patterns-ai-core / langchainrb

💎🔗 Langchain.rb
---
⚡ Building LLM-powered applications in Ruby ⚡
For deep Rails integration see: langchainrb_rails gem.
Available for paid consulting engagements! Email me.
## Use Cases
* Retrieval Augmented Generation (RAG) and vector search
* Assistants (chat bots)
## Table of Contents
- Installation
- Usage
- Unified Interface for LLMs
- Prompt Management
- Output Parsers
- Building RAG
- Assistants
- Evaluations
- Examples
- Logging
- Problems
- Development
- Discord
## Installation
Install the gem and add to the application's Gemfile by executing:
 bundle add langchainrb
If bundler is not being used to manage dependencies, install the gem by executing:
 gem install langchainrb
Additional gems may be required. They're not included by default so you can include only what you need.
## Usage
 [code] 
# Unified Interface for LLMs
The `Langchain::LLM` module provides a unified interface for interacting with various Large Language Model (LLM) providers. This abstraction allows you to easily switch between different LLM backends without changing your application code.
## Supported LLM Providers
- Anthropic
- AWS Bedrock
- Azure OpenAI
- Cohere
- Google Gemini
- Google Vertex AI
- HuggingFace
- Mistral AI
- Ollama
- OpenAI
- Replicate
## Usage
All LLM classes inherit from `Langchain::LLM::Base` and provide a consistent interface for common operations:
1. Generating embeddings
2. Generating prompt completions
3. Generating chat completions
### Initialization
Most LLM classes can be initialized with an API key and optional default options:
 [code] 
### Generating Embeddings
Use the `embed` method to generate embeddings for given text:
 [code] 
#### Accepted parameters for `embed()`
- `text`: (Required) The input text to embed.
- `model`: (Optional) The model name to use or default embedding model will be used.
### Prompt completions
Use the `complete` method to generate completions for a given prompt:
 [code] 
#### Accepted parameters for `complete()`
- `prompt`: (Required) The input prompt for completion.
- `max_tokens`: (Optional) The maximum number of tokens to generate.
- `temperature`: (Optional) Controls randomness in generation. Higher values (e.g., 0.8) make output more random, while lower values (e.g., 0.2) make it more deterministic.
- `top_p`: (Optional) An alternative to temperature, controls diversity of generated tokens.
- `n`: (Optional) Number of completions to generate for each prompt.
- `stop`: (Optional) Sequences where the API will stop generating further tokens.
- `presence_penalty`: (Optional) Penalizes new tokens based on their presence in the text so far.
- `frequency_penalty`: (Optional) Penalizes new tokens based on their frequency in the text so far.
### Generating Chat Completions
Use the `chat` method to generate chat completions:
 [code] 
#### Accepted parameters for `chat()`
- `messages`: (Required) An array of message objects representing the conversation history.
- `model`: (Optional) The specific chat model to use.
-