ElasticVectorSearch: Add in vector search backed by Elastic (#67)

![image](https://user-images.githubusercontent.com/6690839/200147455-33a68e20-c3c0-4045-9bff-598b38ae8fb2.png)

woo!

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

README.md

@@ -47,6 +47,11 @@ The following use cases require specific installs and api keys:
   - Install requirements with `pip install playwright`
 - _Wikipedia_:
   - Install requirements with `pip install wikipedia`
+- _Elasticsearch_:
+  - Install requirements with `pip install elasticsearch`
+  - Set up Elasticsearch backend. If you want to do locally, [this](https://www.elastic.co/guide/en/elasticsearch/reference/7.17/getting-started.html) is a good guide.
+- _FAISS_:
+  - Install requirements with `pip install faiss` for Python 3.7 and `pip install faiss-cpu` for Python 3.10+.
 
 ## 🚀 What can I do with this
 
@@ -98,6 +103,28 @@ question = "What NFL team won the Super Bowl in the year Justin Beiber was born?
 llm_chain.predict(question=question)
 ```
 
+**Embed & Search Documents**
+
+We support two vector databases to store and search embeddings -- FAISS and Elasticsearch. Here's a code snippet showing how to use FAISS to store embeddings and search for text similar to a query. Both database backends are featured in this [example notebook] (https://github.com/hwchase17/langchain/blob/master/notebooks/examples/embeddings.ipynb).
+
+```
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.faiss import FAISS
+from langchain.text_splitter import CharacterTextSplitter
+
+with open('state_of_the_union.txt') as f:
+    state_of_the_union = f.read()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+texts = text_splitter.split_text(state_of_the_union)
+
+embeddings = OpenAIEmbeddings()
+
+docsearch = FAISS.from_texts(texts, embeddings)
+
+query = "What did the president say about Ketanji Brown Jackson"
+docs = docsearch.similarity_search(query)
+```
+
 ## 📖 Documentation
 
 The above examples are probably the most user friendly documentation that exists,

examples/embeddings.ipynb

@@ -8,6 +8,7 @@
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.elastic_vector_search import ElasticVectorSearch\n",
     "from langchain.faiss import FAISS\n",
     "from langchain.text_splitter import CharacterTextSplitter"
    ]
@@ -24,8 +25,7 @@
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "texts = text_splitter.split_text(state_of_the_union)\n",
     "\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "docsearch = FAISS.from_texts(texts, embeddings)"
+    "embeddings = OpenAIEmbeddings()"
    ]
   },
   {
@@ -35,6 +35,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "docsearch = FAISS.from_texts(texts, embeddings)\n",
+    "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "docs = docsearch.similarity_search(query)"
    ]
@@ -65,10 +67,49 @@
     "print(docs[0].page_content)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4906b8a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = ElasticVectorSearch.from_texts(\"http://localhost:9200\", texts, embeddings)\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "95f9eee9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25500fa6",
+   "id": "70a253c4",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -90,7 +131,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

langchain/__init__.py

@@ -16,6 +16,7 @@ from langchain.chains import (
     SQLDatabaseChain,
 )
 from langchain.docstore import Wikipedia
+from langchain.elastic_vector_search import ElasticVectorSearch
 from langchain.faiss import FAISS
 from langchain.llms import Cohere, HuggingFaceHub, OpenAI
 from langchain.prompts import BasePrompt, DynamicPrompt, Prompt
@@ -39,4 +40,5 @@ __all__ = [
     "SQLDatabaseChain",
     "FAISS",
     "MRKLChain",
+    "ElasticVectorSearch",
 ]

langchain/elastic_vector_search.py

@@ -0,0 +1,146 @@
+"""Wrapper around Elasticsearch vector database."""
+import uuid
+from typing import Callable, Dict, List
+
+from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
+
+
+def _default_text_mapping(dim: int) -> Dict:
+    return {
+        "properties": {
+            "text": {"type": "text"},
+            "vector": {"type": "dense_vector", "dims": dim},
+        }
+    }
+
+
+def _default_script_query(query_vector: List[int]) -> Dict:
+    return {
+        "script_score": {
+            "query": {"match_all": {}},
+            "script": {
+                "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",
