dh2SSKJr SSKrSSKrSSKrSSKrSSKrSSKrSSKJ r SSK J r J r J r JrJrJrJrJrJrJr SSKrSSKJr SSKJr SSKJr SSKJr SS KJ r J!r! SS K"J#r# SS K$J%r%J&r& \RN"\(5r)SSS jjr*SS jr+"SS\5r,g)) annotationsN)Path) AnyCallableDictIterableListOptionalSequenceSizedTupleUnion)Document) Embeddings)run_in_executor) VectorStore) AddableMixinDocstore)InMemoryDocstore)DistanceStrategymaximal_marginal_relevancecUc3S[R;a[[R"S55nU(aSSKJn U$SSKnU$![ a [ S5ef=f)a1 Import faiss if available, otherwise raise error. If FAISS_NO_AVX2 environment variable is set, it will be considered to load FAISS with no AVX2 optimization. Args: no_avx2: Load FAISS strictly with no AVX2 optimization so that the vectorstore is portable and compatible with other devices. N FAISS_NO_AVX2r) swigfaisszCould not import faiss python package. Please install it with `pip install faiss-gpu` (for CUDA supported GPU) or `pip install faiss-cpu` (depending on Python version).)osenvironboolgetenvfaissr ImportError)no_avx2rs ^/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/faiss.pydependable_faiss_importr#'si?bjj8ryy12   0 L  L   H   s AAA$c[U[5(a[[U[5(aF[U5[U5:wa.[USUSUS[U5SUS[U53 5eg)Nz and z% expected to be equal length but len(z)=z and len() isinstancer len ValueError)xyx_namey_names r"_len_check_if_sizedr,Bsq!U 1e 4 4Q3q69IheF8$("SVHIfXRAx A   c\rSrSrSrSS\R 4S3Sjjr\S4Sj5r S5Sjr S5Sjr S6S jr S6S jr S7S8S jjrS7S9S jjrS7S9S jjrS7S:SjjrS;SSjjrS;S?SjjrS;S@SjjrS;S@SjjrSSSSS.SASjjrSSSSS.SASjjrSBSCSjjrSBSCSjjrSBSDSjjrSBSDS jjrSESFS!jjr SGS"jr!\"SSS\R 4SHS#jj5r#\"S7SIS$jj5r$\"S7SJS%jj5r%\"S7SKS&jj5r&\"S7SKS'jj5r'SLSMS(jjr(\"SLSS).SNS*jjj5r)SOS+jr*\"SS).SPS,jj5r+SQS-jr,S;S=S.jjr-S;S=S/jjr.\/SRS0j5r0SSS1jr1S2r2g)TFAISSKus FAISS vector store integration. See [The FAISS Library](https://arxiv.org/pdf/2401.08281) paper. Setup: Install ``langchain_community`` and ``faiss-cpu`` python packages. .. code-block:: bash pip install -qU langchain_community faiss-cpu Key init args — indexing params: embedding_function: Embeddings Embedding function to use. Key init args — client params: index: Any FAISS index to use. docstore: Docstore Docstore to use. index_to_docstore_id: Dict[int, str] Mapping of index to docstore id. Instantiate: .. code-block:: python import faiss from langchain_community.vectorstores import FAISS from langchain_community.docstore.in_memory import InMemoryDocstore from langchain_openai import OpenAIEmbeddings index = faiss.IndexFlatL2(len(OpenAIEmbeddings().embed_query("hello world"))) vector_store = FAISS( embedding_function=OpenAIEmbeddings(), index=index, docstore= InMemoryDocstore(), index_to_docstore_id={} ) Add Documents: .. code-block:: python from langchain_core.documents import Document document_1 = Document(page_content="foo", metadata={"baz": "bar"}) document_2 = Document(page_content="thud", metadata={"bar": "baz"}) document_3 = Document(page_content="i will be deleted :(") documents = [document_1, document_2, document_3] ids = ["1", "2", "3"] vector_store.add_documents(documents=documents, ids=ids) Delete Documents: .. code-block:: python vector_store.delete(ids=["3"]) Search: .. code-block:: python results = vector_store.similarity_search(query="thud",k=1) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python * thud [{'bar': 'baz'}] Search with filter: .. code-block:: python results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"}) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python * thud [{'bar': 'baz'}] Search with score: .. code-block:: python results = vector_store.similarity_search_with_score(query="qux",k=1) for doc, score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python * [SIM=0.335304] foo [{'baz': 'bar'}] Async: .. code-block:: python # add documents # await vector_store.aadd_documents(documents=documents, ids=ids) # delete documents # await vector_store.adelete(ids=["3"]) # search # results = vector_store.asimilarity_search(query="thud",k=1) # search with score results = await vector_store.asimilarity_search_with_score(query="qux",k=1) for doc,score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python * [SIM=0.335304] foo [{'baz': 'bar'}] Use as Retriever: .. code-block:: python retriever = vector_store.as_retriever( search_type="mmr", search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, ) retriever.invoke("thud") .. code-block:: python [Document(metadata={'bar': 'baz'}, page_content='thud')] NFcT[U[5(d[RS5 XlX lX0lX@lXplXPl X`l UR[R:wa6UR(a$[R"SUR35 ggg)z%Initialize with necessary components.t`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.z2Normalizing L2 is not applicable for metric type: N)r%rloggerwarningembedding_functionindexdocstoreindex_to_docstore_iddistance_strategyoverride_relevance_score_fn _normalize_L2rEUCLIDEAN_DISTANCEwarningswarn)selfr5r6r7r8relevance_score_fn normalize_L2r9s r"__init__FAISS.__init__s,j99 NNB #5  $8!!2+=()  " "&6&I&I I"" MM $ 6 679 # Jr-c\[UR[5(a UR$S$N)r%r5rr?s r" embeddingsFAISS.embeddingss1$11:>>  # #  r-c[UR[5(aURRU5$UVs/sHo RU5PM sn$s snfrE)r%r5rembed_documents)r?textstexts r"_embed_documentsFAISS._embed_documentssM d--z : :**::5A A>CDed++D1eD DDsAc# [UR[5(a#URRU5IShvN $[ S5eN7fNr2)r%r5raembed_documents Exception)r?rKs r"_aembed_documentsFAISS._aembed_documentssM d--z : :00AA%HH H B  I>AAAc[UR[5(aURRU5$URU5$rE)r%r5r embed_queryr?rLs r" _embed_queryFAISS._embed_querys= d--z : :**66t< <**40 0r-c# [UR[5(a#URRU5IShvN $[ S5eN7frP)r%r5r aembed_queryrRrXs r" _aembed_queryFAISS._aembed_querysM d--z : :00==dCC CB DrUc &[5n[UR[5(d[ SURS35e[ XSS5 U=(d. UVs/sH!n[ [R"55PM# snn[ XSS5 U=(d SU5n[XAU5VV V s/sHupn [XU S9PM n n nn [ XSS 5 U(a,[U5[[U55:wa [ S 5e[R"U[RS 9n UR (aUR#U 5 UR$R'U 5 URR'[XK5VV s0sHupX_M sn n5 [UR(5n[+U5VVs0sH upX-U_M nnnUR(R-U5 U$s snfs sn n nfs sn nfs snnf) NzSIf trying to add texts, the underlying docstore should support adding items, which z does notrK metadatasidsc3$# UHn0v M g7frE).0_s r" FAISS.__add..+s"5u!2us)id page_contentmetadata documentsrGz$Duplicate ids found in the ids list.dtype)r#r%r7rr'r,struuiduuid4ziprr&setnparrayfloat32r;rAr6addr8 enumerateupdate)r?rKrGr`rarre _metadatasid_tmrkvectordoc starting_lenj index_to_ids r"__add FAISS.__adds()$--66''+}}oY@  Eg{C77Ac$**,'7E75"5u"5 !Z8 8  a 88  I; M 3s8s3s8},CD D*BJJ7      v & v C4GH4G384GHI4445 ;DS>J>|',> J !!((5 18 IJs'G;6H H H c Z[U5nURU5nURXX#S9$)a4Run more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of unique IDs. Returns: List of ids from adding the texts into the vectorstore. r`ra)listrM _FAISS__addr?rKr`rakwargsrGs r" add_textsFAISS.add_textsBs0"U **51 zz%yzJJr-cv# [U5nURU5IShvN nURXX#S9$N7f)aGRun more texts through the embeddings and add to the vectorstore asynchronously. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of unique IDs. Returns: List of ids from adding the texts into the vectorstore. Nr)rrSrrs r" aadd_textsFAISS.aadd_textsWs;$U 11%88 zz%yzJJ9s 979c 6[U6upVURXVX#S9$)aNAdd the given texts and embeddings to the vectorstore. Args: text_embeddings: Iterable pairs of string and embedding to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of unique IDs. Returns: List of ids from adding the texts into the vectorstore. r)rqr)r?text_embeddingsr`rarrKrGs r"add_embeddingsFAISS.add_embeddingsms$& 1zz%yzJJr-c [5n[R"U/[RS9nUR(aUR U5 UR RXscUOU5up/n