From 507cee5ee5038d3758e1d477668498c0083d0a21 Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Tue, 11 Apr 2023 21:32:17 -0700 Subject: [PATCH] Harrison/pinecone hybrid update (#2742) Co-authored-by: acatav <39461369+acatav@users.noreply.github.com> Co-authored-by: Amnon Catav --- .../examples/pinecone_hybrid_search.ipynb | 156 ++++-- .../retrievers/pinecone_hybrid_search.py | 101 ++-- poetry.lock | 501 ++++++------------ pyproject.toml | 8 +- 4 files changed, 311 insertions(+), 455 deletions(-) diff --git a/docs/modules/indexes/retrievers/examples/pinecone_hybrid_search.ipynb b/docs/modules/indexes/retrievers/examples/pinecone_hybrid_search.ipynb index 79b9e375..a3680aa9 100644 --- a/docs/modules/indexes/retrievers/examples/pinecone_hybrid_search.ipynb +++ b/docs/modules/indexes/retrievers/examples/pinecone_hybrid_search.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "ab66dd43", "metadata": {}, @@ -9,12 +10,12 @@ "\n", "This notebook goes over how to use a retriever that under the hood uses Pinecone and Hybrid Search.\n", "\n", - "The logic of this retriever is largely taken from [this blog post](https://www.pinecone.io/learn/hybrid-search-intro/)" + "The logic of this retriever is taken from [this documentaion](https://docs.pinecone.io/docs/hybrid-search)" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 75, "id": "393ac030", "metadata": {}, "outputs": [], @@ -31,43 +32,61 @@ ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "15390796", + "attachments": {}, + "cell_type": "markdown", + "id": "95d5d7f9", "metadata": {}, - "outputs": [], "source": [ - "import pinecone # !pip install pinecone-client\n", + "You should only have to do this part once.\n", "\n", - "pinecone.init(\n", - " api_key=\"...\", # API key here\n", - " environment=\"...\" # find next to api key in console\n", - ")\n", - "# choose a name for your index\n", - "index_name = \"...\"" + "Note: it's important to make sure that the \"context\" field that holds the document text in the metadata is not indexed. Currently you need to specify explicitly the fields you do want to index. For more information checkout Pinecone's [docs](https://docs.pinecone.io/docs/manage-indexes#selective-metadata-indexing)." ] }, { - "cell_type": "markdown", - "id": "95d5d7f9", + "cell_type": "code", + "execution_count": 76, + "id": "3b8f7697", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "WhoAmIResponse(username='load', user_label='label', projectname='load-test')" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "You should only have to do this part once." + "import os\n", + "import pinecone\n", + "\n", + "api_key = os.getenv(\"PINECONE_API_KEY\") or \"PINECONE_API_KEY\"\n", + "# find environment next to your API key in the Pinecone console\n", + "env = os.getenv(\"PINECONE_ENVIRONMENT\") or \"PINECONE_ENVIRONMENT\"\n", + "\n", + "index_name = \"langchain-pinecone-hybrid-search\"\n", + "\n", + "pinecone.init(api_key=api_key, enviroment=env)\n", + "pinecone.whoami()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "id": "cfa3a8d8", "metadata": {}, "outputs": [], "source": [ - "# create the index\n", + " # create the index\n", "pinecone.create_index(\n", " name = index_name,\n", " dimension = 1536, # dimensionality of dense model\n", - " metric = \"dotproduct\",\n", - " pod_type = \"s1\"\n", + " metric = \"dotproduct\", # sparse values supported only for dotproduct\n", + " pod_type = \"s1\",\n", + " metadata_config={\"indexed\": []} # see explaination above\n", ")" ] }, @@ -81,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 78, "id": "bcb3c8c2", "metadata": {}, "outputs": [], @@ -90,18 +109,19 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "dbc025d6", "metadata": {}, "source": [ - "## Get embeddings and tokenizers\n", + "## Get embeddings and sparse encoders\n", "\n", "Embeddings are used for the dense vectors, tokenizer is used for the sparse vector" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 79, "id": "2f63c911", "metadata": {}, "outputs": [], @@ -110,19 +130,51 @@ "embeddings = OpenAIEmbeddings()" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "96bf8879", + "metadata": {}, + "source": [ + "To encode the text to sparse values you can either choose SPLADE or BM25. For out of domain tasks we recommend using BM25.