+                "params": {"query_vector": query_vector},
+            },
+        }
+    }
+
+
+class ElasticVectorSearch:
+    """Wrapper around Elasticsearch as a vector database.
+
+    Example:
+        .. code-block:: python
+
+            from langchain import ElasticVectorSearch
+            elastic_vector_search = ElasticVectorSearch(
+                "http://localhost:9200",
+                "embeddings",
+                mapping,
+                embedding_function
+            )
+
+    """
+
+    def __init__(
+        self,
+        elastic_url: str,
+        index_name: str,
+        mapping: Dict,
+        embedding_function: Callable,
+    ):
+        """Initialize with necessary components."""
+        try:
+            import elasticsearch
+        except ImportError:
+            raise ValueError(
+                "Could not import elasticsearch python packge. "
+                "Please install it with `pip install elasticearch`."
+            )
+        self.embedding_function = embedding_function
+        self.index_name = index_name
+        try:
+            es_client = elasticsearch.Elasticsearch(elastic_url)  # noqa
+        except ValueError as e:
+            raise ValueError(
+                "Your elasticsearch client string is misformatted. " f"Got error: {e} "
+            )
+        self.client = es_client
+        self.mapping = mapping
+
+    def similarity_search(self, query: str, k: int = 4) -> List[Document]:
+        """Return docs most similar to query.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents most similar to the query.
+        """
+        embedding = self.embedding_function(query)
+        script_query = _default_script_query(embedding)
+        response = self.client.search(index=self.index_name, query=script_query)
+        texts = [hit["_source"]["text"] for hit in response["hits"]["hits"][:k]]
+        documents = [Document(page_content=text) for text in texts]
+        return documents
+
+    @classmethod
+    def from_texts(
+        cls, elastic_url: str, texts: List[str], embedding: Embeddings
+    ) -> "ElasticVectorSearch":
+        """Construct ElasticVectorSearch wrapper from raw documents.
+
+        This is a user friendly interface that:
+            1. Embeds documents.
+            2. Creates a new index for the embeddings in the Elasticsearch instance.
+            3. Adds the documents to the newly created Elasticsearch index.
+
+        This is intended to be a quick way to get started.
+
+        Example:
+            .. code-block:: python
+
+                from langchain import ElasticVectorSearch
+                from langchain.embeddings import OpenAIEmbeddings
+                embeddings = OpenAIEmbeddings()
+                elastic_vector_search = ElasticVectorSearch.from_texts(
+                    "http://localhost:9200",
+                    texts,
+                    embeddings
+                )
+        """
+        try:
+            import elasticsearch
+            from elasticsearch.helpers import bulk
+        except ImportError:
+            raise ValueError(
+                "Could not import elasticsearch python packge. "
+                "Please install it with `pip install elasticearch`."
+            )
+        try:
+            client = elasticsearch.Elasticsearch(elastic_url)
+        except ValueError as e:
+            raise ValueError(
+                "Your elasticsearch client string is misformatted. " f"Got error: {e} "
+            )
+        index_name = uuid.uuid4().hex
+        embeddings = embedding.embed_documents(texts)
+        dim = len(embeddings[0])
+        mapping = _default_text_mapping(dim)
+        # TODO would be nice to create index before embedding,
+        # just to save expensive steps for last
+        client.indices.create(index=index_name, mappings=mapping)
+        requests = []
+        for i, text in enumerate(texts):
+            request = {
+                "_op_type": "index",
+                "_index": index_name,
+                "vector": embeddings[i],
+                "text": text,
+            }
+            requests.append(request)
+        bulk(client, requests)
+        client.indices.refresh(index=index_name)
+        return cls(elastic_url, index_name, mapping, embedding.embed_query)

requirements.txt

@@ -1,6 +1,7 @@
 -r test_requirements.txt
 # For integrations
 cohere
+elasticsearch
 openai
 google-search-results
 nlpcloud