UbURU5n [U S5HupU S:XaM URU nURRU5n[U[5(d[SUSU35eUb3W "UR5(aU R!XSU 45 MMU R!XSU 45 M UR#S5nUbvUR$[&R([&R*4;a[,R.O[,R0nU VVs/sHunnU"UU5(dMUU4PM n nnU SU$s snnf)a$Return docs most similar to query. Args: embedding: Embedding vector to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Union[Callable, Dict[str, Any]]]): Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool. fetch_k: (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20. **kwargs: kwargs to be passed to similarity search. Can include: score_threshold: Optional, a floating point value between 0 to 1 to filter the resulting set of retrieved docs Returns: List of documents most similar to the query text and L2 distance in float for each. Lower score represents more similarity. rlNrCould not find document for id , got score_threshold)r#rsrtrur;rAr6search_create_filter_funcrwr8r7r%rr'rjappendgetr9rMAX_INNER_PRODUCTJACCARDoperatorgele)r? embeddingkfilterfetch_krrr}scoresindicesdocs filter_funcri_idr~rcmp similaritys r"&similarity_search_with_score_by_vector,FAISS.similarity_search_with_score_by_vectors4()9+RZZ8      v &**++FAWU  226:Kgaj)DABw++A.C--&&s+Cc8,, #B3%vcU!STT!s||,,KKQil 34- S)A,/0*!**%67  &))$668H8P8PQR [[ (,'+OCz?3"j!'+  BQx s -GGcV# [SURU4UUUS.UD6IShvN $N7f)a#Return docs most similar to query asynchronously. Args: embedding: Embedding vector to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, Any]]): Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool. fetch_k: (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20. **kwargs: kwargs to be passed to similarity search. Can include: score_threshold: Optional, a floating point value between 0 to 1 to filter the resulting set of retrieved docs Returns: List of documents most similar to the query text and L2 distance in float for each. Lower score represents more similarity. Nrrr)rr)r?rrrrrs r"'asimilarity_search_with_score_by_vector-FAISS.asimilarity_search_with_score_by_vectorsG:%   7 7        s )')c VURU5nUR"UU4UUS.UD6nU$)a4Return docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool. fetch_k: (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20. Returns: List of documents most similar to the query text with L2 distance in float. Lower score represents more similarity. rr)rYrr?queryrrrrrrs r"similarity_search_with_score"FAISS.similarity_search_with_scoresH0%%e, ::        r-c# URU5IShvN nUR"UU4UUS.UD6IShvN nU$N%N7f)aCReturn docs most similar to query asynchronously. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool. fetch_k: (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20. Returns: List of documents most similar to the query text with L2 distance in float. Lower score represents more similarity. Nr)r]rrs r"asimilarity_search_with_score#FAISS.asimilarity_search_with_score s]0,,U33 AA         4 sA=A?AAc hUR"UU4UUS.UD6nUVVs/sHupxUPM snn$s snnf)aReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool. fetch_k: (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20. Returns: List of Documents most similar to the embedding. r)r r?rrrrrdocs_and_scoresr~res r"similarity_search_by_vector!FAISS.similarity_search_by_vector/sM.EE       #22//222s .c# UR"UU4UUS.UD6IShvN nUVVs/sHupxUPM snn$Ns snnf7f)aReturn docs most similar to embedding vector asynchronously. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool. fetch_k: (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20. Returns: List of Documents most similar to the embedding. rN)rrs r"asimilarity_search_by_vector"FAISS.asimilarity_search_by_vectorOs\.!% L L  !  !  !  #22//22 3sA8 A :AAc dUR"X4X4S.UD6nUVVs/sHupxUPM snn$s snnf)aReturn docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. fetch_k: (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20. Returns: List of Documents most similar to the query. r)r r?rrrrrrr~res r"similarity_searchFAISS.similarity_searchosC(;;  # 8> #22//222s ,c# UR"X4X4S.UD6IShvN nUVVs/sHupxUPM snn$Ns snnf7f)aReturn docs most similar to query asynchronously. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. fetch_k: (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20. Returns: List of Documents most similar to the query. rN)rrs r"asimilarity_searchFAISS.asimilarity_searchsR(!% B B ! #! 8>!  #22//22 3s>6 > 8>>?rr lambda_multrcURR[R"U/[RS9UcUOUS-5upgUbUR U5n/n USHn U S:XaM UR U n URRU 5n [U [5(d[SU SU 35eU"U R5(dMtU RU 5 M [R"U /5nUSV s/sH.oS:wdM URR[U 55PM0 n n [[R"U/[RS9U UUS9n/nUHn USU S:XaMUR USU n URRU 5n [U [5(d[SU SU 35eURXSU 45 M U$s sn f)aReturn docs and their similarity scores selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch before filtering to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents and similarity scores selected by maximal marginal relevance and score for each. rlrrrr)rr)r6rrsrtrurr8r7r%rr'rjr reconstructintr)r?rrrrrrrrfiltered_indicesrrr~rG mmr_selectedrs r"2max_marginal_relevance_search_with_score_by_vector8FAISS.max_marginal_relevance_search_with_score_by_vectors8**++ HHi[ 3~G7Q;   226:K! QZ7//2mm**3/!#x00$'Fse6RUQV%WXXs||,,$++A. hh 012G>EajTjQSG4djj,,SV4j T1 HHi[ 3 #  Aqz!}"++GAJqM:C--&&s+Cc8,, #B3%vcU!STT  " "C1#6 7'Us G8(G8c P# [SURUUUUUS9IShvN $N7f)a Return docs and their similarity scores selected using the maximal marginal relevance asynchronously. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch before filtering to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents and similarity scores selected by maximal marginal relevance and score for each. Nr)rr)r?rrrrrs r"3amax_marginal_relevance_search_with_score_by_vector9FAISS.amax_marginal_relevance_search_with_score_by_vectors9:%   C C #    s &$&c \URXX4US9nUVV s/sHupUPM sn n$s sn nf)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch before filtering to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. r)r r?rrrrrrrr~res r"'max_marginal_relevance_search_by_vector-FAISS.max_marginal_relevance_search_by_vector s=4QQ GVR #22//222s (cx# URXX4US9IShvN nUVV s/sHupUPM sn n$Ns sn nf7f)aReturn docs selected using the maximal marginal relevance asynchronously. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch before filtering to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. rN)rrs r"(amax_marginal_relevance_search_by_vector.FAISS.amax_marginal_relevance_search_by_vector+sP6JJQWK   #22//22  3s:2 : 4::c XURU5nUR"U4UUUUS.UD6nU$)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch before filtering (if needed) to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. r)rYr r?rrrrrrrrs r"max_marginal_relevance_search#FAISS.max_marginal_relevance_searchLsI4%%e, ;;  #     r-c# URU5IShvN nUR"U4UUUUS.UD6IShvN nU$N&N7f)aReturn docs selected using the maximal marginal relevance asynchronously. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch before filtering (if needed) to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. Nr)r]rrs r"amax_marginal_relevance_search$FAISS.amax_marginal_relevance_searchqs^4,,U33 BB  #      4 sA>AAAAc Uc [S5e[U5RURR 55nU(a[SU35eURR 5VVs0sHupEXT_M nnnUVs1sHoVUiM nnUR R[R"U[RS95 URRU5 [URR 55VVs/sHupX;dM UPM n nn[U 5VVs0sHupX_M snnUlgs snnfs snfs snnfs snnf)zDelete by ID. These are the IDs in the vectorstore. Args: ids: List of ids to delete. