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 # chromem-go
-In-memory vector database for Go with Chroma-like interface
+
+[![Go Reference](https://pkg.go.dev/badge/github.com/philippgille/chromem-go.svg)](https://pkg.go.dev/github.com/philippgille/chromem-go)
+
+In-memory vector database for Go with Chroma-like interface.
+
+It's not a library to connect to the Chroma database. It's an in-memory database on its own, meant to enable retrieval augmented generation (RAG) applications in Go *without having to run a separate database*.  
+As such, the focus is not scale or performance, but simplicity.
+
+> ⚠️ The initial implementation is fairly naive, with only the bare minimum in features. But over time we'll improve and extend it.
+
+## Interface
+
+Our inspiration, the [Chroma](https://www.trychroma.com/) interface, is the following (taken from their [README](https://github.com/chroma-core/chroma/blob/0.4.21/README.md)).
+
+```python
+import chromadb
+# setup Chroma in-memory, for easy prototyping. Can add persistence easily!
+client = chromadb.Client()
+
+# Create collection. get_collection, get_or_create_collection, delete_collection also available!
+collection = client.create_collection("all-my-documents")
+
+# Add docs to the collection. Can also update and delete. Row-based API coming soon!
+collection.add(
+    documents=["This is document1", "This is document2"], # we handle tokenization, embedding, and indexing automatically. You can skip that and add your own embeddings as well
+    metadatas=[{"source": "notion"}, {"source": "google-docs"}], # filter on these!
+    ids=["doc1", "doc2"], # unique for each doc
+)
+
+# Query/search 2 most similar results. You can also .get by id
+results = collection.query(
+    query_texts=["This is a query document"],
+    n_results=2,
+    # where={"metadata_field": "is_equal_to_this"}, # optional filter
+    # where_document={"$contains":"search_string"}  # optional filter
+)
+```
+
+Our Go library exposes the same interface:
+
+```go
+package main
+
+import "github.com/philippgille/chromem-go"
+
+func main() {
+    // Set up chromem-go in-memory, for easy prototyping. Persistence will be added in the future.
+    client := chromem.NewClient()
+
+    // Create collection. GetCollection, GetOrCreateCollection, DeleteCollection will be added in the future.
+    collection := client.CreateCollection("all-my-documents", nil, nil)
+
+    // Add docs to the collection. Update and delete will be added in the future.
+    // Row-based API will be added when Chroma adds it!
+    _ = collection.Add(ctx,
+        []string{"doc1", "doc2"}, // unique ID for each doc
+        nil, // We handle embedding automatically. You can skip that and add your own embeddings as well.
+        []map[string]string{{"source": "notion"}, {"source": "google-docs"}}, // Filter on these!
+        []string{"This is document1", "This is document2"},
+    )
+
+    // Query/search 2 most similar results. Getting by ID will be added in the future.
+    results, _ := collection.Query(ctx,
+        "This is a query document",
+        2,
+        map[string]string{"metadata_field": "is_equal_to_this"}, // optional filter
+        map[string]string{"$contains": "search_string"},         // optional filter
+    )
+}
+```
+
+Initially, only a minimal subset of all of Chroma's interface is implemented or exported, but we'll add more in future versions.
+
+## Features
+
+- Embedding creators:
+  - [X] [OpenAI ada v2](https://platform.openai.com/docs/guides/embeddings/embedding-models) (default)
+  - [X] Bring your own
+  - [ ] [Mistral (API)](https://docs.mistral.ai/api/#operation/createEmbedding)
+  - [ ] [ollama](https://ollama.ai/)
+  - [ ] [LocalAI](https://github.com/mudler/LocalAI)
+- Similarity search:
+  - [X] Exact nearest neighbor search using cosine similarity
+  - [ ] Approximate nearest neighbor search with index
+    - [ ] Hierarchical Navigable Small World (HNSW)
+    - [ ] Inverted file flat (IVFFlat)
+- Filters:
+  - [X] Document filters: `$contains`, `$not_contains`
+  - [X] Metadata filters: Exact matches
+  - [ ] Operators (`$and`, `$or` etc.)
+- Storage:
+  - [X] In-memory
+  - [ ] Persistent (file)
+  - [ ] Persistent (others (S3, PostgreSQL, ...))
+
+## Usage
+
+For a full, working example, using the vector database for retrieval augmented generation (RAG), see [example/main.go](example/main.go)