Compatibility with Haystack v2

Co-authored-by: Peter Izsak <[email protected]>
Co-authored-by: Moshe Berchansky <[email protected]>

Demo.md

@@ -0,0 +1,134 @@
+
+# Chat Demo with Chainlit
+
+We provide the conversation demo with a multi-modal agent, using the [chainlit](https://github.com/Chainlit/chainlit) framework. For more information, please visit their official website [here](https://docs.chainlit.io/get-started/overview).
+
+For a simple chat experience, we load an LLM, such as [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), by specifying the configuration of like so:
+
+```yaml
+task: text-generation
+model: "meta-llama/Meta-Llama-3-8B-Instruct"
+do_sample: false
+max_new_tokens: 300
+```
+
+Then, run the following:
+
+```sh
+CONFIG=config/regular_chat.yaml chainlit run fastrag/ui/chainlit_no_rag.py
+```
+
+For a chat using a RAG pipeline, specify the tools you wish to use in the following format:
+
+```yaml
+chat_model:
+  generator_kwargs:
+      model: microsoft/Phi-3-mini-128k-instruct
+      task: "text-generation"
+      generation_kwargs:
+        max_new_tokens: 300
+        do_sample: false
+      huggingface_pipeline_kwargs:
+        torch_dtype: torch.bfloat16
+        max_new_tokens: 300
+        do_sample: false
+        trust_remote_code: true
+  generator_class: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator
+tools:
+  - type: doc
+    query_handler:
+        type: "haystack_yaml"
+        params:
+            pipeline_yaml_path: "config/empty_doc_only_retrieval_pipeline.yaml"
+    index_handler:
+        type: "haystack_yaml"
+        params:
+            pipeline_yaml_path: "config/empty_index_pipeline.yaml"
+    params:
+      name: "docRetriever"
+      description: 'useful for when you need to retrieve text to answer questions. Use the following format: {{ "input": [your tool input here ] }}.'
+      output_variable: "documents"
+```
+
+Then, run the application using the command:
+
+```sh
+CONFIG=config/rag_pipeline_chat.yaml chainlit run fastrag/ui/chainlit_pipeline.py
+```
+
+## Screenshot
+
+![alt text](./assets/chainlit_demo_example.png)
+
+
+# Multi-Modal Conversational Agent with Chainlit
+
+In this demo, we use the [```xtuner/llava-llama-3-8b-v1_1-transformers```]https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers) model as a conversational agent, that can decide which retriever to use to respond to the user's query.
+To perform that, we use dynamic reasoning with [ReAct](https://arxiv.org/abs/2210.03629) prompts, resulting in multiple logical turns.
+To explore all the steps to build the agent system, you can check out our [Example Notebook](../examples/multi_modal_react_agent.ipynb).
+For more information on how to use ReAct, feel free to visit [Haystack's original tutorial](https://haystack.deepset.ai/tutorials/25_customizing_agent), which our demo is based on.
+
+To run the demo, simply run:
+
+```sh
+CONFIG=config/visual_chat_agent.yaml chainlit run fastrag/ui/chainlit_multi_modal_agent.py
+```
+
+## Screenshot
+
+![alt text](./assets/chainlit_agent_example.png)
+
+# Available Chat Templates
+
+## Default Template
+
+```
+The following is a conversation between a human and an AI. Do not generate the user response to your output.
+{memory}
+Human: {query}
+AI:
+```
+
+## Llama 2 Template (Llama2)
+
+```
+<s>[INST] <<SYS>>
+The following is a conversation between a human and an AI. Do not generate the user response to your output.
+<</SYS>>
+
+{memory}{query} [/INST]
+```
+
+Notice that here we, the user messages will be:
+
+```
+<s>[INST] {USER_QUERY} [/INST]
+```
+
+And the model messages will be:
+
+```
+ {ASSISTATN_RESPONSE} </s>
+```
+
+## User-Assistant (UserAssistant)
+
+```
+### System:
+The following is a conversation between a human and an AI. Do not generate the user response to your output.
+{memory}
+
+### User: {query}
+### Assistant:
+```
+
+## User-Assistant for Llava (UserAssistantLlava)
+
+For the v1.5 llava models, we define a specific template, as shown in [this post regardin Llava models](https://huggingface.co/docs/transformers/model_doc/llava).
+
+```
+{memory}
+
+USER: {query}
+ASSISTANT:
+```