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. zNo ids provided to delete.zESome specified ids do not exist in the current store. Ids not found: rlT)r'rr differencer8valuesitemsr6 remove_idsrsfromiterint64r7deletesortedrw) r?rar missing_idsidxrzreversed_indexindex_to_deleter remaining_idss r"r FAISS.deletesD ;9: :#h))$*C*C*J*J*LM W-!  483L3L3R3R3TU3Txs#(3TU:=>#3#.#> bkk/JK S!!!:!:!@!@!BC C' C  ;DM:R$S:RQV:R$S!V>  %Ts3 E E E!E8 E c [UR[5(d [S5e[ UR 5nUR RUR 5 /nUR R5HUupEURRU5n[U[5(d [S5eURX$-XV45 MW URRUVVVs0sHupxohU_M snnn5 UV VVs0sHupoyU_M n nn nUR RU 5 gs snnnfs snnn f)zMerge another FAISS object with the current one. Add the target FAISS to the current one. Args: target: FAISS object you wish to merge into the current one Returns: None. z'Cannot merge with this type of docstorezDocument should be returnedN)r%r7rr'r&r8r6 merge_fromrrrrrvrx) r?targetr full_infor target_idr~rerr6rs r"rFAISS.merge_froms$--66FG G4445  fll+ "77==?LA//((3Cc8,, !>??   l. ? @ @ yAy 8yAB7@Ayme!czy A !!((5BAs 5E Ec N[5n U[R:XaU R[ US55n OU R [ US55n UR S[55n UR S05n U"UU U U 4UUS.UD6n U RXXES9 U $)Nrr7r8)rAr9r) r#rr IndexFlatIPr& IndexFlatL2poprr)clsrKrGrr`rarAr9rrr6r7r8vecstores r"__from FAISS.__froms()  0 B B B%%c*Q-&89E%%c*Q-&89E::j*:*<=%zz*@"E     &/    uIGr-c TURU5nUR"UUU4UUS.UD6$)aConstruct FAISS wrapper from raw documents. This is a user friendly interface that: 1. Embeds documents. 2. Creates an in memory docstore 3. Initializes the FAISS database This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import FAISS from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() faiss = FAISS.from_texts(texts, embeddings) r)rJ _FAISS__fromrrKrr`rarrGs r" from_textsFAISS.from_textssE6..u5 zz          r-cp# URU5IShvN nUR"UUU4UUS.UD6$N7f)aConstruct FAISS wrapper from raw documents asynchronously. This is a user friendly interface that: 1. Embeds documents. 2. Creates an in memory docstore 3. Initializes the FAISS database This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import FAISS from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() faiss = await FAISS.afrom_texts(texts, embeddings) Nr)rQrrs r" afrom_textsFAISS.afrom_textssP6%55e<< zz          =s 646c j[U6upgUR"[U5[U5U4UUS.UD6$)azConstruct FAISS wrapper from raw documents. This is a user friendly interface that: 1. Embeds documents. 2. Creates an in memory docstore 3. Initializes the FAISS database This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import FAISS from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() text_embeddings = embeddings.embed_documents(texts) text_embedding_pairs = zip(texts, text_embeddings) faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings) r)rqrr)rrrr`rarrKrGs r"from_embeddingsFAISS.from_embeddingsBsK: 1zz K          r-c8# UR"UU4UUS.UD6$7f)z:Construct FAISS wrapper from raw documents asynchronously.r)r)rrrr`rars r"afrom_embeddingsFAISS.afrom_embeddingsis7""         scR[U5nURSSS9 [5nURUR[ X2S3- 55 [ X2S3- S5n[R"URUR4U5 SSS5 g!,(df  g=f)zSave FAISS index, docstore, and index_to_docstore_id to disk. Args: folder_path: folder path to save index, docstore, and index_to_docstore_id to. index_name: for saving with a specific index file name T)exist_okparents.faiss.pklwbN) rmkdirr# write_indexr6rnopenpickledumpr7r8)r? folder_path index_namepathrfs r" save_localFAISS.save_local{sK  D$ /() $**c$Load FAISS index, docstore, and index_to_docstore_id from disk. Args: folder_path: folder path to load index, docstore, and index_to_docstore_id from. embeddings: Embeddings to use when generating queries index_name: for saving with a specific index file name allow_dangerous_deserialization: whether to allow