weaviate-vector-db-ag-2637

cookbook/agent_concepts/knowledge/vector_dbs/README.md

@@ -165,3 +165,13 @@ docker run -p 6333:6333 -p 6334:6334 -v $(pwd)/qdrant_storage:/qdrant/storage:z
 ```shell
 python cookbook/vector_dbs/qdrant_db.py
 ```
+
+### Weaviate
+
+```shell
+./cookbook/scripts/run_weviate.sh
+```
+
+```shell
+python cookbook/vector_dbs/weaviate_db.py
+```

cookbook/agent_concepts/knowledge/vector_dbs/weaviate_db.py

@@ -0,0 +1,52 @@
+"""
+This example demonstrates using Weaviate as a vector database for semantic search.
+
+Installation:
+    pip install weaviate-client
+
+You can use either Weaviate Cloud or a local instance.
+
+Weaviate Cloud Setup:
+1. Create account at https://console.weaviate.cloud/
+2. Create a cluster and copy the "REST endpoint" and "Admin" API Key. Then set environment variables:
+    export WCD_URL="your-cluster-url" 
+    export WCD_API_KEY="your-api-key"
+
+Local Development Setup:
+1. Install Docker from https://docs.docker.com/get-docker/
+2. Run Weaviate locally:
+    docker run -d \
+        -p 8080:8080 \
+        -p 50051:50051 \
+        --name weaviate \
+        cr.weaviate.io/semitechnologies/weaviate:1.28.4
+   or use the script `cookbook/scripts/run_weviate.sh` to start a local instance.
+3. Remember to set `local=True` on the Weaviate instantiation.
+"""
+
+from agno.agent import Agent
+from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
+from agno.vectordb.search import SearchType
+from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate
+
+vector_db = Weaviate(
+    collection="recipes",
+    search_type=SearchType.hybrid,
+    vector_index=VectorIndex.HNSW,
+    distance=Distance.COSINE,
+    local=True, # Set to False if using Weaviate Cloud and True if using local instance
+)
+# Create knowledge base
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=vector_db,
+)
+knowledge_base.load(recreate=False)  # Comment out after first run
+
+# Create and use the agent
+agent = Agent(
+    knowledge=knowledge_base,
+    search_knowledge=True,
+    show_tool_calls=True,
+)
+agent.print_response("How to make Thai curry?", markdown=True)

cookbook/scripts/run_weviate.sh

@@ -0,0 +1,5 @@
+docker run -d \
+        -p 8080:8080 \
+        -p 50051:50051 \
+        --name weaviate \
+        cr.weaviate.io/semitechnologies/weaviate:1.28.4 

libs/agno/agno/vectordb/weaviate/__init__.py

@@ -0,0 +1,2 @@
+from agno.vectordb.weaviate.index import Distance, VectorIndex
+from agno.vectordb.weaviate.weaviate import Weaviate

libs/agno/agno/vectordb/weaviate/index.py

@@ -0,0 +1,15 @@
+from enum import Enum
+
+
+class VectorIndex(Enum):
+    HNSW = "hnsw"
+    FLAT = "flat"
+    DYNAMIC = "dynamic"
+
+
+class Distance(Enum):
+    COSINE = "cosine"
+    DOT = "dot"
+    L2_SQUARED = "l2-squared"
+    HAMMING = "hamming"
+    MANHATTAN = "manhattan"