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"pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] +[[package]] +name = "sniffio" +version = "1.3.1" +description = "Sniff out which async library your code is running under" +optional = false +python-versions = ">=3.7" +files = [ + {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"}, + {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"}, +] + [[package]] name = "swagger-ui-bundle" version = "0.0.9" @@ -559,6 +783,17 @@ files = [ [package.dependencies] Jinja2 = ">=2.0" +[[package]] +name = "typing-extensions" +version = "4.10.0" +description = "Backported and Experimental Type Hints for Python 3.8+" +optional = false +python-versions = ">=3.8" +files = [ + {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"}, + {file = 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parameters provided to the Elasticsearch " @@ -23,7 +26,28 @@ def __init__(self, connection_params=None, index=None, search_limit=10): self.index = index self.search_limit = search_limit + self.do_vector = False + if search_type == "vector": + assert ( + "embedding" + in self.client.indices.get(index=index)[index]["mappings"]["properties"] + ), f"Supplied index, {index}, is not a vector index" + + if api_key := app.config.get("COHERE_API_KEY"): + self.co = cohere.Client(api_key=api_key) + else: + raise ValueError( + "If doing vector search, a Cohere API key must be provided." + ) + + self.do_vector = True + def search(self, query): + if self.do_vector: + return self.vector_search(query) + return self.default_search(query) + + def default_search(self, query): es_query_body = { "query": {"multi_match": {"query": query}}, "highlight": { @@ -44,6 +68,32 @@ def search(self, query): return response["hits"]["hits"] + def vector_search(self, query): + response = self.client.search( + index=self.index, + knn={ + "field": "embedding", + "query_vector": self.get_query_embedding(query), + "num_candidates": 50, + "k": 10, + }, + size=self.search_limit, + ) + + if response.get("hits", {}).get("hits") is None: + raise UpstreamProviderError( + "Error while searching Elasticsearch with " f"query: '{query}'." + ) + + return response["hits"]["hits"] + + def get_query_embedding(self, query): + r = self.co.embed( + texts=[query], model="embed-english-v3.0", input_type="search_query" + ) + + return r.embeddings[0] + def get_client(): global client @@ -77,6 +127,10 @@ def get_client(): assert (index := app.config.get("INDEX")), "ELASTIC_INDEX must be set" search_limit = app.config.get("SEARCH_LIMIT", 10) - client = ElasticsearchClient(connection_params, index, search_limit) + search_type = app.config.get("SEARCH_TYPE") + + client = ElasticsearchClient( + connection_params, index, search_limit, search_type + ) return client diff --git a/elastic/pyproject.toml b/elastic/pyproject.toml index 6a24618dd..7ff743332 100644 --- a/elastic/pyproject.toml +++ b/elastic/pyproject.toml @@ -12,6 +12,7 @@ flask = "2.2.5" connexion = {extras = ["swagger-ui"], version = "^2.14.2"} gunicorn = "^20.1.0" python-dotenv = "^1.0.0" +cohere = "^5.1.2" [build-system]