deserialization of the data which involves loading a pickle file. Pickle files can be modified by malicious actors to deliver a malicious payload that results in execution of arbitrary code on your machine. BThe de-serialization relies loading a pickle file. Pickle files can be modified to deliver a malicious payload that results in execution of arbitrary code on your machine.You will need to set `allow_dangerous_deserialization` to `True` to enable deserialization. If you do this, make sure that you trust the source of the data. For example, if you are loading a file that you created, and know that no one else has modified the file, then this is safe to do. Do not set this to `True` if you are loading a file from an untrusted source (e.g., some random site on the internet.).rrrbN)r'rr# read_indexrnrrload) rrrGr r%rr!rr6r"r7r8s r" load_localFAISS.load_locals./ "  K ')  Tl&,A%A!BC$'> >22 2  r-c UR5nUc [S5eUR"U4UUUS.UD6nUVV s/sHupX"U 54PM n nn U $s sn nf)?Return docs and their similarity scores on a scale from 0 to 1.Lrelevance_score_fn must be provided to FAISS constructor to normalize scoresr)r;r'r r?rrrrrr@rr~scoredocs_and_rel_scoress r"(_similarity_search_with_relevance_scores.FAISS._similarity_search_with_relevance_scoress"<<>  %9 ;;      @O ?NS$U+ ,  #" sAc# UR5nUc [S5eUR"U4UUUS.UD6IShvN nUVV s/sHupX"U 54PM n nn U $N"s sn nf7f)r>Nr?r)r;r'rr@s r")_asimilarity_search_with_relevance_scores/FAISS._asimilarity_search_with_relevance_scoress"<<>  %9 !% B B !  !  !  @O ?NS$U+ ,  #"  s!9A&A A&A A& A&c^ ^ ^ ^[U5(aU$[U[5(d[S[ U535eSSKJnJnJnJ nJ nJ n UUUUUUS.nSSS.nXx-m [[T 5/SQ-5n S m UH7n U (dM U RS 5(dM$X;dM+[S U 35e SU U 4S jjmSU U4S jjm T "U5$)av Create a filter function based on the provided filter. Args: filter: A callable or a dictionary representing the filter conditions for documents. Returns: A function that takes Document's metadata and returns True if it satisfies the filter conditions, otherwise False. Raises: ValueError: If the filter is invalid or contains unsupported operators. z5filter must be a dict of metadata or a callable, not r)eqrgtrltne)z$eqz$neqz$gtz$ltz$gtez$ltec X;$rErcabs r"+FAISS._create_filter_func..`sr-c X;$rErcrNs r"rQrRasr-)z$inz$nin)$and$or$not $&filter contains unsupported operator: c^>^^^^[T[5(aP/mTR5H/up#UT;a[SU35eTR TUU45 M1 SUU4SjjnU$[T[ 5(a([ T5T:a[T5mUU4Sj$UU4Sj$UU4Sj$)a$ Creates a filter function based on field and condition. Args: field: The document field to filter on condition: Filter condition (dict for operators, list for in, or direct value for equality) Returns: A filter function that takes a document and returns boolean rYcR>^URT5m[U4SjT55$)a? Evaluates a document against a set of predefined operators and their values. This function applies multiple comparison/sequence operators to a specific field value from the document. All conditions must be satisfied for the function to return True. Args: doc (Dict[str, Any]): The document to evaluate, containing key-value pairs where keys are field names and values are the field values. The document must contain the field being filtered. Returns: bool: True if the document's field value satisfies all operator conditions, False otherwise. c38># UHupU"TU5v M g7frErc)rdopvalue doc_values r"rfYFAISS._create_filter_func..filter_func_cond..filter_fn..sOY r)U33Ys)rall)r~r_field operatorss @r" filter_fnFFAISS._create_filter_func..filter_func_cond..filter_fns#$!$IOYOOOr-c,>URT5T;$rEr)r~ condition_setrbs r"rQEFAISS._create_filter_func..filter_func_cond..sswwu~'Fr-c,>URT5T;$rErgr~ conditionrbs r"rQris3775>Y#>r-c,>URT5T:H$rErgrks r"rQrisswwu~:r-)r~Dict[str, Any]returnr)r%dictrr'rrr& frozenset) rbrlr]r^rdrhrc OPERATIONSSET_CONVERT_THRESHOLDs `` @@r"filter_func_cond3FAISS._create_filter_func..filter_func_condls)T** !