Harrison/elastic search (#2419)

docs/modules/indexes/retrievers/examples/elastic_search_bm25.ipynb

@@ -0,0 +1,164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ab66dd43",
+   "metadata": {},
+   "source": [
+    "# ElasticSearch BM25\n",
+    "\n",
+    "This notebook goes over how to use a retriever that under the hood uses ElasticSearcha and BM25.\n",
+    "\n",
+    "For more information on the details of BM25 see [this blog post](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "393ac030",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.retrievers import ElasticSearchBM25Retriever"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aaf80e7f",
+   "metadata": {},
+   "source": [
+    "## Create New Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bcb3c8c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "elasticsearch_url=\"http://localhost:9200\"\n",
+    "retriever = ElasticSearchBM25Retriever.create(elasticsearch_url, \"langchain-index-3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b605284d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Alternatively, you can load an existing index\n",
+    "# import elasticsearch\n",
+    "# elasticsearch_url=\"http://localhost:9200\"\n",
+    "# retriever = ElasticSearchBM25Retriever(elasticsearch.Elasticsearch(elasticsearch_url), \"langchain-index\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c518c42",
+   "metadata": {},
+   "source": [
+    "## Add texts (if necessary)\n",
+    "\n",
+    "We can optionally add texts to the retriever (if they aren't already in there)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "98b1c017",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['386c76c9-4355-4c12-aaeb-7b80054caf93',\n",
+       " 'fffd279c-a0c9-4158-a904-6e242c517c99',\n",
+       " '7f5528a3-18d0-43b0-894d-f6770a002219',\n",
+       " 'e2ef5e32-d5bd-44e2-b045-cfc5a8e0a0a1',\n",
+       " 'cce8ba48-e473-4235-bca2-2c8d65e73ccf']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever.add_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08437fa2",
+   "metadata": {},
+   "source": [
+    "## Use Retriever\n",
+    "\n",
+    "We can now use the retriever!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c0455218",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = retriever.get_relevant_documents(\"foo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7dfa5c29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='foo', metadata={}),\n",
+       " Document(page_content='foo bar', metadata={})]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74bd9256",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

langchain/retrievers/__init__.py

@@ -1,4 +1,5 @@
 from langchain.retrievers.chatgpt_plugin_retriever import ChatGPTPluginRetriever
+from langchain.retrievers.elastic_search_bm25 import ElasticSearchBM25Retriever
 from langchain.retrievers.metal import MetalRetriever
 from langchain.retrievers.pinecone_hybrid_search import PineconeHybridSearchRetriever
 from langchain.retrievers.remote_retriever import RemoteLangChainRetriever
@@ -8,4 +9,5 @@ __all__ = [
     "RemoteLangChainRetriever",
     "PineconeHybridSearchRetriever",
     "MetalRetriever",
+    "ElasticSearchBM25Retriever",
 ]

langchain/retrievers/elastic_search_bm25.py

@@ -0,0 +1,126 @@
+"""Wrapper around Elasticsearch vector database."""
+from __future__ import annotations
+
+import uuid
+from typing import Any, Iterable, List
+
+from langchain.docstore.document import Document
+from langchain.schema import BaseRetriever
+
+
+class ElasticSearchBM25Retriever(BaseRetriever):
+    """Wrapper around Elasticsearch using BM25 as a retrieval method.
+
+
+    To connect to an Elasticsearch instance that requires login credentials,
+    including Elastic Cloud, use the Elasticsearch URL format
+    https://username:password@es_host:9243. For example, to connect to Elastic
+    Cloud, create the Elasticsearch URL with the required authentication details and
+    pass it to the ElasticVectorSearch constructor as the named parameter
+    elasticsearch_url.
+
+    You can obtain your Elastic Cloud URL and login credentials by logging in to the
+    Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
+    navigating to the "Deployments" page.
+
+    To obtain your Elastic Cloud password for the default "elastic" user:
+
+    1. Log in to the Elastic Cloud console at https://cloud.elastic.co
+    2. Go to "Security" > "Users"
+    3. Locate the "elastic" user and click "Edit"
+    4. Click "Reset password"
+    5. Follow the prompts to reset the password
+
+    The format for Elastic Cloud URLs is
+    https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
+    """
+
+    def __init__(self, client: Any, index_name: str):
+        self.client = client
+        self.index_name = index_name
+
+    @classmethod
+    def create(
+        cls, elasticsearch_url: str, index_name: str, k1: float = 2.0, b: float = 0.75
+    ) -> ElasticSearchBM25Retriever:
+        from elasticsearch import Elasticsearch
+
+        # Create an Elasticsearch client instance
+        es = Elasticsearch(elasticsearch_url)
+
+        # Define the index settings and mappings
+        index_settings = {
+            "settings": {
+                "analysis": {"analyzer": {"default": {"type": "standard"}}},
+                "similarity": {
+                    "custom_bm25": {
+                        "type": "BM25",
+                        "k1": k1,
+                        "b": b,
+                    }
+                },
+            },
+            "mappings": {
+                "properties": {
+                    "content": {
+                        "type": "text",
+                        "similarity": "custom_bm25",  # Use the custom BM25 similarity
+                    }
+                }
+            },
+        }
+
+        # Create the index with the specified settings and mappings
+        es.indices.create(index=index_name, body=index_settings)
+        return cls(es, index_name)
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        refresh_indices: bool = True,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the retriver.
+
+        Args:
+            texts: Iterable of strings to add to the retriever.
+            refresh_indices: bool to refresh ElasticSearch indices
+
+        Returns:
+            List of ids from adding the texts into the retriever.
+        """
+        try:
+            from elasticsearch.helpers import bulk
+        except ImportError:
+            raise ValueError(
+                "Could not import elasticsearch python package. "
+                "Please install it with `pip install elasticsearch`."
+            )
+        requests = []
+        ids = []
+        for i, text in enumerate(texts):
+            _id = str(uuid.uuid4())
+            request = {
+                "_op_type": "index",
+                "_index": self.index_name,
+                "content": text,
+                "_id": _id,
+            }
+            ids.append(_id)
+            requests.append(request)
+        bulk(self.client, requests)
+
+        if refresh_indices:
+            self.client.indices.refresh(index=self.index_name)
+        return ids
+
+    def get_relevant_documents(self, query: str) -> List[Document]:
+        query_dict = {"query": {"match": {"content": query}}}
+        res = self.client.search(index=self.index_name, body=query_dict)
+
+        docs = []
+        for r in res["hits"]["hits"]:
+            docs.append(Document(page_content=r["_source"]["content"]))
+        return docs
+
+    async def aget_relevant_documents(self, query: str) -> List[Document]:
+        raise NotImplementedError