Trustgraph, first drop of code

tests/test-embeddings

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+
+import pulsar
+from trustgraph.embeddings_client import EmbeddingsClient
+
+embed = EmbeddingsClient(pulsar_host="pulsar://localhost:6650")
+
+prompt="Write a funny limerick about a llama"
+
+resp = embed.request(prompt)
+
+print(resp)
+
+
+

tests/test-graph-rag

@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+
+import pulsar
+from trustgraph.graph_rag_client import GraphRagClient
+
+rag = GraphRagClient(pulsar_host="pulsar://localhost:6650")
+
+query="""This knowledge graph describes the Space Shuttle disaster.
+Present 20 facts which are present in the knowledge graph."""
+
+resp = rag.request(query)
+
+print(resp)
+

tests/test-llm

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+
+import pulsar
+from trustgraph.llm_client import LlmClient
+
+llm = LlmClient(pulsar_host="pulsar://localhost:6650")
+
+prompt="Write a funny limerick about a llama"
+
+resp = llm.request(prompt)
+
+print(resp)
+
+llm.close()
+

tests/test-milvus

@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+
+from langchain_huggingface import HuggingFaceEmbeddings
+
+from edge_map import VectorStore
+
+client = VectorStore()
+
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+text="""A cat is a small animal.  A dog is a large animal.
+Cats say miaow.  Dogs go woof.
+"""
+
+embeds = embeddings.embed_documents([text])[0]
+
+text2="""If you couldn't download the model due to network issues, as a walkaround, you can use random vectors to represent the text and still finish the example. Just note that the search result won't reflect semantic similarity as the vectors are fake ones.
+"""
+
+embeds2 = embeddings.embed_documents([text2])[0]
+
+client.insert(embeds, "animals")
+client.insert(embeds, "vectors")
+
+query="""What noise does a cat make?"""
+
+qembeds = embeddings.embed_documents([query])[0]
+
+res = client.search(
+    qembeds,
+    limit=2
+)
+
+print(res)
+