docs/fix links (#6498)

docs/extras/ecosystem/integrations/awadb.md

@@ -18,4 +18,4 @@ whether for semantic search or example selection.
 from langchain.vectorstores import AwaDB
 ```
 
-For a more detailed walkthrough of the AwaDB wrapper, see [this notebook](../modules/indexes/vectorstores/examples/awadb.ipynb)
+For a more detailed walkthrough of the AwaDB wrapper, see [here](/docs/modules/data_connection/vectorstores/integrations/awadb.html).

docs/extras/ecosystem/integrations/databricks.md

@@ -12,12 +12,12 @@ Databricks embraces the LangChain ecosystem in various ways:
 
 Databricks connector for the SQLDatabase Chain
 ----------------------------------------------
-You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain. See the notebook [Connect to Databricks](./databricks/databricks.html) for details.
+You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain. See the notebook [Connect to Databricks](/docs/ecosystem/integrations/databricks/databricks.html) for details.
 
 Databricks-managed MLflow integrates with LangChain
 ---------------------------------------------------
 
-MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. See the notebook [MLflow Callback Handler](./mlflow_tracking.ipynb) for details about MLflow's integration with LangChain.
+MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. See the notebook [MLflow Callback Handler](/docs/ecosystem/integrations/mlflow_tracking.ipynb) for details about MLflow's integration with LangChain.
 
 Databricks provides a fully managed and hosted version of MLflow integrated with enterprise security features, high availability, and other Databricks workspace features such as experiment and run management and notebook revision capture. MLflow on Databricks offers an integrated experience for tracking and securing machine learning model training runs and running machine learning projects. See [MLflow guide](https://docs.databricks.com/mlflow/index.html) for more details.
 
@@ -26,11 +26,11 @@ Databricks-managed MLflow makes it more convenient to develop LangChain applicat
 Databricks as an LLM provider
 -----------------------------
 
-The notebook [Wrap Databricks endpoints as LLMs](../modules/models/llms/integrations/databricks.html) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. 
+The notebook [Wrap Databricks endpoints as LLMs](/docs/modules/model_io/models/llms/integrations/databricks.html) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. 
 
 Databricks endpoints support Dolly, but are also great for hosting models like MPT-7B or any other models from the Hugging Face ecosystem. Databricks endpoints can also be used with proprietary models like OpenAI to provide a governance layer for enterprises.
 
 Databricks Dolly
 ----------------
 
-Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [Hugging Face Hub](../modules/models/llms/integrations/huggingface_hub.html) for instructions to access it through the Hugging Face Hub integration with LangChain. 
+Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [Hugging Face Hub](/docs/modules/model_io/models/llms/integrations/huggingface_hub.html) for instructions to access it through the Hugging Face Hub integration with LangChain. 

docs/extras/ecosystem/integrations/google_search.mdx

@@ -29,4 +29,4 @@ from langchain.agents import load_tools
 tools = load_tools(["google-search"])
 ```
 
-For more information on this, see [this page](/docs/modules/agents/tools/getting_started.md)
+For more information on tools, see [this page](/docs/modules/agents/tools/).

docs/extras/ecosystem/integrations/google_serper.mdx

@@ -70,4 +70,4 @@ from langchain.agents import load_tools
 tools = load_tools(["google-serper"])
 ```
 
-For more information on this, see [this page](/docs/modules/agents/tools/getting_started.md)
+For more information on tools, see [this page](/docs/modules/agents/tools/).

docs/extras/ecosystem/integrations/huggingface.mdx

@@ -66,4 +66,4 @@ For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_
 
 The Hugging Face Hub has lots of great [datasets](https://huggingface.co/datasets) that can be used to evaluate your LLM chains.
 
-For a detailed walkthrough of how to use them to do so, see [this notebook](../use_cases/evaluation/huggingface_datasets.html)
+For a detailed walkthrough of how to use them to do so, see [this notebook](/docs/use_cases/evaluation/huggingface_datasets.html)

docs/extras/ecosystem/integrations/openweathermap.mdx

@@ -41,4 +41,4 @@ from langchain.agents import load_tools
 tools = load_tools(["openweathermap-api"])
 ```
 
-For more information on this, see [this page](/docs/modules/agents/tools/getting_started.md)
+For more information on tools, see [this page](/docs/modules/agents/tools/).

docs/extras/ecosystem/integrations/promptlayer.mdx

@@ -40,7 +40,7 @@ for res in llm_results.generations:
 ```
 You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. [Read more about it here](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
 
-This LLM is identical to the [OpenAI LLM](./openai.md), except that
+This LLM is identical to the [OpenAI](/docs/ecosystem/integrations/openai.html) LLM, except that
 - all your requests will be logged to your PromptLayer account
 - you can add `pl_tags` when instantializing to tag your requests on PromptLayer
 - you can add `return_pl_id` when instantializing to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).

docs/extras/ecosystem/integrations/searx.mdx

@@ -87,4 +87,4 @@ arxiv_tool = SearxSearchResults(name="Arxiv", wrapper=wrapper,
                                 })
 ```
 
-For more information on tools, see [this page](../modules/agents/tools/getting_started.md)
+For more information on tools, see [this page](/docs/modules/agents/tools/).

docs/extras/ecosystem/integrations/vectara/index.mdx

@@ -54,7 +54,7 @@ The results are returned as a list of relevant documents, and a relevance score
 
 
 For a more detailed examples of using the Vectara wrapper, see one of these two sample notebooks:
-* [Chat Over Documents with Vectara](./vectara/vectara_chat.html)
-* [Vectara Text Generation](./vectara/vectara_text_generation.html)
+* [Chat Over Documents with Vectara](./vectara_chat.html)
+* [Vectara Text Generation](./vectara_text_generation.html)
 
 

docs/extras/ecosystem/integrations/wolfram_alpha.mdx

@@ -36,4 +36,4 @@ from langchain.agents import load_tools
 tools = load_tools(["wolfram-alpha"])
 ```
 
-For more information on this, see [this page](/docs/modules/agents/tools/getting_started.md)
+For more information on tools, see [this page](/docs/modules/agents/tools/).