\n", + "\n", + "For more information about the sparse encoders you can checkout pinecone-text library [docs](https://pinecone-io.github.io/pinecone-text/pinecone_text.html)." + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 80, "id": "c3f030e5", "metadata": {}, "outputs": [], "source": [ - "from transformers import BertTokenizerFast # !pip install transformers\n", + "from pinecone_text.sparse import BM25Encoder\n", + "# or from pinecone_text.sparse import SpladeEncoder if you wish to work with SPLADE\n", "\n", - "# load bert tokenizer from huggingface\n", - "tokenizer = BertTokenizerFast.from_pretrained(\n", - " 'bert-base-uncased'\n", - ")" + "# use default tf-idf values\n", + "bm25_encoder = BM25Encoder().default()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "23601ddb", + "metadata": {}, + "source": [ + "The above code is using default tfids values. It's highly recommended to fit the tf-idf values to your own corpus. You can do it as follow:\n", + "\n", + "```python\n", + "corpus = [\"foo\", \"bar\", \"world\", \"hello\"]\n", + "\n", + "# fit tf-idf values on your corpus\n", + "bm25_encoder.fit(corpus)\n", + "\n", + "# store the values to a json file\n", + "bm25_encoder.dump(\"bm25_values.json\")\n", + "\n", + "# load to your BM25Encoder object\n", + "bm25_encoder = BM25Encoder().load(\"bm25_values.json\")\n", + "```" ] }, { @@ -137,12 +189,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 81, "id": "ac77d835", "metadata": {}, "outputs": [], "source": [ - "retriever = PineconeHybridSearchRetriever(embeddings=embeddings, index=index, tokenizer=tokenizer)" + "retriever = PineconeHybridSearchRetriever(embeddings=embeddings, sparse_encoder=bm25_encoder, index=index)" ] }, { @@ -157,23 +209,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 82, "id": "98b1c017", "metadata": {}, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4d6f3ee7ca754d07a1a18d100d99e0cd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1 [00:00 List[Dict]: - # store a batch of sparse embeddings - sparse_emb = [] - # iterate through input batch - for token_ids in input_batch: - indices = [] - values = [] - # convert the input_ids list to a dictionary of key to frequency values - d = dict(Counter(token_ids)) - for idx in d: - indices.append(idx) - values.append(d[idx]) - sparse_emb.append({"indices": indices, "values": values}) - # return sparse_emb list - return sparse_emb +def hash_text(text: str) -> str: + return str(hashlib.sha256(text.encode("utf-8")).hexdigest()) def create_index( - contexts: List[str], index: Any, embeddings: Embeddings, tokenizer: Any + contexts: List[str], + index: Any, + embeddings: Embeddings, + sparse_encoder: Any, + ids: Optional[List[str]] = None, ) -> None: batch_size = 32 _iterator = range(0, len(contexts), batch_size) @@ -37,28 +28,33 @@ def create_index( except ImportError: pass + if ids is None: + # create unique ids using hash of the text + ids = [hash_text(context) for context in contexts] + for i in _iterator: # find end of batch i_end = min(i + batch_size, len(contexts)) # extract batch context_batch = contexts[i:i_end] - # create unique IDs - ids = [str(x) for x in range(i, i_end)] + batch_ids = ids[i:i_end] # add context passages as metadata meta = [{"context": context} for context in context_batch] # create dense vectors dense_embeds = embeddings.embed_documents(context_batch) # create sparse vectors - sparse_embeds = generate_sparse_vectors(context_batch, tokenizer) + sparse_embeds = sparse_encoder.encode_documents(context_batch) for s in sparse_embeds: s["values"] = [float(s1) for s1 in s["values"]] vectors = [] # loop through the data and create dictionaries for upserts - for _id, sparse, dense, metadata in zip(ids, sparse_embeds, dense_embeds, meta): + for doc_id, sparse, dense, metadata in zip( + batch_ids, sparse_embeds, dense_embeds, meta + ): vectors.append( { - "id": _id, + "id": doc_id, "sparse_values": sparse, "values": dense, "metadata": metadata, @@ -69,38 +65,10 @@ def create_index( index.upsert(vectors) -def generate_sparse_vectors(context_batch: List[str], tokenizer: Any) -> List[Dict]: - # create batch of input_ids - inputs = tokenizer( - context_batch, - padding=True, - truncation=True, - max_length=512, # special_tokens=False - )["input_ids"] - # create sparse dictionaries - sparse_embeds = build_dict(inputs) - return sparse_embeds - - -def hybrid_scale( - dense: List[float], sparse: Dict, alpha: float -) -> Tuple[List[float], Dict]: - # check alpha value is in range - if alpha < 0 or alpha > 1: - raise ValueError("Alpha must be between 0 and 1") - # scale sparse and dense vectors to create hybrid search vecs - hsparse = { - "indices": sparse["indices"], - "values": [v * (1 - alpha) for v in sparse["values"]], - } - hdense = [v * alpha for v in dense] - return hdense, hsparse - - class PineconeHybridSearchRetriever(BaseRetriever, BaseModel): embeddings: Embeddings + sparse_encoder: Any index: Any - tokenizer: Any top_k: int = 4 alpha: float = 0.5 @@ -110,15 +78,32 @@ class PineconeHybridSearchRetriever(BaseRetriever, BaseModel): extra = Extra.forbid arbitrary_types_allowed = True - def add_texts(self, texts: List[str]) -> None: - create_index(texts, self.index, self.embeddings, self.tokenizer) + def add_texts(self, texts: List[str], ids: Optional[List[str]] = None) -> None: + create_index(texts, self.index, self.embeddings, self.sparse_encoder, ids=ids) + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate that api key and python package exists in environment.""" + try: + from pinecone_text.hybrid import hybrid_convex_scale # noqa:F401 + from pinecone_text.sparse.base_sparse_encoder import ( + BaseSparseEncoder, # noqa:F401 + ) + except ImportError: + raise ValueError( + "Could not import pinecone_text python package. " + "Please install it with `pip install pinecone_text`." + ) + return values def get_relevant_documents(self, query: str) -> List[Document]: - sparse_vec = generate_sparse_vectors([query], self.tokenizer)[0] + from pinecone_text.hybrid import hybrid_convex_scale + + sparse_vec = self.sparse_encoder.encode_queries(query) # convert the question into a dense vector dense_vec = self.embeddings.embed_query(query) # scale alpha with hybrid_scale - dense_vec, sparse_vec = hybrid_scale(dense_vec, sparse_vec, self.alpha) + dense_vec, sparse_vec = hybrid_convex_scale(dense_vec, sparse_vec, self.alpha) sparse_vec["values"] = [float(s1) for s1 in sparse_vec["values"]] # query pinecone with the query parameters result = self.index.query( diff --git a/poetry.lock b/poetry.lock index 3478b4d0..8e2bdb52 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry and should not be changed by hand. 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"pyowm"] cohere = ["cohere"] -llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "torch", "transformers"] +llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"] openai = ["openai"] qdrant = ["qdrant-client"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "a8fde2558f92b4c5ec1dce45f830adc6158dd2cf8c425a34a06523ee8e74487d" +content-hash = "11bbe0042c3c1e56a5d1abbaab33185efad5dcdacb095fc91e91c382f2c9ebb7" diff --git a/pyproject.toml b/pyproject.toml index 68da49ca..74af55eb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,10 +28,11 @@ spacy = {version = "^3", optional = true} nltk = {version = "^3", optional = true} transformers = {version = "^4", optional = true} beautifulsoup4 = {version = "^4", optional = true} -torch = {version = "^2", optional = true} +torch = {version = "^1", optional = true} jinja2 = {version = "^3", optional = true} tiktoken = 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