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https://github.com/hwchase17/langchain.git
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commit
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@ -21,8 +21,8 @@
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.embeddings import OpenAIEmbeddings, HypotheticalDocumentEmbedder\n",
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"from langchain.chains import LLMChain\n",
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"from langchain.embeddings import OpenAIEmbeddings\n",
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"from langchain.chains import LLMChain, HypotheticalDocumentEmbedder\n",
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"from langchain.prompts import PromptTemplate"
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]
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},
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@ -220,7 +220,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "llm-env",
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"language": "python",
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"name": "python3"
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},
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@ -234,7 +234,12 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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"version": "3.9.0 (default, Nov 15 2020, 06:25:35) \n[Clang 10.0.0 ]"
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},
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"vscode": {
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"interpreter": {
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"hash": "9dd01537e9ab68cf47cb0398488d182358f774f73101197b3bd1b5502c6ec7f9"
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}
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}
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},
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"nbformat": 4,
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@ -1,6 +1,7 @@
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"""Chains are easily reusable components which can be linked together."""
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from langchain.chains.api.base import APIChain
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from langchain.chains.conversation.base import ConversationChain
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from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
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from langchain.chains.llm import LLMChain
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from langchain.chains.llm_bash.base import LLMBashChain
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from langchain.chains.llm_checker.base import LLMCheckerChain
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@ -41,4 +42,5 @@ __all__ = [
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"OpenAIModerationChain",
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"SQLDatabaseSequentialChain",
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"load_chain",
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"HypotheticalDocumentEmbedder",
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]
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@ -4,18 +4,19 @@ https://arxiv.org/abs/2212.10496
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"""
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from __future__ import annotations
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from typing import List
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from typing import Dict, List
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import numpy as np
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from pydantic import BaseModel, Extra
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from langchain.chains.base import Chain
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from langchain.chains.hyde.prompts import PROMPT_MAP
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from langchain.chains.llm import LLMChain
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from langchain.embeddings.base import Embeddings
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from langchain.embeddings.hyde.prompts import PROMPT_MAP
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from langchain.llms.base import BaseLLM
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class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
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class HypotheticalDocumentEmbedder(Chain, Embeddings, BaseModel):
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"""Generate hypothetical document for query, and then embed that.
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Based on https://arxiv.org/abs/2212.10496
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@ -30,10 +31,24 @@ class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Input keys for Hyde's LLM chain."""
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return self.llm_chain.input_keys
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@property
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def output_keys(self) -> List[str]:
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"""Output keys for Hyde's LLM chain."""
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return self.llm_chain.output_keys
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Call the base embeddings."""
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return self.base_embeddings.embed_documents(texts)
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def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
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"""Combine embeddings into final embeddings."""
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return list(np.array(embeddings).mean(axis=0))
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def embed_query(self, text: str) -> List[float]:
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"""Generate a hypothetical document and embedded it."""
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var_name = self.llm_chain.input_keys[0]
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@ -42,9 +57,9 @@ class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
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embeddings = self.embed_documents(documents)
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return self.combine_embeddings(embeddings)
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def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
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"""Combine embeddings into final embeddings."""
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return list(np.array(embeddings).mean(axis=0))
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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"""Call the internal llm chain."""
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return self.llm_chain._call(inputs)
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@classmethod
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def from_llm(
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@ -2,7 +2,6 @@
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from langchain.embeddings.cohere import CohereEmbeddings
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
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from langchain.embeddings.hyde.base import HypotheticalDocumentEmbedder
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from langchain.embeddings.openai import OpenAIEmbeddings
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__all__ = [
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@ -10,5 +9,4 @@ __all__ = [
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"HuggingFaceEmbeddings",
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"CohereEmbeddings",
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"HuggingFaceHubEmbeddings",
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"HypotheticalDocumentEmbedder",
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]
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@ -4,9 +4,9 @@ from typing import List, Optional
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import numpy as np
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from pydantic import BaseModel
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from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
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from langchain.chains.hyde.prompts import PROMPT_MAP
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from langchain.embeddings.base import Embeddings
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from langchain.embeddings.hyde.base import HypotheticalDocumentEmbedder
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from langchain.embeddings.hyde.prompts import PROMPT_MAP
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from langchain.llms.base import BaseLLM
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from langchain.schema import Generation, LLMResult
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