id stringlengths 14 15 | text stringlengths 23 2.21k | source stringlengths 52 97 |
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619b5efe033c-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message History... | https://python.langchain.com/docs/integrations/memory/ |
619b5efe033c-2 | is a memory server implemented in Rust. It automatically handles incremental summarization in the background and allows for stateless applications.📄� Postgres Chat Message HistoryThis notebook goes over how to use Postgres to store chat message history.📄� Redis Chat Message HistoryThis notebook goes over how ... | https://python.langchain.com/docs/integrations/memory/ |
d0d3bd72cb6f-0 | Motörhead Memory (Managed) | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/memory/motorhead_memory_managed |
d0d3bd72cb6f-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message History... | https://python.langchain.com/docs/integrations/memory/motorhead_memory_managed |
d0d3bd72cb6f-2 | verbose=True, memory=memory,)llm_chain.run("hi im bob") > Entering new LLMChain chain... Prompt after formatting: You are a chatbot having a conversation with a human. Human: hi im bob AI: > Finished chain. ' Hi Bob, nice to meet you! How are you doing today?'llm_chain.run("... | https://python.langchain.com/docs/integrations/memory/motorhead_memory_managed |
d0d3bd72cb6f-3 | I'm sorry, I'm not sure what you're asking. Could you please rephrase your question?"PreviousMotörhead MemoryNextPostgres Chat Message HistorySetupCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/memory/motorhead_memory_managed |
169310d2cf52-0 | Momento Chat Message History | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/memory/momento_chat_message_history |
169310d2cf52-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message History... | https://python.langchain.com/docs/integrations/memory/momento_chat_message_history |
c948729e86e5-0 | Redis Chat Message History | 🦜�🔗 Langchain
Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storage... | https://python.langchain.com/docs/integrations/memory/redis_chat_message_history |
e849dc4a20fc-0 | Dynamodb Chat Message History | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history |
e849dc4a20fc-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message History... | https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history |
e849dc4a20fc-2 | additional_kwargs={}, example=False), AIMessage(content='whats up?', additional_kwargs={}, example=False)]DynamoDBChatMessageHistory with Custom Endpoint URL​Sometimes it is useful to specify the URL to the AWS endpoint to connect to. For instance, when you are running locally against Localstack. For those cases ... | https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history |
e849dc4a20fc-3 | agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory,)agent_chain.run(input="Hello!") > Entering new AgentExecutor chain... { "action": "Final Answer", "action_input": "Hello! How can I assist you today?" } > Finished chain. 'Hello! How can I ... | https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history |
e849dc4a20fc-4 | name is Bob.") > Entering new AgentExecutor chain... { "action": "Final Answer", "action_input": "Hello Bob! How can I assist you today?" } > Finished chain. 'Hello Bob! How can I assist you today?'agent_chain.run(input="Who am I?") > Entering new AgentExecutor chain.... | https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history |
7b35985d3863-0 | Postgres Chat Message History | 🦜�🔗 Langchain
Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite stor... | https://python.langchain.com/docs/integrations/memory/postgres_chat_message_history |
de730d9c7df0-0 | Cassandra Chat Message History | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/memory/cassandra_chat_message_history |
de730d9c7df0-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message History... | https://python.langchain.com/docs/integrations/memory/cassandra_chat_message_history |
de730d9c7df0-2 | CASSANDRA_CONTACT_POINTS = input( "Contact points? (comma-separated, empty for localhost) " ).strip()depending on whether local or cloud-based Astra DB, create the corresponding database connection "Session" object​from cassandra.cluster import Clusterfrom cassandra.auth import PlainTextAuthProviderif datab... | https://python.langchain.com/docs/integrations/memory/cassandra_chat_message_history |
de730d9c7df0-3 | Chat Message HistoryPlease provide database connection parameters and secrets:Creation and usage of the Chat Message HistoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/memory/cassandra_chat_message_history |
a1c610e35c1a-0 | Entity Memory with SQLite storage | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite |
a1c610e35c1a-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message History... | https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite |
a1c610e35c1a-2 | in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand. You are constantly lea... | https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite |
a1c610e35c1a-3 | on a hackathon project with Deven.'PreviousDynamodb Chat Message HistoryNextMomento Chat Message HistoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite |
babbe4a6a02c-0 | Motörhead Memory | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/memory/motorhead_memory |
babbe4a6a02c-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message History... | https://python.langchain.com/docs/integrations/memory/motorhead_memory |
babbe4a6a02c-2 | after formatting: You are a chatbot having a conversation with a human. Human: hi im bob AI: > Finished chain. ' Hi Bob, nice to meet you! How are you doing today?'llm_chain.run("whats my name?") > Entering new LLMChain chain... Prompt after formatting: You are a chatbot havi... | https://python.langchain.com/docs/integrations/memory/motorhead_memory |
