v0.1.3 📚 [books]

What's new in v0.1.3?

Elastic vector store

The ElasticSearch and Kibana services are now started with Docker Compose. The certificates are generated and stored in the certs directory.

Start Elasticsearch and Kibana

docker compose up -d

👀 you will find a complete example in examples/33-rag-with-elastic

v0.1.2 📕 [red-textbook]

What's new in v0.1.2?

Elastic vector store

Create a store, and open an existing store:

cert, _ := os.ReadFile(os.Getenv("ELASTIC_CERT_PATH"))

elasticStore := embeddings.ElasticSearchStore{}
err := elasticStore.Initialize(
	[]string{
		os.Getenv("ELASTIC_ADDRESS"),
	},
	os.Getenv("ELASTIC_USERNAME"),
	os.Getenv("ELASTIC_PASSWORD"),
	cert,
	"chronicles-index",
)

v0.1.1 📗 [green-textbook]

What's new in v0.1.1?

Redis vector store

Create a store, and open an existing store:

redisStore := embeddings.RedisVectorStore{}
err := redisStore.Initialize("localhost:6379", "", "chronicles-bucket")

if err != nil {
	log.Fatalln("😡:", err)
}

👀 you will find a complete example in examples/32-rag-with-redis

• examples/32-rag-with-redis/create-embeddings: create and populate the vector store
• examples/32-rag-with-redis/use-embeddings: search similarities in the vector store

v0.1.0 📘 [blue-book]

What's new in v0.1.0?

Completion

Verbose mode:

options := llm.Options{
    Temperature: 0.5,
    RepeatLastN: 2,
    RepeatPenalty: 2.0,
    Verbose: true,
}

You will get an output like this (with the query and the completion):

[llm/query] {
  "model": "deepseek-coder",
  "messages": [
    {
      "role": "system",
      "content": "You are an expert in computer programming.\n\tPlease make friendly answer for the noobs.\n\tAdd source code examples if you can."
    },
    {
      "role": "user",
      "content": "I need a clear explanation regarding the following question:\n\tCan you create a \"hello world\" program in Golang?\n\tAnd, please, be structured with bullet points"
    }
  ],
  "options": {
    "repeat_last_n": 2,
    "temperature": 0.5,
    "repeat_penalty": 2,
    "Verbose": true
  },
  "stream": false,
  "prompt": "",
  "context": null,
  "tools": null,
  "TokenHeaderName": "",
  "TokenHeaderValue": ""
}

[llm/completion] {
  "model": "deepseek-coder",
  "message": {
    "role": "assistant",
    "content": "Sure, here's a simple \"Hello, World!\" program in Golang.\n\t1. First, you need to have Golang installed on your machine.\n\t2. Open your text editor, and write the following code:\n\t```go\n\tpackage main\n\timport \"fmt\"\n\tfunc main() {\n\t    fmt.Println(\"Hello, World!\")\n\t} \n\t```\n\t3. Save the file with a `.go` extension (like `hello.go`).\n\t4. In your terminal, navigate to the directory containing the `.go` file.\n\t5. Run the program with the command:\n\t```\n\tgo run hello.go\n\t```\n\t6. If everything goes well, you should see \"Hello, World!\" printed in your terminal.\n\t7. If there's an error, you will see the error message.\n\t8. If everything is correct, you'll see \"Hello, World!\" printed in your terminal.\n"
  },
  "done": true,
  "response": "",
  "context": null,
  "created_at": "2024-08-19T05:57:23.979361Z",
  "total_duration": 3361191958,
  "load_duration": 2044932125,
  "prompt_eval_count": 79,
  "prompt_eval_duration": 95034000,
  "eval_count": 222,
  "eval_duration": 1216689000
}

Vector store

Add additional data to a vector record (embedding):

embedding.Text()
embedding.Reference()
embedding.MetaData()

Protected endpoint

If your Ollama endpoint is protected with a header token, you can specify the token like this:

query := llm.Query{
    Model: model,
    Messages: []llm.Message{
        {Role: "system", Content: systemContent},
        {Role: "user", Content: userContent},
    },
    Options: options,
    TokenHeaderName: "X-TOKEN",
    TokenHeaderValue: "john_doe",
}

🦜 Parakeet v0.0.9 📙 [orange-book]

Add SearchTopNSimilarities to the MemoryVectorStore

🦜 Parakeet v0.0.8 📝 [memo]

Chunkers and Splitters

There are three methods in the content package to help you chunk and split text:

• ChunkText
• SplitTextWithDelimiter
• SplitTextWithRegex

Other similarity search methods

SearchTopNSimilarities searches for vector records in the BboltVectorStore that have a cosine distance similarity greater than or equal to the given limit and returns the top n records.

🦜 Parakeet v0.0.7 🛠️ [tools]

Support of Ollama tools calling

Ollama API: chat request with tools https://github.com/ollama/ollama/blob/main/docs/api.md#chat-request-with-tools

Since Ollama 0.3.0, Ollama supports tools calling, blog post: https://ollama.com/blog/tool-support.
A list of supported models can be found under the Tools category on the models page: https://ollama.com/search?c=tools

🦜 Parakeet v0.0.6 🟣 [purple]

What's new:

• Some fixes
• Some helpers:
  • gear.PrettyString
  • gear.JSONParse
  • gear.JSONStringify
• Function Calling
• Wasm support

🦜 Parakeet v0.0.5 🐞 [ladybug]

Fix on the completion stream

🦜 Parakeet v0.0.4 📝 [memo]

• More examples: ama sample: "Ask me anything about Parakeet" (RAG/Embeddings demo)
• Helpers:
  • content.GenerateContextFromDocs(docs []string) string
  • content.FindFiles(dirPath string, ext string) ([]string, error)
  • content.ForEachFile(dirPath string, ext string, callback func(string) error) ([]string, error)
  • embeddings.GenerateContextFromSimilarities(similarities []llm.VectorRecord) string