*!2IB+(+QRTQU)VWW$$jne%<="3 PP*! )T**y>$99$-i$8MFF>>: :r-cT>^^^SU;a!USVs/sH nT"U5PM snmU4Sj$SU;a!USVs/sH nT"U5PM snmU4Sj$SU;aT"US5mU4Sj$UR5VVs/sH up#T"X#5PM snnmU4Sj$s snfs snfs snnf)a Creates a filter function that evaluates documents against specified filter conditions. This function processes a dictionary of filter conditions and returns a callable that can evaluate documents against these conditions. It supports logical operators ($and, $or, $not) and field-level filtering. Args: filter (Dict[str, Any]): A dictionary containing filter conditions. Can include: - Logical operators ($and, $or, $not) with lists of sub-filters - Field-level conditions with comparison or sequence operators - Direct field-value mappings for equality comparison Returns: Callable[[Dict[str, Any]], bool]: A function that takes a document (as a dictionary) and returns True if the document matches all filter conditions, False otherwise. rTc0>^[U4SjT55$)Nc30># UH o"T5v M g7frErcrdr"r~s r"rfSFAISS._create_filter_func..filter_func....&?w!qvvwrar~filterss`r"rQ@FAISS._create_filter_func..filter_func..3&?w&?#?r-rUc0>^[U4SjT55$)Nc30># UH o"T5v M g7frErcrys r"rfrzr{r|)anyr~s`r"rQrrr-rVc>T"U5(+$rErc)r~conds r"rQrs tCy=r-c0>^[U4SjT55$)Nc30># UH o"T5v M g7frErc)rdrlr~s r"rfrzs"N:i9S>>:r|r})r~ conditionss`r"rQrss"N:"NNr-)r) r sub_filterrbrlrrrrrts @@@r"r.FAISS._create_filter_func..filter_funcs*EKF^T^z;z2^T??EKE]S]z;z2]S??"6&>200)/ (6$E!2(6JO NUTsBB=B$)rbrnrlz%Union[Dict[str, Any], List[Any], Any]ro Callable[[Dict[str, Any]], bool])rrnror)callabler%rpr'typerrIrrJrrKrLrqr startswith)rrIrrJrrKrLCOMPARISON_OPERATORSSEQUENCE_OPERATORSVALID_OPERATORSr]rrrsrrts @@@@r"rFAISS._create_filter_func;s $ F  M&$''GV ~V  43  '+ *> #D$47N$NO "BrbmmC((R-F #I"!NOO2 ;2 ;#H2 ; -2 ;2 ;h% O% ON6""r-cUVs/sHo RRU5PM nnUVs/sHn[U[5(dMUPM sn$s snfs snfrE)r7rr%r)r?rarzrr~s r" get_by_idsFAISS.get_by_idssI589Sc $$S)S9#Atz#x'@tAA:As#AA A)r;r9r7r5r6r8r:)r5z/Union[Callable[[str], List[float]], Embeddings]r6rr7rr8zDict[int, str]r@z"Optional[Callable[[float], float]]rArr9r)rozOptional[Embeddings])rK List[str]roList[List[float]])rLrnro List[float])NN) rK Iterable[str]rGzIterable[List[float]]r`Optional[Iterable[dict]]raOptional[List[str]]ror) rKrr`Optional[List[dict]]rarrrror) r!Iterable[Tuple[str, List[float]]]r`rrarrrror)rNr) rrrrr)Optional[Union[Callable, Dict[str, Any]]]rrrrroList[Tuple[Document, float]]) rrnrrrrrrrrror) rrrrrzOptional[Dict[str, Any]]rrrrroList[Document]) rrrrrrrrrrror) rrnrrrrrrrrror) rrrrrrrfloatrrror)rrrN)rrrrrrrrrrrrror)rrnrrrrrrrrrrrorrE)rarrrroOptional[bool])rr/roNone)rKrrGrrrr`rrarrArr9rrrror/) rKrrrr`rrarrrror/) rKz list[str]rrr`rrarrrror/) rrrrr`rrarrrror/)r6)rrnr rnror) rrnrGrr rnr%rrrror/)robytes) r3rrGrr%rrrror/)rozCallable[[float], float])rrror)raz Sequence[str]rozlist[Document])3__name__ __module__ __qualname____firstlineno____doc__rr<rBpropertyrGrMrSrYr]rrrrrrrrrrrrrrrrrrrr classmethodrr r rrr#r+r/r4r;rCrF staticmethodrr__static_attributes__rcr-r"r/r/Ksd }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fT 16 PPP *. P  P PP><<@ ## #: #  #  # &#@<@ ## #: #  #  # &#:K#9K# )K#K#ZBr-r/rE)r!rror) r(rr)rr*rnr+rnror)- __future__rloggingrrrror=pathlibrtypingrrrrr r r r r rnumpyrslangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.runnables.configrlangchain_core.vectorstoresr!langchain_community.docstore.baserr&langchain_community.docstore.in_memoryr&langchain_community.vectorstores.utilsrr getLoggerrr3r#r,r/rcr-r"rst"    -0;3DC   8 $6 @BK@Br-