README.md

@@ -1,6 +1,3 @@
-> [!IMPORTANT]
-> We're migrating `fastRAG` to Haystack v2.0 API and will release a major update soon. Stay tuned!
-
 <div align="center">
     <img src="assets/fastrag_header.png" width="300"/>
 
@@ -10,7 +7,7 @@
     <p>Build and explore efficient retrieval-augmented generative models and applications</p>
 </h4>
 
-:round_pushpin: <a href="#round_pushpin-installation">Installation</a> • :rocket: <a href="components.md">Components</a> • :books: <a href="examples.md">Examples</a> • :red_car: <a href="getting_started.md">Getting Started</a> • :pill: <a href="demo/README.md">Demos</a> • :pencil2: <a href="scripts/README.md">Scripts</a> • :bar_chart: <a href="benchmarks/README.md">Benchmarks</a>
+:round_pushpin: <a href="#round_pushpin-installation">Installation</a> • :rocket: <a href="components.md">Components</a> • :books: <a href="examples.md">Examples</a> • :red_car: <a href="getting_started.md">Getting Started</a> • :pill: <a href="Demo.md">Demos</a> • :pencil2: <a href="scripts/README.md">Scripts</a> • :bar_chart: <a href="benchmarks/README.md">Benchmarks</a>
 
 
 </div>
@@ -21,11 +18,13 @@ with a comprehensive tool-set for advancing retrieval augmented generation.
 
 Comments, suggestions, issues and pull-requests are welcomed! :heart:
 
+> [!IMPORTANT]
+> Now compatible with Haystack v2+. Please report any possible issues you find.
+
 ## :mega: Updates
 
-- **2024-04**: [(Extra Demo)](extras/rag_on_client.ipynb) **Chat with your documents on Intel Meteor Lake iGPU**.
-- **2023-12**: Gaudi2, ONNX runtime and LlamaCPP support; Optimized Embedding models; Multi-modality and Chat demos; [REPLUG](https://arxiv.org/abs/2301.12652) text generation.
-- **2023-06**: ColBERT index modification: adding/removing documents; see [IndexUpdater](https://github.com/stanford-futuredata/ColBERT/blob/main/colbert/index_updater.py).
+- **2023-12**: Gaudi2 and ONNX runtime support; Optimized Embedding models; Multi-modality and Chat demos; [REPLUG](https://arxiv.org/abs/2301.12652) text generation.
+- **2023-06**: ColBERT index modification: adding/removing documents; see [IndexUpdater](libs/colbert/colbert/index_updater.py).
 - **2023-05**: [RAG with LLM and dynamic prompt synthesis example](examples/rag-prompt-hf.ipynb).
 - **2023-04**: Qdrant `DocumentStore` support.
 
@@ -57,6 +56,10 @@ For a brief overview of the various unique components in fastRAG refer to the [C
     <td><a href="components.md#fastrag-running-llms-with-onnx-runtime">ONNX Runtime</a></td>
     <td><em>Running LLMs with optimized ONNX-runtime</td>
   </tr>
+  <tr>
+    <td><a href="components.md#fastrag-running-quantized-llms-using-openvino">OpenVINO</a></td>
+    <td><em>Running quantized LLMs using OpenVINO</td>
+  </tr>
   <tr>
     <td><a href="components.md#fastrag-running-rag-pipelines-with-llms-on-a-llama-cpp-backend">Llama-CPP</a></td>
     <td><em>Running RAG Pipelines with LLMs on a Llama CPP backend</td>
@@ -117,10 +120,6 @@ pip install .[qdrant]              # Support for Qdrant store
 pip install .[colbert]             # Support for ColBERT+PLAID; requires FAISS
 pip install .[faiss-cpu]           # CPU-based Faiss library
 pip install .[faiss-gpu]           # GPU-based Faiss library
-pip install .[knowledge_graph]     # Libraries for working with spacy and KG
-
-# User interface (for demos)
-pip install .[ui]
 
