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docs: fix links (#15848)
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Bagatur
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18411c379c
@ -201,7 +201,7 @@
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"\n",
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"* E.g., for Llama-7b: `ollama pull llama2` will download the most basic version of the model (e.g., smallest # parameters and 4 bit quantization)\n",
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"* We can also specify a particular version from the [model list](https://github.com/jmorganca/ollama?tab=readme-ov-file#model-library), e.g., `ollama pull llama2:13b`\n",
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"* See the full set of parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain.llms.ollama.Ollama.html)"
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"* See the full set of parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.ollama.Ollama.html)"
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]
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},
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{
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@ -241,7 +241,7 @@
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"\n",
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"As noted above, see the [API reference](https://api.python.langchain.com/en/latest/llms/langchain.llms.llamacpp.LlamaCpp.html?highlight=llamacpp#langchain.llms.llamacpp.LlamaCpp) for the full set of parameters. \n",
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"\n",
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"From the [llama.cpp docs](https://python.langchain.com/docs/integrations/llms/llamacpp), a few are worth commenting on:\n",
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"From the [llama.cpp API reference docs](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.llamacpp.LlamaCpp.htm), a few are worth commenting on:\n",
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"\n",
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"`n_gpu_layers`: number of layers to be loaded into GPU memory\n",
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"\n",
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@ -378,9 +378,9 @@
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"source": [
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"### GPT4All\n",
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"\n",
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"We can use model weights downloaded from [GPT4All](https://python.langchain.com/docs/integrations/llms/gpt4all) model explorer.\n",
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"We can use model weights downloaded from [GPT4All](/docs/integrations/llms/gpt4all) model explorer.\n",
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"\n",
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"Similar to what is shown above, we can run inference and use [the API reference](https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html?highlight=gpt4all#langchain.llms.gpt4all.GPT4All) to set parameters of interest."
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"Similar to what is shown above, we can run inference and use [the API reference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.gpt4all.GPT4All.html) to set parameters of interest."
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]
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},
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{
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@ -390,7 +390,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install gpt4all\n"
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"%pip install gpt4all"
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]
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},
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{
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@ -582,9 +582,9 @@
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"source": [
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"## Use cases\n",
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"\n",
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"Given an `llm` created from one of the models above, you can use it for [many use cases](docs/use_cases).\n",
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"Given an `llm` created from one of the models above, you can use it for [many use cases](/docs/use_cases/).\n",
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"\n",
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"For example, here is a guide to [RAG](docs/use_cases/question_answering/local_retrieval_qa) with local LLMs.\n",
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"For example, here is a guide to [RAG](/docs/use_cases/question_answering/local_retrieval_qa) with local LLMs.\n",
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"\n",
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"In general, use cases for local LLMs can be driven by at least two factors:\n",
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"\n",
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@ -611,7 +611,7 @@
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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": "3.10.12"
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -399,24 +399,6 @@
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"\n",
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"\n",
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"\n",
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"**Improvements**\n",
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"\n",
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"The performance of the `SQLDatabaseChain` can be enhanced in several ways:\n",
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"\n",
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"- [Adding sample rows](#adding-sample-rows)\n",
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"- [Specifying custom table information](/docs/integrations/tools/sqlite#custom-table-info)\n",
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"- [Using Query Checker](/docs/integrations/tools/sqlite#use-query-checker) self-correct invalid SQL using parameter `use_query_checker=True`\n",
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"- [Customizing the LLM Prompt](/docs/integrations/tools/sqlite#customize-prompt) include specific instructions or relevant information, using parameter `prompt=CUSTOM_PROMPT`\n",
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"- [Get intermediate steps](/docs/integrations/tools/sqlite#return-intermediate-steps) access the SQL statement as well as the final result using parameter `return_intermediate_steps=True`\n",
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"- [Limit the number of rows](/docs/integrations/tools/sqlite#choosing-how-to-limit-the-number-of-rows-returned) a query will return using parameter `top_k=5`\n",
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"\n",
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"You might find [SQLDatabaseSequentialChain](/docs/integrations/tools/sqlite#sqldatabasesequentialchain)\n",
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"useful for cases in which the number of tables in the database is large.\n",
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"\n",
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"This `Sequential Chain` handles the process of:\n",
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"\n",
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"1. Determining which tables to use based on the user question\n",
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"2. Calling the normal SQL database chain using only relevant tables\n",
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"\n",
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"**Adding Sample Rows**\n",
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"\n",
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@ -1269,7 +1251,7 @@
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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": "3.10.1"
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -11,7 +11,7 @@
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"\n",
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"LangChain has [integrations](https://integrations.langchain.com/) with many open-source LLMs that can be run locally.\n",
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"\n",
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"See [here](docs/guides/local_llms) for setup instructions for these LLMs. \n",
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"See [here](/docs/guides/local_llms) for setup instructions for these LLMs. \n",
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"\n",
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"For example, here we show how to run `GPT4All` or `LLaMA2` locally (e.g., on your laptop) using local embeddings and a local LLM.\n",
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"\n",
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@ -141,11 +141,11 @@
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"\n",
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"Note: new versions of `llama-cpp-python` use GGUF model files (see [here](https://github.com/abetlen/llama-cpp-python/pull/633)).\n",
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"\n",
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"If you have an existing GGML model, see [here](docs/integrations/llms/llamacpp) for instructions for conversion for GGUF. \n",
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"If you have an existing GGML model, see [here](/docs/integrations/llms/llamacpp) for instructions for conversion for GGUF. \n",
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" \n",
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"And / or, you can download a GGUF converted model (e.g., [here](https://huggingface.co/TheBloke)).\n",
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"\n",
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"Finally, as noted in detail [here](docs/guides/local_llms) install `llama-cpp-python`"
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"Finally, as noted in detail [here](/docs/guides/local_llms) install `llama-cpp-python`"
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]
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},
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{
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@ -201,7 +201,7 @@
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"id": "fcf81052",
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"metadata": {},
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"source": [
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"Setting model parameters as noted in the [llama.cpp docs](https://python.langchain.com/docs/integrations/llms/llamacpp)."
