].append({ 'content': result['chunk']['content'][:200] + "...", 'score': result['score'] }) print("=== Sources Found ===") for filename, chunks in sources.items(): print(f"\nšŸ“„ {filename}") for i, chunk in enumerate(chunks[:3]): # Top 3 chunks per file print(f" {i+1}. Score: {chunk['score']:.3f}") print(f" {chunk['content']}") # Calculate average similarity avg_score = sum(r['score'] for r in results['results']) / len(results['results']) print(f"\nšŸ“Š Average Similarity: {avg_score:.3f}") - id: synthesize_response name: synthesize_response tool: ANTHROPIC_INVOKE config: - name: token value: "{{secrets.ANTHROPIC_API_KEY}}" input: - name: prompt value: | You are a research assistant. Synthesize the following search results into a comprehensive response. Original Query: {{steps.search_research_base.query}} Search Results: {{#each steps.search_research_base.results}} **Source: {{file.filename}}** (Similarity: {{score}}) {{chunk.content}} --- {{/each}} Please provide: 1. A direct answer to the question 2. Key insights from multiple sources 3. Any conflicting information found 4. Suggestions for further research ``` ## Example: Content Recommendation ```yaml theme={null} - id: get_user_interests name: get_user_interests tool: INPUT_TEXT input: - name: description value: "Describe topics or content you're interested in" - id: find_related_content name: find_related_content tool: JINBA_VECTOR_SEARCH config: - name: token value: "{{secrets.JINBA_API_TOKEN}}" input: - name: query value: "{{steps.get_user_interests.result}}" - name: knowledgeBaseId value: "{{secrets.CONTENT_KB_ID}}" - name: topK value: 8 - name: threshold value: 0.25 - id: format_recommendations name: format_recommendations tool: PYTHON_SANDBOX_RUN input: - name: code value: | results = {{steps.find_related_content.results}} print("šŸ” **Content Recommendations**") print(f"Based on: *{results['query']}*\n") recommendations = [] for i, result in enumerate(results['results'][:5], 1): chunk = result['chunk'] file_info = result['file'] score = result['score'] # Extract first sentence or 100 characters content_preview = chunk['content'][:100].split('.')[0] + "..." recommendations.append({ 'rank': i, 'title': file_info['filename'], 'preview': content_preview, 'relevance': f"{score:.1%}", 'content_type': file_info['contentType'] }) for rec in recommendations: print(f"**{rec['rank']}. {rec['title']}**") print(f" šŸ“‹ {rec['preview']}") print(f" šŸŽÆ Relevance: {rec['relevance']}") print(f" šŸ“„ Type: {rec['content_type']}") print() if len(results['results']) == 0: print("No relevant content found. Try adjusting your search terms or lowering the threshold.") ``` ## Best Practices ### Query Optimization * **Use natural language**: Write queries as you would ask a human * **Be specific**: More specific queries often yield better results * **Include context**: Add relevant keywords and context terms ### Threshold Selection * **0.7-1.0**: Very high similarity, exact or near-exact matches * **0.4-0.7**: High similarity, closely related content * **0.2-0.4**: Moderate similarity, potentially relevant content * **0.0-0.2**: Low similarity, may include tangentially related content ### Performance Tips * **Limit topK**: Don't retrieve more results than needed * **Adjust threshold**: Higher thresholds = fewer, more relevant results * **Use metadata**: Leverage chunk metadata for additional filtering ## Knowledge Base Setup Before using vector search, ensure your knowledge base contains relevant documents: 1. **Upload Documents**: Add PDFs, text files, or other supported formats 2. **Processing**: Allow time for document chunking and vectorization 3. **Test Queries**: Start with simple queries to understand your data 4. **Iterate**: Refine queries and thresholds based on results ## Use Cases * **Customer Support**: Find relevant documentation for user questions * **Research Assistant**: Discover related research papers and documents * **Content Discovery**: Recommend similar articles or resources * **FAQ Automation**: Automatically answer common questions * **Document Analysis**: Find specific information across large document sets * **Knowledge Management**: Quick access to institutional knowledge * **Legal Research**: Search through contracts and legal documents * **Product Information**: Find technical specifications and manuals