b10209f5a73e-0 | Zep Memory | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message History... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-2 | Docs: https://docs.getzep.com/from langchain.memory import ZepMemoryfrom langchain.retrievers import ZepRetrieverfrom langchain import OpenAIfrom langchain.schema import HumanMessage, AIMessagefrom langchain.utilities import WikipediaAPIWrapperfrom langchain.agents import initialize_agent, AgentType, Toolfrom uuid impo... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-3 | the memory. The default message window is 12 messages. We want to push beyond this to demonstrate auto-summarization.test_history = [ {"role": "human", "content": "Who was Octavia Butler?"}, { "role": "ai", "content": ( "Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an ... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-4 | "What awards did she win?"}, { "role": "ai", "content": ( "Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur" " Fellowship." ), }, { "role": "human", "content": "Which other women sci-fi writers might I want to read?", }, { ... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-5 | to" " environmental disasters, poverty, and violence." ), "metadata": {"foo": "bar"}, },]for msg in test_history: memory.chat_memory.add_message( HumanMessage(content=msg["content"]) if msg["role"] == "human" else AIMessage(content=msg["content"]), metadata=msg... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-6 | and timestamps.Summaries are biased towards the most recent messages.def print_messages(messages): for m in messages: print(m.type, ":\n", m.dict())print(memory.chat_memory.zep_summary)print("\n")print_messages(memory.chat_memory.messages) The human inquires about Octavia Butler. The AI identifies her as a... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-7 | ai : {'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'additional_kwargs': {'uuid': '2f6d80c6-3c08-4fd4-8d4e-7bbee341ac90', 'created_at': '2023-07-09T19:23:16.618947Z', 'token_count': 21, 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-8 | ai : {'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'additional_kwargs': {'uuid': '7977099a-0c62-4c98-bfff-465bbab6c9c3', 'created_at': '2023-07-09T19:23:16.631721Z', 'token_count': 18, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursul... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-9 | 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}], 'intent': 'The subject is requesting a brief summary or explanation of the book "Parable of the Sower" by Butler.'}}}, 'example': False} ai : {'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It f... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-10 | including its genre, publication date, and a brief summary of the plot.'}}}, 'example': False} human : {'content': "What is the book's relevance to the challenges facing contemporary society?", 'additional_kwargs': {'uuid': '7dbbbb93-492b-4739-800f-cad2b6e0e764', 'created_at': '2023-07-09T19:23:19.315182Z', 'tok... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-11 | the Zep memory​Zep provides native vector search over historical conversation memory via the ZepRetriever.You can use the ZepRetriever with chains that support passing in a Langchain Retriever object.retriever = ZepRetriever( session_id=session_id, url=ZEP_API_URL, api_key=zep_api_key,)search_results = memor... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-12 | K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting that the person should consider reading the works of Ursula K. Le Guin or Joanna Russ.'}}, 'token_count': 18} 0.8534346954749745 ... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-13 | Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ."}}, 'token_count': 27} 0.8523831524040919 {'uuid': 'e346f02b-f854-435d-b6ba-fb394a416b9b', 'created_at': '2023-07-09T19:23:16.556587Z', 'role': 'human', 'content': 'Who was Octavia Butler?', 'metadata': {'system': {'entities': [{'... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-14 | 1947'}, {'Label': 'DATE', 'Matches': [{'End': 57, 'Start': 40, 'Text': 'February 24, 2006'}], 'Name': 'February 24, 2006'}, {'Label': 'NORP', 'Matches': [{'End': 74, 'Start': 66, 'Text': 'American'}], 'Name': 'American'}], 'intent': 'The subject is providing information about Octavia Estelle Butler, who was an American... | https://python.langchain.com/docs/integrations/memory/zep_memory |
b10209f5a73e-15 | Chat Message HistoryNextRetrieversREACT Agent Chat Message History with Zep - A long-term memory store for LLM applications.More on Zep:Initialize the Zep Chat Message History Class and initialize the AgentAdd some history dataRun the agentInspect the Zep memoryVector search over the Zep memoryCommunityDiscordTwitterGi... | https://python.langchain.com/docs/integrations/memory/zep_memory |
68b8715a0937-0 | Mongodb Chat Message History | 🦜�🔗 Langchain
Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite stora... | https://python.langchain.com/docs/integrations/memory/mongodb_chat_message_history |
70a6030f17d5-0 | Retrievers | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/ |
70a6030f17d5-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/ |
70a6030f17d5-2 | ChaindeskChaindesk platform brings data from anywhere (Datsources: Text, PDF, Word, PowerPpoint, Excel, Notion, Airtable, Google Sheets, etc..) into Datastores (container of multiple Datasources).📄� ChatGPT PluginOpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact w... | https://python.langchain.com/docs/integrations/retrievers/ |
70a6030f17d5-3 | LOTR (Merger Retriever)Lord of the Retrievers, also known as MergerRetriever, takes a list of retrievers as input and merges the results of their getrelevantdocuments() methods into a single list. The merged results will be a list of documents that are relevant to the query and that have been ranked by the different re... | https://python.langchain.com/docs/integrations/retrievers/ |