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"Setting model parameters as noted in the [llama.cpp docs](/docs/integrations/llms/llamacpp)."
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]
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},
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{
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@ -230,7 +230,7 @@
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"id": "3831b16a",
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"metadata": {},
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"source": [
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"Note that these indicate that [Metal was enabled properly](https://python.langchain.com/docs/integrations/llms/llamacpp):\n",
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"Note that these indicate that [Metal was enabled properly](/docs/integrations/llms/llamacpp):\n",
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"\n",
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"```\n",
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"ggml_metal_init: allocating\n",
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@ -304,7 +304,7 @@
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"\n",
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"Similarly, we can use `GPT4All`.\n",
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"\n",
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"[Download the GPT4All model binary](https://python.langchain.com/docs/integrations/llms/gpt4all).\n",
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"[Download the GPT4All model binary](/docs/integrations/llms/gpt4all).\n",
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"\n",
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"The Model Explorer on the [GPT4All](https://gpt4all.io/index.html) is a great way to choose and download a model.\n",
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"\n",
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@ -27,7 +27,7 @@
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"\n",
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"**Step 2: Add that parameter as a configurable field for the chain**\n",
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"\n",
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"This will let you easily call the chain and configure any relevant flags at runtime. See [this documentation](docs/expression_language/how_to/configure) for more information on configuration.\n",
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"This will let you easily call the chain and configure any relevant flags at runtime. See [this documentation](/docs/expression_language/how_to/configure) for more information on configuration.\n",
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"\n",
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"**Step 3: Call the chain with that configurable field**\n",
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"\n",
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@ -298,14 +298,6 @@
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" config={\"configurable\": {\"search_kwargs\": {\"namespace\": \"ankush\"}}},\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e3aa0b9e",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -55,7 +55,7 @@
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"\n",
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"#### Retrieval and generation\n",
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"4. **Retrieve**: Given a user input, relevant splits are retrieved from storage using a [Retriever](/docs/modules/data_connection/retrievers/).\n",
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"5. **Generate**: A [ChatModel](/docs/modules/model_io/chat) / [LLM](/docs/modules/model_io/llms/) produces an answer using a prompt that includes the question and the retrieved data"
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"5. **Generate**: A [ChatModel](/docs/modules/model_io/chat/) / [LLM](/docs/modules/model_io/llms/) produces an answer using a prompt that includes the question and the retrieved data"
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]
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},
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{
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@ -449,7 +449,7 @@
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"`TextSplitter`: Object that splits a list of `Document`s into smaller chunks. Subclass of `DocumentTransformer`s.\n",
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"- Explore `Context-aware splitters`, which keep the location (\"context\") of each split in the original `Document`:\n",
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" - [Markdown files](/docs/modules/data_connection/document_transformers/markdown_header_metadata)\n",
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" - [Code (py or js)](docs/integrations/document_loaders/source_code)\n",
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" - [Code (py or js)](/docs/integrations/document_loaders/source_code)\n",
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" - [Scientific papers](/docs/integrations/document_loaders/grobid)\n",
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"- [Interface](https://api.python.langchain.com/en/latest/text_splitter/langchain.text_splitter.TextSplitter.html): API reference for the base interface.\n",
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"\n",
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@ -865,7 +865,7 @@
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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": "3.10.1"
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -1422,7 +1422,7 @@
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},
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{
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"source": "/docs/integrations/tools/sqlite",
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"destination": "/docs/use_cases/qa_structured/sqlite"
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"destination": "/docs/use_cases/qa_structured/sql"
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},
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{
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"source": "/en/latest/modules/callbacks/filecallbackhandler.html",
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