docs: add pgvector page

docs/docs/modules/data_connection/vector_stores/pgvector.mdx

@@ -0,0 +1,84 @@
+---
+sidebar_label: pgvector
+sidebar_position: 1
+draft: true
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import ExamplePGVector from "@examples/pgvector-vectorstore-example/pgvector_vectorstore_example.go";
+
+# Getting Started: pgvector
+
+[PGVector](https://github.com/pgvector/pgvector) is an open-source vector similarity search for Postgres
+
+PGVector supports:
+* exact and approximate nearest neighbor search
+* L2 distance, inner product, and cosine distance
+* IVFFlat and HNSW index types
+
+See the [installation instructions](https://github.com/pgvector/pgvector#installation-notes).
+
+## Usage with Langchain Go
+
+In code, create an embedder based on an LLM (OpenAI, Ollama, etc.):
+```go
+	llm, _:= openai.New()
+	emb, _ := embeddings.NewEmbedder(llm)
+```
+
+For OpenAI embeddings, you will need obtain an API key and provide as an environment variable to the program:
+
+```bash
+   export OPENAI_API_KEY=your_openai_api_key_here
+```
+
+Create a vector store:
+```go
+	ctx := context.Background()
+	store, err := pgvector.New(
+		ctx,
+		pgvector.WithConnectionURL("postgres://testuser:testpass@localhost:5432/testdb?sslmode=disable"),
+		pgvector.WithEmbedder(emb),
+	)
+```
+
+Document tables will be created automatically.
+
+Add documents:
+```go
+	_, err = store.AddDocuments(context.Background(), []schema.Document{
+		{
+			PageContent: "Tokyo",
+			Metadata: map[string]any{
+				"population": 38,
+				"area":       2190,
+			},
+		},
+        {
+            PageContent: "Sao Paulo",
+			Metadata: map[string]any{
+				"population": 22.6,
+				"area":       1523,
+			},
+		},
+    })
+```
+
+Run a similarity search using cosine distance (`<=>`):
+
+```go
+	filter := map[string]any{"area": "1523"}
+
+	docs, err = store.SimilaritySearch(ctx, "only cities in south america",
+		10,
+		vectorstores.WithScoreThreshold(0.80),
+		vectorstores.WithFilters(filter),
+	)
+```
+
+For now, pgvector integration only supports simple key-value filters and cosine distance search.
+
+## Full example
+
+Here is the entire program (from [pgvector-vectorstore-example](https://github.com/tmc/langchaingo/blob/main/examples/pgvector-vectorstore-example/pgvector_vectorstore_example.go)):
+<CodeBlock language="go">{ExamplePGVector}</CodeBlock>