 # Benchmarking
 pip install .[benchmark]

assets/multi_modal_files/intel_image_data.json

@@ -0,0 +1,17 @@
+[
+    {
+        "image_url": "https://upload.wikimedia.org/wikipedia/commons/1/13/2200_Mission_College_Boulevard.jpg",
+        "title": "Intel Headquarters",
+        "content": "Headquarters in Santa Clara, California, in 2023"
+    },
+    {
+        "image_url": "https://upload.wikimedia.org/wikipedia/commons/6/64/Intel_8742_153056995.jpg",
+        "title": "Intel 8742",
+        "content": "The die from an Intel 8742, an 8-bit microcontroller that includes a CPU running at 12 MHz, 128 bytes of RAM, 2048 bytes of EPROM, and I/O in the same chip"
+    },
+    {
+        "image_url": "https://upload.wikimedia.org/wikipedia/commons/f/fc/Intel_Costa_12_2007_SJO_105b.jpg",
+        "title": "microprocessor facility",
+        "content": "Intel microprocessor facility in Costa Rica was responsible in 2006 for 20% of Costa Rican exports and 4.9% of the country's GDP."
+    }
+]

chainlit.md

@@ -0,0 +1 @@
+# Chat with an LLM 📚

components.md

@@ -1,7 +1,5 @@
 # fast**RAG** Components Overview
 
-
-
 ## REPLUG
 <image align="right" src="assets/replug.png" width="600">
 
@@ -12,9 +10,9 @@ process a larger number of retrieved documents, without limiting ourselves to th
 method works with any LLM, no fine-tuning is needed. See ([Shi et al. 2023](#shiREPLUGRetrievalAugmentedBlackBox2023))
 for more details.
 
-We provide implementation for the REPLUG ensembling inference, using the invocation layer
-`ReplugHFLocalInvocationLayer`; Our implementation supports most Hugging FAce models with `.generate()` capabilities (such that implement the generation mixin); For a complete example, see [REPLUG Parallel
-Reader](examples/replug_parallel_reader.ipynb) notebook.
+We provide implementation for the REPLUG ensembling inference, using the generator `ReplugGenerator`; Our implementation
+supports most Hugging FAce models with `.generate()` capabilities (such that implement the generation mixin); For a
+complete example, see [REPLUG Parallel Reader](examples/replug_parallel_reader.ipynb) notebook.
 
 ## ColBERT v2 with PLAID Engine
 
@@ -82,11 +80,10 @@ We enabled support for running LLMs on Habana Gaudi (DL1) and Habana Gaudi 2 by
 See below an example for loading a `PromptModel` with Habana backend:
 
 ```python
-from fastrag.prompters.invocation_layers.gaudi_hugging_face_inference import GaudiHFLocalInvocationLayer
+from fastrag.generators import GaudiGenerator
 
-PrompterModel = PromptModel(
+generator = GaudiGenerator(
     model_name_or_path= "meta-llama/Llama-2-7b-chat-hf",
-    invocation_layer_class=GaudiHFLocalInvocationLayer,
     model_kwargs= dict(
         max_new_tokens=50,
         torch_dtype=torch.bfloat16,
@@ -137,18 +134,60 @@ quantizer.quantize(save_dir=os.path.join(converted_model_path, 'quantized'), qua
 
 ### Loading the Quantized Model
 
-Now that our model is quantized, we can load it in our framework, by specifying the ```ORTInvocationLayer``` invocation layer.
+Now that our model is quantized, we can load it in our framework, by using the ```ORTGenerator``` generator.
 