91475250ccea-0 | Azure Cognitive Search | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/azure_cognitive_search |
91475250ccea-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/azure_cognitive_search |
91475250ccea-2 | or Query key, but as we only read data it is recommended to use a Query key.Using the Azure Cognitive Search Retriever​import osfrom langchain.retrievers import AzureCognitiveSearchRetrieverSet Service Name, Index Name and API key as environment variables (alternatively, you can pass them as arguments to AzureCogniti... | https://python.langchain.com/docs/integrations/retrievers/azure_cognitive_search |
87daecbbd973-0 | Vespa | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/vespa |
87daecbbd973-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/vespa |
87daecbbd973-2 | Vespa Cloud instance
or a local
Docker instance.After connecting to the service, you can set up the retriever:from langchain.retrievers.vespa_retriever import VespaRetrievervespa_query_body = { "yql": "select content from paragraph where userQuery()", "hits": 5, "ranking": "documentation", "locale": "en-us"... | https://python.langchain.com/docs/integrations/retrievers/vespa |
c3b2e44a6457-0 | ElasticSearch BM25 | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/elastic_search_bm25 |
c3b2e44a6457-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/elastic_search_bm25 |
c3b2e44a6457-2 | and BM25.For more information on the details of BM25 see this blog post.#!pip install elasticsearchfrom langchain.retrievers import ElasticSearchBM25RetrieverCreate New Retriever​elasticsearch_url = "http://localhost:9200"retriever = ElasticSearchBM25Retriever.create(elasticsearch_url, "langchain-index-4")# Alternati... | https://python.langchain.com/docs/integrations/retrievers/elastic_search_bm25 |
c3b2e44a6457-3 | RetrieverNextGoogle Cloud Enterprise SearchCreate New RetrieverAdd texts (if necessary)Use RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/retrievers/elastic_search_bm25 |
9d5e87bbd763-0 | Zep | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-2 | Docs: https://docs.getzep.com/Retriever Example​This notebook demonstrates how to search historical chat message histories using the Zep Long-term Memory Store.We'll demonstrate:Adding conversation history to the Zep memory store.Vector search over the conversation history.from langchain.memory.chat_message_histories... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-3 | "Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American" " science fiction author." ), }, {"role": "human", "content": "Which books of hers were made into movies?"}, { "role": "ai", "content": ( "The most well-known adaptation of Octavia Butler's... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-4 | ), }, { "role": "human", "content": "Which other women sci-fi writers might I want to read?", }, { "role": "ai", "content": "You might want to read Ursula K. Le Guin or Joanna Russ.", }, { "role": "human", "content": ( "Write a short synopsis of But... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-5 | else AIMessage(content=msg["content"]) )Use the Zep Retriever to vector search over the Zep memory​Zep provides native vector search over historical conversation memory. Embedding happens automatically.NOTE: Embedding of messages occurs asynchronously, so the first query may not return results. Subsequent queries ... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-6 | 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}, 'token_count': 56}), Document(page_content="Write a sh... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-7 | 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject wants to know about the identity or background of an individual named Octavia Butler.'}}, 'token_count': 8}), Document(page_con... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-8 | about Octavia Butler's contemporaries."}}, 'token_count': 27}), Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7594731095320668, 'uuid': '6951f2fd-dfa4-4e05-9380-f322ef8f72f8', 'created_at': '2023-06-26T23:40:22.80464Z', 'role': 'ai', 'metadata': {'system': {'... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-9 | 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name':... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-10 | was Octavia Butler?', metadata={'score': 0.7758688965570713, 'uuid': '361d0043-1009-4e13-a7f0-8aea8b1ee869', 'created_at': '2023-06-26T23:40:22.709886Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], ... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
9d5e87bbd763-11 | 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': "The subject is providing information about Octavia Butler's contemporaries."}}, 'token_count': 27}), Document(page_content='You might want to read ... | https://python.langchain.com/docs/integrations/retrievers/zep_memorystore |
c920bdb54ed4-0 | SVM | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/svm |
c920bdb54ed4-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/svm |
c920bdb54ed4-2 | use the retriever!result = retriever.get_relevant_documents("foo")result [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={}), Document(page_content='hello', metadata={}), Document(page_content='world', metadata={})]PreviousPubMedNextTF-IDFCreate New Retriever with Te... | https://python.langchain.com/docs/integrations/retrievers/svm |
676c29d0d5d0-0 | Wikipedia | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/wikipedia |
676c29d0d5d0-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/wikipedia |
676c29d0d5d0-2 | langchain.retrievers import WikipediaRetrieverretriever = WikipediaRetriever()docs = retriever.get_relevant_documents(query="HUNTER X HUNTER")docs[0].metadata # meta-information of the Document {'title': 'Hunter × Hunter', 'summary': 'Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter")... | https://python.langchain.com/docs/integrations/retrievers/wikipedia |
676c29d0d5d0-3 | totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter × Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been al... | https://python.langchain.com/docs/integrations/retrievers/wikipedia |
676c29d0d5d0-4 | ChatOpenAI(model_name="gpt-3.5-turbo") # switch to 'gpt-4'qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)questions = [ "What is Apify?", "When the Monument to the Martyrs of the 1830 Revolution was created?", "What is the Abhayagiri Vih�ra?", # "How big is Wikipédia en français?",... | https://python.langchain.com/docs/integrations/retrievers/wikipedia |
676c29d0d5d0-5 | web monitoring, content aggregation, and much more. Additionally, it offers various features such as proxy support, scheduling, and integration with other tools to make web scraping and automation tasks easier and more efficient. -> **Question**: What is the Abhayagiri Vih�ra? **Answer**: Abhayagiri Vi... | https://python.langchain.com/docs/integrations/retrievers/wikipedia |
bee3b6edc0bc-0 | Metal | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/metal |
bee3b6edc0bc-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/metal |
bee3b6edc0bc-2 | [Document(page_content='foo1', metadata={'dist': '1.19209289551e-07', 'id': '642739a17559b026b4430e40', 'createdAt': '2023-03-31T19:50:57.853Z'}), Document(page_content='foo1', metadata={'dist': '4.05311584473e-06', 'id': '642738f67559b026b4430e3c', 'createdAt': '2023-03-31T19:48:06.769Z'})]PreviousLOTR (Merger Ret... | https://python.langchain.com/docs/integrations/retrievers/metal |
bb2e92064692-0 | DocArray Retriever | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-2 | BaseDocfrom docarray.typing import NdArrayimport numpy as npfrom langchain.embeddings import FakeEmbeddingsimport randomembeddings = FakeEmbeddings(size=32)Before you start building the index, it's important to define your document schema. This determines what fields your documents will have and what type of data each ... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-3 | = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 56', metadata={'id': '1f33e58b6468ab722f3786... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-4 | index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 28', metadata={'id': 'ca9f3f4268eec7c97a7d6e77f541cb82', 'year': 28,... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-5 | year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"path": ["year"], "operator": "LessThanEqual", "valueInt": "90"}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, ... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-6 | color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"range": {"year": {"lte": 90}}}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field=... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-7 | color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = rest.Filter( must=[ rest.FieldCondition( key="year", range=rest.Range( gte=10, lt=90, ), ) ... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-8 | the task of planting an idea into the mind of a CEO.", "director": "Christopher Nolan", "rating": 8.8, }, { "title": "The Dark Knight", "description": "When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychologica... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-9 | "When a simple jewelry heist goes horribly wrong, the surviving criminals begin to suspect that one of them is a police informant.", "director": "Quentin Tarantino", "rating": 8.3, }, { "title": "The Godfather", "description": "An aging patriarch of an organized crime dynasty transfers... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-10 | add datadb.index(docs)Normal Retriever​from langchain.retrievers import DocArrayRetriever# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="description_embedding", content_field="description",)# find the relevant documentdoc = retriever.get_relevant_documents... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-11 | 'director': 'Christopher Nolan'}), Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nol... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bb2e92064692-12 | the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.', metadata={'id': '91dec17d4272041b669fd113333a65f7', 'title': 'The Dark Knight', 'rating': 9.0, 'director': 'Christopher Nolan'})]Prev... | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever |
bba6e2ef5336-0 | TF-IDF | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/tf_idf |
bba6e2ef5336-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/tf_idf |
bba6e2ef5336-2 | ])Use Retriever​We can now use the retriever!result = retriever.get_relevant_documents("foo")result [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={}), Document(page_content='hello', metadata={}), Document(page_content='world', metadata={})]PreviousSVMNextVespaCre... | https://python.langchain.com/docs/integrations/retrievers/tf_idf |
767d5b975f00-0 | Google Cloud Enterprise Search | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/retrievers/google_cloud_enterprise_search |
767d5b975f00-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterpr... | https://python.langchain.com/docs/integrations/retrievers/google_cloud_enterprise_search |
767d5b975f00-2 | the Enterprise Search Search Service API.Install pre-requisites​You need to install the google-cloud-discoverengine package to use the Enterprise Search retriever.pip install google-cloud-discoveryengineConfigure access to Google Cloud and Google Cloud Enterprise Search​Enterprise Search is generally available for ... | https://python.langchain.com/docs/integrations/retrievers/google_cloud_enterprise_search |
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