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Add chroma example
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| { | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0, | ||
| "metadata": { | ||
| "colab": { | ||
| "provenance": [] | ||
| }, | ||
| "kernelspec": { | ||
| "name": "python3", | ||
| "display_name": "Python 3" | ||
| }, | ||
| "language_info": { | ||
| "name": "python" | ||
| } | ||
| }, | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "# Use ChromaDocumentStore with Haystack\n", | ||
| "\n" | ||
| ], | ||
| "metadata": { | ||
| "id": "ZjlwUPWugM37" | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| ">[Use ChromaDocumentStore with Haystack](#scrollTo=ZjlwUPWugM37)\n", | ||
| "\n", | ||
| ">>[Install dependencies](#scrollTo=135w48jbgRRU)\n", | ||
| "\n", | ||
| ">>[Indexing Pipeline: preprocess, split and index documents](#scrollTo=gt_XhGXBgU-I)\n", | ||
| "\n", | ||
| ">>[Query Pipeline: build retrieval-augmented generation (RAG) pipelines](#scrollTo=44cRT55agw2e)\n", | ||
| "\n" | ||
| ], | ||
| "metadata": { | ||
| "colab_type": "toc", | ||
| "id": "TjEesvJKiYKT" | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "## Install dependencies" | ||
| ], | ||
| "metadata": { | ||
| "id": "135w48jbgRRU" | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "id": "znSRD-hO2doM" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Install the Chroma integration, Haystack will come as a dependency\n", | ||
| "!pip install -U chroma-haystack" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "## Indexing Pipeline: preprocess, split and index documents\n", | ||
| "In this section, we will index documents into a Chroma DB collection by building a Haystack indexing pipeline. Here, we are indexing documents from the [VIM User Manuel](https://vimhelp.org/) into the Haystack `ChromaDocumentStore`.\n", | ||
| "\n", | ||
| " We have the `.txt` files for these pages in the examples folder for the `ChromaDocumentStore`, so we are using the [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument) and [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter) components to build this indexing pipeline." | ||
| ], | ||
| "metadata": { | ||
| "id": "gt_XhGXBgU-I" | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "# Fetch data files from the Github repo\n", | ||
| "!curl -sL https://github.com/deepset-ai/haystack-core-integrations/tarball/main -o main.tar\n", | ||
| "!mkdir main\n", | ||
| "!tar xf main.tar -C main --strip-components 1\n", | ||
| "!mv main/integrations/chroma/example/data ." | ||
| ], | ||
| "metadata": { | ||
| "id": "fGxsA9C74BWr" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "import os\n", | ||
| "from pathlib import Path\n", | ||
| "\n", | ||
| "from haystack import Pipeline\n", | ||
| "from haystack.components.converters import TextFileToDocument\n", | ||
| "from haystack.components.writers import DocumentWriter\n", | ||
| "\n", | ||
| "from chroma_haystack import ChromaDocumentStore\n", | ||
| "\n", | ||
| "file_paths = [\"data\" / Path(name) for name in os.listdir(\"data\")]\n", | ||
| "\n", | ||
| "# Chroma is used in-memory so we use the same instances in the two pipelines below\n", | ||
| "document_store = ChromaDocumentStore()\n", | ||
| "\n", | ||
| "indexing = Pipeline()\n", | ||
| "indexing.add_component(\"converter\", TextFileToDocument())\n", | ||
| "indexing.add_component(\"writer\", DocumentWriter(document_store))\n", | ||
| "indexing.connect(\"converter\", \"writer\")\n", | ||
| "indexing.run({\"converter\": {\"sources\": file_paths}})\n" | ||
| ], | ||
| "metadata": { | ||
| "id": "ayyBKQIC3jGo" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "## Query Pipeline: build retrieval-augmented generation (RAG) pipelines\n", | ||
| "\n", | ||
| "Once we have documents in the `ChromaDocumentStore`, we can use the accompanying Chroma retrievers to build a query pipeline. The query pipeline below is a simple retrieval-augmented generation (RAG) pipeline that uses Chroma's [query API](https://docs.trychroma.com/usage-guide#querying-a-collection).\n", | ||
| "\n", | ||
| "You can change the idnexing pipeline and query pipelines here for embedding search by using one of the [`Haystack Embedders`](https://docs.haystack.deepset.ai/v2.0/docs/embedders) accompanied by the `ChromaEmbeddingRetriever`.\n", | ||
| "\n", | ||
| "\n", | ||
| "In this example we are using:\n", | ||
| "- The `HuggingFaceTGIGenerator` with the Mistral 8x7B model. (You will need a Hugging Face token to use this model). You can repleace this with any of the other [`Generators`](https://docs.haystack.deepset.ai/v2.0/docs/generators)\n", | ||
| "- The `PromptBuilder` which holds the prompt template. You can adjust this to a prompt of your choice\n", | ||
| "- The `ChromaQueryRetriver` which expects a list of queries and retieves the `top_k` most relevant documents from your Chroma collection." | ||
| ], | ||
| "metadata": { | ||
| "id": "44cRT55agw2e" | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "from getpass import getpass\n", | ||
| "\n", | ||
| "hf_token = getpass(\"Enter Hugging Face API key:\")" | ||
| ], | ||
| "metadata": { | ||
| "id": "WGGApIR3pllW" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "from chroma_haystack.retriever import ChromaQueryRetriever\n", | ||
| "from haystack.components.generators import HuggingFaceTGIGenerator\n", | ||
| "from haystack.components.builders import PromptBuilder\n", | ||
| "\n", | ||
| "prompt = \"\"\"\n", | ||
| "Answer the query based on the provided context.\n", | ||
| "If the context does not contain the answer, say 'Answer not found'.\n", | ||
| "Context:\n", | ||
| "{% for doc in documents %}\n", | ||
| " {{ doc.content }}\n", | ||
| "{% endfor %}\n", | ||
| "query: {{query}}\n", | ||
| "Answer:\n", | ||
| "\"\"\"\n", | ||
| "prompt_builder = PromptBuilder(template=prompt)\n", | ||
| "\n", | ||
| "llm = HuggingFaceTGIGenerator(model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\", token=hf_token)\n", | ||
| "llm.warm_up()\n", | ||
| "retriever = ChromaQueryRetriever(document_store)\n", | ||
| "\n", | ||
| "querying = Pipeline()\n", | ||
| "querying.add_component(\"retriever\", retriever)\n", | ||
| "querying.add_component(\"prompt_builder\", prompt_builder)\n", | ||
| "querying.add_component(\"llm\", llm)\n", | ||
| "\n", | ||
| "querying.connect(\"retriever.documents\", \"prompt_builder.documents\")\n", | ||
| "querying.connect(\"prompt_builder\", \"llm\")" | ||
| ], | ||
| "metadata": { | ||
| "id": "YQJTPYNreNV-" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "query = \"Should I write documentation for my plugin?\"\n", | ||
| "results = querying.run({\"retriever\": {\"queries\": [query], \"top_k\": 3},\n", | ||
| " \"prompt_builder\": {\"query\": query},\n", | ||
| " \"llm\":{\"generation_kwargs\": {\"max_new_tokens\": 350}}})" | ||
| ], | ||
| "metadata": { | ||
| "id": "O8jcmcdqrGu1" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "print(results[\"llm\"][\"replies\"][0])" | ||
| ], | ||
| "metadata": { | ||
| "id": "Pa7f7EzjtBXw" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| } | ||
| ] | ||
| } |