 ```python
-PrompterModel = PromptModel(
-    model_name_or_path= "my/local/path/quantized",
-    invocation_layer_class=ORTInvocationLayer,
+generator = ORTGenerator(
+    model="my/local/path/quantized",
+    task="text-generation",
+    generation_kwargs={
+        "max_new_tokens": 100,
+    }
+)
+```
+
+## fastRAG running quantized LLMs using OpenVINO
+
+We provide a method for running quantized LLMs with [OpenVINO](https://docs.openvino.ai/2024/home.html) and [optimum-intel](https://github.com/huggingface/optimum-intel).
+We recommend checking out our [notebook](examples/rag_with_openvino.ipynb) with all the details, including the quantization and pipeline construction.
+
+### Installation
+
+Run the following command to install our dependencies:
+
+```
+pip install -e .[openvino]
+```
+
+For more information regarding the installation process, we recommend checking out the guides provided by [OpenVINO](https://docs.openvino.ai/2024/home.html) and [optimum-intel](https://github.com/huggingface/optimum-intel).
+
+### LLM Quantization
+
+We can use the [OpenVINO tutorial notebook](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/254-llm-chatbot/254-llm-chatbot.ipynb) to quantize an LLM to our liking.
+
+### Loading the Quantized Model
+
+Now that our model is quantized, we can load it in our framework, by using the ```OpenVINOGenerator``` component.
+
+```python
+from fastrag.generators.openvino import OpenVINOGenerator
+
+openvino_compressed_model_path = "path/to/model"
+
+generator = OpenVINOGenerator(
+    model="microsoft/phi-2",
+    compressed_model_dir=openvino_compressed_model_path,
+    device_openvino="CPU",
+    task="text-generation",
+    generation_kwargs={
+        "max_new_tokens": 100,
+    }
 )
 ```
 
 ## fastRAG Running RAG Pipelines with LLMs on a Llama CPP backend
 
-To run LLM effectively on CPUs, especially on client side machines, we offer a method for running LLMs using the [llama-cpp](https://github.com/ggerganov/llama.cpp).
+To run LLMs effectively on CPUs, especially on client side machines, we offer a method for running LLMs using the [llama-cpp](https://github.com/ggerganov/llama.cpp).
 We recommend checking out our [tutorial notebook](examples/client_inference_with_Llama_cpp.ipynb) with all the details, including processes such as downloading GGUF models.
 
 ### Installation
@@ -176,7 +215,6 @@ PrompterModel = PromptModel(
 )
 ```
 
-
 ## Optimized Embedding Models
 
 Bi-encoder Embedders are key components of Retrieval Augmented Generation pipelines. Mainly used for indexing documents and for online re-ranking. We provide support for quantized `int8` models that have low latency and high throughput, using [`optimum-intel`](https://github.com/huggingface/optimum-intel) framework.

config/data/wikipedia_hf_6M.yaml

@@ -5,3 +5,4 @@ dataset_info:
     split: "train"
 encoding_method: "wikipedia_hf_multisentence"
 batch_size: 500
+limit:

config/data/wikitext_hf.yaml

@@ -0,0 +1,8 @@
+type: fastrag.data_loaders.HFDatasetLoader
+dataset_info:
+    path: wikitext
+    name: wikitext-2-v1
+    split: train
+encoding_method: text
+batch_size: 100
+limit:

config/doc_chat_ort.yaml

@@ -6,5 +6,4 @@ chat_model:
       session.intra_op_thread_affinities: '3,4;5,6;7,8;9,10;11,12'
     intra_op_num_threads: 6
   model_name_or_path: '/tmp/facebook_opt-iml-max-1.3b/quantized'
-  invocation_layer_class: fastrag.prompters.invocation_layers.ort.ORTInvocationLayer
 doc_pipeline_file: "config/empty_retrieval_pipeline.yaml"

config/embedder/sentence-transformer-docs.yaml

@@ -0,0 +1,7 @@
+type: haystack.components.embedders.SentenceTransformersDocumentEmbedder
+init_parameters:
+    model: "BAAI/llm-embedder"
+    meta_fields_to_embed: ['title']  # optional
+    prefix: "Represent this document for retrieval: "
+    batch_size: 256
+    device:

config/embedder/sentence-transformer-text.yaml

@@ -0,0 +1,6 @@
+type: haystack.components.embedders.SentenceTransformersTextEmbedder
+init_parameters:
+    model: "BAAI/llm-embedder"
+    prefix: "Represent this query for retrieving relevant documents: "
+    batch_size: 256
+    device: